Proceedings of Seventh International Congress on Information and Communication Technology

This work synthesizes two didactic experiences carried out in the Digital Graphics Workshop, an optional course of the School of Architecture, Design, and Urbanism of the Universidad Nacional del Litoral, based in the city of Santa Fe (Argentina). The workshop proposes a transdisciplinary look that integrates knowledge and practices of three careers—architecture, visual communication design, and industrial design—to achieve a creative appropriation of the available technological instruments, in order to contribute from the design to specific problematic situations. From the professorship, we take ICT as a means to operate in the design processes, either in the ideation, development, production and/or communication stage. Technologies are not an end in themselves but a means to enhance our practices as designers operating in a community. Thus, the question is not what can we do with the available technologies? But, in what way, can we use them to help improve the living conditions of our society?

This paper presents a prototype for a Web application recommendation system’s content applied to movies’ recommendations. It learns the pattern of user content consumption, predicting what he will consume in future based on similar items to those he has shown interest. It considers similarity with neighbor users, thus creating a user model. Content-based filtering, collaborative filtering, and memory-based on hybrid filtering techniques are used. Content-based filtering allows to extract the fundamental features or attributes of the items and select similar items. Moreover, it proposes predicted classifications for the items of interest not yet classified by the active user. Collaborative filtering allows applying the KNN methodology to identify the similarity between the active user located in the neighborhood and propose predicted classifications for items of interest not yet classified. Hybrid filtering combines the two methodologies to overcome their drawbacks. A weighted approach is applied, allowing a dynamic linear combination of collaborative and content-based filtering. The results obtained were empirically relevant in the experimental evaluation, matching with the results presented in similar studies validated with RMSE metrics.

The purpose of this study is to determine the Web site's success in providing public information for the Ngawi Regency Local Government. The Delone and Mclean approach is used to evaluate a Web site as an information system. The study employs a quantitative methodology. The data were gathered by the distribution of questionnaires to 100 respondents. Structural equation modeling (SEM) and Smart PLS 3.0 partial least squares (PLSs) software were used to analyze the data. The findings indicated that all indicators were deemed genuine, and the variables had a high degree of reliability—information quality has an effect on Web site-user happiness. Meanwhile, service quality has a favorable influence on Web site users’ use and satisfaction. Meanwhile, the quality of the system has minimal influence on its usability or user satisfaction.

This study aims to determine the factors that influence public trust in the government. Indonesian people's belief in the government has begun to decline, especially during the COVID-19 pandemic. So that it is crucial in this study to test the theory of the independent hypothesis built to see the relationship between people's intentions to trust the government. This study uses data from questionnaires that the people of Bantul Regency have given responses to. The number of research respondents was 100 respondents and processed using SmartPLS 3.0 Software. The results showed that of the three hypotheses tested; all showed a positive relationship concerning trust in the government. Variable perceived usefulness has become an accepted variable according to the hypothesis. This research is used as input for the Bantul Regency Government to increase public trust in the government, mainly focusing on variables that have positive implications.

Internet of things (IoT) has significantly altered the traditional lifestyle to a highly technologically advanced society. Some of the significant transformations that have been achieved through IoT are smart homes, smart transportation, smart city, and control of pollution. A considerable number of studies have been conducted and continues to be done to increase the use of technology through IoT. Furthermore, the research about IoT has not been done fully in improving the application of technology through IoT. Besides, IoT experiences several problems that need to be considered in order to get the full capability of IoT in changing society. This research paper addresses the key applications of IoT, the architecture of IoT, and the key issues affecting IoT. In addition, the paper highlights how big data analytics is essential in improving the effectiveness of IoT in various applications within society.

The rapid development of technology that has involved neurosciences and human–computer interaction has provided solutions to several problems. Brain-computer interface so-called BCI has opened the door to several new research areas and has given way out to critical issues. It has provided solutions to support paralyzed patients to interact with the outside world. This review work presents the state-of-the-art methods and techniques of feature extraction and classifications. These are the methods used to extract and classify the EEG signals. In another way, the features of interest that we are looking for in the EEG-BCI analyzes. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers understand state-of-the-art methods available in this field, their pros and cons, their mathematical representations, and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) categorization/classifications of the SSVEP-based BCI extraction and classification methods, (2) stating most of the prominent methods used in this field in a hierarchical way, and (3) explaining pros and cons of each method and their performance.

Biomechanical models of the myocardium provide more details of the heart behavior and several biomechanical parameters. Thus, biomechanical heart models are important for improving clinical treatment and interventions for patients with heart failure. The aims of this study are to present a biomechanical human left ventricle (LV) models that are derived from clinical imaging data of 20 healthy subjects. End-systolic volume (ESV), end-diastolic volume (EDV), and end-diastole wall thickness from 20 health subjects were computed using cardiac CMR data and personalized cardiac modeling. The results reveal that the computed parameters are in accordance with the normal values of healthy subjects. The outcome of this study suggests that the proposed 3D model of the LV is able to describe the physiological function of the heart and to differentiate between normal and pathological heart function.

Classification for multi-class dataset provides exciting and explorative domain to be studied in data science domain. And yet, the challenges of measuring the accuracy of multi-class performance rise an issue worth detailed research to be explored. Due to multi-class accuracy may be lower due to imbalance dataset, this paper aimed to analyze the usage of macro and micro-accuracy in classifying text data with multi-class label. This research focused on text data of movie reviews being classified by three multi-class classifier which are Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). We set five performance measure to be analyzed; recall, precision, f-score, sensitivity and specificity with regards of micro and macro-accuracy. We successfully yielded a significant result of comparative analysis where average micro-accuracy (87.3%) produced 14.8% higher than macro-accuracy (72.5%) for imbalance dataset. Result also shown a significant gap between balanced and imbalanced dataset. For further analysis, the flexibility of class label in multi-class may be studied to obtain the changing of learning behavior of the classifier as future work.

This paper has presented a novel concept of a food constraint satisfaction system based on the genetic algorithm (GA) and random walk (RW). The system is a part of the food recommendation system under consideration for recommending proper calorie daily food for obese individuals with overweight. The system estimates the values of overweight, daily needed food calorie (DNC), and saturated DNC (DNCsat) of the individuals from their Body Mass Index (BMI) and Basal Metabolic Rate (BMR) values. The daily food set is the food items for breakfast (BF), lunch (LN), and dinner (DN) selected by an individual. Individuals can choose any food item from the predefined food variables for their daily food set. The overall daily food calorie (ODC) is the total food calories estimated from the daily food set. The system aims to assign only the DNCsat matching food items for the daily food set of each individual from the food list of the system. The system has utilized two random-based algorithms such as GA and RW, to find an appropriate daily food set that satisfies the individual's food choices and DNCsat values. The fitness ratio (FR) of GA and RW’s node evaluation (NDeval) are estimated based on the individuals’ ODC and DNCsat values. The carried-out simulations show that RW outperforms GA in performance and computational complexity. The presented food constraint satisfaction system could effectively satisfy individuals with different food habits and tastes.

The study of the evacuation of buildings in emergency fire situations has deserved the attention of researchers for decades, particularly regarding the real-time guiding of occupants in their way to exit the building. However, finding solutions to guide the occupants evacuating a building requires a thorough knowledge of that domain. Using ontological models to model the knowledge of a domain allows the understanding of that domain to be shared. This paper presents an ontological model that pretends to reinforce and deepen knowledge of the domain under study and help develop solutions and systems capable of guiding the occupants during a building evacuation. The ontology was developed following the METHONTOLOGY methodology, and for implementation, the Protégé tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.

Artificial satellites, as a vital part of our infrastructures, are launching to space for different purposes such as the Internet, forecasting weather/disaster, and space exploration. International Space Station (ISS), as a space exploration research center in the Earth orbit, is the largest satellite in space. Astronauts in ISS do research on astrobiology, astronomy, and testing spacecraft equipment for a long-term mission to Moon and Mars. ISS is considered a unique satellite to provide some valuable information about space exploration; however, according to research, the ISS is vulnerable to different cyberattacks. In other words, cybersecurity challenges may compromise the ISS performance, which is controlled via ground/space stations and communication between them. Thus, this paper considers cyberattack threats compromising the ISS and mathematically models false data injection (FDI) attack threatening ISS power system. Two types of FDI are applied to the ISS power system, which results in (1) battery depletion and (2) load shedding. Then, security defense mechanisms are recommended to protect the ISS power system from the cyber threats.

This paper aims to provide a tool to assist rural energy planning in developing countries. Indeed, classical methods seem to be limited for the implementation of power generation plants in these often remote and isolated areas. These localities are home to populations living on less than USD2 (2$) per day with a typical and complex conditions for the implementation of power plants. Thus, on the basis of surveys carried out in several villages in the northern region of Madagascar, a data classification method is used in order to provide electrification operators with a tool to help them make decisions on the potential technico-economic and cultural feasibility of electrification projects. The implicative and cohesive hierarchical classification method according to the interestingness measure $$M_\mathrm{GK}$$ M GK (ICHC- $$M_\mathrm{GK}$$ M GK ), which has already proven itself in several didactic studies, is used herein for an economic-socio-energy study of households in some rural areas of the DIANA region of Antsiranana, in order to highlight the most used and consumed sources of energy in rural areas of the northern area of Madagascar. The results are conclusive because they highlight the potential of the sites in terms of electrification. Also, the approach makes it possible to make the link between the economic activities, the cultural practices of the villages, and the various elements relating to the energy component.

Texture is a term used to characterize the surface of objects and region and represents main features in pattern recognition and image processing. The concept of image or object, and is defined as a function of the brightness’s spatial fluctuation intensity of pixel or shape, analysis of texture may be classified into four distinct categories (structural, statistical, transform, and a model-based approach). The purpose of feature extraction is to convert an image to a matrix vector and to create a unique representation of signal values. The term “feature extraction” refers to the process of extracting features from an image without the use of any processing procedure. This article presents the main texture strategies extracted such as co-occurrence matrix, gradient, contrast, DCT, DWT, fractal, and PCA are used for analysis image skin tumors and compare, combine these strategies to reach a high diagnostic accuracy by computational complexity to reduce the challenge example (rotation, noise, etc.) to become familiar with the many sorts of features that may be utilized in DIA (digital image analysis) for future researchers are provided.

Construction projects are becoming more complicated, and there are a growing number of elements that contribute to project failure. As a result, a more systematic approach to selecting an appropriate project delivery system and kind of contract is required. The selection of a construction project delivery system and type of contract greatly influences the construction project outcome and considers one of the most important factors of the construction project success. So, the present study aims to identify the key criteria of selecting the project delivery system and also identify the key criteria of selecting the type of contract of the construction project. This study used the Delphi survey and principal components analysis to achieve this aim. The results of this study showed that there are Thirteen key criteria for selecting the construction project delivery system, where the criterion of “Clarity of contractual treatments” was of the highest importance with a value of Mean is (4.334). In addition, this study identified Eleven key criteria for selecting the type of contract in the construction project, where the criterion of “Bear the risk of unexpected cost increases” was of the highest importance with a value of Mean is (4.5). While the results of applying principal components analysis on the Delphi survey outcomes showed that these criteria are most valuable on the decision-making process and there is no possibility to reduce its number.

The weather monitoring system is used to provide pharmacists, farmers, event planners, and others with a precise statistic to guide them in taking an appropriate action. Today, with the last increased in smart technologies, the system is developed in too many sensing techniques to capture the real-time climate data for a wide area. In this paper, the proposed system is used to calculate the weather (hot, cold, wind speed, humidity, temperature, and dry) in an indoor and outdoor environment in order to monitor and control the weather conditions. This system is simple and easy to implement which consists of two parts: The first part is used only in the outdoor environment, and the second one is used in an indoor and outdoor environment. A very simple hardware components are used to build the whole system such as the DHT-11 sensor, LCD (16×4) screen, Arduino microcontroller, and an encoder to calculate the wind speed. The system can be extended to control different tasks based on weather conditions. The results are summarized in a table and sent to the control unit system.

In today’s world, we find that life is becoming increasingly busy and hectic with each passing day, and as a result, employees of any corporate organization work extremely hard to meet project deadlines. Furthermore, as evidenced by numerous cases from the corporate sector, some of them used to drink after work and before work. So the issue is how to keep track of these activities in the office. To address such flaws in the system, a proposal has been made to detect people who come to work while inebriated. There will be no additional setup required in the office, according to the proposal. At each entry gate where each employee must punch before entering, a small alcohol sensor is all that is required. The alcohol sensor will detect each person’s alcohol sensitivity and send the data to the server storage, where the database developers will perform the ETL process on the data and save it in the form of OLAP cubes, which will help in the future in generating reports with multidimensional data from which the admin and HR will get the record of each employee through application. In this way, the company can keep a hold on the employee, which will improve the company’s rating and market growth.

This study intends to explore legal frameworks and ethical considerations that would regulate social media use to promote politics and for social interaction in the context of Ethiopia. Apparently, social medias are found convenient, effective, and less expensive to market political agendas and to reach out voters easily. Particularly, they are ideal to attract young voters who have low interest in politics because of ease of generating contents, commenting and reflecting on posts, ease of sharing contents to large audience breaking down barriers to freedom of expression. Data for the study were gathered through domain expert involved focus group discussion (FGD). Thematic analysis approach was used to generate themes and insights from the data. The study reveals interesting results on two key relevant issues: media law and individual rights. From the perspectives of media law and policy, the study uncovered paucity and loopholes connected to nurturing a culture of constructive critique on political matters, the need to have sound communication policy at all key governmental institutions, weak regulatory system in terms of crafting and enforcing relevant media laws, the tendency of demeaning accountability on the side of social media companies and social media users, the need to explore legal and diplomatic pressures on social media companies to discharge their corporate social responsibilities by protecting the platform they have developed not to be used to incite conflict, violence, and human right abuses. With regard to upholding fundamental human rights, social medias have provided great opportunities to the general public to easily air out their views and promote political agendas they support. Social medias also paved the way to realize freedom of speech, which presupposes orderly liberty that do not grant a license to threaten individual’s existential rights opening the door for gross human right violations.

Background: mHealth applications have added new dimensions for managing individual and community health and healthcare aspects. To date, information about the prevalence and factors associated with mHealth applications’ use among the Saudi population is scarce. Objectives: This study aimed to estimate the prevalence and to explore determinants of mHealth Apps use among Saudi adults. Methods: A cross-sectional study was carried out to approach patients and caregivers using healthcare facilities in Hail region, Saudi Arabia. Results: Overall, 470 participants completed the survey questionnaire. Females were 336 (71.6%) and 288 (61.3%) were university educated. Almost all 464 (98.7%) participants were smartphone owners and 423 (90.0%) of them have Internet access, however, 268 (57.0%) only were mHealth applications users. The multivariate logistic regression model revealed that higher education (P = 0.023), easy to access and upload the applications (P = 0.030), having no difficulty in registration (P = 0.005), little effort needed for mastering the applications (0.005) were the independent factors favoring mobile health applications use. Contrariwise, feeling discomfort in using the applications (P = 0.003) and having concerns regarding violation of privacy (P = 0.036), were factors independently associated with not using these applications. Conclusion: While almost all the participants were smartphone owners and have Internet access, only a fair percent use mobile health-related apps.

The research carries out a bibliographic review of state-of-the-art factors that affect the process of adopting technology and the models for its acceptance, focusing specifically on the Peruvian government’s information systems so that administrative procedures are faster and more efficient. Competitiveness in productive activities is important to guarantee quality and lower prices in products and services delivered to the economy. The methodology considers the planning review, the conduct review, and the report review; in the process, the existing evidence of methods and models to identify knowledge gaps is summarized. The objective of reviewing the state-of-the-art of TAM models from 2001–2019 was completed, finding 10 models and 36 factors proposed by researchers on this subject, all of these are related to the perceived usefulness and ease of use associated with the use of the system, as well as some elements that complement the adoption process.

Today, the Internet is one of the most expanding and changing technologies, and it has grown popular all around the globe. The Internet of Things (IoT) is a system that includes a device, a sensor, a network, cloud storage and an application. Every interface to communicate with another device over the Internet to share information and achieve specific objectives Internet of Things (IoT) is a new future technology that is gaining traction in various fields around the world. Kuwait is one of the nations in the planning stages of expanding IoT development, comparable to other countries with rising IoT application development. However, owing to several obstacles and challenges in integrating IoT devices, it was not simple to design IoT devices. This article highlights IoT’s key concerns, barriers and solutions to these issues. IoT’s future trends and uses were also briefly explored in this article to acquire a more in-depth understanding of IoT equipment.

Today, information and communications technologies (ICTs) are present in all sectors, and the agricultural sector is no exception. Access to information is important for gaining knowledge and making better decisions. Several factors determine the adoption of ICTs in the agricultural sector. This article presents the literature review on the factors that condition the adoption of ICTs in the agricultural sector. A total of 17 scientific articles were reviewed and analyzed. The results of the review show that some factors mentioned in the literature are education, ICT availability, age of farmers, subjective norm, and farm size. The models that explain the technology acceptance used in the studies were technological acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) and theory that explains human behavior such as theory of planned behavior (TPB). In future adoption model proposals, the factors, models, and theory can be taken into account.

The research has proposed a detailed study into the perceptive of government employees about the entry of the concept of biometric authentication like voice recognition at the respective workplaces in the State of Kuwait. Studies were carried out on employees regarding the factors that influence the employees to adopt any new technology, which helps in improved reception of biometric technology in the Big data applications. To derive the required data for this study, a mix of interviews and questionnaires was done; managers had to give the interviews, and employees had to complete questionnaires which were provided by four particular government organizations in the State of Kuwait to understand the perception of the employees using voice recognition biometrics. It was seen from the study that there was a notable cultural and digital space between the required authentication solutions issued by the management and the technological knowledge of the employees. Due to the situation of misuse, and mainly a lack of trust in the newly introduced technology and different management intentions, managers feel more responsible for minimizing the prevalent gaps. In the context of voice recognition implementation, it was more essential to tackle the employees’ resistance. It is highly recommended to have proper orientation and knowledge in voice biometrics much before introducing the technology into the organization.

One crucial topic in higher education that have negative consequences is student dropout. In academic organizations there is a duality between retention and dropout. This situation is not only detrimental to the student who sees his or her dreams of completing a professional career cut short, but also to the educational organizations. Knowing the factors that cause the student dropout allows educational organizations to take actions to prevent dropout. The results of the literature review on factors related to dropout, as well as the categories used by the authors to categorize them, are presented in this article. The results show that institutional, academic, individual, economic, and vocational are the categories most used by authors. Knowing the determining factors will make it possible to analyze how these factors influence dropout.

With the rapid development of social media platforms as well as the current pandemic, the majority of activities are performed online. The user comments obtained from the digital channels are crucial in order that the agencies or organizations can improve and develop their brand. Thus, an automatic system is necessary to analyze the sentiment of a customer feedback. Recently, the well-known pre-trained language models for Vietnamese (PhoBERT) have achieved high performance in comparison with other approaches. However, this method may not focus on the local information in the sentiment like phrases or fragments. In this paper, we propose a PhoBERT-based convolutional neural networks (CNN) for text classification. The output of contextualized embeddings of the PhoBERT’s last four layers is fed into the CNN. This makes the network capable of obtaining more local information from the text. Besides, the PhoBERT output is also given to the transformer encoder layers in order to employ the self-attention technique, and this also makes the model more focused on the important information of the text segments. The experimental results demonstrate that the proposed approach gives competitive performance compared to the existing studies on three public datasets with Vietnamese texts.

Applications using IoT sensory data, such as in Industry 4.0, are a classic example of an organized database. This paper focuses on evaluating three types of DBMS, MongoDB, PostgreSQL using JSON and the relational PostgreSQL, measuring average, jitter, and loss of response Time and achieved throughput. Three scenarios were thoroughly tested, (i) data insertions, (ii) select/find queries, and (iii) queries related to correlation functions. Experimentations concluded that MongoDB is between 19–30% faster than Postgres in the insert queries, achieving 51–55% higher throughput. Additionally, relational Postgres is x4 times faster than MongoDB and x2 times faster than Postgres JSON in the selection queries, achieving 31–35% higher throughput. Finally, the two versions of Postgres performed equally concerning response time in the correlation function queries, while both of them outperformed MongoDB by x3.6 times. Contrariwise, in the correlation function queries, MongoDB achieved 19–24% higher throughput than both versions of Postgres.

Under mutual exclusion is understood preventing of any opportunity more than one active object (process, thread, task) to access a shared resource at a time. The distributed ring-based (aka token-ring) mutual exclusion algorithm is executed over logical circular topology. Due the chosen topology the ring-based algorithm is the simplest of this kind. Its pros are simplicity and minimalistic preliminary information required to be known a priori from each system process. The main drawback of this attractive algorithm in its basic definition is the strong presumption of absolute system reliability which makes it impractical. After all, the failure model of distributed systems itself assumes that failures should not be treated as exceptions but as a norm. Three working recovery schemes are considered through the overall project “Class of Fault-Tolerant Distributed Algorithms for Mutual Exclusion over Elastic Logical Ring Topology”. They evolve consistently and complement each other: Failure recovery without reconfiguration (Scheme 1); Failure recovery with one-way reconfiguration/resiliency (Scheme 2); Failure recovery with two-way reconfiguration/resiliency (Scheme 3). The first two recovery schemes are described in previous works. Here is presented the third one. It supposes both exclusion of the faulty processes and inclusion (injection) of faultless spare processes as replacement of faulty ones. Combining both Scheme 2 and Scheme 3 the communication ring acquires the property of elasticity or two-way resiliency. So, we eliminate the impractical assumption of “absolute” reliability. The full code of the algorithm test bed is placed in the GitHub under MIT license.

This paper is involved in the performance analysis of protection mechanisms utilized in common time division multiplexing-based passive optical networks. First, possible protection schemes for this kind of passive optical networks involved into the performance analysis are presented. This analysis is based on corresponding reliability diagrams and parameters used for the evaluation of the total network availability for each considered protection scheme. For evaluating possible migration scenarios related to protection schemes and comparing of protection possibilities of various passive optical networks, an appropriate simulation program must be prepared. Subsequently, the PON network availability evaluator is realized for obtaining relevant results appropriate for the performance analysis. The simulation program utilizes specific parameters for particular optical components utilized in a selected protection scheme and presents its resultant network availability. Values of considered parameters are changing according to the well-known data resources or specific network operator’s data. Finally, an evaluation of the total network availability for considered traffic protection schemes utilized in TDM-PON networks is included.

Taking optimal decisions in many cases is related to knowledge, acquired in a relevant domain. The amount of information that forms that knowledge directly affects the quality of decisions. Neither large nor too small amounts of information lead to perfect problem solutions in areas where knowledge is particularly important. The use of case-based reasoning systems can help improve performance in situations where such knowledge is scarce or non-existent at all. Our goal is to offer a model of a system that will help diagnose and solve problems met by users when they use technical products, consisting of many software and hardware components.

Doctors and health workers are the key front-liners fighting relentlessly to save human lives from various diseases and hidden enemies, such as germs and microbes, since the dawn of civilisation. However, extreme work pressure due to colossal number of patient with finger-countable health workers and doctors makes it hard to maintain the quality of their service. Medical science is a vast, incredibly complicated, and ever-growing field that make the task of health workers harder and urge them to remain updated always. Hence, an AI assistant to suggest possible diagnosis and treatment options can ease the life of doctors to some extent and keep them updated. Existing clinical decision support systems (CDSSs) are either knowledge-based or pattern-based, and very few of them are for personalised care. In this paper, we propose and develop (a prototype version of) a heterogeneous CDSS using pattern mining, machine learning, and knowledge-based techniques. Finally, we conclude the paper with scopes for future extension of the proposed system.

With the dawn of the Fourth Industrial Revolution (Industry 4.0), following three previous industrial revolutions, many disruptive technologies such as cloud computing, the Internet of Things (IoT), Internet of Services, Cyber Physical Systems (CPS) and big data are rapidly advancing throughout the world. Smart Manufacturing integrates these expeditious advancement of technologies related to the Fourth Industrial Revolution at the center of advancing automated structures. Preceding research within the Industry 4.0 research environment has for the most part focused on connecting machines and digital systems in an autonomous environment. However, the integration of humans within Industry 4.0 need to be considered as many industrial settings still make use of mixed environments. Within these environments the completion of processes are dependent on the cooperation between human workers and automated systems. At this time there is limited research on the development of collaborative decision-making where the human’s adjustment and acknowledgment to the method is taken into consideration. A research gap identifying the lack of processes involving collaborative decision-making, has been established as the productive setup of an automated system relies greatly on the Human–Machine Interaction (HMI) between the machines and the human operator. The case study of a water bottling plant is utilized in this research to create a separation between the collection of tasks that need to be done by a machine as well as a human related to a Smart Manufacturing Environment. Even though the new technologies employed in Industry 4.0 may predict or detect a shift in procedure, human intervention and decision-making are still of critical importance. The paper initially discusses how the water bottling plant was modeled. Secondly, the paper then discusses collaborative decision-making with reference to possible models for collaborative decision-making and introduces a HMI used for intervention in the automated process. The paper is rounded off by discussing the results and impact of collaborative decision-making in the automated procedure related to the case study.

This research focuses on the Ministry of Social Affairs in distributing social assistance. This study aims to find out how the process of distributing social aid carried out by the Ministry of Social Affairs has been going well following the procedures. And during the distribution, there were any obstacles that hindered the distribution process so that the distribution of social assistance was not right on target. The method used in this research is descriptive qualitative using online news media as data processed through NVivo 12 Plus software. This approach was chosen to see the extent to which the Ministry of Social Affairs was distributing social assistance to people affected by the COVID-19 pandemic with indicators of resources, bureaucratic structure, communication, and commitment as comparison material. Based on the data analysis that has been carried out, the bureaucratic structure is a major problem that causes delays in the distribution of social assistance with a percentage of 81.25%, not without reasons for lack of accountability and the occurrence of abuse of office by bureaucrats who control the distribution of social assistance and use the situation to commit acts of corruption. Furthermore, Resources became the second factor that caused the distribution of social assistance not to go well with a presentation of around 37.50%. Furthermore, the results of data analysis with the commitment indicator, which is not much different from the bureaucratic structure, become the third reason for the problems in the distribution of social assistance due to factors from the stakeholders in the distribution of social service who lack a strong commitment with a value presentation of around 21.25% as the primary basis in implementing responsibility. In contrast to the results of data analysis from the communication indicator, 18.75% is the lowest problem in the distribution of social assistance by the Ministry of Social Affairs to the results of data analysis from the communication indicator of 18.75%.

This research aims to see how far the application of “Tangerang LIVE” is in realizing the principles of agile governance in the Tangerang City Government. The current era of technology is a challenge for every government to have information and communication technology (ICT) capabilities in the application of public services. The Tangerang City Government answered this challenge by making innovation in an application called “Tangerang LIVE”. The method in this study is a descriptive analysis that explains a clear picture of the application of agile governance principles to applications made by the Tangerang City Government. Researchers analyzed the data using the “Tangerang LIVE” application found on the google play store for users’ smartphone Android. This study’s data collection techniques were carried out through secondary data searches obtained from books, articles, news, comments, government publications (applications), and journals. This research is necessary because it can see how far the Tangerang City Government is in realizing the principles of agile governance through public services. The results of this study indicate that of the three principles, agile governance discussed, the Tangerang City Government has implemented several principles of agile governance, namely based on quick wins and simple design and continuous refinement. However, on the focus of the systematic and adaptive approach, it is still not implemented optimally. This can be seen through the number of user complaints related to the verification process, which takes a long time to respond after registering on the application.

This study aims to determine the success of using the PeduliLindungi application in Indonesia. The government created the PeduliLindungi application in dealing with COVID-19 in Indonesia, integrated nationally. The PeduliLindungi application is one of the government’s breakthroughs in handling the COVID-19 pandemic. The PeduliLindungi application has several challenges: how people trust the application, the role of the application in daily life, and how the government can make a strategy so that people are sure of using the PeduliLindungi application. The data used in this study is a qualitative descriptive data and Twitter data related to public responses. This study used a QDAS approach to analyze the NVIVO Plus 12 software. The results showed that the PeduliLindungi application provided convenience to users through the features on the application offered. The PeduliLindungi application components include COVID-19 test results, Ehac, teledokter, COVID-19 statistical information, and vaccination registration. The government also has a good strategy in convincing the public to use the PeduliLindungi application by making rules that explain the obligation to use the PeduliLindungi application in daily life during the COVID-19 pandemic. This rule makes the number of PeduliLindungi application users increase each month. However, the PeduliLindungi application also received criticism from application users regarding complaints in using the PeduliLindungi application, such as application errors, data leaks, and user data errors. This criticism can be used as an evaluation material for the government to improve the quality of the PeduliLindungi application.

This study aims to determine the application of the JKN Mobile Application in improving the quality of health services during the COVID-19 period. In this study, researchers used the E-GovQual indicator. Researchers in conducting research using a qualitative descriptive approach. This research data was taken from the JKN Mobile Application, Twitter, Online Media, JKN Website, and Play Store and then presented descriptively. This study indicates that the JKN Mobile Application has improved the quality of public services in the health sector during the COVID-19 period. There are several assessment indicators. First, the Ease of use indicator, namely, the JKN Mobile Application, has made it easier for the public to access the presence of several features, and this application can overcome queue buildup and prevent the spread of COVID-19. Second, the indicator of trust, namely, the JKN Mobile Application, has won the public’s trust. Third, reliability, namely, the JKN Mobile Application, which has provided easy access for the community to obtain health services. The JKN Mobile Application also provides features that support the community’s needs in health services. Fourth, Content and Information Display, the features provided get a positive response from the community because it is easy to understand; people need to choose the health services they need. Finally, the Citizen Support, JKN Mobile Application provides information and complaints features that are useful for serving the problems and difficulties of the community in accessing health services and information needed.

The Papuan conflict is classified as an internal conflict, which occurs within sovereign state. This conflict cannot be separated from the community’s dissatisfaction with the Indonesian government’s services to the Papuan people. The Papuan conflict has its roots in a long historical event, starting with the history of the entry Dutch colonialism, and the Dutch ending in 1969, in the same year Indonesia entered through the Act of Free Choice. But, some of the Papuan people and some international elements think it is not finished yet. The purpose of this research is to know the dynamics of cyberactivism in handling cases of conflict in Papua, Indonesia. The method used is a qualitative method using a descriptive approach that produces data on the dynamics of cyber-activists in the digital era of the conflict in Papua, Indonesia which will produce results data. Data obtained were om documents, journals, theses, and news. Data are managed through Nvivo. The data processing mechanism describes, analyzes phenomena, social activity events, attitudes, beliefs, perceptions, and human thoughts that are conveyed through the media and other documents that have been digitized. In this study, the author uses the theory of cyberactivism, as an analytical material to answer the title that will be discussed regarding the dynamics of cyber-activists in the digital era of Papua, Indonesia. This theory is used as a basic reference material to find out the use of social media in Indonesia, more specifically the problem of the dynamics of the conflict in Papua, Indonesia.

This paper proposes the construction of the software and hardware of an omnidirectional mobile robot with the ability to receive linear and angular velocity commands to move from one place to another. In the prototype can be used controllers suitable for their configuration, capable of performing movements of the omnidirectional platform for the execution of specific tasks, the robot will also transmit the current speeds to have a feedback of any control, i.e., a control law for the execution of positioning, trajectory tracking, road tracking, among others.

This paper represents the flow pattern classification utilizing the recorded sound of flows and their spectrograms synthesized images. The sound of four different flow patterns, stratified, churn, annular and slug flow, are recorded and converted to spectrogram for further analysis. The proposed method uses a new version of the local binary pattern (LBP) to extract robust features from the returned audio noise. We propose an adaptive threshold function based on Gaussian distribution's cumulative density function (CDF). The proposed algorithm performance is analyzed with both the 836 recorded sound of flow pattern and RWCP database to validate enhancements. The validation is done for two scenarios, one with the same noise signals for the training and test sets and one for different noises for each set. Furthermore, the new method is compared with three other methods (e.g., MFCC-HMM, SIF-SVM and MC-BDLBP). The comparisons show the new method's better performance with reduced complexity.

This paper discusses the importance teaching–learning process for quality assurance and as efforts of affiliated technical institutes in India for getting accredited. Various problems related to the long-lasting acquisition of knowledge, skills, and attitude by the millennials are discussed with their implementable solutions to overcome it. The methodology suggested for attaining the course outcomes as well as program outcomes are supported with the values received after processing results of the learner-cohort for three consecutive years. The paper also discusses the action taken to fill the gap in the curriculum, if any.

Today, as a result of the pandemic we are going through due to the infectious disease COVID-19, many small, medium and large companies worldwide have had difficulties to stay in the market, because they have not made appropriate strategic decisions causing most of them to close. It is therefore important that companies adapt business intelligence and note the effect it has on them. The present investigation is a systematic review of the literature, compiling 55 articles from the following databases; Springer Link, IEEE Xplore, Wiley and Scopus that according to our inclusion and exclusion criteria 26 articles were systematized showing how business intelligence adapts and positively influences companies.

Digital public diplomacy has become a new method implied by the state to interact with foreign publics in the Internet era, particularly by utilizing social media. One of the most used social media by the state to carry out the interaction is Twitter. This phenomenon has also made communist states like China utilize Twitter in interacting with foreign publics. Therefore, this paper aims to examine how social media posts intended for public diplomacy can reach a high level of engagement and interact with a more significant number of the public abroad. This paper analyzes posts created by six Chines diplomats on social media Twitter by capturing their engagement, sentiment, as well as the subject. The focus of this paper is to discover that most high-engagement posts contain positive sentiment, that which subject becomes popular to rely on each account’s audience, and that some engagement takes the form of negative replies.

Convolutional neural networks have come a long way since AlexNet. Each year the limits of the state-of-the-art are being pushed to new levels. EfficientNet pushed the performance metrics to a new high and EfficientNetV2 even more so. Even so, architectures for mobile applications can benefit from improved accuracy and reduced model footprint. The classic Inverted Residual block has been the foundation upon which most mobile networks seek to improve. EfficientNet architecture is built using the same Inverted Residual block. In this paper we experiment with Harmonious Bottlenecks in place of the Inverted Residuals to observe a reduction in the number of parameters and improvement in accuracy.

In recent years, the demand for digitalization, automation, and smart systems in the airline industry has accelerated. Furthermore, due to the ongoing global pandemic as of 2022, airlines are faced with the challenge of offering flexibility in both cargo and passenger capacity. Studies show that the use of smart products and autonomous agents are expected to play a key part in the digital transformation of the logistics industry. This paper aims to examine the current state-of-the-art in multi-agent systems and reinforcement learning with special interest in intelligent baggage handling systems. How to simplify, implement, and simulate a system of autonomous baggage carts as a software model in order to examine congestion situations will be the main topics of this paper. Furthermore, how the findings from the software model may be applied to real-world scenarios related to Industry 4.0, and baggage handling will also be discussed.

The paper describes the results of an empirical study of relationships between time attitudes and debt attitudes. The total sample was 3022 respondents, aged 18–84. Inventories used: author’s questionnaire for socio-demographic data, express debt behavior instrument, long-term orientation questionnaire, short version of the Zimbardo time perspective inventory, and a short Russian version of the consideration of future consequences scale. Future, hedonistic present and positive past TPs reinforce long-term orientation, while negative past can have different influences on its components. The motivational value and behavioral components of long-term orientation support a willingness to take into account future consequences, which in turn increases the willingness to lend and reduces the willingness to borrow and fulfill obligations. Statistics: Structural equation modeling was carried out with the Mplus 7 program. Results: It was found that different types of time perspectives are differently associated with long-term orientation, but long-term orientation is positively associated with consideration of future consequences. Debt behavior is related in different ways to consideration of immediate consequences: positively with avoidance of borrowing and negatively with debt rationality and disapproval of lending.

In programming education, it is desirable for instructors to stand beside learners and monitor their answering process to assess the individual’s actual ability. However, it seems to be impossible in large-group lectures at educational institutions or newcomer education at companies. Therefore, instructors attempt to grasp the understanding status of many learners at once by using written tests and e-learning to find out the learners who need instruction. They examine the learner’s knowledge such as algorithm and syntax. However, in reality, not a few learners fail to acquire the skill of writing source codes. This kind of situation implies that the programming ability of learners cannot be measured only by knowledge tests or the data obtained from answer results. The purpose of this study is to estimate the understanding of programming, focusing on the thinking process. This paper analyzes a time series of operations of learners working on code puzzles, where they arrange code fragments. Since we assumed learners with low understanding are different from those with high in terms of the consistency of blocks of code fragments to be touched, we modeled it using a hidden Markov model. The proposed method estimates their perspectives on how fragments are built up to achieve given requirements. The results of an experiment have shown that the calculated hidden Markov model produces meaningful interpretable values. Furthermore, the values show significant indices that machine learning models can explain the understanding of learners.

Perception is a fundamental component of any autonomous driving system. Semantic segmentation is the perception task of assigning semantic class labels to sensor inputs. While autonomous driving systems are currently equipped with a suite of sensors, much focus in the literature has been on semantic segmentation of camera images only. Research in the fusion of different sensor modalities for semantic segmentation has not been investigated as much. Deep learning models based on transformer architectures have proven successful in many tasks in computer vision and natural language processing. This work explores the use of deep learning transformers to fuse information from LiDAR and camera sensors to improve the segmentation of LiDAR point clouds. It also addresses the question of which fusion level in this deep learning framework provides better performance.

In the current era, global data have been rising at a very fast speed due to the excessive use of technologies including cloud and IoT. It leads to the development of big data that can handle and analyze a high volume of data regularly. Cloud computing provides a reliable, available, and scalable environment for the processing of this huge data. MapReduce has become an important computing model for processing and generating high-volume datasets on a cluster of machines. It not only allows distributed processing but also significant attributes like flexibility, versatility, load adjusting, and adaptation to internal failure. Despite these benefits, the performance of this framework gets affected by multiple factors, namely indexing, data skew, joining, caching, and load balancing. The main objective of this paper is to identify the factors to resolve its performance issues and investigate alternate strategies to improve the MapReduce query performance in the cloud-based environment.

A prominent application of blockchain smart contracts is in the modern manufacturing supply chain. This is mostly done to ensure data provenance. This research paper analyse the factors influencing the selection of a blockchain platform for incorporating data provenance into smart contracts. This research paper is written based on the literature study of various blockchain platforms. The current challenge that necessitated this research stems from the low performance, auction-based consensus mechanism, and high transaction fees of the blockchain platforms. Such contributing factors have negative financial implications associated with the deployment or interaction of smart contracts incorporating data provenance in the modern manufacturing supply chain. This research paper first gives details of some of the factors influencing the selection of smart contracts, then goes to compare how some of the latest blockchain technologies perform against these factors. Finally, analysis of the best possible blockchain platform for incorporating smart contracts is proposed.

Recently, artificial intelligence (AI) methods are becoming popular due to their efficacy when applied to different fields; hence, there has been an increasing effort to provide meaningful explanations of the decision-making process of such methods. For computer vision methods, one of the most utilized is called layer-wise relevance propagation (LRP), which computes the relevance of each layer in the process of classification and generates a comprehensible relevance map image representing the importance of each part of the image during the classification process. In this work, we take one step toward the utilization of explainable artificial intelligence (XAI) methods, such as LRP, to improve the accuracy of well-established AI models. We propose a novel methodology that creates a meta-model that is able to learn the classification process of a model through observation of LRP relevance map images. This meta-model is able to improve the accuracy of the baseline model in up to 30% in the best case and 1% on average.

In this paper a brief analysis has been carried out targeting a DC-DC step-up converter used within renewable energy applications. The converter topology involved is called as an “independent double boost interleaved converter” (IDBIC) with three-level output. The operation mode of the proposed power electronics conversion stage has been analyzed in the context of a photovoltaic application. The converter is used (in the present application) as an interfacing stage between three PV modules connected in series and the deserved load. An MPPT algorithm was also implemented as the control law that drives the application, in order to extract the maximum power from the solar panels array. A series of tests were performed in two study cases: In first case an off-line co-simulation using Plexim—PLECS and MATLAB—Simulink has been done. In second case an on-line Real-Time, Rapid Control Prototyping (RCP) simulation using C2000 F28069M Texas Instruments DSP with HOTLINK jTAG connection to the host computer that runs Altair/Solid Thinking Embed (formerly known as VisSim) as RCP development environment has been made. The resulting current and voltage waveforms for different cases of solar irradiation values were represented and analyzed. Also, the efficiency of the power stage was determined at different levels of solar irradiation.

Today’s supply chains (SC) are immersed in extremely dynamic environments, and supply chain management (SCM) has to deal with a multitude of risks. The domain of supply chain risk management (SCRM) has emerged, providing approaches on how to cope with risks in SC. However, due to increased complexity, volatility, and uncertainty, the number of risks in global SC has increased significantly. Harnessing the power of predictive analytics (PA), implemented in intelligent decision support systems (IDSS), offers huge potential in SCRM. However, research at the intersection of the domains of SCRM, PA, and IDSS is still in its infancy, and several research gaps have yet to be addressed. The paper elaborates on these research gaps by means of a systematic literature review. The results include a set of seven research questions and proposed research directions for future studies. Future research is presented with a plethora of starting points, which originate from the business perspective (i.e., the SCRM domain), the data-driven (i.e., the PA domain) as well as an IT-system perspective (i.e., the IDSS domain).

The COVID-19 pandemic brought the connectivity and mobility of Northern Irish residents to a halt forcing the bike hiring service Belfast Bikes to cease operations. After reintroducing the service in Summer 2020, the usage of the bike hiring service was severely impacted. This paper investigates the usage of Belfast Bikes pre-pandemic and during the pandemic. Research includes how consumer trends for Belfast Bikes customers changed due to the pandemic and predictive modelling is used to predict whether customers hire a Belfast Bike for direct travel from stations or for an indirect, leisurely trip. We will conclude how the connectivity and mobility of Belfast residents have changed due to the pandemic and provide recommendations for Belfast Bikes and Belfast City to recover after the pandemic.

We aim to develop an agent for understanding the distribution of public opinions and preferences; thus, this agent needs to have causality knowledge. When discussing social issues, Wikidata, a knowledge base, can provide linked data and play an important role in analyzing discussion content. However, there is a lack of causal content on Wikidata, and some content has errors. Therefore, it is necessary to automatically extract knowledge from news and add it to Wikidata. We propose a method of automatically determining causality in text and directly extracting effect from news. We collected news and used GPT-3 to infer whether a news article is causally related to the entity and further infer the effect of this entity. We also attempted to increase the reliability of extracted causality knowledge by dealing with multilingual texts.

The tortuosity characterisation of vascular networks in digital retinal fundus images plays important roles in biomedicine for the diagnosis and early detection of different human illness such as diseases of the artery and vein vessels of the retina, hypertension, and varying forms of retinopathies. Although literature findings have revealed that varying techniques have been proposed, studies have shown that there are needs for further investigation to improve the performance of automated vascular network tortuosity characterisation. This paper investigates the suitability of multi-objective evolutionary-fuzzy classification approach for the tortuosity characterisation of the vascular networks utilising the extracted geometric features of the vascular networks. The method proposed in this study seems promising as the performance accuracy rates of 88.57%, 90%, 95%, and 100% are obtained for varying training sample sizes.

The paper presents a hardware-in-the-loop (HiL) simulation regarding the power flow control in an AC-DC microgrid. This microgrid topology employs the use of a combination of two bidirectional interlinking converters (BIC), with bipolar DC distribution voltage. A hybrid HiL setup is configured using the Plecs RT Box 1 and dSpace MicroLabBox 1202, on which the AC-DC microgrid hardware structure and control strategy are implemented. Observations of the microgrid’s abilities to perform power factor correction/compensation (PFC) at the point of common coupling (PCC) are presented. By using the BIC, power flow control is achieved regardless of the load type and mode of operation. Also, independent control of each individual BIC is possible, this feature being highlighted in the experimental stage.

The upsurge in the number of criminal cases in Saudi Arabia is a cause for concern. More so, with the recent emergence of COVID-19, the government has forbidden specific social behaviors, which means that any breach of these prohibitions will be classified as a criminal. This work leverages the immense ability of deep learning architectures to develop and evaluate models to detect images of people or a person either violating or observing COVID-19 rules. For instance, an image of a person/s wearing a face mask would definitely fall under the category of non-violation, whereas an image of people hugging or shaking hands is an indication of a violation of COVID-19 rules. The model is trained and evaluated on a bunch of images that we have extracted from social media sites, and it produces exceptional results in the image classification assignment that we have performed.

The most common cause of death among patients with cardiovascular diseases is myocardial infarction (MI). Identifying predictors for in-hospital mortality is an essential step toward MI prevention and consequent reduction in mortality. We aimed to develop machine learning (ML) methods for predicting in-hospital mortality in MI patients and apply novel techniques for models’ interpretability to detect the predictive importance of the variables. Random forest (RF) and extreme gradient boosting (XGB) are applied to a dataset of 2035 MI patients who underwent percutaneous coronary intervention. When comparing the models’ AUC (RF—0.9712 vs. XGB—0.9666) and accuracy (RF—97% versus XGB—98%), both techniques achieved similar performance. However, the RF model obtained a higher sensitivity (86%) than the XGBoost classifier (80%). Hypertension, cardiogenic shock, ejection fraction were identified as some of the main contributors to the outcome. Our paper contributes to the global effort of reducing mortality in patients with myocardial infarction by proposing two interpretable ML models that accurately predict in-hospital mortality in MI patients. These results are essential steps in improving current preventive strategies. However, future studies on larger datasets enriched with both categorical and continuous variables, and models validated on external data from other centers are needed to accurately assume generalizability in clinical practice.

The trend of adopting container-based systems become increasingly relevant for companies and their IT departments. Improved scalability and shorter deployment cycles in IT production are the most mentioned benefits of the technology. The adoption of container-based technologies in an existing IT system landscape requires a consideration of migration strategies. The paper at hand examines general migration patterns for the transition of virtual systems into container-based systems from a cloud computing perspectives. Several strategies are derived for a specific use case.

The pandemic COVID-19 has caused significant changes in many areas of public life, including education. This article introduces how the pandemic COVID-19 affected higher education and how it can reduce the negative impacts of the pandemic and respond to future challenges with the use of ICT. Problems and consequences related to the educational process in educational institutions of Ukraine in the conditions of quarantine restrictions are analyzed. The experience of Kyiv National University of Culture and Arts in adapting the educational process to today's challenges is presented. The main directions of the university's work on reducing the consequences of the pandemic and ensuring the stability and flexibility of the educational process in the conditions of quarantine restrictions are revealed. Accented the importance of developing digital competence for all participants in the learning process.

As a technique applied with artificial neural networks, deep learning is widely used in the field of image recognition. However, a lack of available datasets leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in graphical user interface (GUI) generation, it was found that the collection of GUI datasets is a time- and labour-consuming project. This makes it difficult to meet the dataset needs of current deep learning networks. To solve this problem, we propose the user interface generative adversarial network (UIGAN), a semi-supervised deep learning model, to produce a large number of reliable GUI datasets. By combining a cyclic neural network with a generated countermeasure network, UIGAN can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the selected Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to them. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

The work present improved methods for stabilizing ball-plate system with different variant of sliding mode control (SMC) like classical SMC, SSMC, RENN-optimized SMC, and SSTSMC based on their level of robustness in the presence of bounded disturbances. In this research, double-feedback loop was used in controlling the system, where PID controller, tuned with wAFSA, was used for the inner loop, while the variant SMC was used for the outer loop control. The results from the variant SMCs were compared as regards performance (rise-time and steady-state error) and disturbance rejection in order to determine the best variant. Therefore, best variant was the smoothed super-twisting SMC (3.2462 s and 1.2244e-04%, respectively) and robust disturbance rejection ability. The work is based on simulation in MATLAB.

Vision is one of the human senses that influences the way we perceive the world. There are traditional and technological ophthalmic tests used to identify visual deficiencies. Two visual tests using virtual reality were implemented in our project. The results of both studies showed correlation with the traditional tests. To improve the accuracy of all tests, we proposed to build an ophthalmic testing machine that allows traditional and technological tests to be integrated. This paper presents the prototype design of an ophthalmic tests machine that will allow to integrate different devices and tests. The general method of development of Ulrich products was used for the design stages of the machine. An adaptive mesh analysis was used to verify the chosen materials and components with more forces applied on them. This obtained a smaller divergence of 2.5% on all elements.

Knowledge Domain Organization (KDO) allows us to understand relationships, terms, or topics and the conceptual structure of specific knowledge domains. In complex domains, given by the heterogeneity of actors as well as specific domains/sub-domain features, they also impose further challenges in defining structures/features. In the AEC-AI 4.0 domain, digital and cyber-physical technologies are transforming the sector. However, big unsolved challenges are still foreseen. One of these challenges is the need for development, testing, selection, integration, and orchestration of AI and IoT technology. Here, an integrated approach involving AI, scientometrics, knowledge engineering, and metalearning would enable the development of an integrated operational system for the AEC’s life cycle operations. It would provide a dynamic framework for the assessment of digital and cyber-physical technology.

This research proposes the design of a portable electronic dispenser for personal hygiene, which will help reduce the spread of COVID-19, or other diseases or infections that can appear due to lack of hygiene. This dispenser will contain alcohol, will be attachable to the desired arm, either left or right, and will also have a drying mechanism. The disinfectant dispenser and air dispenser will be voice controlled by means of a microphone; the words “alcohol” or “air” must be spoken. When the word “alcohol” is said, the dispenser will begin to expel this liquid toward the direction that is being pointed, be it our hands, body, surroundings, etc. When the word “air” is said, the drying mechanism will be activated, and the air that will start to be expelled will go in the direction that is being pointed, be it to our hands, body, environment, etc. Fourier transform algorithms were used to perform the signal processing. In this work, the MATLAB software was used for the voice recognition of this portable electronic dispenser for the toilet.

This study aims to develop a reliable model for the two-way shear capacity of reinforced concrete slabs with FRP reinforcements, which is a complicated problem with many effective parameters. In this study, an ANN capacity model was developed and proposed. The model predicted the capacity accurately with respect to the available models. In addition, the effect of the main parameters on the capacity using the proposed model was investigated and thus provided an insight on the influence and interrelation of effective parameters on such a problem which can help further design code development.

Introduction Alcohol use disorder is a global problem and is associated with various physical, mental and social complications. Symptom network analysis can provide an unique insight into the complex interaction of the various factors as regards to their contribution to all the social and mental health-related issues and vice versa. Objective This study attempts to understand this relationship in patients with alcohol use disorders by using principles of social network. Methods Relevant information was collected from patients attending Drug Deaddiction Centre of Assam Medical College, Dibrugarh, by using a structured tool containing questions related to pattern of use of alcohol and social and mental complications attributable to alcohol. Results The analysis of data by Gephi software showed that regular drinking had high degree score of 19 for social complications. In addition, fights with family members and decreased work performance (weighted degree 19 and 17, respectively) emerged as prominent contributors to social difficulties apart from regularity of drinking in the study population. Harmonic centrality values reveal that frequency of drinking behaviour is more influential than other factors in causing social harms. The degree centrality for network related to alcohol use and mental health-related complications was highest for regularity of drinking followed by stress and depression. Conclusion Frequency of drinking, fights with family members and decreased work performance are prominent contributors to social difficulties while regular drinking is a factor that is even more important than stress or depression in affecting mental health of persons with a history of alcohol.

This paper presents a rigorous evaluation of Botswana’s e-government system users’ satisfaction using a multicriteria satisfaction analysis (MUSA) model. While most experiments found the MUSA model rigorous and very useful, one study suggested that the model does not always give an interpretable result. The present study evaluates the usefulness of MUSA in the analysis of users’ satisfaction of Botswana e-government systems with a sample of 136 users comprising 35 IT professionals and 101 non-IT professionals. This sample was selected using a purposive sampling to select employees who have used at least one e-government system, and focusing on Botswana government ministries who first took up e-government systems initiatives. The results indicate that MUSA was appropriate in evaluating the system, and it gives interpretable results. Also, Botswana e-government systems has a global satisfaction index of 0.86 (86%) rating, a partial satisfaction index of 0.5447 (54.5%), a demand index of 0.385 (38.5%). The partial satisfaction index measurements for individual system components are actual use (44%), usability (81%), functionality (19%), efficiency (51%), reliability (27%), maintainability (29%), portability (41%), and operational readiness (25%). The sampled users are non-demanding users and are satisfied with the systems. Based on the partial satisfaction indices, components of the system with low ratings need improvement.

Nowadays, face expression technology is widespread. For instance, 2D pain detection is utilized in hospitals; nevertheless, it has some disadvantages that should be considered. Our goal was to design a 3D pain detection system that anybody may use before coming to the hospital, supporting all orientations. We utilized a dataset from the University of Northern British Columbia (UNBC) as a training set in this study. Pain is classified as not hurting, becoming painful, and painful in our system. The system’s effectiveness was established by comparing its results to those of a highly trained medical and two-dimensional pain identification. To conclude, our study has developed an uncomplicated, cost-effective, and easy to comprehend alternative tool for screening for pain before admission for the public in general and health provider.

While the 5G generation of mobile communications system has delimited its focus on Internet of Things (IoT) connection and industrial automation systems. The 6G generation will offer an extrasensory experiences through the fusion of the digital, physical, and human world. It will redefine the way we live, work, and manage the world, and it will make us more efficient thanks to the combination of intelligence and vigorous computation capabilities. The sixth generation of mobile communications is still under investigation. Several projects and research have been launched. 6G promises a specific type of communication with very high data rate and capacity, very low latency, maximum coverage, very high reliability, extremely massive connectivity, and very low cost and power. The current article provides an overview of what has been discussed about the future of 6G so far. The major goal of this paper is to present a comprehensive picture of 6G based on the research and projects that have been launched. We describe the potential architectural characteristics of 6G that will give users the experience they expect. We present an important list of technologies that will be the critical element in the rollout of 6G such as artificial intelligence, VLC communications, 3D beamforming, massive MIMO aircraft, and drones. We also exhibit scenarios and use cases that might be lived in this next-generation networks. Finally, we identify the challenges that could be faced by the 6G in different sides ...

The ongoing development and improvements of digital technologies lead to changes on markets and industries. The implementation of new digital approaches has the potential to secure the competitiveness and corporate success. Companies must anticipate these changes and respond to them adequately. As a result, organizations need to be more agile and ambidextrous. For further insights on these organizational requirements an empirical study has been conducted in conjunction with a literature review. The study examined whether digitization leads to an improvement in agility and ambidexterity, and thus to a better firm performance. This revealed drivers and inhibiting factors that have an influence on the potentials of agility and ambidexterity. Finally, a conceptual model has been developed to enable future quantitative-based research.

The perfection of information is an important notion in games when considering sequential and simultaneous game. It is a key concept when analyzing the possibility of strategies and decision making for the next move. Scrabble board game is an imperfect information game where each opponent is unaware of the letters on the opposite rack. It is board game of luck, vocabulary and strategy. Our computer board game strategy focuses on maximizing the score and minimizing response time while searching for the best word possible from letters through the dictionary of words where best word could be a word containing all letters of rack and with best scoring letters (Vyas and Gahlot in A theoretical approach to reinforcement learning algorithm and Nash equilibrium to maximize the score in imperfect information game. Springer, Singapore, 2021) [1].

The biggest issues found in fostering economic potential and yield value of precision agriculture is intelligence because of this we are not able to apply innovative techniques to complete variety of task online and offline. As agriculture affects long chain of supply management from crop production till its delivery to end user, it can be seen as field witch is going to produce high number of jobs. As India’s economy is hugely depend upon the field of agriculture there is need to includes technology due to which it should be able to increase its 15.40% share of total crop production at the global level. Therefore, the need for an hour is to optimize the output per unit drop of water. Therefore, in today’s sense, great emphasis is put on enhancing irrigation practices in order to increase crop production and preserve productivity levels. Use of advanced fertilizer irrigation system is going to strengthen root of the current agricultural system. The reduction in water usage due to the drip irrigation system varies from 30 to 70% for the surface irrigation method and the productivity benefit ranges from 20 to 80% for the various crops. Fertigation is a process of delivering water soluble fertilizer at the time of irrigation. Use of the fertigation process is going to significantly reduce the work load of the farmers and will save huge amount of time. Fertigation enables the placement of NPK nutrients directly into the plant root zone at the necessary dose during critical times. It is possible to increase crop yield capacity by three times more with the same amount of water by incorporating drip fertigation. Application all the above techniques will help in the process of quality and the production enhancement of the yield.