Systems, Volume 11, Issue 9 (September 2023) – 48 articles

Prebaked carbon anodes are a critical consumable in the aluminum electrolysis industry. Prebaked carbon anode paste is the intermediate product of the prebaked carbon anode, and its quality significantly impacts the prebaked carbon anode. Therefore, inspecting the quality of the prebaked carbon anode paste is essential. Currently, the quality inspection of the paste still relies on laboratory analysis or manual experience. A laboratory inspection cannot obtain results in real time, while manual inspection poses potential risks. To address these issues, an online intelligent inspection method for prebaked carbon anode paste based on an anomaly detection algorithm was proposed. Firstly, we acquired the temperature of the paste and the power of the kneading motor. Secondly, we transformed these time-series data into images using the Gramian Angular Field (GAF) technique and joined them to create the paste anomaly detection dataset. Thirdly, we trained a matched anomaly detection model based on the PatchCore algorithm. Finally, we compared two advanced models: HaloAE and TSRD. PatchCore performs best on our dataset with an AUC-ROC score of 0.9943, followed by HaloAE (0.9906) and TSRD (0.9811). Our proposed method enables on-time intelligent inspection of prebaked carbon anode paste quality. This eliminates the need for manual inspection, reduces labor requirements, and ensures worker safety. Full article

Text classification has been highlighted as the key process to organize online texts for better communication in the Digital Media Age. Text classification establishes classification rules based on text features, so the accuracy of feature selection is the basis of text classification. Facing fast-increasing Chinese electronic documents in the digital environment, scholars have accumulated quite a few algorithms for the feature selection for the automatic classification of Chinese texts in recent years. However, discussion about how to adapt existing feature selection algorithms for various types of Chinese texts is still inadequate. To address this, this study proposes three improved feature selection algorithms and tests their performance on different types of Chinese texts. These include an enhanced CHI square with mutual information (MI) algorithm, which simultaneously introduces word frequency and term adjustment (CHMI); a term frequency–CHI square (TF–CHI) algorithm, which enhances weight calculation; and a term frequency–inverse document frequency (TF–IDF) algorithm enhanced with the extreme gradient boosting (XGBoost) algorithm, which improves the algorithm’s ability of word filtering (TF–XGBoost). This study randomly chooses 3000 texts from six different categories of the Sogou news corpus to obtain the confusion matrix and evaluate the performance of the new algorithms with precision and the $F_1$-score. Experimental comparisons are conducted on support vector machine (SVM) and naive Bayes (NB) classifiers. The experimental results demonstrate that the feature selection algorithms proposed in this paper improve performance across various news corpora, although the best feature selection schemes for each type of corpus are different. Further studies of the application of the improved feature selection methods in other languages and the improvement in classifiers are suggested. Full article

Free Space Optical Communication (FSO) is a promising technology to address wired Data Center Network (DCN) challenges like power consumption, low scalability and flexibility, congestion and cabling. Scholars have developed indirect line-of-sight (LoS) FSO schemes by reflecting the FSO beams via switchable mirrors. These schemes have introduced extra overhead delay to establish indirect LoS links, defined herein as the rack-to-rack FSO link setup process. The purpose of this work is to study and model this setup process with the consideration of the DC workloads. We found that the process involves a sequence of i.i.d random variables that contribute differently to its delay. Also, the process shows a statistical characteristic close to M/M/K. However, the number of FSO links, K, is random with time, which necessitates careful modeling. Finally, the PDF of the process total response time is close to the hypoexponential distribution, and it maintains its main characteristics even with different distributions for the service time. Full article

To scientifically evaluate the implementation of the River Chief System (RCS), accelerate the overall improvement of the water ecological environment, and promote the sustainable development of river and lake functions, this study selects 26 evaluation indicators from six aspects, including the effectiveness of organization and management, the effectiveness of water resources protection, the effectiveness of water environment management, the effectiveness of water pollution prevention and control, the effectiveness of water ecological restoration, and the effectiveness of the management of the waterside shoreline, and establishes an evaluation system for the effectiveness of the implementation of the RCS. Among the 26 indicators, data for the qualitative indicators mainly come from a series of statistical yearbooks and RCS reports, while data for the quantitative indicators are obtained through scoring by more than 20 experts and calculating the average. The CRITIC objective weighting method is improved from three aspects of comparison intensity, correlation coefficient, and degree of variation, and the subjective weighting of indicators is carried out using the AHP 1–5 scale method. The optimal linear combination of subjective and objective weighting results is obtained using the combination weighting method with game theory, which is auxiliary to the set pair analysis. Considering the “certainty” and “uncertainty” in the evaluation process, the four-element connection number model of set pair analysis is established to evaluate the implementation effect of the RCS in Henan Province from 2018 to 2021. The results show that the implementation effect of the RCS in Henan Province improves year by year and reaches excellent in 2019. The results of this study can be used as a reference for evaluating the work of the RCS in other regions and can also provide a reference for the study of evaluation problems in other fields. Full article

In product development, important specification and design decisions must be made at various stages of the lifecycle that include design, manufacturing, operations, and support. However, making these decisions becomes more complex when a multi-disciplinary team of stakeholders is involved in system-level or subsystem-level architecture and design decisions. Model-Based Engineering (MBE) approaches are enabling a digital thread of connected data and models. This work demonstrates a novel MBE approach that incorporates a model-based systems engineering (MBSE) method and a multi-criteria decision-making (MCDM) method to determine the best architecture solution that aligns with stakeholder needs and objectives over multiple domains. This approach demonstrates the connection of a system descriptive model, modeled using the systems modeling language (SysML), to underlying physics-based engineering models for the purpose of better predicting the technical performance of systems during the architecture development phase. This approach is demonstrated for a common aerospace communications application, a software-defined radio. This novel MBE approach supports digital transformation at organizations and allows for earlier design validation, enabling designers to test and select the best system architecture from many candidates and validate that the design meets stakeholder needs. Full article

Digital transformation is seen as an “elixir” for companies to improve their economic performance and expand their market power in the digital economy. Therefore, how does digital transformation affect enterprises’ market power? This paper used machine learning to construct a digital transformation index and used panel data of listed enterprises from 2008 to 2020 to study the impact of digital transformation on market power and its mechanism of action. The findings showed that digital transformation significantly increases market power, and this conclusion still holds after considering potential endogeneity issues and conducting robustness tests. The results of mechanism analysis revealed that digital transformation facilitates endogenous scale expansion and promotes merger and acquisition (M&A), which reshapes firm boundaries and, thus, enhances market power. This paper revealed new changes in the micro-organization of enterprises in the context of digital transformation and provided micro-evidence for the industrial organization effect of digital transformation. Full article

Industrial sectors’ innovative and sustainable development relies not only on solid government, society, academia, and industry but also on how they interact to set and implement strategic goals. In the fashion industry context, the new sociocultural scenario is increasingly driven by pressures from stakeholders to limit the impacts of industrial practices and to move toward new open ecosystems to create and maintain sustainable innovation. This article explores how such innovation can be enabled by design-driven actions in the context of the quadruple helix. Such a model aims to revitalize the areas of technological innovation and gradually advance the construction of the infrastructure needed for sustainable fashion growth, combining and integrating different knowledge. An initial literature review, complemented by case studies analysis, identifies the European fashion industry dynamics of innovation and the roles of industry, government, university, and society. In particular, the government is transforming from a mere controller to a facilitator of innovation synergies. Society relies on citizens revising their consumption habits by shifting toward a performative economy. Industry understands the need for collaboration and adopts new closed-loop supply chains to create and maintain its sustainable development. Universities enable new open system flows to make innovations concerning knowledge, technologies, and systems thrive, from technology transfer to knowledge co-creation. Based on the analysis, we propose a conceptual framework to understand the micro- and macro-dynamics of open innovation with a quadruple helix model to implement sustainability practices in the fashion sector through design-driven actions—reuse, repair, recycle, and refashion—that aim to eliminate the concept of waste to support local ecosystems toward establishing a closed-loop chain. Full article

The integration of building information modeling (BIM) and the integrated project delivery (IPD) mode effectively promotes collaboration among project members and enhances project profitability. However, the issue of profit sharing significantly impacts the successful implementation of IPD projects. To enhance the profit-sharing mechanism of IPD projects and ensure their smooth implementation, a game analysis model of profit sharing in IPD projects was established based on the Stackelberg game theory, taking into account the multidimensional fair preferences of the participants and the application of BIM technology. Through simulation, the impact of various parameters of participants on output utility, total revenue, and sharing coefficient in IPD projects was analyzed. The results show that: (1) participants achieve their highest output utility and total revenue under vertical–horizontal fairness preferences; (2) under vertical fairness preferences, the profit sharing coefficient is the highest, while the output utility and total revenue are the lowest; (3) although the output utility and total revenue of participants under horizontal fairness preferences exceed those under neutral fairness preferences, the profit-sharing coefficient is lower; (4) the output utility, the total revenue, and the profit-sharing coefficient of the participants all increase with the increase in effort utility value and decrease with the increase in the effort cost coefficient and the risk avoidance coefficient. The research findings provide valuable theoretical support for the profit sharing of IPD projects, thereby further promoting the advancement and implementation of the IPD model. Full article

Rice is one of the major food crops across the globe, and its quality and safety highly influence human health. It is the basis of many different products, including rice flour, rice bread, noodles, rice vinegar, and others. Therefore, the rice supply chain has garnered increasing attention due to the high demand for food safety. Furthermore, malpractices in the rice supply chain can impact farmers by generating low revenues despite their great efforts in rice cultivation. In addition, they would cause governments to suffer significant economic losses due to the high cost of importing rice crops from other countries during the off-season. These issues derive from the lack of reliability, trust, transparency, traceability, and security in the rice supply chain. In this research, we propose a secure, trusted, reliable, and transparent framework based on a Blockchain for rice crop supply chain’s traceability from farm to fork. A new crypto token, the Rice Coin (RC), is introduced to keep a record of all transactions between the stakeholders of the rice supply chain. Moreover, the proposed framework includes an economic model and a crypto wallet and introduces an Initial Coin Offering (ICO) for the RC. Based on smart contracts, a transaction processing system was developed for the transparency and traceability of rice crops, including the conversion of the RC to fiat currency. Furthermore, the InterPlanetary File System (IPFS) is proposed in this research to store encrypted data of companies, retailers, and farmers, so to increase data security, transparency, and availability. In the end, the experimental results showed a better performance of the proposed framework compared to already available supply chain solutions in terms of transaction verification time, transaction average gas cost, and new block latency. Full article

Highways are one of the most suitable scenarios for automated driving technology. For conditionally automated driving, drivers are required to take over the vehicle when the system reaches its boundary. Therefore, it is necessary to evaluate the driver’s takeover performance and take-over safety differences under typical segments of highways. The experiment was conducted in a driving simulator. Three typical highway segments were constructed: a long straight segment, a merging segment and a diverging segment. Under each segment, a 2 × 2 factorial design was adopted, including two traffic densities (high density and low density) and two kinds of time budget (5 s and 7 s). The results showed that time budget and traffic density affected drivers’ take-over performance and safety. As the time budget decreased, the driver’s reaction time decreased and the braking amplitude increased. As traffic density increased, the lateral deviation rate increased. The maximum steering angle and steering wheel reversal rate in general tended to increase with scenario urgency. Meanwhile, drivers paid more attention to the longitudinal control on the long straight segment, which was reflected in the maximum braking amplitude and directional reversal rate. However, drivers paid more attention to the lateral control on the diverging segment, which was reflected in the maximum lateral deviation rate and the minimum steering wheel reversal rate. The study will contribute to the safety assessment of take-over behavior in highway avoidance scenarios and provide a theoretical basis for the design of a human–machine interaction system. Full article

Change and environmental trends are enormously influencing the globe. Businesses, societies, and people are all attempting to do their part to safeguard the environment. This study examines the impact of supportive leadership on organizational citizenship behavior for the environment (OCBE) and the mediating effect of psychological empowerment and affective commitment. The survey method was utilized. Data were gathered from 362 employees of the banking and pharmaceutical sectors for the present research. This research employed AMOS-SEM to analyze data and test the formulated hypotheses. The empirical results established that supportive leadership significantly influences workers’ OCBE. The results further corroborate that psychological empowerment and affective commitment mediate between supportive leadership and organizational citizenship behavior for the environment. These findings have vital implications for managers and enterprises that seek to increase their sustainability and organizational citizenship behavior for the environment. This research highlights the significant role of supportive leadership in stimulating psychological empowerment and affective commitment, which in turn affect organizational citizenship behavior for the environment. The present research broadens our understanding of leadership style and its influence on OCBE. The theoretical and managerial implications of organizational environmental sustainability and future research prospects are highlighted. Full article

The global total amount of generated solid waste is currently on a rapid growth trend. China, as the largest developing country, promulgated its Pilot Work Plan for the Construction of Zero-Waste Cities led by the new development concept in 2018 after recognizing the inadequacy and urgency of solid waste management, and the lack of valuable experience and benchmark cities for the construction of zero-waste cities. This study uses the undesirable super-efficiency model and kernel density estimation method to measure the efficiency of zero-waste city construction in 16 prefecture-level cities in Shandong Province and analyze their spatial and temporal differences. Three major problems were found, namely, low regional coordination, the rigid policies of some local governments, and the unbalanced development of scale efficiency and pure technical efficiency. Results show that the zero-waste city construction efficiency as a whole shows a declining and then fluctuating growth trend, and that low-scale efficiency is the main reason behind the decrease in construction efficiency. Suggestions are then provided considering three aspects: improving regional synergy; improving government quality and capacity, and strengthening government supervision and revitalizing the market; and introducing social capital for environmental pollution treatment. These suggestions ultimately help improve the level of zero-waste city construction. Full article

The propagation of information risk in complex public opinion environments not only leads to severe direct reputational losses for companies but also results in significant economic damages. Therefore, during the nascent stage of information risk, identifying potential propagation pathways, determining key dissemination channels, and taking timely measures become crucial. To address this issue, this paper proposes a multi-criteria decision-making method for evaluating information risk propagation in complex public opinion environments. In this method, this paper utilizes probabilistic hesitant fuzzy sets to express the evaluation information, and provide several distance and similarity measurement methods for probabilistic hesitant fuzzy elements. To ensure the rationality of the evaluation indicator weights, this study first applies these distance measurement methods to improve the Grey Relational Analysis—Decision Making Trial and Evaluation Laboratory (GRA-DEMATEL) method for determining the objective weights of evaluation indicators. Next, this paper uses the Delphi method to establish the subjective weights of each evaluation indicator. Finally, by employing a weight synthesis operator, this paper combines the subjective and objective weights to obtain the final indicator weights. Additionally, this paper utilizes the similarity measurement methods for probabilistic hesitant fuzzy elements to improve the combined compromise solution (CoCoSo) method in evaluating and ranking potential information risk propagation pathways. Furthermore, this paper incorporates the “Probability Splitting Algorithm” to handle probabilistic hesitant fuzzy elements, enabling their application in these methodologies. Finally, based on a case study of information risk propagation in the catering industry, we conducted a sensitivity analysis and effectiveness verification of the proposed approach. The results demonstrate the effectiveness of the method and its ability to address real-world issues. Full article

With the rapid spread of mobile devices and the Internet, mobile interactive video advertising has become an increasingly popular means of accessing advertising information for a large number of users. Interactive narratives are advertisements that require collaboration between consumers and designers to complete the story. Interactive narratives influence marketing impact and the advertising experience. Building on previous research, this study delves deeper into the design methods of interactive narratives for mobile video advertisements. We developed various interactive narrative samples by controlling video quality parameters, content, and product involvement, and then measured consumer perceptions of these samples in a laboratory environment. The results indicate that six design methods for interactive narratives foster positive perceptions, immersion, and satisfaction in advertisements with low product involvement. For ads with a high degree of product involvement, two design methods can achieve positive consumer perceptions of interactive narratives. This study offers insights for businesses and interaction designers aiming to advance the commercial use of mobile interactive video advertising. At the same time, we propose a design method for mobile interactive video advertising that can also serve as an entry point for theoretical research on interactive narratives. Full article

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in this study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we assessed the performance of eight deep learning models: long short-term memory (LSTM), a convolutional neural network (CNN), bidirectional LSTM (BiLSTM), CNN Attention, a bidirectional gated recurrent unit (BiGRU) CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market. Full article

Studies on Critical Success Factors (CSFs) in Healthcare Information Systems (HIS) development projects have traditionally often been limited to retrospectively identifying CSFs in a finished project. In this paper, we focus on how to prospectively apply and manage CSFs in HIS projects. Based on a holistic perspective and systems thinking, an inductive research strategy was applied and a single in-depth case study was conducted. The findings include detailed descriptions that contribute to further understanding of how to prospectively apply and manage CSFs in HIS projects. The analysis reveals that CSFs must be applied differently and managed on various system levels. Furthermore, it shows how interactions exist between different system levels, both in the case of a specific CSF and between different CSFs on various system levels. Our analysis framework and findings indicate new directions for future research: how to prospectively apply and manage CSFs in HIS development projects can now be investigated both in a more holistic way and more in detail. Finally, healthcare practitioners can use the descriptions as practical checklists for guiding them in how to realize situational adaptation of CSFs in HIS projects across different system levels. Full article

Operational readiness, activation, and Transition (ORAT) is a series of processes that aims to convert an airport into an efficient business and make the personnel, processes, operations, and systems work in full harmony with each other. This paper aimed to determine the importance level of the fundamental prerequisites of the ORAT process. To fulfil this aim, the ORAT activities were first determined within a comprehensive literature review, and a descriptive case study was presented for illustrating the effects and the relations between the ORAT activities. Finally, the Pythagorean fuzzy analytical hierarchy process (PFAHP) method was used. “Management of airport systems”, “Preparation of ORAT management and tracking systems”, and “Providing process, and documentation requirements for operational transition of the facility” were found out as the three prominent ORAT activities. The study contributes to the body of knowledge with an empirical investigation of which of up-front activities in the ORAT process should be primarily focused. Thus, operational and risk management strategies can be developed by considering the fundamental prerequisites of ORAT on a preferential basis by practitioners. Additionally, the results of this study will also be an informative resource for the prevention of failures that are frequently encountered in ORAT experiences. Full article

Efficient and effective operation of an emergency department is necessary. Since patients can visit the emergency department without making an appointment, the emergency department always treats a lot of critical patients. Moreover, the severity of the ailment determines which patients should be prioritized. Therefore, the patients are greatly impacted as a consequence of longer waiting times caused primarily by incorrect resource allocation. It frequently happens that patients leave the hospital or waiting area without treatment. Certainly, the emergency department’s operation can be made more effective and efficient by examining its work and making modifications to the number of resources and their allocation. This study, therefore, investigates the emergency department of a public hospital to improve its functioning. The goal of this research is to model and simulate an emergency department to minimize patient wait times and also minimize the number of patients leaving the hospital without service. A comprehensive simulation model is developed using the Arena simulation platform and goal programming is undertaken to conduct simulation optimization and resource allocation analysis. Hospital management should realize that all resources must be prioritized rather than just focusing on one or two of them. The case scenario (S3) in this study that implements goal programming with variable weights yields the most favorable results. For example, it is observed in this instance that the number of patients leaving the system without service drops by 61.7%, and there is also a substantial drop in waiting times for various types of patients. Full article

Global industrial chain resilience refers to the capability of industrial chains, on a global scale, to maintain or restore their normal operations and value-creating ability in the face of various risks and uncertainties. This resilience is crucial for addressing crises, promoting economic growth, and upholding national security. However, there is currently a lack of unified standards and methods for measuring and enhancing global industrial chain resilience. This study constructs a global industrial chain production model in a multi-country and multi-stage open economy context. It utilizes data from the 1990–2021 Eora MRIO (Multi-Regional Input–Output) dataset to analyze the formation, measurement, and influencing factors of global industrial chain resilience. The research findings indicate that since 2010, the disparity in industrial chain resilience between different countries has gradually widened. Manufacturing plays a pivotal role in maintaining industrial chain stability. Additionally, factors such as input costs and technological levels have been found to positively impact the enhancement of global industrial chain resilience. Therefore, this study provides theoretical and empirical support for exploring and improving global industrial chain resilience, offering valuable guidance for policymakers and entrepreneurs. Full article

As big-science infrastructures (BSIs) become the new infrastructure to support the construction of strong science and technology in China, how to choose an operation mode that is more conducive to achieving the construction goals of BSIs has become a current focus issue. The existing literature focuses more on the governance relationship between BSIs and universities or research institutes, while the important role of government has not yet been thoroughly analyzed. This study argues that government plays a fundamental role in the selection of operation modes for BSIs. Therefore, this study builds an evolutionary game model between the government and the contractor based on the perspective of asset specificity by analyzing the practical basis for the strategic choices of the government and the contractor for the operation of BSIs. The model is numerically simulated and analyzed. The research results indicate that the government’s decisions on operation strategies, outsourcing strategies, and the combination of the two significantly affect the strategic choices of the contractor, thereby affecting whether the government can obtain the value of asset specificity of BSIs. The government’s choice of the “independent operation” strategy or the combination “dependent operation + controlled outsourcing” strategy is more conducive to encouraging the contractor to choose the “cooperation” strategy for producing specific value for BSIs. The main contribution of this study is to clarify that the allocation of the government control right is the key factor in obtaining the value of asset specificity of BSIs. Full article

The advancements in the technology landscape and software development in recent years mandate paying attention to Software Quality Assurance (SQA) because it is becoming significantly important and complex. SQA is a set of activities within the software development lifecycle that aims at reducing development and testing costs, improving the quality of the software systems, and increasing customer satisfaction. Thus, the objective of this paper is to build a SQA maturity model, particularly in the telecommunication industry. To achieve this, this research identified perspectives and factors based on a comprehensive literature review and experts’ inputs using Hierarchical Decision Modeling (HDM) as the methodology. The proposed model consists of five perspectives, which are requirements validation, testing, software change management control, technology, and organization and culture with every perspective containing relevant factors. The factors and perspectives are validated and quantified using SQA inputs from subject matter experts. The findings of this study suggest that requirements validation is the most important perspective. Two case studies were conducted to identify the maturity score for each case, demonstrate the practicality of the research model, identify areas of deficiencies, and propose corrective actions. This paper provides an in-depth look at software quality factors and their relative importance, targeting to help SQA practitioners understand and assess their SQA practices. Full article

This case study examines the effectiveness and industry relevance of a collaborative systems engineering master’s program in Kongsberg, Norway. Through close collaboration with industry partners, students gain practical experience and tackle real engineering challenges. The authors used statistical data, meeting notes, and an alumni survey to assess the program’s impact. The results indicate a high success rate of 87%, with alumni holding desirable positions in various engineering disciplines. The alumni expressed satisfaction with flexibility and teacher quality but desired more focus on leadership and soft skills. Strategic inputs highlight digitalization, sustainability, security, and progress in technology as critical topics for the industry, shaping the program’s evolution for continued relevance. Full article

In complex health systems, referral and outreach systems (ROS) are formal strategies for connecting and managing interdependencies between facilities in service delivery pathways. Well-functioning maternal and newborn ROS are critical to successful outcomes, and therefore, a good lens through which to examine the management of local interdependencies. We conducted a qualitative study of maternal–newborn ROS, involving interviews with 52 senior, middle, and frontline managers, in three health districts of three different provinces in South Africa. We analyse the differences in functioning of ROS as an interplay of setting (urban, rural), individual facility strengths and weaknesses, the quality of emergency medical services (EMS), and the wider provincial strategic and organisational context. ROS are strengthened by sub-district governance arrangements that recognise and enable connectedness—in particular, between primary health care and district hospital services; by informal, day-to-day communication and collaboration across levels and professions; and by hybrid clinical–managerial players as system brokers and systems thinkers. We also identify leverage points, places where small shifts could have wider system effects, most notably in the design and functioning of EMS, and in addressing small, but significant bottlenecks in supply chains in lower level facilities that negatively impact the system as a whole. Full article

Our study explores the association between the adoption of green technology and the development of green dynamic capabilities to achieve green competitive advantage. This research concentrates explicitly on the mediating function of green product innovation. The study is grounded in the dynamic capabilities theory and seeks to improve understanding regarding how organizations can attain a competitive edge by employing green practices and capabilities. Data were obtained from 312 manufacturing business managers in Bangladesh. We utilized the partial least squares structural equation modeling (PLS-SEM) method to examine the data and evaluate the proposed hypotheses. The empirical evidence suggests that both green technology adoption and green dynamic capabilities significantly impact firms’ green product innovation and competitive advantage. Additionally, the findings indicate that green product innovation is a mediating variable in the association between green technology adoption-green competitive advantage and green dynamic capabilities-green competitive advantage. This research adds to the current body of literature by presenting empirical findings highlighting the crucial role of green technology and dynamic capabilities in promoting green competitive advantage. Our results reveal that it would be beneficial for organizations to prioritize adopting eco-friendly technologies and cultivating dynamic capabilities to improve their overall green performance. The present study contributes significantly to the literature by offering insights into the strategies managers and policymakers can employ to attain sustainable competitive advantage in the manufacturing sector. Full article

In the current globalized business environment, multinational competition has become the norm for companies. This paper considers technology spillovers among manufacturers and develops a global supply chain network equilibrium model. Firstly, the optimal decision-making behaviors of manufacturers, retailers, and demand markets are characterized separately. Secondly, based on the variational inequality theory, the optimal decision-making behaviors of global supply chain members are transformed. Finally, the model is solved and analyzed using the Euler algorithm. The primary objective is to explore the impact of research and development (R&D) subsidies and intellectual property protection (IPP) strategies on manufacturers’ research and development technological levels. Furthermore, the study delves into their effects on the production and transactions of the global supply chain network and social welfare. The following conclusions are drawn: (1) Technology spillovers have a positive effect on the technological level achieved by manufacturers through research and development investment and social welfare. However, intense technological competition may harm manufacturers’ profits. (2) Under the symmetric subsidy policy, higher subsidies may lead to a decrease in social welfare. (3) Under symmetric intellectual property protection policies, increasing the intensity of intellectual property protection benefits manufacturers but is detrimental to retailers and social welfare. However, under an asymmetric intellectual property protection strategy, implementing high-intensity intellectual property protection by high-technology countries is advantageous for retailers and social welfare. This conclusion has contributed to the technical research and development and production operation decision making of global supply chain members, as well as government policy formulation, and has also provided a new perspective for theoretical research in the field of global supply networks. Full article

The iteration of entrepreneurial opportunities is vital to the growth and maintenance of long-term competitive advantages of user enterprises. However, there needs to be more comprehensive theoretical discussion within the academic community on how entrepreneurial opportunity iteration contributes to the entrepreneurial performance of user enterprises. In this study, we investigate Smartmi Technology and Zepp Technology as the research subjects and employ the case study method encoded in the programmed rootedness theory to uncover the intrinsic mechanism by which the entrepreneurial opportunity iteration of user enterprises affects entrepreneurial performance and explicate the mechanistic model between different types of entrepreneurial opportunity iteration and the dual strategic orientations and entrepreneurial performance. Specifically, the entrepreneurial opportunity iterations of user enterprises are mainly categorized into efficiency-based and innovative entrepreneurial opportunity iterations. The dual strategic orientations of stakeholder long-termism and professionalism play a significant moderating role in promoting user and growth performance improvement. Full article

With the popularization of digital technology, the problem of information pollution caused by fake news has become more common. Malicious dissemination of harmful, offensive or illegal content may lead to misleading, misunderstanding and social unrest, affecting social stability and sustainable economic development. With the continuous iteration of artificial intelligence technology, researchers have carried out automatic and intelligent news data mining and analysis based on aspects of information characteristics and realized the effective identification of fake news information. However, the current research lacks the application of multidisciplinary knowledge and research on the interpretability of related methods. This paper focuses on the existing fake news detection technology. The survey includes fake news datasets, research methods for fake news detection, general technical models and multimodal related technical methods. The innovation contribution is to discuss the research progress of fake news detection in communication, linguistics, psychology and other disciplines. At the same time, it classifies and summarizes the explainable fake news detection methods and proposes an explainable human-machine-theory triangle communication system, aiming at establishing a people-centered, sustainable human–machine interaction information dissemination system. Finally, we discuss the promising future research topics of fake news detection technology. Full article

This paper considers a pickup and delivery problem in automobile logistics. In the daily operations of a third-party logistics company (3PL), decisions must be made for two kinds of demands: delivering finished automobiles from an outbound warehouse to distribution centers (DCs) and transferring automobiles among the DCs according to specific customer orders. The problem is to assign a set of automobiles to a set of heterogeneous auto-carriers and deliver them to their destinations considering the outbound and transfer demands. Each automobile is assigned a value indicating its urgency level to be handled and a car type: small, medium, or large. Each of the auto-carriers has a specific number of slots with different types indicating the largest size of an automobile that can be loaded into the slot. An integer programming (IP) model is formulated for the problem to maximize the total loaded value and minimize the total transportation cost depending on the routing of the carriers. An improved adaptive large neighborhood search algorithm is developed to solve the problem efficiently, where a heuristic generates an initial solution, and a series of operators update the solution iteratively. Experimental results based on multi-scale instances show that the proposed algorithm can generate near-optimal solutions in an acceptable amount of time, and outperforms solving the IP model directly by CPLEX to a large extent. The algorithm can help 3PL companies make efficient and economical decisions in daily operations. Full article

The growth of urban areas and the management of energy resources highlight the need for precise short-term load forecasting (STLF) in energy management systems to improve economic gains and reduce peak energy usage. Traditional deep learning models for STLF present challenges in addressing these demands efficiently due to their limitations in modeling complex temporal dependencies and processing large amounts of data. This study presents a groundbreaking hybrid deep learning model, BiGTA-net, which integrates a bi-directional gated recurrent unit (Bi-GRU), a temporal convolutional network (TCN), and an attention mechanism. Designed explicitly for day-ahead 24-point multistep-ahead building electricity consumption forecasting, BiGTA-net undergoes rigorous testing against diverse neural networks and activation functions. Its performance is marked by the lowest mean absolute percentage error (MAPE) of 5.37 and a root mean squared error (RMSE) of 171.3 on an educational building dataset. Furthermore, it exhibits flexibility and competitive accuracy on the Appliances Energy Prediction (AEP) dataset. Compared to traditional deep learning models, BiGTA-net reports a remarkable average improvement of approximately 36.9% in MAPE. This advancement emphasizes the model’s significant contribution to energy management and load forecasting, accentuating the efficacy of the proposed hybrid approach in power system optimizations and smart city energy enhancements. Full article

Although previous research has explored the correlation between teacher characteristics and teaching quality, effective methods for identifying key factors that influence teaching quality are still lacking. This study aims to address this issue by developing an identification methodology based on a computational pedagogy research paradigm to identify the key characteristics of teachers and courses that influence their teaching quality. We developed quantitative models to quantify the characteristics of teaching quality, based on those identified in previous studies. Correlation and multiple correlation analyses were conducted to identify the key influencing characteristics, and grey correlation analysis was used to calculate the degree of correlation between these key characteristics and teaching quality. Our methodology was applied to 27 computer science discipline teachers and 82 courses, and validated with teaching data from eight additional teachers. Our findings demonstrate the effectiveness of our method in identifying the key influence characteristics of teachers and courses on teaching quality and confirm significant correlations between these key influential characteristics and teaching quality. This innovative approach provides new insights and tools for predicting and improving the teaching quality across disciplinary majors. Our research has significant implications for future education studies, particularly for the development of effective methods for identifying key factors that influence teaching quality. By providing a more comprehensive understanding of the key factors that influence teaching quality, our study can inform the development of evidence-based strategies to improve the teaching effectiveness for different disciplinary majors. Full article