Frontier Computing

Speaking of new teaching methodology, “Flipped Classroom” is undoubtedly a very popular one. The basic concept of flipped classroom is to have students learn by themselves before attending a “real” class at school. Once the background learning stage is performed outside of the class time, tutors have free time to lead students to participate in higher-order thinking. However, as shown in the report of Katie Ash, the performance of the flipped classroom method is in fact still arguable. Our survey shows that the contents offered by most modern e-learning systems are relatively static. Consider how fast new information appeared on the Web! Of course, teachers, or material providers, can upload new contents to e-learning systems. However, creating contents requires efforts. Today, work load of our teachers is already heavy, so expecting teachers to update contents very frequently is not practical. The researchers believe that one of the major challenges faced by e-learning systems today is the richness of contents.

In a Smarter City, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Enabling City information as a utility, through a robust (expressive, dynamic, scalable) and (critically) a sustainable technology and socially synergistic ecosystem could drive significant benefits and opportunities. In this paper we propose a model based on Grid Management System. This model is based on grid cycle providing grid capturing, grid sharing, grid enhancing and grid preserving. Moreover, our model shares grid that supports the law of knowledge dynamics. Later we illustrate a scenario of Pudong District of Shanghai for independence issues. An Artificial Neural network (ANN) based simulation applying the proposed Grid Management System model is also described at the end of this paper to validate its applicability.

As the Web enters Big Data age, users and search engines may find it more and more difficult to effectively use and manage such big data. On one hand, people expect to get more accurate information with less search steps. On the other hand, search engines are expected to incur fewer resources of computing, storage and network, while serving the users more effectively. After more and more personal data becomes available, the basic issue is how to generate Cyber-I’s initial models and make the models growable. The ultimate goal is for the growing models to successively approach to or become more similar as individual’s actual characteristics along with increasing personal data from various sources covering different aspects. In this paper, we propose the concept of search pattern, summarize search engines into three search patterns and compare them in order to seek the more efficient one. We propose a new search pattern termed as ExNa, which can be incorporated into search engines to support more efficient search with better results.

Traditional health care system in the family-oriented application of monitoring system has some disadvantage, which is relatively small, and function relatively single operability is more complex, real-time performance is poor, the price is relatively expensive. In recent years, with the progress of integrated circuit technology and wireless communication technology, wireless body area network (WBAN) systems have got fast development. The application system base on BAN technology also has received more and more people’s attention. This paper presents a general framework BAN-based health care system, mainly introduces the design of the sensor from the perception layer and network protocol.

The paper did a visualization analysis of co-citation data records regarding to financial engineering which were retrieved from Web of Knowledge by making use of CiteSpaceII software. Through establishing the knowledge map of financial engineering fields, this analysis reflects important figures, articles, knowledge structures, evolution rules of financial engineering industry. Confirms and the research edge and trend of international research on Financial Engineering by detecting subject headings whose word frequency fluctuation are significant.

Soft-tissue segmentation has always been difficult point in the medical research of diagnosis of soft-tissue defects. Especially for Anterior Cruciate Ligament (ACL) rebuilding surgery, ACL segmentation from all soft-tissue inside knee joint, including Posterior Cruciate Ligament (PCL) and meniscus, is a very important task. In this paper, we propose a novel ACL segmentation method: Space Model Contrast Clustering-based (SMC-based) ACL Segmentation. Unlike the widely used processing method, such as segmentation by MRI gray values and Mimics segmentation drawing, the proposed method relies 3D model of knee joint to segment soft tissue by self-adaptive K-means clustering. Extensional experiments demonstrate that the proposed method can be capable of solving the problem of soft-tissue segmentation well and has achieved higher ACL segmentation efficiency.

In the domains of computer vision and graphical computation, image haze removal has been a significant issue. By the use of haze removal process, it can significantly improve the visibility of the scene in the image. However, most of the haze removal algorithms bring high computational cost and make algorithms failed in processing huge amount of images. In this paper, we propose a parallel image haze remove algorithm, adopting optimized contrast enhancement approach, to optimize the performance based on GPU platform. The optimization from the proposed algorithm obtains performance acceleration with about 5 times as compared the original version while the haze removal effect is the same. Some haze free images and its original hazy images are shown in the later chapter during this paper. Our work after improvement can process a single picture in a much higher speed after optimization and make it more sufficiently fast for large-scale application which needs image haze removal in computer vision area.

Thunderstorms pose great threat to human survival. As traditional statistics and fore-casts have great limitations, this paper applies credit scoring model into thunderstorm warning system. We select related data of the electric field as variables before and during the thunder-storms. And by actual monitoring system of thunderstorm data preprocessing, sampling, binning and so on, by using the method of logistic regression and neural network to deal with, the model based on the combination of location and data of electric field is an effective way of thunderstorm warning. From the angle of the method, the model based on the theory of credit scoring can be faster and more accurate. It can make the probability of forecasting thunderstorm more quantization.

With the development of cloud applications for marketing, cloud-based marketing applications are becoming more and more important. The purpose of this study was to demonstrate whether cloud applications for marketing bring positive identification and evaluation through simultaneously considering identification with the community and the company and investigate the behavioral implications from the Facebook community members’ perspective. A questionnaire investigation with consumers was conducted in this research for examining five hypotheses. The findings of this study indicated that the interaction on the Facebook brand community can enhance both C-C identifications and consumer post-purchase behaviors. Also this study focused on the 2 × 2 relationship with C-C identifications and consumer post-purchase behaviors which all had significant and positive effects.

Facing the fastest ageing fact, Taiwan implements long term care (LTC) program to lessen the load of some demanded home-care disabled-elderly-family. However, the provided medical resources, including the physician, nurse and physiotherapist (PT) would never catch up to the demands for home care. Using Linear Programming to model the current and the alternative home care medical demand-supply situations, it provides the decision maker with a deep insight of the resource allocation problem. The scenario includes three hospitals around the Meinong District in Kaohsiung to be the medical givers. Study shows that current medical resources are insufficient. PT and nurses are in serious shortage. The optimal solution of (physician, nurse, PT) is (15, 27, 22). Elderly under the age of 65 and local home care facilities are excluded in this study.

Vehicle recognition is the important content of intelligent transportation system, there have been many researches on vehicle recognition, and the technology of vehicle recognition based on CPU and DSP cannot meet the needs of the present. This article is about the study of Vehicle recognition and how to realize the GPU algorithm on the CUDA transplantation, make the algorithm parallel, thus speeding up the computation efficiency of vehicle recognition. This thesis is based on the Jeston TK1 development board as the experimental object, achieving high efficiency of GPU image processing.

In this paper, we propose a new reliable classification approach, called the pseudo nearest centroid neighbor rule, which is based on the pseudo nearest neighbor rule (PNN) and nearest centroid neighborhood (NCN). In the proposed PNCN, the nearest centroid neighbors rather than nearest neighbors per class are first searched by means of NCN. Then, we calculate k categorical local mean vectors corresponding to k nearest centroid neighbors, and assign the weight to each local mean vector. Using the weighted k local mean vectors for each class, PNCN designs the corresponding pseudo nearest centroid neighbor and decides the class label of the query pattern according to the closest pseudo nearest centroid neighbor among all classes. The classification performance of the proposed PNCN is evaluated on real data sets in terms of the classification accuracy. The experimental results demonstrate the effectiveness of PNCN over the competing methods in many practical classification problems.

Having more than one professional certification is one of the various indicators for the Ministry of Education to promote and evaluate their competencies that the vocational education system students in Taiwan. This is also one way to find the ideal jobs that enhance their competitiveness for vocational students on-the-job. As a result, it is very important for students in vocational education systems to obtain professional certifications. In particular, the more professional licenses they have, the more job opportunities for them. Therefore, we propose a RS (Recommended System) that combines two-phase Blue-Red trees of Rule-Space Model and the best learning path, and it is used to remedy and analyze the learning situation of MTA courses and enhance the pass rate of MTA licenses for students. We classify three SGs (Skill Groups) from the Certiport of Microsoft certification center in the first phase, and the three SGs (Skill Groups) can be produced as a concept map and Blue-Red trees. In the second phase, The ten chapters of MTA course are classified within the three SGs (Skill Groups) of phase one according to the most similarity in contents between ten chapters and three SGs (Skill Groups). That is, three groups will be created in a MTA course from previous ten chapters. The three groups result in three concept maps and three groups of Blue-Red trees. After that, it is based on the analysis of Rule-Space Mode for all learning objects in each skill group of phase two. We can define the RW (Relation Weight) of every learning object associated with the other learning objects, and separately calculate the Confidence Level values of between two adjacent learning objects from all learning paths. Finally, the optimal learning path can be obtained by the inferred optimal learning path algorithm from the combination of RW (Relation Weight) and CL (Confidence Level). The proposed method can be used to OCLS (Online Course Learning System) that recommended the best learning path of learning objects for learners to online self-learning, or to RS (Recommended System) that provides the basis of self-learning remedies for RFRC (Recommended Form of Remedial Course).

There are a lot of redundancy and over all issues in the artificial immune system (AIS) because of using the traditional negative selection algorithm (NSA) to generate detectors. It is the main reason for the high false percentage and high missed percentage in the intrusion detection system (IDS). Therefore, an improved immune detector generation algorithm is put forward. By calculating the optimal size of mature detector set and using the twice match in the improved algorithm. The efficiency of the IDS can be guaranteed. In the last, simulation experiments show that the improved algorithm can cover the more nonself and had a higher detection rate in the IDS.

The present work mainly focuses on the issue of risk model in spacial data analysis. Through the analysis on morbidity data of influenza A (H1N1) across China’s administrative regions from 2009 to 2012, a comparative study was carried out among four different estimators SMR, EBPG, EBLN and EBMarshall as risk model to explore and make improvements for the problems of risk model and pattern of survival distribution in spacial disease analysis. By using R programming language, the feasibility of the above analysis method was verified and the variability of the estimated value generated by each model was calculated. The research on spacial variability of disease morbidity is helpful in detecting epidemic area and forewarning the pathophoresis of prospective epidemic disease.

Although the traditional TV (Total Variation) model owns excellent image denoising ability, there are staircase effect problems for TV model. In this article, two detection operators for staircase effect problem are proposed. The staircase effect problem can be solved effectively by introducing two operators into traditional TV model. On the basis, it proposes an adaptive total variation model for image denoising. When dealing with image edge, it can still use the traditional TV model. Its purpose is to maintain the advantages in edge protection for TV model. When it is in the smooth area of image, linear diffusion is used to avoid the staircase effect.

With the popularity of microblog, more and more people like to use microblog to speak, to communicate feelings, sharing anecdotes, microblog has increasingly become the important platform for people to share information. Social events, which are concerned by a considerable amount of people in the society, will inevitably be reflected in the microblog data, and this often has the characteristics of timeliness, high speed, thus it become a good place to start for the social event detection and analysis. Social event tracking and detection is important for improving the level of social governance, improving the ability of the enterprise brand management and enhancing the level of anticipation and intervention of the problem. The study is based on SAS text analysis and natural language processing technology, through many experiments, finally found the effective ways of event detection and tracking. Combining event mining of main body, auxiliary information exhibition and trend tracking, this subject constructs a preliminary prototype system.

We used Spark as a platform for large-scale, real-time intelligent video stream analysis. We observed that the default task scheduling algorithm of Spark was not efficient for scheduling image frame data processing tasks, incurring problems such as poor data locality, high network traffic, low utilization of computing resources, etc. This paper investigates why Spark’s default task scheduling algorithm is not suitable for real-time video stream processing. Further, we present a new real-time task scheduling algorithm that leverages the notion of data locality. This algorithm schedules tasks based on data locality and information collected at runtime, including task execution time and workload of each node. Experiments show that our proposed algorithm increases data locality and CPU utilization while reducing network traffic and latency.

As microblog is becoming more and more popular, people not only begin to use microblog, getting and sharing information at any time, but also begin to do more based on microblog, such as using microblog for recruitment. Microblog recruitment has gradually become a kind of fashion, at the same time, also there are a lot of people using personal microblog to release information about the job or focus on hiring dynamics in real-time. This study classifies the recruitment information based on the text analysis tools SAS, extracts the specific information such as recruitment position, recruiters, hiring requirement, salary, work place, contact information, so that the job seekers can understand industry supply and demand dynamics, grasp the recruitment information in time easily.

With increasing of using smart phones and the social network, the numerous and confused data makes people could hardly easily get what they really want. Although the recommendation systems have been vigorously searched for decades and successfully applied in the business world, they have faced old and new challenges now in the social network. In this paper, we propose a Community Trust model based on Probability Matrix Factorization (CT-PMF) and by using the ratings and networks we make predictions about users’ no-rated items. Extensive experiments have been conducted to evaluate our model on the real data set obtained from Dianping. The experimental results demonstrate that CT-PMF outperforms competitor methods, in terms of predicting the missing rating.

Finite mixture model (FMM) obtains good results when it is applied to image segmentation under non-noise condition. But it cannot obtain satisfied segmentation results when the image is degraded by noise. The main reason is that it regards the relationship of pixels are statistical independent and the position information has no effect for image segmentation. However, in fact, the spatial relationship between these pixels play an important role for image segmentation. Markov Random Field (MRF) considers the spatial information of pixels and has been widely applied image segmentation. In this paper, combine FMM and MRF, a robust MRF model is proposed. The proposed model effectively captures the spatial information of pixels and is applied to color real world image segmentation. Visual and quantitative experimental results demonstrate the robustness, effectiveness and correctness of proposed model.

Community clustering means the vertices in networks are often used to cluster into tightly-knit group with a high density of within-group edges and a lower density of between-group edges. However, most community clustering algorithms do not involve the node attributes and relationship, and these approaches lead to inaccuracy clustering. In this paper, we propose two algorithms which involve both node attributes and link structure in social networks based on Girvan-Newman algorithm (GN) and Weighted Informative Graph (WIG). The related experimental results verify the effectiveness of our proposed algorithms.

In this paper, we present a novel algorithm for performing k-means clustering using cuckoo search. A pending problem of K-Means clustering algorithm is that the performance is affected by the original cluster centers. In this paper the K-Means algorithm is improved by cuckoo search and the initial cluster centers are generated by cuckoo search. The experiments and comparisons with the classical K-Means algorithm indicate that the improved k-mean clustering algorithm has obvious advantages on execution time.

A new setting up logistics distribution centers algorithm based on Bacterial Foraging Optimization is proposed in this paper. Logistics distribution centers are the bridges to connect the supplying points and the demanding points; therefore, how to set up the logistics distribution centers is the important problem of a logistics system. Firstly, the logistics centers location model is discussed and then a new setting up logistics distribution centers algorithm based on Bacterial Foraging Optimization is proposed in this paper. To solve discrete space problems, the chemotaxis procedure is modified in the new algorithm. The experiments show that, the proposed algorithm in this paper can return the solution of setting up logistics distribution centers problems.

High performance encryption is a key means to minimize security risks as protecting private data in cloud or big data environments. In this paper, a new encryption model SIDA is proposed based on the information dispersal and multi-layer encryption. From theoretical analysis and experiments, it shows that SIDA is secure, can not only significantly improve the speed of data encryption and decryption, but also reduce the bandwidth consumption and re-encryption overhead when revoking authority. Taking SIDA4 algorithm as an example, the encryption speed is about 1.6 times of AES. While the overhead of re-encryption when revoking authority, SIDA4 in communication and computation are 1/4 of AES.

Software security is not only related to our life, but also close to the security of our society. This paper proposed a method called software behaviors analysis method based on behavior template (SABT). According to the context of source code, we build and form a behavior template as a system to detect malicious behavior of software, including function transfer map and function block transfer map. We utilize some relative algorithms and technology in SABT, which include the method of stubbing interrupts, building behavior template and forming automaton to detect abnormal software behavior. Behavior template consists of function transfer map and minimum function transfer map. Compared with traditional method, such as N-gram, FSA, Var-gram, SABT can get higher cover rate of code and detect abnormal more effetely and efficiently.

In order to ensure the correct transmission of concurrent data and resolve the uncertainties of communication channels in the across-organizational business process, a new modeling method of dynamic service interaction based on pi-calculus is proposed in this paper. The pi-calculus is selected as the formal modeling language in this method. And three service interaction patterns, which includes request with referral, relayed response and dynamic routing, is studied to build the formal model with the channel mobility and the messaging mechanism of pi-calculus. In case of the bidding activities, a formal model is established and simulated based on pi-calculus in this paper. Furthermore, a tool named MWB is used to automatically validate the model in order to ensure the accuracy and the consistency of process. It proved the applicability and feasibility of the modeling based on the pi-calculus in the dynamic service interaction.

Action analysis and semantic interpretation in surveillance video have recently attracted increasing attention in the computer vision community. In this paper, video structural description model is proposed for practical applications for traffic violation monitoring. Conceptual space is defined to bridge the gap between low-level syntax which is quantitative and high-level semantic where information is handled by qualitative means. Based on the conceptual space, conceptual relating model is proposed to simulate and recognize the targets’ behaviors in the scene. Applications for traffic violation monitoring experimental results demonstrate the performance of the proposed semantic interpretation model of video structural description.

With the rapid development of Internet and the popularization of information technology and computers nowadays, data mining technology is becoming increasingly sophisticated. The main purpose of data mining is to obtain potentially relevant information from large databases correctly and efficiently. As to the association rule model, the main purpose of it is to find out possibly related product items. For example, with each transaction records in stores, we can dig out association rules like “80 % of customers that purchase PCs may also purchase screens.” The aim of this essay is to construct mining multilevel association rules and to analyze and discuss its integrity. The original multilevel association rules only explore associations at single concept level, so this essay will examine the integrity of multilevel association rules and use the original rules to find association relationships at multiple concept levels, coupling with the operation of filtering information from databases. The analysis of this essay can help companies in making marketing strategies and providing customized services to raise their overall sales.

This study quantifies the usage and evaluation of Data Reduction. Within Data Reduction, there are three different measurement of methods: association measurement, discrimination measurement, and information measurement. Through analysis of the importance of each measurement stage, we generated sequences of forward generation to select the best combination of Data Reduction. The purpose of the sequences of forward generation is to increase efficiency and accuracy from the selected combination of Data Reduction. Based on the method of generating our model, we want only a single field to appear, in order to measure the amount of information based on the most suitable model law for the three measurement methods. The purpose of this model is to allow users of data mining to explore the selected field, in addition to the single characteristic attribute field as a reference, but also according to different dimensions of the resulting combination of all the chaos of the target attributes and how they affect the relationship, so that users can analyze and use the field to solve the most troublesome mining field dimension selections.

For latency-sensitive applications, a key issue is how to estimate the latencies between any couple of nodes. Latency Matrix Completion method provides a simple but efficient way to estimate the latencies instead of measure them directly. In this paper, we make comparative studies on several Internet latency data sets, and report an easy overlooked shortcoming exists in Latency Matrix Completion. For short latencies, their relative estimation errors are much higher than those of long latencies. In this paper, we propose a brief model to analyze why this bias exists. We believe that the loss function which used in the optimizing process is a possible reason for this phenomenon. How to remove the short latency bias should cause our consideration in the future.

In order to improve the robustness of face identification with the changes of illumination, expression and facial alteration, a new facial feature extraction algorithm based on weighted adaptive lifting wavelet (ALW) scheme and pulse-coupled neural network (PCNN) is involved in this paper. The face images are decomposed into several subbands by weighted adaptive lifting scheme. Then the PCNN is utilized to decompose each weighted subbands into a series of binary images, the entropies of which are calculated and regarded as facial features. Experimental results show that the method yields a good robustness against the illumination, expression and facial variability and reduces the computer burden.

Events have become central elements in the representation of information from various semantic web applications. It is necessary to develop a formal language for describing and reasoning event knowledge. Description logic is a well-defined knowledge representation language, but it is difficult to represent the event and elements with different characteristics. This paper proposes an event element projection method which is combined with SROIQ to build a new formalization method for event-centered knowledge. Event element projection unifies representation framework of event and event status, establishes the semantic relations between event and its elements. Through element projection and SROIQ, event classes, event instances and event elements can be effectively described in a unified style. An example of formalization on water pollution emergencies ontology is provided based on SROIQ and element projection method. The semantics of event relations based on element projection and reasoning on event relations are also discussed at last.

Hospital drug business is complex, the data integration and the analysis are imperfect. Lacks of data warehouse system in which information is comprehensive and data is integrated, research the hospital drugs data, and carried on the design, the development and the deployment of the data warehouse project using “business dimension lifecycle method”. Created the data warehouse bus structure; established topic model; used dimension modelling carries on the logical modelling; studied in detail of Indexing strategy, granularity conversion algorithm and form design strategy in the design process, the overall logical organization pattern layout was clear. The construction method was practical. Gives a good hospital drug analysis model of data warehouse.

Question analysis is an important component of a general Question Answering (QA) system. Question analysis has different angles and functions. The paper focuses on recognition of question subject in QA system. The goal of subject recognition is to identify given question according to special domain. We discuss three approaches to identify subjects of questions, then quantificationally evaluate effect of machine learning methods by a series of experiments. The results show that Naive Bayes gains the best accuracy and efficiency than other learning methods and two ways of feature extraction proposed by the paper improve accuracy for most of learning methods.

A number of research studies have explored the impact of applying Augmented Reality (AR) technology to real-world learning environments. These studies have asserted that AR can improve students’ perceptions and enhance overall cognitive abilities when engaged in real-world learning activities. However, it is not easy for teachers to implement AR-based learning systems in classrooms because many teachers lack the skills and abilities of computer professionals or coding experts. In this study, we created an easy to use mobile augmented reality system that can support teachers in creating and designing AR materials. This mobile system provides teachers with the ability to combine course content and multimedia materials in a way that promotes learning within an engaging and intuitive AR-based environment. A quasi-experimental research design was used to evaluate the feasibility of using our proposed system to implement a variety of teaching activities. From the results of the questionnaire survey, we discovered that respondents rated the proposed system positively and were willing to formally incorporate mobile augmented reality into their future teaching plans. Therefore, we believe that teachers do regard our mobile augmented reality system as a useful tool that can supplement existing real-world learning activities with distinctive AR capabilities.

Achieving energy efficient monitoring of targets is a critical issue in sensor networks and, thus various power efficient coverage algorithms have been proposed. These algorithms divide the sensors into monitor sets, where each monitor set is able to cover all the targets. However, even the number of monitor sets is set to 3, the considered problem has been proven to be NP-hard. In this paper, we propose a randomized and efficient coverage algorithm that produces disjoint monitor sets, i.e., monitor sets with no common sensors. The monitor sets are activated successively and only the sensor nodes from the current active set are responsible for monitoring all the target nodes, while all other nodes are in a low-energy sleep mode. Our algorithm can generate a solution with guaranteed probability $$ 1 - \varepsilon \, (0 < \varepsilon < 1) $$1-ε(0<ε<1). Simulation results are presented to verify our approach.

Analysis of heart sound (HS) signal is a significant approach for detecting cardiovascular diseases (CVDs). Specifically, heart murmurs are regarded as the first indication of pathological occurrences and carry important diagnostic information. With the aids of computer and artificial intelligence technologies, a lot of HS analysis methods are suggested, which principally fall into two kinds: acoustic analysis and time-frequency analysis. However, most of existing methods are associated poorly with diagnostic information in heart murmurs, which restricts severely further interpretations. Aiming to handle this bottleneck problem, a novel enveloped-form heart murmur feature extraction methods is proposed, which extracts features merely and directly from heart murmurs. Initially, the influences of fundamental HSs are eliminated and the envelopes of heart murmurs are acquired, by employing discrete wavelet transform, Shannon envelope, as well as detecting and selecting peaks of heart murmurs. Thereafter, two key features SP and TS (the ratios of start position and time span of the envelopes of heart murmurs to the length of a HS cycle respectively) are extracted directly from the envelopes of heart murmurs, which are according to that the envelopes of different heart murmurs are of diverse shapes. By applying the key features to artificial neural network for classification and CVD diagnosis, the diagnostic accuracy is up to 96 %, which significantly validates the practicability and effectiveness of the proposed method.

Nowadays integrated network ensures the security of information transmission by adding encrypt, simple authentication and so on, but it lacks effective means to secure the verification, authorization, confidentiality and completeness of the information, especially in the wireless network, and as a result of the openness of the transmission medium, it is particularly important to guarantee its security. This paper focuses on the modern cryptosystem to establish and realize a safe and practical integrated network security strategy model. The architecture of the model consists of three portions, namely security system, secure connection of network and security transmission of data, key management.

Using the Fluka Monte Carlo method, we argue the activity of lead-bismuth eutectic (LBE) and lead (Pb) spallation targets in Accelerator Driven Subcritical System (ADS). Results reveal that the radioactivity of Pb target is lower than that of LBE target when they have the same beam energy and target diameter, the accumulation activity of two target is enhanced as the increase of proton energy and target diameter. In addition, we also discuss the respective contribution of prominent radionuclides of LBE target under various cooling time, it is specified that the used LBE target can be seen as a permanent radioactive waste. For the present works, we expect that it can offer some valuable hints for the future experiment and the assessment of safety hazards of nuclear facilities.

To change the traditional management of agricultural production, using ZigBee technology for short distance wireless transmission to design intelligent farmland environment remote monitoring system, which integrated communication, computer and network all aspects of technology. The real-time, accurate data collection of farmland soil PH value, temperature and humidity surrounding the plant, light intensity, crop growth and bacteria occur posture, provide reliable data for the intelligent agricultural production, thereby increasing the level of intelligence of agricultural management, and promote modernization of agricultural production process.

This paper discusses the design and implementation of an interactive attractions and monuments touring system based on cloud computing and augmented reality technology of Google Earth. The system accesses the user’s motion information through the wireless motion detection module, and then transforms the three-dimensional scenes of real objects correspondingly. It also provides text, sound and other forms of information to make an immersive touring experience possible. With the help of system tools, the content managed by the system (information about attractions, monuments and recommended tourist routes, etc.) can be added, deleted, and modified to improve system extensibility and usability.

Here we constructed a weighted gene correlation network in human glioblastoma cells by developing the graphics processing units (GPUs) algorithm. The strength distributions of entire network, housekeeping genes and hubs in protein interaction network were calculated and the differences between them were found. Six definitions of clustering coefficient previously proposed for weighted networks were calculated in this paper and behaved quite differently. Interestingly, the clustering coefficient distributions of housekeeping genes and hubs are similar to that of entire network, as the strengths of them are generally bigger. This work explored how to calculate the network indices in weighted biological networks on GPUs and whether these indices can reflect the characteristics of biological networks.

Controllers is responsible for the entire network centralized control in SDN (soft-defined network). It attaches great importance to grasping the entire network resources view and improving the quality of network resources delivery. However, centralized controllers call for more responsibility in control apparatus, making the expansibility of the controller plane the key issues of the SDN. This article designs an extensible SDN controller layer. With the use of existing cluster management technology, it avoids single point failure of the controllers and gives full play to every controller. Every controller is able to effectively allocate network resources according to the entire network information, so that the entire network resources can be effectively scheduled.

It was an important thing of record and track learning behavior in the learning process. On account of the complexity of the structure and simplicity of the data transmission, the SCORM make it unable to obtain complete learning record. Under the analysis of the network learning model and relevant semantic elements, this paper presents learning record model and learning record tracking System architecture based on Experience API. The method of learning record be transferred from LMS to Learning Record System is described in detail. And this paper also gives the method of reading the learning record from the LRS which can achieve multidimensional analysis for cross environment learning record, and that will lead to a better support for mobile learning and individualized learning.

The main objective of this paper is to explore an information platform for sharing, managing, downloading, analyzing and visualizing of a diverse range of hydrologic observation data to support investigators, geotechnical experts do some research about watershed in Northwestern China. For this reason, we develop a Watershed Datacenter System (WDC) which adopts an Entity Framework 6 (EF6) approach based on Model-View-Controller (MVC) architecture pattern and several other useful technologies like cross-platform JavaScript libraries (jQuery, D3 and Dojo), ArcGIS API and Responsive web design. Besides, Observation Database Model (ODM), Web Services and Time-Series analysis tools are seamlessly integrated into our WDC with the help of open source HIS (Hydrologic Information System) by CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.). The result shows that the WDC brings a lot of convenience for managing and analyzing of data onto watershed research.

In this paper, a Student-t mixture model is proposed for image segmentation based on Markov random field (MRF). For the clusters of pixels, their prior probabilities are regarded as a MRF. In the proposed model, at first, a factor of capture the spatial relationships between the pixels is given. Furthermore, student-t distribution is adopted to the component function of the distribution of pixels instead of the Gaussian distribution. To inference the parameters of the proposed model, gradient descent method is used during the inference process. Comprehensive experiments conducted on grayscale noisy image and real-world color images shows the effectiveness and robustness of the proposed model.

This paper presents an efficient control scheme of integrating mathematical model, fuzzy logic and genetic algorithm to solve the path planning problem of the CCD wheeled robots, which is based on the specific monocular structure and electric properties. Prior knowledge about the problem domain will cause deviation between the practical path and the ideal path when wheeled robot works in high-speed. This system integrates image information of CCD sensor, current position of the robot, current velocity, deflection angle and the battery capacity and so on, to plan the ideal path of autonomous mobile robot by means of establishing image model and path model. Meanwhile, the fuzzy logic controller based on genetic algorithm saves relevant feedback in the process of system running and updates data parameters to reconstruct the rule database, in order to control the robot move by the ideal path. The proposed control scheme could be useful for planning path of mobile robots. Experimental studies show that the scheme has good feasibility and it can achieve high control precision.

In practical clinical site, recording of heart auscultation signal is often challenged by the contamination of various non-cardiac noises. To address such key challenge, many researchers have developed various heart sound (HS) de-noising methods. Though, many of them on literature show promising results after adding Gaussian white noise to the ideal samples artificially and subsequently filter them out for performance evaluation. It has been proven that the noise which is recorded with the actual HS signal in clinical site does not pose the characteristics of Gaussian white noise. There is lack of study of true characteristics of site-sampled HS signal even it is fundamental and such important. As the first attempt, this paper investigates in depth the characteristics of several typical and common noise interferences that occur during HS clinical site acquisition. After summarizing the key features of such actual noises in time and frequency domains, a dynamic time warping based similarity algorithm is applied to indicate the destruction index of each noise type in contaminating the HS signal. The result show that lung sound and abdominal sound are the greatest disturbances in realistic HS acquisition, which should be brought to the forefront in designing the HS acquisition system as well as de-noising method.

This study proposed a method based on Independent Component Analysis (ICA) to understand the mechanisms that cause regional groundwater head variations. To verify the capability of the proposed method, this method is applied to an ideal numerical groundwater model, which was developed by using MODFLOW. The unconfined aquifer parameters are set as homogeneous and isotropic. The values of the two groups of pumpages (sinks) and one rainfall recharge (sources) were time-variant, and the frequencies among the three sink/sources were different. The simulated heads were sampled from 64 selected observation wells within the model boundary with a daily time step for 5 years. The simulated heads of the 64 wells were inputted to ICA. The study results show that the ICA can successfully decompose the sampled heads into three independent components (ICs) resulted from the three sink/source. To identifying the physical meanings of the three ICs, the correlation coefficients between ICs and the three sinks/sources were computed, and their values are 0.9816, 0.888 and 0.684, respectively. The separating matrix of ICA was also used to identify the pumping well locations. The study results show that the proposed method provides a novel and efficient method to understand the spatiotemporal head variations of groundwater system and can be used to locate the pumping wells, which is crucial for the regional groundwater management.

MOOCs (Massive Open Online Courses), is a new network teaching mode which is currently popular in the world. Its specific features such as openness and massiveness are conducive to the reform and development of Chinese University Education. From the original theory of MOOCs, its connotation, features and types are explored, which helps to learn from the excellent practical experiences from foreign countries. The analysis of the advantages and limitations of the MOOCs can help us make the dialectical understanding of it, make full use of its positive effects on Higher Education in China, and try to avoid its negative effect.

Information technology is developing rapidly today, which makes our life more convenient. Network is not only one of the important information technology products, but it also brings cybercrime, for example, Botnet infected and controlled computers which are usually established through a virus infection in many organizations, such as companies, schools or our home. Botnet do DDOS, phishing, sending spam and stealing of personal information. Every year the amount of infected victims is increasing. The botnet detection is more important day after day. However, Botnet often changed communication tools and transmission to hide, the detection has become difficult and botnet have multiple different implementations. We analyze three types of botnet traffic. There are IRC based, HTTP based and Peer to Peer based botnet. In this paper we construct simulation network to obtain different botnet traffic and extract flow data as some features. To find the important feature of botnet traffic, we use the Support Vector Machine as classifier with Swarm intelligence.

In order to acquire a kind of stable and efficient feature sequences to identify different shape of seal images in different angles. Pulse Coupled Neural Networks (PCNN) are adopted to extract the energy logarithmic sequences of seal images, the input image is a binary image, different shape of seal images used as input data of PCNN network to acquire their energy logarithmic sequence as the standard sequence. Then the same flows are used to match the logarithmic sequences of images to be recognized with the standard sequences. In addition, angle rotated seal images also be recognized as identified images. Statistical results analyzed are based on Pearson correlation coefficient. The experimental results of different shapes stamp statistics show that using Pearson correlation coefficient and for statistical experiments compared the sequence that obtained more desirable results. Through many seals experiments proved that the result of Pearson correlation coefficient can reach more than 0.99. The energy logarithmic sequence of different shape of seal images can be used as the feature sequences, which is not impact by the seal’s chop angles, and the feature has a certain stability.

The key successful factor of the new product design (NPD) of sensor product industry is the selections of the best parameter level. General speaking, decreasing the error rate of product parameter selection by increasing the number of experiments is the commodity trend in NPD goals. For above reasons, previous studies focus on structured approach for the replacement and management of selection of the parameter level in product design with the purpose of increasing efficiency and effectiveness, but rarely on a dynamic environment. Consequently, this work presents a novel algorithm, the Taguchi System-two steps optimal algorithm, which combines the Taguchi System (TS) with two steps optimal (TSO) method, which is shown how product adjusted under a dynamic environment in product design. The utility of the parameter level are selected. The two step optimal (TSO) method links the decisions for selections of parameter level in two different times and can be used to focus on dynamic sensor product design system (DSPDS). From the results, the proposed method might possibly be useful for our problem by selecting the parameter level size and adjusting the parameters by TSO and neural network (NN) in the DSPDS is observed in this study.

This paper proposes a movie recommendation service with linked film-related data as dataset, and the recommender supplies recommendation services with a number of present item-based collaborative filtering algorithms. In order to effectively predict user movie preference, mechanisms developed in MapReduce are adopted for rapid individual recommendation computation, whereas the recommender is combined by three types of movie resources, Movielens, Douban, and movie Trailer to be the dataset of recommendation service. NoSQL database is used to support for the movie dataset maintenance. The proposed prototype collects user feedback, the metrics, precision rate, recall rate, and F-score, and then applied to similarity mechanisms from Mahout.

Ontology mapping is an important solution to ensure interoperability while integrating heterogeneous and distributed data sources. This paper proposes an approach for ontology mapping based on event, which enable the mapping between event-based information with more abundant semantics. Firstly, this paper gives the definition of event ontology mapping, and then proposes an comprehensive semantic similarity calculation model based on the similarity of events and event structures. Experiments show that the proposed model can effectively find the semantic relations of event in two event ontologies.

Due to the development of computer technology and the mature development of 3D motion capture technology, the applications of 3D motion databases become more and more important. How to analysis the huge data stored in the database and efficiently retrieved the matched data is an important research issue. 3D animation design is one of the important applications of 3D motion databases. Based on our teaching experience, the bottleneck of the students’ learning of 3D animation is the motion animation of the 3D characters. Therefore, the 3D motion database can be used to assist the design of the motion for 3D characters. However, it is still a difficult problem because of the high complexity of the matching mechanism and the difficult of user interface design. In this paper, the 3D motion data can be represented as multi-dimensional multi-attribute sequences while the corresponding index structures and query processing mechanism are proposed for efficiently processing the 3D motion queries. Moreover, Microsoft Kinect is used in this project as the user interface. The captured data can be used as the user query and the further comparison will be performed to find the matched motion data.

It is considered that Web services have had a tremendous impact on the web as a potential silver bullet for supporting a distributed service-based economy on a global scale. However, despite the outstanding progress, their uptake on a web scale has been significantly less than initially anticipated due to higher usage thresholds. For instance, it is a hard task for service provider to seek appropriate semantic information such as OWL ontologies for service annotation in the service publication stage due to the fact that nowadays we are suffering from serious lack of available and ubiquitous ontologies for global consensus. Also it is not realistic for query users who do not possess much semantic knowledge to specify their requests with associated semantic information in the service discovery stage. In this paper, we propose an approach to publish services based on Linked data principles and discover services by service cluster with visualization for reducing the using thresholds. First, we propose Linked social service which is published on the open web by following Linked data principles with social link, and then we suggest a new method to calculate service similarity with tree structure. Then, a spatial clustering algorithm is proposed to enable visualization for reducing the using thresholds. Finally, experiment is conducted to show the effectiveness of our proposed approach.

The popularity of cloud computing has been growing among enterprises since its inception. It is an emerging technology which promises competitive advantages, significant cost savings, enhanced business processes and services, and various other benefits. The aim of this paper is to propose a decision modelling using Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) for the factors that have impact in SMEs cloud computing adoption process.

Statin use for cancer may be a potential protective effect in patients with chronic hepatitis B virus infection, according to our research. Statin use reduced the associated risk of liver cancer. With people eating westernized in Taiwan, the use of statins increases year by year. In addition to prevention of cardiovascular disease, also for patients with chronic hepatitis on the preventive effect of hepatocellular carcinoma, the economic benefits of statin use is worth studying. We used the National Health insurance data to explore so far, the statin use since 1997 and the clinical efficacy, including adverse effects. According to the results of our study, statin use for HCC had a potential protective effect in patients with chronic hepatitis B virus infection. Statin use reduced the associated risk of HCC, in particular, our studies found that statin use significantly reduced risk of HCC with cost-effectiveness.

This paper presents a proposed alternative system for queuing management that could reduce inconvenience to the public. The motivation of this system is depicted from an observation on the people queuing for services in the hospitals and the government offices without committing to the estimated time for their demand. Waiting for the service is counterproductive which consumes an unacceptable amount of productive time for the patients. We develop the system to manage the queue without physically lining up and allow people to monitor their queue status by their wireless handheld devices. The project accomplishes its objective as a tool to manage the hospital queue online where customers, patients and stakeholder can access theirs queues remotely over the Internet through a web application. The results benefit to both stakeholder to manage their time for other desire activities and hospitals in utilizing its spacious area for other business proposes.

In this paper, we present a smartphone-based hand-held indoor positioning system. The system collects data using the accelerometer, gyroscope and gravity virtual sensor sensors embedded in the smartphone. The accelerometer and gravity data are used to detect zero vertical speed and calculate the vertical displacement of each walking step, and then the Pythagorean Theorem is applied to calculate the step length of every step. Gyroscope data is used to estimate the direction angle. The step length and the direction angle of each step is combined to determine the coordinates of each step. A Kalman filter is used to reduce the vertical speed offset caused by accelerometer drift errors. The testing results show good performance of the proposed system.

Gaussian Mixture Models are among the most statistically mature methods which are used to make statistical inferences as well as performing unsupervised clustering. Formally, a gaussian mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the data set. In this paper, a probabilistic clustering based on the finite mixture models of the data distribution is suggested. An important issue in the finite mixture model-based clustering approach is to select the number of mixture components of clusters. In this sense, we focus on statistical inference for finite mixture models and illustrate how the variational Bayesian approach can be used to determine a suitable number of components in the case of a mixture of Gaussian distributions.

Whether a person’s upper limbs healthy or not will seriously impact his/her daily life. For those who have healthy, normal function of upper limbs or those who suffered from impairment but in the process of rehabilitation training, it is very important to remain appropriate, reasonable and moderate exercise to keep upper limbs functioning normally or in gradual recovery. This study use Kinect somatosensory device with Unity software to develop 3D situational games such as: “Shoulder finger ladder” and “Single curved shoulder”. The collected data from this training process can be uploaded via the internet to the cloud or server for participants to do self-inspection or it can be a reference for medical staffs to assess training effectiveness for those with impairments and planning in rehabilitation courses. In order to have more effective ways, researchers have imported games and virtual reality training effect to help those participants to train their upper limbs in a relaxed and extricated environment. In Shoulder finger ladder and Single curved shoulder training activities, the results of 8 subjects with normal upper limbs function represented that the system has good stability and reproducibility. And it also showed that motion of dominant side is more flexible than the non-dominant side. Flexibility and responsiveness in the elders are slightly behind the young. Another six weeks of training were held for subjects with frozen shoulder combined with Shoulder finger ladder and Single curved shoulder games. It showed that on the 3rd week, the average performances were stable. The T-test average score from 1–2 week and 3–4 weeks/5–6 weeks showed significant difference (P < 0.05).

In the modern busy work environment, the limb functional damage caused by career injury, sport injury, accident, and illness emerge in endlessly, which makes the rehabilitation training becoming one of the important projects in medical service. The upper limbs are the most frequently used part in the daily activities, so it is important to maintain the sound function of upper limbs; the function of upper limbs could perform normally by tempering the use or exercise of upper limbs, and making proper maintenance. The limb function injured person shall need proper reasonable rehabilitation training, in the expectation of recovering the normal function, to avoid influencing the future daily life. This paper takes the upper limbs rehabilitation training system development as the research topic, with the hand gliding cart of barcode scanner with wireless transmission function, aiming at the several barcode arrangement types, via the participator push the hand gliding cart to detect the move location, motion time, and whether within the preset track in the scanning way, to record the quantifying information. Through the analysis and integration of software program in the host, its result could be provided for the medical personnel as the key reference of training effect of the participator.

University campus may be very large or it may have many campuses. Every year lots of new students come in the university. Students need to buy books, stationeries and need to find something to eat. In those huge campus, it’s very difficult to find where to buy they needs. It creates problem to the new comers and visitors to reach easily and timely. Location-based services (LBS) now are very popular marketing tools. In this paper, we discuss the development a mobile application that delivers personalized campus maps for universities. Everyone can build own list to save that’s needed stores. In this paper, we discuss the development a mobile application it will combine campus maps and LBS with stores around universities. It will save problems for visitors, new comers and also new faculty; staff may found some stores they never find. For those stores, revenues will increase and profits will improve. If this feature is integrated with Google Maps, it will be very helpful both for existing and new comers of University campus. There will be an administrator who will update event information on server.

This text provides an efficient energy scheme of wireless sensor node dynamic deployment. A single sensor node communication model is defined initial. Sensors are co-working with their neighbors in their transmitting range. Suitable neighbor node number and sensing radius make sensors field more efficient. A value CAPR is defined in this text for power efficiency discriminating. A self-regulated mechanism is proposed here that sensor can adjust its radius of sensing range for high efficient energy working.

The Long Term Evolution (3GPP-LTE) combines the Multi-input multi-output (MIMO) antenna and the Orthogonal Frequency Division Multiple Access (OFDMA) techniques to accomplish high speed data transmission. Comparisons of zero forcing (ZF), minimum mean square error (MMSE) and sphere decoding (SD) equalization methods with Turbo code are given under time-varying multi-path fading channel with Doppler frequency shift. The simulation shows that the MMSE and SD equalizer are more robust than the ZF equalizer as the Doppler frequency shift increases.

This paper proposed a novel all-digital pulsewidth lock loops which adopted cyclic binary pulsewidth detector and cyclic delay line mechanism. This design has reduced the circuit area of delay line length increase under lower frequency operation, and it utilizes binary pulsewidth detection to lock output pulsewidth rapidly, whose locking time costs in only 25 duty cycles. Moreover, two delay lines is adopted in the pulsewidth generation mechanism circuit, and the cyclic delay line is employed under low frequency operation, but bypassed under high frequency operation for simplifying pulsewidth generating path. The output pulsewidth 25, 50, 75 % (by setting) could be generated by shift register, and the operating frequency range is 100 MHz to 3 GHz at CMOS 90 nm process simulation.

This paper presents multi-chip temperature sensing technique with simple identification circuits for system in package (SiP) application, to monitor temperature of each functional chip on a system package substrate. The temperature sensor is activated by a simple decoding circuit, and then sends back the temperature dependent pulse. Moreover, by using the BUS topology the control unit connects every sensor chips with only two wires to transceive data, and the routing complexity will not be increased with increased the number of sensor chips. This circuit has implemented in 0.18 μm CMOS process, chip area is 0.02 mm2.

The optical alignment of an off-axis parabolic (OAP) mirror has been successfully developed using the portable alignment device, the wedge optical plate lateral shearing interferometer and the CCD camera. In this method the optical axis of an OAP mirror is made parallel to the “five incident parallel laser beams” in the plane of incidence, by checking direction of these five reflected laser beams and changing the height and orientation of the OAP mirror. The lateral interferometer is referred to as the layout where opposite beams travel in difference directions, encountering exactly the same components until they emerge to form interference pattern. The lateral shearing interferometer uses to examine the parallel of five incident parallel laser beams. This fast aligning method for finding the optical axis of an OAP mirror can measure the Slant Focal Length deviation to an accuracy of 0.5 %.

In this paper, the two wavelength laser color alignment device, the additive color mixing method and the CCD camera is used to rapidly and accurately align the optical axis of a two-mirror telescope (TMT). In this method the optical axis of a TMT is made parallel to the “five incident parallel laser beams” in the plane of incidence, by checking direction of these five reflected laser beams and changing the height and orientation of the Two-mirror telescope. The two wavelength laser color alignment device emit five laser beams at two different visible wavelengths, including blue lasers with 405 nm at horizontal axis and the other red lasers with 645 nm at vertical axis. The combined dot will become magenta once parallel laser beams focus on focus point of two-mirror telescope and their beams overlap. The blue laser beam and red laser beam are added to the magenta light by the additive color mixing. The additive color mixing method uses to find the best focus and get minimum spot diameter of the TMT. This fast aligning method for finding the optical axis of a two-mirror telescope can measure the effective focal length deviation to an accuracy of 0.9 %.

Seidel aberration has been used successfully in finding starting points for a lens system, especially in designing a relay lens, which may have the symmetric condition with aperture stop, and the benefit of zero Seidel aberrations of com, distortion, and transverse chromatic aberration. In this paper, a relay lens has been designed based on the principle of zero Seidel aberrations. The MTF of the design is near diffraction limited, which has an excellent image performance of high resolution.

As the prosperous development of LED fabrication technology, the solid lighting has been a very popular issue in illumination. Not only does the LEDs light source provide a choice of energy-saving and longer lifetime, but also it could change its color-temperature depending on weather or traffic conditions. However, due to the droplets in fog, the propagation of light will be affected differently according to the wavelength. In this paper, the spectra of 4 LED white-light sources, which were RGBY-mixed or phosphors-activated LEDs with high or low color temperature, were explored before and after passing through 5 different frog conditions. The results showed that the color temperature alteration of each white-light source was so different from clear to heaviest fogs. The spectra analysis suggested that the short wavelength (blue) suffered more scattering attenuation than the longer one (red) while passing through fog.

Tablet pc with camera has become the standard function in recent years. Especially high definition camera is the primary specification of high level tablet pc. In this paper, we design a 13 mega-pixels lens system by Zemax. The system includes five plastic aspheric lenses, an IR cut off filter and a sensor cover glass. The F number and FOV of the camera are 2.4 and 64° respectively. The sensor have 13 mega-pixels which is made by OmniVision, the maximum resolution is 4224 × 3120 and the pixel size is 1.3 μm. The design result shows that RMS spot radius at different fields are small, so it is closed to the diffraction limit. The MTF value is more than 0.45 in most fields of view at 1/2 Nyquist sampling frequency.

The optical alignment of two-wavelength optical microscope (TWOM) has been successfully developed using the portable alignment device, right-angle prism and the CCD camera. In this method the optical axis of two-wavelength optical microscope is made parallel to the “five incident parallel laser beams” in the plane of incidence, by checking direction of these five reflected laser beams and changing the height and orientation of the MWOM. Right angle prisms are generally used to achieve a 90° light path bend. This produces a left-handed image and depending on the orientation of the prism, the image may be inverted or reverted. The prism uses to examine the vertical angle of between input laser beam and output laser beam of a plurality of light source. This novel method can rapidly and accurately align the optical axis of MWOM.

This paper discusses the relevance of the non-contact intraocular pressure (non-contact tonometer, NCT) and the diopter value measurements of refractive errors. Methods for the measurement of both eyes IOP of 192 patients, and binocular eye refraction as well, then the measured data were statistically analyzed. Research on age, gender, which eye, refractive power and refractive status (i.e. myopia, face, and hyperopia) affects the values of non-contact tonometer measurements. The results of refractive measurements were as follows: myopia, emmetropia and hyperopia accounted as 47.92, 15.62 and 36.46 % respectively. IOP measurement of all ages: age of 10 years old group (17.74 ± 3.34) mmHg, 10–20 years old group (17.85 ± 3.34) mmHg. According to gender, the male gender (17.94 ± 3.12) mmHg, and female (17.92 ± 3.23) mmHg. Group of the different eye, right eyes IOP was (18.05 ± 3.12) mmHg, left eyes IOP was (17.88 ± 3.17) mmHg. According to refractive errors: myopia group IOP was (17.83 ± 2.95) mmHg, hyperopia group IOP was (18.12 ± 2.94) mmHg, and emmetropia group IOP was (17.81 ± 3.16) mmHg. The results are: As we age, non-contact tonometry values tended to increase; gender has no effect on IOP measurement; detecting IOP found the value of the right eyes higher than the left eye; the refractive errors of (hyperopia, myopia and emmetropia); The values of IOP of myopia and hyperopia are higher than the emmetropia eyes.

Automated spray painting is an important process in the manufacturing of many products. In order to ensure computational efficiency, a new tool trajectory optimization scheme based on T-Bézier curve is developed. And a T-Bézier basis is presented in trajectory optimization problem. The tool trajectory is formed through offsetting the distance between spray gun and the free-form surface along the normal vectors. Automotive body parts, which are free-form surfaces, are used to test the scheme. The results of experiments have shown that the trajectory planning algorithm achieves satisfactory performance. This algorithm can also be extended to other applications.

The convenience and popularity of nowadays internet has more influenced as we know and the internet is actually essential on the daily life of individuals. Thus, internet addiction issues become topics as well as the main purpose of this study to identify and conclude how the generally problem—game addiction caused one’s interpersonal communication difficulties accompany disorder live and behavioral bias. This research refereed the remarkable internet addiction theory from S. Young Dr. Kimberly, and support from his contribution of Internet Addiction Rating Scale (Internet Addiction Test, IAT), we’ve turned it into a “game addiction questionnaire” continually, and via the questionnaire analysis to rank one’s addiction level to the hot games aims to find the time interval versus addiction for each hot game. The statistics of survey questionnaire questions are Likert 5 point questions, 20 questions to answer and calculate, and the value of average scores and time spent then be analysis to propose the Game Addiction interval with incremental increasing function, that we can recognize how long people will be addictive while they began to play and when they’ll appear to be such regular and continually addition, namely, we may hint the addition level of games, then according these data analysis to specify different types of game addition classification and provide better users notification whether it is hazardous, therefore prevent users to become the next victims of game addiction.

The trailing suction hopper dredger dredging model contains many parameters related to the soil types. And the parameters have big difference under different soil conditions. In this paper, genetic algorithms are used to estimate the parameters associated with soil type in a hopper dredger dredging model. The results were compared with the actual monitoring data, and the optimal parameter estimating values were obtained. The example showed that this approach of parameter estimation, based on genetic algorithms, is applicable.

Control system of controllable pitch propeller (CPP) is the key device of ship propulsion system. Along with better requests of the response speed and handling quality in ship propulsion system, the control system has been designed based on Siemens PLC and PROFIBUS, in order to enhance the performance of real-time, maneuverability, stability and reliability.

Line Heating (LH) is the main method for forming ship-hull plates. And it’s mainly operated by skilled workers manually, so the accuracy of final shape and the productivity solely depend on the experience of the workers. In order to predict the surface deformation of plates, a new method is developed to determine the processing parameters and improve the productivity. Firstly, LH process is simulated by Finite Element Analysis (FEA) according to the complexity of LH mechanism. Secondly, a model of Artificial Neural Network (ANN) is established. Finally, the computation results of simulation by FEA are applied to train the ANN model. In the way, a method of surface deformation prediction is proposed for real time analysis.

Through the researches of emergency supplies dispatching problem have formed a certain scale. However, the sudden and diversity of events can easily lead to the lack of relevant statistical data and errors. Thus, it’s easy to lead the mistakes on analysis and decision-making. The Internet of Things technology in the field of emergency supplies scheduling, it can provide more effective real time information to dispatch emergency supplies acquisition and analysis process, assist decision makers draft the scheduling plan, improve the scheduling efficiency. So that it can reduce the social and economic losses, the maximum time to reach maximum efficiency and cost minimization emergency rescue loss goal.

As the development of China, The logistics industry has also made great strides, and there is lots of software to give the simulation of this system. Flexsim is one of them, which can be used to build up the discrete system and an ideal choice for the simulation of the Automatic Storage and Retrieval System (AS/RS). In this passage, taking the AS/RS as an example, by building the simulation of this system using Flexsim, we make some analysis about and then give some measures for optimization.

The abstract should summarize the contents of the paper and the control system is designed for the new multi-functional and model-switched remotely operated vehicle, which is developed for detection and decontamination of underwater structure in Ocean Engineering. The dynamic propulsion system and controller unit was designed by analyzing the underwater motion force of robot. The surface console consist of power supply, control system, communication interface and PC software. The console can control the robot to complete medium-scale searching and point-fixed detection under remote control or monitoring. And the robot can switch modes each other between floating or climbing. Tests including posture, navigation, depth, monitoring and crawling clean-up verify that the ROV control system has reliable performances and it can satisfy the tasks in underwater complex environment.

As we know, social platform has been very popular for many years, such as Facebook, Twitter, Youtube and so on. User on social platform may retrieve lots of information (breaking news, comments, sharing article…). Those information lead user to learn as their new knowledge, it’s one kind of social learning. Otherwise, the popularity of portable devices (cell phone, Tablet) also help user to access internet and social platform in anytime and anywhere, user can get knowledge from social platform. In this paper, we conduct an experiment to compare the students learning on Elgg social platform on portable devices and the students learning in traditional methods. The results show us the students learning with social learning environment have better learning efficiency.

This research can offer the function for users with different limits of authentication to access the system of control list; it includes the user identification, group identification and authorization. The authentication information stores in the smart cards in user authentication phase and adopts the Elliptic Curve Cryptography (ECC), hash function and symmetric key algorithm to get the authentication. Besides, this scheme contents forward security and backward security in order to protect the information security of patients. Compares with the previous authentication mechanisms which were proposed by other papers, the scheme of this research can provide more functions and higher safety on the application of WMSN without increasing the costs of communication and operation.

Out of Hospital Cardiac Arrest (OHCA) is an important medical and public health issue. Emergency first aid service prior to hospital admission is an important indicator for the quality evaluation of the emergency medical service. OHCA frequently occurs without warning, and while there are clear steps in emergency first aid concerning the treatment of OHCA patients, their survivability diminishes if they cannot receive emergency first aid services in time. Using statistical methods such as chi-square test, logistic regression, and decision tree, the influence factors were analyzed and extracted. In addition, combining the strengths of three independent spatial clustering analysis methods, namely, the Global Moran’s Index for finding the spatial clustering, as well as the Local Moran’s Index and spatial autocorrelation analysis Getis-Ord Gi* algorithm, a novel summary approach to identify high-risk OHCA areas. The Global Moran’s Index of OHCA event locations were 0.025861, with a Z-score of 8.178045, indicating significance spatial clustering phenomenon of OHCA locations, Getis-Ord Gi* covers more towns (urban areas), but the High-High area reaching statistical standards obtained through the Local Moran’s Index also has also appeared in the high clusters Area found through search using the Getis-Ord Gi*. In addition, the important factors found through the decision tree analysis method have more space distribution coverage. When OHCA occurs, based on findings in this study, the 119-dispatch duty officer may make further inquiries regarding medical history of heart disease or diabetes, which shall serve as a reference for future dispatch of senior technicians. Based on the OHCA-prone hot zone generated by the Getis-Ord Gi* and targeting OHCA patients’ past medical history of heart disease or diabetes, public health units may adopt information technology or wearable devices as intervention in order to increase the probability of eyewitnesses and prioritize the dispatch of emergency aid resources into the hot zone, thereby enhancing OHCA patient survival rates.

This research describes the social event collection and analysis system developed by Institute for Information Industry. The system can collect more than 30,000 web data items per day for the government to understand public opinion on policy and for companies needing business insights or seeking to provide exposure for their brands on the Internet.

The goal of this study was to analyze the characteristics and purchase probability of different customers groups in the retail store POYA. We abstracted CheckMe app user records about the retail store POYA. The dates of those user records were between January 2015 and April 2015. Furthermore, we collected the Facebook information of the users. All statistical procedures were performed with our Persona Analysis Platform. The purchase probability of female subjects was nearly twice than male subjects in POYA. Although, the main subjects were 18–39 years old, the purchase probability of 40–44 years old subjects was higher than others. Most of the customers were from the metropolises. However, the purchase probability of the subjects in Hsinchu and Pingtung was higher than that in other cities. After we provided analysis results to POYA, it improved its promotions and got an up to 10 % conversion rate improvement.

In delay tolerant networks (DTNs), simulations used to verify the performance of a routing algorithm usually employ a mobility model, either trace or synthetic. Trace models record the actual movement of individuals in the real world; however, obtaining data can be difficult. Synthetic models use mathematical modelling, which eliminates the need to obtain data from participants; however, this means that the results are not necessarily representative of the actual movement of individuals. This study collected information related to the movement of university students using a specially designed APP featuring location aware and behavior aware functionality. This APP tracks the movement of students in a campus environment and then exports the data for simulation. We also developed a method for the distribution of flyers only to individuals who express an interest in the content of that particular message who can then forward the flyers to others. Simulation results demonstrate that the proposed method is able to enhance the successful delivery ratio while reducing delivery overhead and thereby improve the dissemination of data on campus.

This paper aims to investigate the heterogeneous class grouping operation practiced in elementary and junior high schools and propose a two dimensional heterogeneous class grouping model which facilitates the equality of class size and students’ backgrounds.

This paper demonstrated the efficiency of visualized interface for the music sight-singing learning on the Internet. The self-generated visualization on music sight-singing learning system incorporates pitch recognition engine and visualized pitch distinguishing waveforms with descriptions for each corresponding stave notation on the web page to bridge the gap between singing of pitch and music notation. There are three anticipated effects from the design of web-based learning system of self-generated visualization on pitch recognition for the music education in sight-singing: visualizing sight-singing music notes, tuning errors in sight-singing visually with quantified scale, and transforming hearing into vision resulting in individualized sight-singing learning This paper shows the conducted research results that this web-based sight-singing learning system could scaffold cognition about aural skills effectively for the learner through the Internet.

According to the Internet Crime Complaint Center (IC3) reports from 2006 to 2014, we can find the online fraud cases are increasing rapidly year by year. Although the online auction web site is the biggest platform for online transaction, it brings the huge chances to do the online auction frauds. To prevent the online auction frauds, this research will propose a fuzzy genetic approach to learn the detection rules for detect the fraudster accounts. The goal of this research is to help the users to identify which seller is more dangerous. The seller behavior features will transform into fuzzy rules which can represent the detection rules. Then optimize the fuzzy rules by genetic algorithms to build the auction fraud detection model. For implementation, we collect the real auction data from “Ruten Auction” which is the most popular auction site in Taiwan. Then we use the proposed detection model to analyze the fraudster accounts and find out the optimal detection rules of them. We hope the result of this research can help the website administrators to detect the possible seller fraudsters easier in online auction.

With the development of information technology, many industries imported information system to improve the efficiency and output of work. In recent years, interactive digital learning has become the focus of future development projects domestic educational institutions teaching. The after-class school entrepreneurs to import the interactive e-Learning technology system to assist teachers in teaching. Because the overlap between functionality of the interactive e-Learning technology system and after-class schools teachers, those teachers are using the system would therefore be excluded assisted instruction. When the after-class school teachers use this system for assisted teaching, the factors which affecting their willingness is similar with the factors which affecting to other users suffered new information systems imported in other area or not, is also an important issue in this thesis is wish to explore and to discuss. In this paper, through literature analysis and discussion, the use of technology acceptance model 3 as the theoretical model of this thesis. This thesis collection and research framework and measure dimensions scales converted into suitable investigation cram school teacher for the acceptance of the interactive e-Learning technology system questionnaire. In this thesis, teachers who use the system to support teaching cram as research object, paper questionnaires distributed and collected manner survey and sampling data were described in the statistical analysis of samples, mean test, reliability and validity analysis, regression analysis, to verify the impact of the various facets of each other. The paper hope through this research can help to complement modern e-Learning education industry development. The social event collection and analysis system developed by Institute for Information Industry. The system can collect more than 30,000 web data items per day for the government to understand public opinion on policy and for companies needing business insights or seeking to provide exposure for their brands on the Internet.

This study reviews and demonstrates the diverse issues and findings in the research field of high frequency trading. This diversity may root from the emerging nature of computing technology and its wide appeal as well as unique researcher and practitioner viewpoints. The authors propose Bibliometric Analysis might be used to identify some fruitful research opportunities.

This paper presents wearable computing in the Art and interactive performance. With AR/VR technology, human can play with virtual characters. However, people might feel bored to type keyboard or joysticks to play with computer and video game characters. We develop our motion sensors to capture user’s motion. In this way, it becomes more interesting and fresh to swing your hands or shake your hips to interact with virtual characters. In the future, the wearable computing will be more and more popular, so we try to merge these sensors to let everyone can wear these tiny and cute sensors to experience. We believe this will be a new bright spot of the world.

This study explored a dual-sensory user interface design that met the requirements of elderly people with low vision in mouse manipulation. The study showed significant efficiencies in integration of dual senses assisting the difficulties of the unimodal input, specifically the hearing input. The results demonstrate a new conceptualization of integration that is more than just the combination of ability to move with sight, hear, and integrate. The study focused on assessing the implications of difficult unimodal inputs on mouse manipulation as the physiologies of elderly participants are degenerating. The Acoustic Assistance on Cursor Navigation design works with an aural assistance environment to allow the elderly people to select either the appropriate functions of Talking Aid or Cursor Positioning to recognize what objects they are pointing at and to navigate where the mouse cursor they are moving within the computer windows. An advanced level of GPS-like loudspeaker in the computer windows environment, the Acoustic Assistance on Cursor Navigation design can help guide the elderly people with low vision by hearing sensory information to facilitate identifying and navigating where they are in the computer world.

Collective intention, known as we-intention, should play a role in the adoption of cloud services that provide social networking or collaborative functions. In this research, the author explores the effects of we-intention on adoption of cloud drive, including both direct and moderation effects. The result shows that we-intention is a cause of adoption, but if the effect of usability on adoption is controlled, we-intention has little direct and moderation effects.

Reducing information system project risks and improving organizational performance has become an important research issue. In this study, a research framework is constructed from the Stimulus-Organism-Response (S-O-R) framework, comprising the stimulus of project risk, the organism of project management, and the response of organizational performance. Cloud CRM experts projects management experience has many years in this study for the interview sample. DEMATEL-Based ANP (DANP) is MCDM analysis tool that haven’t any presumptions under the premise to explore dynamic relationship among project risk, project management, and organizational performance. The following empirical results were obtained: (a) effective project management reduced project risk and enhanced organizational performance; (b) of all of the types of project risk, organizational environment risk is the most challenging; (c) support from senior managers is crucial in project management; and (d) the multidimensional aspects of a organizational performance have garnered equal amounts of attention, indicating that financial performance is not the only important target.

In recent years, cloud technology has been widely used, including enterprise CRM systems. To reduce the cloud CRM project risk, that improves organizational performance, so it has become an important issue. In this study, a research framework is constructed by the Stimulus-Organism-Response (S-O-R) framework, that through DANP (DEMATEL based ANP) and VIKOR research methods explore the relationship in project risk, project management and organizational performance. The findings of this research can provide a valuable reference for minimizing project management risk and enhancing organizational performance through effective project management in cloud CRM project.

With the rise of the Internet, e-commerce vigorously grows and more and more websites gradually emerge. If the websites want to keep competitive advantage and sustainable development in the highly competitive environment, they inevitably need to provide consumers with high-quality service to create excellent experience for consumers and win the customers’ heart to establish mutually beneficial and long-term relationship. In this study, we chose three well-known domestic e-stores, use VIKOR method, in the optimal solution way to compare performance of dimension and criteria, and then propose improving suggestions and strategies to reduce the gap.

The purpose of this study is to investigate the relationship between intellectual capital and firm performance for pharmaceutical industry in Taiwan. As we know, the intellectual capital has becoming the main resource for creating companies’ value and the competitiveness of an enterprise. R&D productivity is often considered as the key success factor for creating firms’ value of pharmaceutical industry. The 252 firm-year observations of this research are collected from companies of pharmaceutical industry listed on Taiwan Stock Exchange and Gre Tai Securities Market dated from 2007 through 2013. Financial data of this research is collected from Taiwan Economic Journal (TEJ) database. I find evidence revealing that there is a positive correlation between intellectual capital and firm performance.

This study investigates whether voluntary disclosure provides future earnings information to investor in the capital market of Taiwan. Using 165 firm-year observations collected from the Taiwan Economic Journal (TEJ) financial database for companies listed on the Taiwan Stock Exchange and Gre Tai Securities Market in 2005 with December as the fiscal year-end. The result shows that voluntary disclosure improves the association between current stock returns and future earnings.

Even though many kinds of filters algorithms have been developed for cancelling impulse noise from digital images in the past few decades, the issue of image restoration and reconstruction is still regarded for many researchers nowadays. In this study, a smart design of pre-processing classifier is used to categorize several distribution types which were identified as noise by noise detection. This smart design can be used for extracting useful local information from the corrupted image and hopefully results more image details to preserve for image filter. The pre-processing classifier goes through four-phase detection procedures to determine the condition of central pixel of local image window by using the similarity between neighbouring pixels. Simulation results show that our algorithm has extraordinary effective and accurate ability for classifying the condition of noise type.

The technique of search-based face annotation is implemented by mining weakly labeled facial images that are freely collected from the internet web sites but is incompletely correct label data. In this study, the particle swarm algorithm and binary particle swarm algorithm are used to achieve the technique of Unsupervised Label Refinement (ULR) for refining the labels of web facial images. The experimental data is provided from IMDb website and with 45 % initial incorrect label mark rate. The results show that the particle swarm algorithm and binary particle swarm algorithm have the better correction rate and convergence performance than other approaches.

Data Services is a convenient mechanism to provide a service interface to access data from a database. Accordingly, providing data as a service not only encourages the access to data anywhere, at any time, but also reduces the cost of an application system implementation. The computer game is an interactive medium that can effectively to help us to learn some subjects such as a serious game. An interactive game may include a data exchange function to provide information to run the game in each different game level. This paper utilized a data service framework to build a game server for providing game information to the interactive game. And this paper also included a real case to demonstrate the feasibility and merits of this proposed computer game design approach.

This paper uses empirical financial data, which were announced on the website between 2007 and 2014, of food industry companies listed in the Taiwan Stock Exchange (TWSE) to verify the Financial Diagnosis System (FDS). The FDS sets up criteria by ranking the food industry companies’ financial indicators and grouping each indicator into five classes from the best to the worst. A company, for those listed as well as unlisted in the stock exchange, will be evaluated its performance of the certain financial indicator by ranking it into one of the five classes as the best, the better, the average, the worse, the worst. The FDS also sets up five forces, which is stability, profitability, activity, Growth Potential, productivity, financial analysis criteria by classifying financial indicators into five forces, ranking each force into five classes from the best to the worst.

Housing purchase at different stages of life cycle of various people is a complicated but important decision. Classification rule mining is a common technology in the field of data mining. Classification Rules can be generalized to classify unknown samples or predict the future from the mining of historical data. This paper proposes a rule-based approach to housing purchase life cycle on demands through C4.5 classification method. An example, a real estate transaction data set in a government, is utilized in the paper to illustrate the utility of the proposed approach and further evaluated its prediction accuracy, precision and recall. The established prediction model with the aid of the derived classification rules helps various people make appropriate decisions following different housing purchase life cycle stages on demands.

Internet of things (IoT) system have a large number of sensors, and each sensor will generate a large amount of real-time streaming data. So real-time database technology for IoT network is very important to achieve real-time data stream generated by data aggregation, query, analysis and data mining. The biggest problem of the real time data management is data overload and how to efficiently find the data, temporal data index is a good solution. The paper investigated AP+ tree design idea, operation algorithms including query, insertion, deletion and reconstruction. AP+ tree index structure is adopted for the real-time database in order to improve the efficiency of temporal queries. The result shows that the search efficiency of AP+ tree is 1.2 time of B+ tree under certain condition.

In big data era, how to reasonably divide various types of data and efficiently store so large and complex data sets is an important challenge. This paper proposes a layered and configurable storage model to improve the storage capability of big data. First, three-layer hybrid row-column-store storage model is presented, which contains metadata definition layer, key-value model layer, and data physical storage layer. This model combines the advantages of row-store and column-store. Second, based on the three-layer storage model, the realization process is presented. According to the characteristics of each column of data, the suitable storage mode of row-store or column-store is chosen. The proposed model can provide a new technology support for the storage of big data.

High Performance computing (HPC) are becoming not only more complex but also challenging in terms of speedup and scalability. As the size of compute intensive problems increases, Intel MIC architecture comes true. In this paper, PCG method based on Intel MIC architecture is employed to solve large scale linear equations. The numerical results show that PCG method based on Intel MIC architecture has a considerable speedup and scalability.

At present, consumers are accustomed to judge the quality of goods according to online reviews. However, e-commerce sites are always filled with lots of less useful reviews, which is inconvenient for customers. This paper proposes a method for assessing the helpfulness of Chinese online reviews based on writing behavior. The proposed method recognizes the writing behavior, such as Tail-Insertion, Non-Tail-Insertion and Selected-Modification by monitoring the change of the comment input box on goods page, and then a linear weighted model is established on the writing behavior, writing speed and product features of the review and used to assess the helpfulness of reviews. Experimental results show that the model can accurately and efficiently recognize the useful reviews.

Association Link Network (ALN) which can effectively support many Web intelligent activities such as Web-based learning, Web knowledge discovery and semantic search. As one of the most robust measures of network topology, average path length plays an important role in the transport and communication within a network. This paper analyzes the average path length of Association Link Network (ALN). First, the network density of ALN is analyzed to get the functional relation between network density and its parameters. Then, based on the achieved result of network density function, the approximate solution of average path length for ALN is deduced. The experimental results show our approximate solution has high precision.

Currently, with the explosion of multimedia data (image, video and audio) from remote sensors, mobile image captures, social sharing, the web, TV shows and movies, huge volume of images are being generated and consumed daily. The availability of massive images has created fundamental challenges to image processing and analysis. Big Data is a term used to refer to massive and complex datasets made up of a variety of data structures, including structured, semi-structured, and unstructured data. To address these challenges, we propose a model design methodology using collective intelligence for big data analytics. The data and data-transfer contracts then become the primary organizing constructs. With controlled data relations and timing, the system can then be built from independent agents with loosely coupled behaviors. This data-driven design technique is naturally supported by the Data Distribution Service (DDS) specification, which is a standard from the Object Management Group.

The traffic surveillance system is used to quickly and accurately determine the traffic, release traffic information in time, and reduce the number of traffic accidents, traffic jams and road damage. The traffic surveillance system makes the highway fast, safe, comfortable and efficient. The existing surveillance system mainly relies on the surveillance personnel to monitor, but there are too many surveillance videos so that it is difficult to find the user’s interested content. In this paper, we propose Video surveillance system which is based on the technology of video structural description. Then we apply this system to the high-speed service area. Video structural description (VSD) is according to the semantic relation and adopting spatiotemporal segmentation, feature extraction, object recognition and so on, putting video content into the text information which understood by the human and computer.

How to automatically realize acquisition, refining and fast retrieval of the pedestrian face image in surveillance video is of great importance in public security visual surveillance field. This paper proposes a new gun-dome camera cooperative system which solves the above problem partly. The system adopts static panorama-variable view dual-camera cooperative video-monitoring system. As respect to the face detection, the deep learning architecture is exploited and proves it effectiveness. The experimental results show the effectiveness and efficiency of the dual-camera system in close-up face image acquisition.

The processing efficiency of Intelligent Transportation System has become an issue which is attracting more and more attention in order to combat vehicle crimes. The vehicle color recognition plays an important role in identifying, searching, improving and enhancing vehicle Intelligent Transportation System. However, area identification or location and surface high light detection are needed before vehicle color recognition. Convolution neural network training algorithm is taken as the best choice to conduct vehicle color recognition for its parameters are less and computation speed is faster. Neural network algorithm is of high parallelism, and its algorithm can even be further optimized if CUDA acceleration is selected. This research results have reference value for improving the processing efficiency of Intelligent Transportation System.

This paper proposes a video retargeting method for intelligent sensing of surveillence devices. The spatiotemporal saliency model is firstly built by evaluating the contrasts between the global histograms and each regional histogram. Based on the spatiotemporal saliency map, a salient object detection method is used to locate salient object regions in the video. Then the size of cropping window is evaluated based on the moving objects. Finally cropping and scaling operations are performed on the basis of salient object regions to generate the retargeted video.

Web oriented knowledge acquisition has become the important way for people to acquire knowledge. How to help user accurately to obtain personalized knowledge in network, is an important problem for Web service. In this paper, we present a web knowledge acquisition model based on human cognitive process that can improve the precision of knowledge acquisition and meet the personalized requirements of users. The user cognitive model (UCM) designed in this paper can improve the interaction with web, and then promote the web services development of personalized, accurate and intelligent.

With rapid economic development, enterprises start to much concern about employees’ stress while working. The higher the job stress, the lower the job performances. The present research investigated the relationship among job stress, satisfaction, and performance by examining the employees’ attitudes through collecting data from the industries of medical care, plastic spare parts, and automobile spare parts. The results indicated that employees’ performance in working places was somehow related to their work stress and the extent of job satisfaction. Based on the results, suggestions were provided for managers for better employees’ management.

With the development of science and technology and the increasingly fierce competition of innovation, the virtual innovation cluster (VIC) has become an important way of innovation. This paper studies the influence factors of the formation of VIC, in order that more VICs can be built well. A new model of factors is proposed, in which some new variables such as searching for partners, negotiation, internal recognition, external recognition and structure level are used to describe the situation of VIC’s formation, and other new variables such as members’ quality and relationship are increased into the influence factors. The empirical research shows that the factors of the government’s promotion, members’ quality, members’ relationship and innovative spirit play an important role in the formation of VIC.

In China, new-type R&D organization have become a new policy way to explore and deepen the reform of system of science and technology. After reviewing the developing history of new-type R&D organization in Guangdong, summing up its development path, analysing the reasons for its success, this paper put three suggestions for the development of chinese new research institutions. Firstly, the new-type R&D organization is an effective way to promote the sustainable development of local economy. Secondly, scientific research institutions and development cannot do without government’s support and guidance. Thirdly, the planning of the development path of scientific research institutions in different province should be combined with the situation of itself.

With Chinese agricultural developments, agricultural industry clusters emerge as a new medium to promote agricultural technology. The extent of government intervention, the completeness of market, the internal competition in clusters, and the extent of technology demands show great differences among the government- and market-led industry clusters. Therefore, each entity’s agricultural technology diffusion mechanisms and dominating force are different as well. Government policies and leading enterprises play important roles to promote agricultural technology diffusion in agricultural industry clusters.

Zhang and Wang proposed a signature scheme without using one-way hash function and message redundancy. It based on Chang-Chang scheme and gives an improvement which overcomes the known forgery attack. In this paper, we show this scheme can not suffer forgery attack where it does not use the one-way hash function. We believe the one-way hash function still demands message recovery and redundancy.

In 2002, Hwang et al. proposed an ElGamal-like cryptosystem for enciphering large message where it modified from ElGamal cryptosystem. They believe their scheme is based on the difficulty of finding the composite exclusive-or operation. Although, they used bitwise exclusive-or to against multiplicative attack. For this scheme, it is still insecure. In this paper, we give a proof to certain that we claimed.

Since the change of economical level and population structure recently in Taiwan, a phenomenon of non-marriage and fertility declination is getting obvious. Therefore, parents emphasize the high quality of childcare education. This study mainly explores the relationship between childcare staffs working pressure as well as their efficiency in kindergartens and the management of children interpersonal conflict. With the results of a questionnaire and a rule base by specialists, the teacher working efficiency and pressure index can be obtained through the fuzzy inference system. From this study, we can conclude the childcare staff working pressure is not identical because of their various individual backgrounds and the management of children interpersonal conflict is totally different in each kindergarten. This study result can be submitted to administrators for reference.

We found an error in the theorem 2.3 from original paper [Gholamreza Zabandan, Abasalt Bodaghi and Adem Kilicman. Journal of Inequalities and Applications, DOI: 10.1186/1029-242X-2012-215]. Hence, the correct statement of the equation is provided in this paper.