Search Results (19)

This paper proposes the development of an IEC 61850-compliant platform that is readily programmable and deployable for future digital twin applications. Given the compatibility between IEC-61850 and digital twin concepts, a focused case study was conducted involving the robust development of a Raspberry Pi platform with protection relay functionality using the open-source libIEC61850 library. Leveraging IEC-61850’s object-oriented data modelling, the relay can be represented by fully consistent virtual and physical models, providing an essential foundation for accurate digital twin instantiation. The relay implementation supports high-speed Sampled Value (SV) subscription, real-time RMS calculations, IEC Standard Inverse overcurrent trip behaviour according to IEC-60255, and Generic Object-Oriented Substation Event (GOOSE) publishing. Further integration includes setting group functionality for dynamic parameter switching, report control blocks for MMS client–server monitoring, and GOOSE subscription to simulate backup relay protection behaviour with peer trip messages. A staged development methodology was used to iteratively develop features from simple to complex. At the end of each stage, the functionality of the added features was verified before proceeding to the next stage. The integration of the Raspberry Pi into Curtin’s IEC = 61,850 digital substation was undertaken to verify interoperability between IEDs, a key outcome relevant to large-scale digital twin systems. The experimental results confirm GOOSE transmission times below 4 ms, tight adherence to trip-time curves, and performance under higher network traffic. Such measured RMS and trip-time errors fall well within industry and IEC limits, confirming the reliability of the relay logic. The takeaways from this case study establish a high-performing, standardised foundation for a digital twin system that requires fast, bidirectional communication between a virtual and a physical system. Full article

Industrial Control Systems (ICS) are fundamental to the operation, monitoring, and automation of critical infrastructure in sectors such as energy, water utilities, manufacturing, transportation, and oil and gas. According to the Purdue Model, ICS encompasses tightly coupled OT and IT layers, becoming increasingly interconnected. Smart grids represent a critical class of ICS; thus, this survey examines encryption and relevant protocols in smart grid communications, with findings extendable to other ICS. Encryption techniques implemented at both the protocol and network layers are among the most effective cybersecurity strategies for protecting communications in increasingly interconnected ICS environments. This paper provides a comprehensive survey of encryption practices within the smart grid as the primary ICS application domain, focusing on protocol-level solutions (e.g., DNP3, IEC 60870-5-104, IEC 61850, ICCP/TASE.2, Modbus, OPC UA, and MQTT) and network-level mechanisms (e.g., VPNs, IPsec, and MACsec). We evaluate these technologies in terms of security, performance, and deployability in legacy and heterogeneous systems that include renewable energy resources. Key implementation challenges are explored, including real-time operational constraints, cryptographic key management, interoperability across platforms, and alignment with NERC CIP, IEC 62351, and IEC 62443. The survey highlights emerging trends such as lightweight Transport Layer Security (TLS) for constrained devices, post-quantum cryptography, and Zero Trust architectures. Our goal is to provide a practical resource for building resilient smart grid security frameworks, with takeaways that generalize to other ICS. Full article

To address deficiencies in the delivery process of O2O takeaway delivery platforms, the concept of priority levels for merchants within the platform is introduced. Focusing on three core objectives—reducing the proportion of late deliveries, lowering delivery costs, and reducing average order fulfillment times—a model is established that considers merchant priorities for multi-objective pickup and delivery vehicle routing. A two-stage optimization model is proposed for efficient management of real-time order generation and dynamic changes in delivery personnel locations. An improved variable neighborhood search algorithm is used to obtain the initial distribution plan; meanwhile, in the subsequent dynamic adjustment phase, a periodic optimization strategy is adopted. Finally, a test case was constructed using partial data provided by the food delivery platform, and the effectiveness of the proposed model and algorithm was verified through numerical experiments. The experimental results show that as the weight of cost increases, costs decrease, which leads to an increase in the proportion of late deliveries and delivery times, which is detrimental to improving customer satisfaction with deliveries. The research findings not only enrich the theoretical study of the pickup and delivery vehicle routing problem (PDVRP) but also provide scientific optimization methods for food delivery platforms, facilitating their sustainable operations and enhancing the efficiency and sustainability of urban transportation networks. Full article

There are three operational modes for restaurant takeaway services: a takeaway platform combined with restaurant delivery (mode A), a takeaway platform paired with platform delivery (mode B), and a self-established platform with delivery (mode C). In the face of intense market competition, determining how restaurants can select appropriate operational delivery modes to maintain competitiveness and profitability has emerged as a pressing issue. In this paper, we develop game models that encompass a restaurant and food delivery platform to investigate the optimal operational mode for restaurants. Our analysis indicates that when the market size is small, mode B results in the lowest ordering costs, the highest sales volume, and the maximum profit. Conversely, in the context of a large market size, if the commission rate is low, mode A minimizes the ordering costs and maximizes sales volume; if the commission rate is high, mode C minimizes the ordering costs while also achieving the highest sales volume. Notably, we find that as long as the market size is sufficiently large, mode C consistently yields the highest restaurant profits. The outcomes of this study contribute to the theoretical framework surrounding the operation and management of food delivery platforms and offer theoretical guidance and decision-making support for selecting restaurant food delivery operational modes. Full article

Wildfires are a critical driver of landscape transformation on Earth, representing a dynamic and ephemeral process that poses challenges for accurate early detection. To address this challenge, researchers have increasingly turned to deep learning techniques, which have demonstrated remarkable potential in enhancing the performance of wildfire detection systems. This paper provides a comprehensive review of fire detection using deep learning, spanning from 1990 to 2023. This study employed a comprehensive approach, combining bibliometric analysis, qualitative and quantitative methods, and systematic review techniques to examine the advancements in fire detection using deep learning in remote sensing. It unveils key trends in publication patterns, author collaborations, and thematic focuses, emphasizing the remarkable growth in fire detection using deep learning in remote sensing (FDDL) research, especially from the 2010s onward, fueled by advancements in computational power and remote sensing technologies. The review identifies “Remote Sensing” as the primary platform for FDDL research dissemination and highlights the field’s collaborative nature, with an average of 5.02 authors per paper. The co-occurrence network analysis reveals diverse research themes, spanning technical approaches and practical applications, with significant contributions from China, the United States, South Korea, Brazil, and Australia. Highly cited papers are explored, revealing their substantial influence on the field’s research focus. The analysis underscores the practical implications of integrating high-quality input data and advanced deep-learning techniques with remote sensing for effective fire detection. It provides actionable recommendations for future research, emphasizing interdisciplinary and international collaboration to propel FDDL technologies and applications. The study’s conclusions highlight the growing significance of FDDL technologies and the necessity for ongoing advancements in computational and remote sensing methodologies. The practical takeaway is clear: future research should prioritize enhancing the synergy between deep learning techniques and remote sensing technologies to develop more efficient and accurate fire detection systems, ultimately fostering groundbreaking innovations. Full article

As consumers increasingly access takeaway food from restaurants, the importance of reducing the use of single-use plastic has emerged. To investigate this complex process, the current study applies the Theory of Planned Behavior (TPB) and the Norm Activation Model (NAM) to discover how this theory and model leads to discovering customers’ behavioral intentions. Data were collected from 436 respondents and reflect people aged 18 years or older who accessed an online food ordering platform within the three months prior to the study data collection. Results present that customers’ attitudes, subjective norms, perceived behavioral control, awareness of consequences, and personal norms regarding reducing single-use plastic utensils influence consumers’ behavioral intentions. These findings are meaningful to academia in providing insights into the link between consumers’ behavior and reducing the use of single-use plastic utensils in the restaurant context. In addition, this study provides marketing strategies and implications for improving consumers’ awareness of the use of single-use plastic utensils in retail food services. Full article

To study the internal formation mechanisms of risky riding behaviors of takeaway e-bikers at urban intersections, we designed a takeaway riding risky behavior questionnaire and obtained 605 valid samples. An exploratory factor analysis was then conducted to extract five scales: individual characteristics, safety attitude, riding confidence, risk perception, and risky riding behavior. On this basis, a structural equation model was constructed to explore the intrinsic causal relationships among the variables that affect the risky riding behaviors of takeaway e-bikers. The results show that the influence of incentive compensation driven by the takeaway platform was the greatest one. Takeaway riders tend to fight against time to improve punctuality and income by red-light running and speeding. They usually need to pay attention to order information and the delivery routes and communicate with customers to pick up meals in real-time, which inevitably lead to the use of cell phone while riding. Road factors such as “no turnaround at the intersection” and “no non-isolation facilities between on-motorized and motorized lane” lead riders to riding against the traffic, riding on the motor lane, and parking outside the stop line. In addition, lax traffic regulations lead to frequent loopholes for takeaway riders. It means that improving the takeaway platform system, strengthening traffic safety education, and adopting mandatory restraint measures are extremely important. The empirical results provide theoretical support for the benign and healthy development of the takeaway industry, which is significant for preventing and reducing risky behaviors of takeaway riders and improving safety at urban intersections. Full article

As the digitization of the supply side continues to advance, the takeaway industry has made a significant contribution to economic growth. However, the rapid expansion of the scale has also brought many social problems, merchants provide low-quality goods out of the psychology of opportunity, and the uneven quality of goods and asymmetric information not only bring great regulatory problems for the takeaway platform, but also make it difficult for consumers to identify the platform, merchants, and consumers as takeaway platform stakeholders, it is difficult to integrate resources to achieve value co-creation. Therefore, in order to realize the value co-creation among the stakeholders of the takeaway platform, a three-party evolutionary game model was constructed to analyze and simulate the strategic choices of stakeholders under different situations through simulation experiments and to explore the sensitive influence of each factor. The results of the study show the following: shaping a scientific reward and punishment system and setting reasonable rewards and punishments within a limited threshold; platforms, consumers using word-of-mouth effects to amplify the loss of network externalities that merchants have to bear when they provide low-quality services, and improving the consumer feedback mechanism to reduce the cost of feedback are all effective measures to promote the active participation of takeaway platform stakeholders in value co-creation and promote the sustainable and healthy development of the takeaway industry. Full article

Drawing upon the conservation of resources (COR) theory, this paper discusses the effect of platform economic dependence on working time and the mediating role of work pressure, as well as the boundary role of platform human resource management practices, with a survey of 9576 takeaway riders. The results suggest that the greater the economic dependence of takeaway riders on the platform organization, the longer they work; work pressure plays a mediating role between platform economic dependence and working time; the amount of platform rewards has no effect on the positive relationship between platform economic dependence and working time, whereas the difficulty of obtaining platform rewards strengthens the positive relationship between platform economic dependence and working time. This paper contributes to the literature on gig work by providing a micro-individual perspective and to the literature on COR theory by enriching the studies of resource caravan passageways. Full article

In the modern catering business model, restaurants usually use established platforms to promote their food and use two channels to sell their food: online and offline sales. We construct demand functions for online and offline, considering promotion and substitution relationships by a revised Bertrand model. We first consider three classic models: the decentralized decision model, the equilibrium decision model, and the centralized decision model. In the decentralized decision model, the platform decides both the promotional effort and the online discount; in the equilibrium decision model, the platform decides the online discount, while the food service provider decides the promotional effort. In the centralized decision model, the takeaway platform and the food service provider have maximized the overall profit as the decisive goal. We find that the online discount decreases in price when the impact factor of the online promotion is high but increases in price when the impact factor of the online promotion is low. Then, we analyze and compare the results under three models. We find that when the substitution factor is low enough, or the impactor factor of online promotion is low enough, the global optimal platform discount is higher than the equilibrium platform discount and the decentralized online discount; otherwise, the results are the opposite. In addition, the global optimal promotional effort is always higher than the optimal promotional effort in the decentralized model. When the substitution factor is low enough, or the impactor factor of online promotion is low enough, the global optimal promotional effort is higher than the equilibrium optimal promotional effort; otherwise, the result is the opposite. Full article

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development. Full article

Sentiment analysis of subjective texts in social media is beneficial to help people adjust and intervene in a negative mental state in time, which is significant to mental health care. At present, limited by the accuracy of word segmentation, sentiment analysis of subjective text has difficulties in dealing with context, sentence patterns, and word co-occurrence. This paper aims to propose an efficient method of semantic feature representation and sentiment analysis, thereby providing a basis for sentiment visualization and interactive applications. Based on Ernie-Tiny and BiGRU, this paper proposes a sentiment analysis model ET_s_BG+p to solve problems in analyzing Chinese subjective texts’ complex semantics, diverse sentence patterns, and shortness. The model inputs the semantic features obtained via Ernie-Tiny into BiGRU and then splices the output with the sentence vectors of Ernie-Tiny to form final text features and perform sentiment classification. Experiments are performed on a dataset integrating text comments from Weibo, takeaway, and e-commerce platforms. The results show that the model proposed in this paper performs best in most of the evaluation indicators compared with baseline models such as CNN, BiLSTM, and GRU. The experiments show that the accuracy of the model on the dataset built in this research is 84.30%, the precision is 83.95%, the recall rate is 88.35%, and the F1 value is 85.98%. At the same time, based on ET_s_BG+p, this paper develops a prototype visual display platform that integrates functions such as text input, sentiment analysis, and agent interaction, which can provide support for daily emotion monitoring and adjustment. Full article

Online food delivery (OFD) platforms have become increasingly popular due to advanced technology, which is changing the way consumers purchase food prepared outside of the home. There is limited research investigating the healthiness of the digital food environment and its influence on consumer choice and dietary behaviours. This study is the first to examine the nutritional quality and marketing attributes of menu items from popular independent and franchise restaurants and takeaway outlets on New Zealand’s market leading OFD platform (UberEATS^®). A total of 374 popular independent and franchise restaurants and takeaway outlets were identified to form a database of complete menus and marketing attributes. All 25,877 menu items were classified into 38 food and beverage categories based on the Australian Dietary Guidelines. Of complete menus, 73.3% (18,955/25,877) were discretionary. Thirty-six percent (9419/25,877) were discretionary cereal-based mixed meals, the largest of the 38 categories. Discretionary menu items were more likely to be categorized as most popular (OR: 2.0, 95% CI 1.7–2.2), accompanied by a photo (OR: 1.7, 95% CI 1.6–1.8), and offered as a value bundle (OR: 4.6, 95% CI 3.2–6.8). Two of the three discretionary mixed meal categories were significantly less expensive than their healthier counterparts (p < 0.001). The overwhelming availability and promotion of discretionary choices offered by restaurants and takeaway outlets on OFD platforms have implications for public health policy. Further research to explore direct associations between nutritional quality and consumers’ dietary choices is required. Full article

Sub-Saharan Africa (SSA) has a significant proportion of populations living in urban slum conditions, where exposure to multiple environmental stressors and social inequalities is ubiquitous. This commentary synthesizes commonalities in recent environmental health studies from urban cities in East and West Africa, presented during a symposium sponsored by the Africa Chapter of the International Society of Environmental Epidemiology (ISEE) in August 2020. A key takeaway from this symposium is the need for harmonization of epidemiologic and exposure data collection in three domains tailored to the SSA context: (1) improvements in socioeconomic status (SES) measurement through harmonization in the conceptualization and operationalization of SES indicators; (2) improvements in air pollution exposure assessment in resource-constrained contexts by better integration, validation, and harmonization of exposure data of air pollution and mitigating factors; and (3) harmonization in the assessment of health outcomes and biomonitoring of contaminants. Focusing on these three domains would galvanize environmental epidemiologists in SSA around shared data collection instruments and shared data platforms and facilitate the pooling of data across the continent. Fostering this collaborative research will enable researchers and decision-makers to glean new insights and develop robust environmental health interventions and policies for SSA urban slums and for improved population health. Full article

The era of mobile information has arrived, and people’s lifestyles have undergone tremendous changes. Ordering takeaways through takeout apps on smartphones is one of them. However, most existing takeaway platforms charge high commissions in the middle. There are many fake reviews in restaurants, the authenticity of restaurant ratings is low, and the recommended dishes have low customer satisfaction. This paper aims to solve this problem by introducing a peer-to-peer architecture based on blockchain smart contracts. The proposed architecture leverages the automation of smart contracts to provide autonomous, commission-free food ordering and delivery services. In addition, the smart contract reward mechanism is used to collect order information and rating information, and a deep learning recommendation model is introduced to analyze the data to recommend restaurants and menus to the client accurately. To demonstrate the usability and efficiency of the proposed method, we conducted a case study using public chain-based technologies. At the same time, comprehensive evaluation experiments are carried out, and the results show the importance of the proposed food delivery system. Full article