Smart Service Systems, Operations Management, and Analytics

The development of the Internet of VehiclesInternet of Vehicles (IoV) (IoV) enables companies to collect an increasing amount of telematics dataTelematics data , which creates plenty of new business opportunities. How to improve the integrity and precision of electric vehicle telematics data to effectively support the operation and management of vehicles is one of the thorniest problems in the electric vehicle industry. With the purpose of accurately collecting and calculating the driving mileage of electric vehicles, a series of data cleaning and processingData cleaning and processing methodologies were conducted on the real-world electric vehicle telematics data. More specifically, descriptive statistics was conducted on the data, and the statistical results showed the quality of the data in general. Above all, the driving mileage data were segmented according to the rotate speed of the electric motor, and the anomaly threshold of the driving mileage data was obtained by the box-plot methodBox-plot method . Then, the typical anomalies in the data were screened out by the threshold and analysed, respectively. Ultimately, the real-time and offline abnormal processing algorithms are designed to process real-time and offline data, respectively. After debugging and improvement, these two sets of abnormal processing algorithms we designed have been able to run on a company’s big data cloud platform. According to the feedback of the operation results of real-world massive data, the two sets of algorithms can effectively improve the statistical accuracy of driving mileage data of electric vehicle.

In recent years, the High-Speed Rail (HSR) in China has continued to thrive rapidly with the development of China’s booming economy, and it has become the preferred mode of transportation for many travelers. This paper investigates the stochastic process of security inspection for passengers in the high-speed rail stationRail station . A queuing model is developed for studying the proposed security-check system via computer simulation. In the numerical experiments, we illustrate the influence of varying model parameters on the average waiting time and safety level of the queuing systemQueuing system . The sensitivity analysis of our simulation model could contribute to improve the service level and efficiency on security check before implementing operation plans for high-speed rail stations.

To improve web search quality and serve a better search experience for users, it is important to capture semantic information from user query which contains user’s intention in web search. Long Short-Term MemoryLong Short-Term Memory (LSTM) (LSTM), a significant network in deep learningDeep learning has made tremendous achievements in capturing semantic information and predicting the semantic relatedness of two sentences. In this study, considering the similarity between predicting the relatedness of sentence pair task and semantic searchSemantic search, we provide a novel channel to process semantic search task: see semantic search as an atypical predicting the relatedness of sentence pair task. Furthermore, we propose an LSTM-Based Neural Network Model which is suitable for predicting the semantic relatedness between user query and potential documents. The proposed LSTM-Based Neural Network Model is trained by Home Depot dataset. Results show that our model outperforms than other models.

With the development of power grid and the widespread use of power, the impact of power safety is becoming more and more important. Meanwhile, Internet and Internet of Things have both provided new technical and management ideas for power safety. Combining literature research and field investigation on safety managementSafety management of Jiangsu Electric Power Company, this paper focuses on solving the problem of electric power companies’ safety capabilitiesElectric power companies’ safety capabilities . First, based on the idea of data-driven management and decision-making, power safety risksSafety risks are identified according to electric power companies’ actual business situation and therefore, safety capability evaluation index systemEvaluation index system is created. Second, the weight of each indicator is determined according to the proportion of safety risksSafety risks in historical accidents. Finally, considering the problem of information lack in electric power safety managementSafety management in China, this paper uses Grey Fixed Weight ClusteringGrey fixed weight clustering Theory including determining thresholds of each indicator and constructing whitening weight functions to make an evaluation of electric power companies’ safety capabilitiesElectric power companies’ safety capabilities which could help managers learn about their own existing safety levels and provide them a reference for future decisions.

Based on the theory of complex networkComplex network , the crude price data for 30 years is processed by coarse-graining method and the price fluctuation network of crude oil is constructed. According to the price of crude oil at different timescalesTimescales , this paper establishes the different complex network models to compare the topological propertiesTopological property of crude oil price fluctuation network and explores the internal characteristics of the original oil price fluctuation system. Meanwhile, using the K-core, the obtained fluctuation loops at different timescalesTimescales are analyzed to achieve certain prediction effect. As is shown in the study, small-world phenomenon and scale-free characteristics exist in the crude oil priceCrude oil price fluctuation network at different timescales. However, with the increase of timescaleTimescales , the small-world phenomenon of the crude oil priceCrude oil price fluctuation network is enhanced, while the scale-free properties are weakened. Ultimately, this paper summarizes the differences and relations between the evolution of crude oil priceCrude oil price fluctuation under different scales, and the next research direction is put forward.

This study analyses the core changes in the entire industrial structure of China from 2002 to 2015. First, utilizing the input–output modelInput–Output model (or interindustry relation model), we evaluate the production-inducing effects across the various manufacturing and service sectors. Employing standard centrality measures that are often used in network analysisNetwork analysis , we identify the status of various industry sectors together with their structural roles on the input–output network and track the evolutionary paths as they change over time. Furthermore, the clustering analysis of industrial sectors also examines critical changes in the overall industrial structure. We will discuss the policy implications of these evolutionary paths and explore the trends and sources of Chinese economicChinese economy development regarding manufacturing and service productions. As a result, it can be seen that the production-inducing effects in the manufacturing sectors—particularly, chemical, electric power, and primary metal products—play the core role in driving the economy. On the other hand, the distribution sector like the traditional wholesale and retail services still shows unstable connections with other industries. However, the sectors like finance and transportation, which constitute another axis of the service industry, have strengthened their connection with the manufacturing sectors since 2012. Moreover, the real estate and lease and the other business support services continue to absorb production resources, deepen their linkages with the key service industries (e.g., transportation), and the various manufacturing sectors. In conclusion, China still has manufacturing as a central driving force for economic development, but with the increasing integration with the service sectors, both manufacturing and service industries show an apparent convergence in their production activities.

Machine learning has gained increasing interests from various application domains for its ability to understand data and make predictions. In this paper, we apply machine learning techniques to predict revenue per customer for Google Merchandise Store. Exploratory Data Analysis (EDA) was conducted for the customer dataset and feature engineeringFeature engineering was applied to the find best subset of features. Four machine learning methods, Gradient Boosting MachineGradient Boosting Machine (GBM) (GBM), Extreme Gradient Boosting (XGBoost), Categorical BoostingCategorical Boosting (CatBoost) (CatBoost), and Light Gradient Boosting MachineLight Gradient Boosting Machine (LightGBM) (LightGBM) have been applied to predict revenue per customer. Results show that LightGBM outperforms other methods in terms of RMSE and running time.

The concept of smart city has been gaining public interests with the considerations of socioeconomic development and quality of life. Smart initiatives have been proposed in multiple domains, such as health, energy, and public safety. One of the key factors that impact the quality of life is the crime rate in a metropolitan area. Predicting crime patterns is a significant task to develop more efficient strategies either to prevent crimes or to improve the investigation efforts. In this research, we use machine learningMachine learning techniques to solve a multinomial classificationMultinomial classification problem where the goal is to predict the crime categories with spatiotemporal data. As a case study, we use San Francisco crime data from San Francisco Police Department (SFPD). Various classification methods such as Multinomial Logistic Regression, Random Forests, Lightgbm, and Xgboost have been adopted to predict the category of crime. Feature engineering was employed to boost the model performance. The results demonstrate that our proposed classifier outperforms other published models.

Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge, especially, in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is the error due to the difference in the average model predictions and actual values, and variance, which is the variability of model predictions, has been the main goal of the studies in this area. An optimization model has been proposed in this paper to design ensembles that try to minimize bias and variance of predictions. Focusing on service sciences, two well-known housing datasets have been selected as case studies: Boston housing and Ames housing. The results demonstrate that our designed ensembles can be very competitive in predicting the house prices in both Boston and Ames datasets.

With the rapid development of e-commerce, an increasing amount of manufacturers are implementing a dual-channel strategy, i.e., introducing an online direct sale channel on the existing traditional retail channel. However, it is noted that for the small and medium-sized manufacturers, they may face the resistance of the dominant retailer caused by this strategy. It is well known as channel conflict, and some challenging issues remain to be further explored, e.g., How to price and coordinate the dual-channel under different pricing powerPricing power structures? And, if possible, when should the retailer offer the value-added service to enhance his competitiveness and mitigate the channel conflict? The paper compares the decision models under a different power structure (i.e., Manufacturer-Stackelberg, Retailer-Stackelberg, and Vertical Nash) and service strategyService strategy (providing the value-added service or not); the analyses show two important findings. First, for the manufacturer or the retailer, they both prefer to be the leader to enjoy the power advantage and higher profits. However, for the entire supply chain and the consumers, they prefer equal power structure to the other two power arrangements. Second, the retailer will get more profits while providingvalue-added service. Interestingly, it is also beneficial to the manufacturer.

Value co-creationValue co-creation requires a system that links actors together for mutual value creation. In our paper, we describe the development of such a system in the context of the new free-floating e-bike-sharing system (BSS) in Zurich, Switzerland. This BSS is based on the idea that users of the BSS co-create value by adapting their usage behavior such that the overall service level is maximized. This creates value for other users and reduces the provider’s costs for redistribution, but requires some kind of incentive system for influencing the user behavior. We describe a systematic approach of designing such a system by operationalizing the concept of value and value generation for the different actors: What exactly is the value that is to be created, and how can it be measured? By which activities is value created, and what are the options for stimulating these activities? Which design options maximize value creation? We found that this required combining two different research approaches: Empirical social researchSocial research was necessary to understand user needs, value perception, motivational patterns in response to incentives, and communication needs. Operational research was necessary for assessing different options for the incentive system with respect to the value creation both for provider and users. By interlinking both research activities, we were able to design an incentive system that allows reducing the number of bikes by 30% without diminishing the service level. Users are offered a reward for dropping-off their bikes in dynamically changing reward zones whose locations are determined based on the bike distribution and the future demand pattern. These incentives lead to two distinct behavioral responses which were assessed and quantified in an extensive real-life field test during a period of 13 weeks. The impact of the measured behavioral change on service level and the required number of bikes was modeled via simulationSimulation .

In view of the applicability of accident causation modelsAccident causation model to different types of accidents, this paper comprehensively considers various kinds of accident causation theories, compares, and analyses seven kinds of accident causation models, and points out the limitations of existing models. Then, according to the mechanism of electricity falling accidentElectricity falling accident , combining the theory of Bode accident causality chain with the theory of man–machine–environment system, this paper presents an accident causation modelAccident causation model suitable for the analysis of electricity falling accidentElectricity falling accident , which has a strong guiding significance for the prevention of electricity falling accident. Finally, the management improvement measures are put forward from the aspects of safety operation standard, safety production responsibility system, safety education, and training, in order to prevent the recurrence of electricity falling accident.

The world’s population is on the rise and in order to feed the world in 2050, food production will need to increase by 70% [1]. As a result, it is of great importance to construct powerful predictive models for phenotype prediction based on Genotype and Environment data (so-called G by E problem). The objective of the G by E analysis is to understand how genotype and the environment jointly determine the phenotype (such as crop yield and disease resistance) of plant or animal species. In this research, deep neural networksDeep neural networks are trained and used as predictive models. Deep neural networks have become a popular tool in supervise learning due to considerable ability in training nonlinear features [5]. Recent articles have stated that the network depth is a vital factor in decreasing classification or regression error. But, deeper networks have a so-called vanishing/exploding gradients problem which makes the training and optimizing deeper networks difficult. He et al. proposed residual learning method which alleviates this problem very well and showed that deep residual networks are significantly better and more efficient than previous typical networks [5]. As a result, residual training has been used in this research to prevent gradient degradation and ease the optimization process. Finally, since it is difficult to predict the yield difference directly, two separate residual neural networks have been trained to predict yield and check yield. After training the networks, the RMSE for check yield and yield are 8.23 and 10.52, respectively, which are very good because of considerable amount of missing values, uncertainty, and complexity in the datasets.

A vital need in the life sciences industry is software that manages large amounts of fast-moving data for manufacturing quality assurance, clinical diagnostics, and research. In the life sciences industry and research labs, lab information management systemsLab Information Management System (LIMS) (LIMS) are often used to manage expensive lab instruments. We propose a new software architecture for cloud-based life sciences manufacturing system through the following two advances: (1) full life cycle support of life science experiment through cloud services, (2) workflow-based easy and automatic experiment management and data analysis. This paper discusses our software architecture and implementation on top of Amazon Web Services by utilizing its services including Lambda architecture, API gateway, serverless computing, and Internet of Things (IoT)Internet of Things (IoT) services. We demonstrate its usage through a real-world life sciences instrument and experimental use case. To our best knowledge, it is the first work on supporting integrated experiment design, experiment instrument operation, experiment data storage, and experiment data analysis all in the cloud for the life sciences.

Data privacy is of great importance for the healthy development of service systemsService systems . Companies and governments that provide services to people often have big concerns in sharing their data. Because of that, data must be preprocessed (e.g., anonymized) before they can be shared. However, without identification, it is difficult to match data from different sources and thus the data cannot be used together. This paper investigates how the performance of two simple individual matching methods was affected by errors in the similarity scores between individuals. The first method is a greedy method (GM) that simply matches individuals based on the maximum similarity scores. The second method is an optimal assignment problem (AP), which maximizes the total similarity scores of the matched individuals. Consistent with the literature, we found that GM outperforms AP in most situations. However, we also discovered that AP could be better in fixing errors.

To make a fundamental shift toward value orientation, manufacturing companies strategically move to integrate services into their portfolio. While manufacturing firms rely on production information systems as the backbone of their operations, these systems are based on product structureProduct structure models (e.g., bill of materials). This poses a problem because services do not adhere to the goods-dominant perspective of product structuresProduct structure . To solve this divide, this paper proposes an integrative mathematical model for both production systemsProduction service system and service systemsService systems . The model draws upon concepts of service-dominant logic and is based on hypergraph theory. To illustrate that the production structure modelProduction structure model includes both product structures and process structures, we further demonstrate that the production structure model can be transformed into either. Therefore, our theoretical contribution lies in introducing a structural model into production systems that is compatible with structures of a service systemService systems model. For practice, the model enables the development of production information systems that can plan and control products, services, and hybrids.

Based on fuzzy neural network, this study explores the quantifying calculation problem of airworthiness of specific rescue operations. Aiming at the problem of quantification assessment of flightworthiness for rescue flight operation, this study starts from the perspective of the mechanical properties of the aircraft itself, and use a fuzzy neural networkFuzzy neural network model for rescue operations of airworthiness evaluation modeling. The rescue historical data of the EC-135 helicopter model is used for model training, in order to form a quantitative model of airworthiness assessment for flight operation. On the one hand, the quantitative output results provide qualitative guidance for aircraft’s competency, and on the other hand, it provides a basis for comparing the advantages and disadvantages of multitask allocation schemes. Optimization of the whole system task allocation effect is formed through the best way of individual utility. Aiming at the heterogeneous problem for rescue system, this paper introduces the concept of “task ability vector”, quantitative representing the ability of heterogeneous aircrafts and requirements of mission. The comprehensive ability of quantitative calculation of multi-aircrafts alliance is discussed, as well as the single and multi-aircrafts cooperative task ability.

The rebalance operations have been an essential problem in car-sharing service. In this paper, a two-level price incentive trip selection process is proposed to mitigate the imbalance issue in an electric vehicle-sharing (EVS) system. Specifically, at the perspective of customers, a trip price plan is made based on the adoption rate incorporating stochastic utility function in the first-level trip selection. The second-level selection adopts part of customers kept in the first-level selection, which brings less reposition cost happened in the scheduling operations in the EVS service. In the two-level trip selection process, the uncertain parameters, i.e., customers’ price expectation, potential travel demand, are assumed as random variables with known statistical measures, e.g., marginal moments, obtained from the real world. And the corresponding worst-case chance constraints combined with these random variables are further approximated as the convex optimization. In a real-world case study, the computational results demonstrate several economic and environmental benefits of our two-level selection program in the EVS system.

Opinion leadersOpinion leader are attracting increasing attention on practitioners and academics. Opinion leaders’ online Word-of-Mouth (WOM) plays a guiding and decisive role in reducing risks and uncertainty faced by users in online shopping. It is of great significance of businesses and enterprises to effectively identify opinion leadersOpinion leader. This study proposes an integrated method by looking at not only essential indicators of reviewers but also the review characteristics. The RFMRFM model is used to evaluate the activity of reviewers. Four variables L (text length), T (period time), P (with or without a picture) and S (sentiment intensity) are derived to measure review helpfulness from review text. And two effective networks are built using the Artificial Neural Network (ANN)Artificial Neural Network (ANN). This study utilizes a real-life data set from Dianping.com for analysis and designs three different experiments to verify the identification effect. The results show that this method can scientifically and effectively identify the opinion leadersOpinion leader and analyze the influence of opinion leaders.

People analyticsPeople analytics is a rapidly growing field and one that can be daunting for many HR professionals. The sophistication and capability of organizations vary considerably. Some organizations are just starting to get a handle on their data and improve its quality while others are on the cutting edge of predictive analytics. This paper outlines an analytics maturity model and various case studies sharing how organizations have moved from data to action. Examples will demonstrate how companies have embarked on a journey to connect data on employee attitudes, customer loyalty and satisfaction, operations, and financial performance to make more informed evidence-based business decisions.

This paper studies multiple-disease risk predictive modelsPredictive modeling to assess a discharged patient’s future disease risks. We propose a novel framework that combines directed disease networksDirected disease network and recommendation system techniques to substantially enhance the performance of multiple-disease risk predictive modelingPredictive modeling . Firstly, a directed disease networkDirected disease network considering patients’ temporal information is developed. Then based on this directed disease network, we investigate different disease risk score computing approaches. We validate the proposed approaches using a hospital’s dataset. Promisingly, the predictive results can be well referenced by healthcare professionals who provide healthcare guidance for patients ready for discharge.

Mobile Edge Computing (MEC)Mobile Edge Computing (MEC) is a fast growing research area that may soon offer alternate service platforms to the clients over today’s Cloud Computing vendors (e.g., Amazon AWS, Google Cloud, or Microsoft Azure, etc.). And when the MEC services are ready and available, just like when Cloud Computing services were presented to the “On-Premises” server clients, despite available research data on compatibility, business owners and engineers will still have to practically implement and host their applications to the MECMobile Edge Computing (MEC) servers, measure performance and be convinced first that MEC services can withstand the real users load without any performance degradation or disruption before finally making the switch. In this paper, we first discuss how today’s “On-Premises” server–clients conduct performance analysis before switching to the Cloud computing vendors, discuss the differences between the Cloud computing and MECMobile Edge Computing (MEC) paradigms and then we try to envision the challenges and opportunities that may unfold for the performance tests on the future MEC service platforms.

With the rapid development of e-commerceE-commerce , the automatic transaction has been a potential demand of enterprises. Concentrating on B2C e-commerce scenario, this paper designs a human-computer negotiation model and algorithm, through which an agentAgent can negotiate with a human using natural language. To validate the model and algorithm, we conducted a between-group experiment based on a prototype system comparing the negotiation effect between the groups with and without arguments. The experimental results show that adding arguments into price negotiation can significantly increase the success rate of the human-computer negotiation. Moreover, there is a significant positive impact on the buyer’s subjective feelings on system using, as well as the seller agent’sAgent economic utility, so that it can finally help both sides to reach a win-win situation in the negotiation. The contribution of our study can apply to B2C e-commerceE-commerce platforms for improving the performance of their intelligent customer service agent. Our study is also meaningful for helping the two parties to increase their trading efficiency and decrease their trading cost.

With the development of the Internet, online reviewsOnline reviews play a more important role than ever. We study the impact of online reviews on a seller’s information provisionInformation provision strategy regarding product quality. The accuracy of the seller-provided information can be uncertain. Yet the presence of online reviewsOnline reviews can resolve such uncertainty. We find that the seller can benefit from a greater capability in supplying more accurate information. We also find that online reviews do not always improve the seller’s expected profit and the uncertainty of the signal accuracy is not always unfavorable to the seller.

Due to the complexity of the last mile delivery, the emerging business called “just-in-time delivery” often has a strict service range limit. As a newly received method for last mile delivery, the UAVUAV can expand the service range of just-in-time delivery with the assistance of vehicles. This paper proposes a model based on the set covering model to locate the UAVUAV take-off point. After that, an improved ant colony algorithm is used to obtain the locationLocation result. Finally, the customer’s allocation method is proposed. Both methods are tested with randomly generated data and proved to be effective. It provides a reference for the future practice of the just-in-time delivery business.

Indexes reflect the mechanism of the stock market and the Gray Wave Forecasting Model (GWFM) which has been confirmed to be one of the most effective methods for forecasting. However, the previous method did not take into account the fact that the larger the index frequency is, the more likely this index is to appear in the future. According to the changing law of indexes, an index frequency-based contour selection of GWFM is put forward in this study where the classical uniformly spaced contour line is used twice to select the contour lines. Using this model, the fluctuation trend of Shanghai stock indexesShanghai stock market indexes is well predicted which demonstrated that this model has certain advantage over the original GWFM at forecasting stock indexes.

The recent emergence of WeChat-based brand communities in China is regarded as an effective channel for user-centric service marketing. However, Brand-related information disseminationInformation dissemination and acceptance are the main hindering force in their sustainability. Despite the growing popularity of these brand communities, there has been only limited research modeling the brand-related information dissemination rule and process. The present study examines the factors affecting brand-related information dissemination and models the process of brand-related information dissemination in WeChat-based brand communities. Based on the characteristics of WeChat, this paper presents a modified information dissemination model. It quantifies the brand information dissemination process through MATLAB simulation and gets the law of information propagation. The influence of user acceptance thresholdUser acceptance threshold and social motivationSocial motivation in information disseminationInformation dissemination is examined. The study findings suggest that the user acceptance threshold and relative motivation have obvious positive effects on the width and the speed of brand information dissemination. The findings have implications for organizations intending to use WeChat-based brand communities to practice user-centric service marketing.

The purposes of this paper are (1) to introduce the process of structure evolvement of international credit ratingCredit rating market in chronological order, (2) to analyze S and P, Moody’s, and Fitch’s rating patterns and rating services, and (3) based on the content stated before, to conclude the characteristics of the evolvement of international credit rating market and analyze the cause of them. By using correlation analysis and statistical analysis we emphasized on doing research on the ratings released by S and P, Moody’s, and Fitch (the Big ThreeThe Big Three) in 5 rating categories, financial institutions, insurance companies, corporate bonds, asset-backed securities, and government securities and gave our own opinion for causes of the current monopolyMonopoly by the Big Three in credit ratingCredit rating market.7.

This paper gives an overview of a somewhat unusual business analyticsBusiness analytics initiative at a southeastern public university in the USA. The massive influx of data and the accessibility of analytics tools have presented a provocative opportunity for businesses to improve decision-making and have also created a demand for talent with data analytics skills. Though many universities have stepped up to meet this growing need for data analytics talent, the vast majority have done so by offering specialized programs at the M.S. level. The school offers embedded undergraduate analytics program with experiential learningExperiential learning . This paper includes our motivations and reasons behind the analytics program as well as how we have implemented it.

Fresh supplier plays a vital role in the whole supply chain. How to evaluate the comprehensive ability of suppliers in a more scientific way so as to assist decision-making and establish long-term cooperative relationship is a problem worth thinking about by the enterprise members of supply chain. At present, there are few discussions targeting at the fresh industry in supplier capacity assessment researches, and most of them take KPI as the assessment standard and only focus on the current business ability of the enterprises while ignoring their long-term development. Even though some researches consider the multidimensional performance of the enterprise, the AHP method is often used to determine the weights, which lacks objectivity. Based on the characteristics of fresh products, this paper discusses the supplier capability evaluation in the field of fresh products. The BSCBSC method is used to divide the four dimensions of evaluation indicators, comprehensively considering the financial and nonfinancial information, short-term performance and future development space of suppliers. This research collects data of the four representative suppliers and divides the weight of the subdivision index by the coefficient of variation method, so as to form the fresh supplier evaluation system. Combing the balanced scorecard and coefficient of variation method to establish an evaluation system, which can not only avoid the disadvantages of one-sidedness and lack of pertinence but also better assist the decision-making implementation in the supply chain, provides a new idea for supplier evaluation.

Due to shorter development cycle and lower cost, CubeSats have been widely used in space science, technology, and business missions. CubeSat usually forms a formation/constellation, which boosts the capability of implementing complex space missions. However, the distributed CubeSat networkDistributed CubeSat network is prone to malfunction. This paper envisions a maintenance architecture that includes launching and replenishing spare CubeSats to replace the faulty one on a regular basis. The major effort is to optimize this architecture in terms of total cost by taking the stochastic failures into consideration. In particular, a parametric model fitted from practical data is used to represent the realistic CubeSat lifetime distribution. A CubeSat lifetime database of 111 CubeSats has been built. The parametric model is obtained via a Bayesian estimation scheme. A cost model that is composed of fixed cost, holding cost, and shortage cost have been proposed. Then, A Monte Carlo simulation-based approach has been adopted to evaluate the cost. Finally, the optimal arrival time and quantity of backup CubeSats corresponding to minimal cost have been obtained by examining all the feasible combinations of arrival time and quantity of backup CubeSats. Results show that the CubeSat network should be replenished in the early stage which agrees with the high infant mortality trend of CubeSats.

Intensive care unit (ICU) has the highest outbreak rate of Multidrug-Resistant OrganismsMultidrug-Resistant Organisms (MDRO) (MDRO) infection. In this paper, the control measuresControl measures of MDRO transmission in ICU were studied. Considering the incubation period and media transmission of MDRO, we combine the compartmental model and continuous time Markov chain (CTMC) to build the stochastic model of MDRO transmission in ICU. The model was expanded into a bidimensional Markov transmission modelBidimensional Markov transmission model based on the heterogeneity of population. By simulation, the key factors of the transmission model were quantitatively analyzed, and the state evolution rulesState evolution rules of patients and medical staff were studied. Then, through the sensitivity analysisSensitivity analysis , we get some manage insights to provide control suggestions for MDROMultidrug-Resistant Organisms (MDRO) transmission in each scenario.

The online healthcare websites bring more healthcare resources to patients, reduce time cost, and break geographical restrictions. However, the information explosion brought by the healthcare website also increased the difficulty in information screening and trust establishment for risky healthcare serviceHealthcare service . The online review is an important resolution for information asymmetry. This paper explores the review significance of healthcare websites by examining the impact of review depthReview depth and valence on the review helpfulnessReview helpfulness , especially in the context of different risk level diseases. We employed a secondary data econometric analysis obtained as 44,938 pieces of reviews from Haodaifu.com . We found that both review depth and valence have a significant impact on review helpfulness. And review depth has a more significant impact when review valence is low. But the disease risk moderates the impact, that is, review depthReview depth is more useful for low-risk diseases than high-risk diseases. Also, the disease risk moderates the impact of review valenceReview valence . For low-risk diseases, neutral reviews have a more positive impact on the review helpfulnessReview helpfulness . For high-risk diseases, extreme reviews have a greater impact on the review helpfulness. These findings can help to understand users’ needs on healthcare websites and establish more effective ways to do communication.

WeChatWeChat social networkSocial network is one of the most popular social platforms in China, providing not only communication services but also enabling a number of service innovations. Understanding how information diffuses in an online social network such as WeChat is critical to the design and evaluation of existing or new services. This paper studies the diffusion pattern and predictability of WeChatWeChat cascade. We propose an analysis framework for WeChat cascade based on the characteristics of cross-scenario diffusion. By analyzing a real WeChat dataset, we reveal some typical diffusion patterns. We also obtain good predictionPrediction performance.

The logistics industryLogistics industry has entered industrial development stage but the statistical data of logistics industry is inaccurate and incomplete. As one of modern service industry, the development of logistics industry needs the support of scientific theory. There is a close relationship between economy and logistics, so the relationship between them needs to be studied to make the logistics industry scientifically develop. This paper analyzes the characteristics and functions of logistics industryLogistics industry development in China, and then uses the method of grey incidenceGrey incidence to study the relationship between logistics scale and related economic factors reflecting the logistics scale with freight turnover, analyzes the reasons for the rapid development of China’s logistics industry, and points out the future development direction of the logistics industry.