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This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student performance in different courses? Which machine-learning classifiers tend to perform consistently well across different courses? Can the authors develop a general model for use in multiple courses to predict student performance based on LMS data?

Three mandatory undergraduate courses with large class sizes were selected from three different faculties at a large Western Canadian University, namely, faculties of science, engineering and education. Course-specific models for these three courses were built and compared using data from two semesters, one for model building and the other for generalizability testing.

The investigation has led the authors to conclude that it is not desirable to develop a general model in predicting course failure across variable courses. However, for the science course, the predictive model, which was built on data from one semester, was able to identify about 70% of students who failed the course and 70% of students who passed the course in another semester with only LMS data extracted from the first four weeks.

The results of this study are promising as they show the usability of LMS for early prediction of student course failure, which has the potential to provide students with timely feedback and support in higher education institutions.

Many higher education institutions are investigating the possibility of developing predictive student success models that use different sources of data available to identify students that might be at risk of failing a course or program. The purpose of this paper is to review the methodological components related to the predictive models that have been developed or currently implemented in learning analytics applications in higher education.

Literature review was completed in three stages. First, the authors conducted searches and collected related full-text documents using various search terms and keywords. Second, they developed inclusion and exclusion criteria to identify the most relevant citations for the purpose of the current review. Third, they reviewed each document from the final compiled bibliography and focused on identifying information that was needed to answer the research questions

In this review, the authors identify methodological strengths and weaknesses of current predictive learning analytics applications and provide the most up-to-date recommendations on predictive model development, use and evaluation. The review results can inform important future areas of research that could strengthen the development of predictive learning analytics for the purpose of generating valuable feedback to students to help them succeed in higher education.

This review provides an overview of the methodological considerations for researchers and practitioners who are planning to develop or currently in the process of developing predictive student success models in the context of higher education.

In novelty detection, the autoencoder based image reconstruction strategy is one of the mainstream solutions. The basic idea is that once the autoencoder is trained on normal data, it has a low reconstruction error on normal data. However, when faced with complex natural images, the conventional pixel-level reconstruction becomes poor and does not show the promising results. This paper aims to provide a new method for improving the performance of novelty detection based autoencoder.

To solve the problem that conventional pixel-level reconstruction cannot effectively extract the global semantic information of the image, a novel model with the combination of attention mechanism and self-supervised learning method is proposed. First, an auxiliary task, reconstruct rotated image, is set to enable the network to learn global semantic feature information. Then, the channel attention mechanism is introduced to perform adaptive feature refinement on the intermediate feature map to optimize the correspondingly passed feature map.

Experimental results on three public data sets show that the proposed method has potential performance for novelty detection.

This study explores the ability of self-supervised learning methods and attention mechanism to extract features on a single class of images. In this way, the performance of novelty detection can be improved.

The purpose of this study is to examine the relation between relationship value and relationship quality in the business relationship between customers and facility management (FM) suppliers. To investigate the relationship value in outsourced FM services, the customer’s perspective is used to identify the dimensions and drivers of relationship value.

A three-stage research design was used. The first stage was a thorough literature review, followed by expert interviews with six senior managers from the customer side, together with workshop and discussion with FM academics. In the third stage, quantitative data were gathered in a survey of 60 senior managers whose companies outsourced FM services.

Findings show that relationship value is an antecedent to relationship quality of the business relationship in the context of FM. In all, 9 dimensions and 34 drivers of relationship value were identified, and a framework of relationship value for FM was established and measured. The sacrifice dimension correlates positively with relationship value, which contrasts with previous studies of relationship value in the context of business markets.

A framework of relationship value has been established for further in-depth investigation. There are limitations related to the sampling procedure: qualitative research selected large-sized organizations; the relationship value was only studied within the customer–FM supplier dyad; and a static view of customers’ perceived value from the relationship with their FM suppliers.

The study provides a set of value dimensions and drivers for customers to assess how a FM supplier adds value in a relationship, and for FM suppliers to improve their services.

This research narrowed the gap in relationship-value studies in FM. The findings can contribute to traditional theory that customer value can be the add-on between benefits (“what you get”) and sacrifices (“what you give”), rather than just a trade-off between these two dimensions.

Localization is one of the critical issues in Ocean Internet of Things (OITs). The existing research results of localization in OITs are very limited. It poses many challenges due to the difficulty of deploy beacon accurately, the difficulty of transmission distance estimation in harsh ocean environment and the underwater node mobility. This paper aims to provide a novel localization algorithm to solve these problems.

This paper takes the ship with accurate position as a beacon, analyzes the relationship between underwater energy attenuation and node distance and takes them into OITs localization algorithm design. Then, it studies the movement regulation of underwater nodes in the action of ocean current, and designs an Energy-aware Localization Algorithm (ELA) for OITs.

Proposing an ELA. ELA takes the ship with accurate position information as a beacon to solve the problem of beacon deployment. ELA does not need to calculate the information transmission distance which solves the problem of distance estimation. It takes underwater node movement regulation into computation to solve the problem of node mobility.

This paper provides an ELA based on the relationship between propagation energy attenuation and node distance for OITs. It solves the problem of localization in dynamic underwater networks.

The goal of the special issue is to review current cigarette smoking trends in China; this article aims to provide an overview of the main themes of the special issue.

The instruments for data collection of the five studies in this special issue are surveys. One study used a random sampling method, one used an intercept survey method, and three used a convenience sampling method.

Highlights of the findings include: among the 677 physicians surveyed, 31.6 percent of the men and 0.9 percent of the women were current smokers; 79.2 percent of the cigarette users reported smoking on duty; 15 percent of the cigarette users smoked in front of patients. Sixty‐one percent of the physicians often advised patients to quit smoking. Two factors significantly influenced a physician's anti‐smoking frequencies: whether they were smokers themselves and whether they had received training on helping patients to quit smoking. About half of the 269 patients surveyed reported seeing someone smoking inside the hospital, and 22.3 percent had seen physicians and/or nurses smoking. Among the 758 medical students surveyed, 26.5 percent of males and 1.6 percent of females had smoked in the previous 30 days.

The exclusive coverage of a western journal on cigarette smoking in China can draw the attention of Chinese and western scholars in the field, as well as the attention of the Chinese Ministry of Health, to this major national problem. This attention should help to advance anti‐smoking educational campaigns in China.

This is the first special issue by a western academic journal on cigarette smoking in China, where rates are far higher than in most other parts of the world, and are a major health concern. Two studies have large sample sizes and all five studies have high response rates.

In this case study, the alternative route designs were observed to significantly decrease transportation costs and the total distance traveled. This decrease in logistics requirements almost halved the annual number of shipments and the time needed for operation and documentation activities. In addition, reduced carbon emissions made this an environmentally friendly transportation model, in line with trends in society.

The basis for this case study was the analysis of Whirlpool Turkey’s transportation system for materials used in the production of white goods. Data obtained through fieldwork and cooperation with company consultants showed that some suppliers have high annual logistics costs. This inefficiency causes time loss and increases the total distance traveled and thus carbon emissions. In the case study, the current application created inefficiency in cost and time management, and therefore, after determining the factors that increase costs, different transportation solutions were developed accordingly.

This case is particularly designed for undergraduates in the final semester of management courses that specialize in supply chain and operation management.

Teaching notes are available for educators only.

CSS 9: Operations and logistics.

The purpose of the study is to address concerns regarding the subjectivity and imprecision of decision-making in table tennis refereeing by developing and enhancing a sensor node system. This system is designed to accurately detect the points on the table tennis table where balls collide. The study introduces the twined-reinforcement chimp optimization (TRCO) framework, which combines two novel approaches to optimize the distribution of sensor nodes. The main goal is to reduce the number of sensor units required while maintaining high accuracy in determining the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through complex optimization procedures, the study aims to improve the efficiency and reliability of decision-making in table tennis refereeing by leveraging sensor technology.

The study employs a design methodology focused on developing a sensor array system to enhance decision-making in table tennis refereeing. It introduces the twined-reinforcement chimp optimization (TRCO) framework, combining dual adaptive weighting strategies and a stochastic approach for optimization. By meticulously engineering the sensor array and utilizing complex optimization procedures, the study aims to improve the accuracy of detecting ball collisions on the table tennis table. The methodology aims to reduce the number of sensor units required while maintaining high precision, ultimately enhancing the reliability of decision-making in the sport.

The optimization research study yielded promising outcomes, showcasing a substantial reduction in the number of sensor units required from the initial count of 60 to a more practical 49. The sensor array system demonstrated excellent accuracy in identifying the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through the implementation of the twined-reinforcement chimp optimization (TRCO) framework, which integrates dual adaptive weighting strategies and a stochastic approach, the study achieved its goal of enhancing the efficiency and reliability of decision-making in table tennis refereeing.

This study introduces novel contributions to the field of table tennis refereeing by pioneering the development and optimization of a sensor array system. The innovative twined-reinforcement chimp optimization (TRCO) framework, integrating dual adaptive weighting strategies and a stochastic approach, sets a new standard for sensor node distribution in sports technology. By substantially reducing the number of sensor units required while maintaining high accuracy in detecting ball collisions, this research offers practical solutions to address the inherent subjectivity and imprecision in decision-making processes. The study’s originality lies in its meticulous design methodology and complex optimization procedures, offering significant value to the field of sports technology and officiating.

This paper aims to investigate the effects of deposition time on the structure and anti-corrosion properties of a micro-arc oxidation (MAO)/Al coating on AZ31B Mg alloy.

The study describes the fabrication of the coating via a combined process of MAO with multi-arc ion plating. The structure, composition and corrosion resistance of the coatings were evaluated using scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction and electrochemical methods.

The Al-layer is tightly deposited with a good mechanical interlock along the rough interface due to the Al diffusion. However, the Al layer reduces the anti-corrosion of MAO-coated Mg alloy because of structural defects such as droplets and cavities, which act as channels for corrosive media infiltration towards the substrate. Fortunately, the Al layer improves the substrate corrosion resistance owing to its passive behaviour, and the corrosion resistance can be enhanced with increasing deposition time. All results indicate that a buffer layer fabricated through the duplex process improves the interfacial compatibility between the hard coating and soft Mg alloys.

An MAO/Al duplex coating was fabricated via a combined process of MAO and physical vapour deposition. MAO/Al duplex coatings exhibit obviously passive behaviours on AZ31 Mg alloy. The structure and corrosion resistance of MAO/Al coatings were investigated.

The purpose of this paper is to contribute to the extant literature about the co-evolvement of Business Process Management (BPM) and the Internet of Things (IoT) by proposing the IoT-enabled Context-aware BPM (IoT-CaBPM) framework to bridge from the IoT infrastructure to context-aware business processes.

Motivated by the “Three Waves” of BPM research, IoT-enabled context-awareness is, therefore, expected to be achieved for enhancing the business process design, which pilots a new wave of BPR (Business Process Redesign/Reengineering) to enable the business process coevolve with IoT and analytics. This paper reports an illustrative case study of BPR in a Chinese bulk port, one of the hub seaports that widely adopted IoT technologies over the last few years.

The IoT implementation and data analytics has increased the efficiency and improve the monitoring effectively. The proposed IoT-CaBPM framework availably helps to identify and match nodes of IoT devices, business decisions and analytic models in order to redesign a business process towards context-aware variability. As IoT is rapidly becoming the new dominant IT paradigm is moving towards mature implementation in various industries, the corresponding BPR must be planned and executed strategically for achieving better benefits.

Despite some research extend BPM standard by integrating IoT devices as a sort of resources or report generically that the ports operations are affected by IoT, there is still a lack of layers from the IoT infrastructure to context-aware business processes. An industrial BPR case with business models in detail is also a lack for presenting the specific implications and effectiveness of the adoption of such technologies. This paper fills in this gap.