Search Results (2,316)

Computational fluid dynamics (CFD) is a numerical tool often used to predict anticipated observations using only the physics involved by numerically solving the conservation equations for energy, momentum, and continuity. These governing equations have been around for more than one hundred years, but only limited analytical solutions exist for specific geometries and conditions. CFD provides a numerical solution to these governing equations, and several commercial software and shareware versions exist that provide numerical solutions for customized geometries requiring solutions. Often, experiments are cost prohibitive and/or time consuming, or cannot even be performed, such as the explosion of a chemical plant, downwind air concentrations and the impact on residents and animals, contamination in a river from a point source loading following a train derailment, etc. A modern solution to these problems is the use of CFD to digitally evaluate the output for a given scenario. This paper discusses the use of CFD at Corteva and offers a flavor of the types of problems that can be solved in agricultural manufacturing for pesticides and environmental scenarios in which pesticides are used. Only a handful of examples are provided, but there is a near semi-infinite number of future possibilities to consider. Full article

Food banks are providing crucial relief as food insecurity increases worldwide. While these services are essential for vulnerable populations, there is variability in foods available and users may experience poor nutritional quality, and an overabundance of discretionary foods, contributing to public health risks including overnutrition and obesity. Understanding how customers perceive food availability, variety, and quality is important to inform relief services and health interventions. This study reports the findings of a convergent parallel mixed-methods investigation of user experiences and perceptions of food availability, variety, and quality at a major food bank in Western Australia. Food bank customers (N = 207) at a food bank branch and mobile van locations completed a survey, with an option to complete a subsequent semi-structured interview (n = 15). Approximately 80% of the survey sample had low (48%) or very low (30%) food security, half of the sample had been using the food bank for longer than 6 months, and 77% reported the food bank as their first choice for food. Three-quarters (77%) reported financial barriers to a balanced diet in the past twelve months and described how limited availability and variety complicated shopping. Interviewees explained complex perceptions of these issues, including favouring healthy food while considering discretionary food as a “luxury” that enhanced their quality of life. Our findings suggest that food bank users experience barriers to maintaining a balanced diet, encounter variable supplies of healthy and nutritious foods, and have concerns about the impacts of frequent discretionary food consumption. These findings have implications for public health promotion. Full article

In today’s competitive digital landscape, banking firms must leverage qualitative and quantitative analysis to enhance their website interfaces, ensuring they meet user needs and expectations. By combining detailed user feedback with data-driven insights, banks can create more intuitive and engaging online experiences, ultimately driving customer satisfaction and loyalty. Thus, the need for website customer behavior analysis to evaluate its interface is critical. This study focused on the five biggest banking firms and collected big data from their websites. Statistical analysis was followed to validate findings and ensure the reliability of the results. At the same time, agent-based modeling (ABM) and System Dynamics (SD) were utilized to simulate user behavior, thereby allowing for the prediction of responses to interface changes and the optimization of their website, and to obtain a comprehensive understanding of user behavior, thereby enabling banking firms to create more intuitive and user-friendly website interfaces. This interdisciplinary approach found that various website analytical metrics, such as organic and paid traffic costs, referral domains, and email sources, tend to impact banking firms’ purchase conversion, display ads, organic traffic, and bounce rate. Moreover, these insights into banking firms’ website visibility, combined with the behavioral data of the neuromarketing study, indicate specific areas for their website interface and performance improvement. Full article

The Industrial Internet of Things has enabled the integration and analysis of vast volumes of data across various industries, with the maritime sector being no exception. Advances in cloud computing and deep learning (DL) are continuously reshaping the industry, particularly in optimizing maritime operations such as Predictive Maintenance (PdM). In this study, we propose a novel DL-based framework focusing on the fault detection task of PdM in marine operations, leveraging time-series data from sensors installed on shipboard machinery. The framework is designed as a scalable and cost-efficient software solution, encompassing all stages from data collection and pre-processing at the edge to the deployment and lifecycle management of DL models. The proposed DL architecture utilizes Graph Attention Networks (GATs) to extract spatio-temporal information from the time-series data and provides explainable predictions through a feature-wise scoring mechanism. Additionally, a custom evaluation metric with real-world applicability is employed, prioritizing both prediction accuracy and the timeliness of fault identification. To demonstrate the effectiveness of our framework, we conduct experiments on three types of open-source datasets relevant to PdM: electrical data, bearing datasets, and data from water circulation experiments. Full article

Inner Mongolia, a major region in China for growing sugar beet, faces challenges caused by unscientific water and fertilizer management. This mismanagement restricts the improvement of sugar beet yield and quality and exacerbates water waste and environmental pollution. This study aims to evaluate the effects of reduced water and fertilizer on the growth and physiological metabolism of sugar beet taproot. Field experiments were conducted in Ulanqab, Inner Mongolia, in 2022 and 2023, using a split-plot design with three levels each of fertilization and irrigation. The study analyzed the effects of reduced water and fertilizer treatments on fresh taproot weight, respiration rate, energy metabolism, respiratory enzyme activity, and gene expression in sugar beet taproot. It was found that a 10% reduction in fertilizer significantly increased the beet taproot fresh weight. Further research revealed that during the rapid leaf growth phase and the taproot and sugar growth period, a 10% reduction in fertilizer upregulated HK and IDH gene expression and downregulated G6PDH gene expression in the beet taproot. This increased HK and IDH activities, decreased G6PDH activity, enhanced the activity of the EMP-TCA pathway, and inhibited the PPP. Taproot weight was positively correlated with the respiration rate, ATP content, EC, and ATPase, HK, and IDH activities, thereby increasing the taproot growth rate and taproot fresh weight, with an average increase of 4.0% over two years. These findings introduce a novel method for optimizing fertilizer use, particularly beneficial in water-scarce regions. Implementing this strategy could help farmers in western Inner Mongolia and similar areas improve crop yield and sustainability. This study offers new insights into resource-efficient agricultural practices, highlighting the importance of customized fertilization strategies tailored to local environmental conditions. Full article

The advancement of 3D printing technology has enabled the fabrication of intricate structures, yet the complexity of the print head’s motion path significantly hampers production efficiency. Addressing the challenges posed by the dataset of section points in 3D-printed workpieces, this study introduces an innovative ant colony optimization algorithm tailored to enhance the print head’s trajectory. By framing the optimization of the motion path as a Traveling Salesman Problem (TSP), the research employs a custom-designed K-means clustering algorithm to categorize the dataset into distinct clusters. This clustering algorithm partitions each printing point into different subsets based on density, optimizes these subsets through improved K-means clustering computations, and then aggregates the results to classify the entire dataset. Subsequently, the ant colony algorithm arranges the printing sequence of these clusters based on the cluster centers, followed by computing the shortest path within each cluster. To form a cohesive motion trajectory, the nearest nodes between adjacent clusters are linked, culminating in a globally optimal solution. Comparative experiments repeatedly demonstrate significant enhancements in the print head’s motion path, leading to marked improvements in printing efficiency. Full article

This paper studies the single-machine scheduling problem with truncated learning effect, time-dependent processing time, and past-sequence-dependent delivery time. The delivery time is the time that the job is delivered to the customer after processing is complete. The goal is to determine an optimal job schedule to minimize the total weighted completion time and maximum tardiness. In order to solve the general situation of the problem, we propose a branch-and-bound algorithm and other heuristic algorithms. Computational experiments also prove the effectiveness of the given algorithms. Full article

V-Formation and echelon formation flights can be seen used by migratory birds throughout the year and have left many scientists wondering why they choose very specific formations. Experiments and analytical studies have been completed on the topic of the formation flight of birds and have shown that migratory birds benefit aerodynamically by using these formations. However, many of these studies were completed using fixed-wing models, while migratory birds both flap and glide while in formation. This paper reports the design of and experiments with a flapping-wing model rather than only a fixed-wing model. In order to complete this study, two different approaches were used to generate a flapping-wing model. The first was a computational study using an unsteady vortex–lattice (UVLM) solver to simulate flapping bodies. The second was an experimental design using both custom-built flapping mechanisms and commercially bought flapping drones. The computations and various experimental trials confirmed that there is an aerodynamic benefit from flying in either V-formation or echelon flight while flapping. It is shown that each row of birds experiences an increase in aerodynamic performance based on positioning within the formation. Full article

Drought stress has serious effects on the growth and yield of wheat in both productivity and quality and is an abiotic factor. Traditional methods of crop drought stress monitoring have some deficits. This work has been conducted in order to enhance these conventional methods by proposing a new deep learning approach. This paper has presented a deep learning-based model customized for monitoring drought stress in winter wheat during the critical growth stages. Drought-afflicted winter wheat images were captured at three crucial phases: rising–jointing, heading–flowering, and flowering–maturity. These images are correlated against soil moisture data to construct a comprehensive dataset. DenseNet121 was chosen as the network model since it extracts features from images relating to phenotypes. Several factors, like training methods, learning rate adjustment, and addition of the attention mechanism, are optimized in eight sets of experiments. This provided the final DenseNet-121 model with an average recognition accuracy of 94.67% on the test set, which means that monitoring drought stress during wheat growth’s key periods is feasible and effective. Full article

The resurgence of the Taliban in Afghanistan has significantly exacerbated educational challenges for marginalized women and girls, deepening gender disparities and impeding socio-economic development. Addressing these issues, this article introduces the Blockchain and Artificial Intelligence Non-Formal Education System (BANFES), an innovative educational solution specifically designed for Afghan girls deprived of formal schooling. BANFES leverages advanced artificial intelligence technologies, including personalized data analysis, to provide customized learning experiences. Additionally, blockchain technology ensures secure record management and data integrity, facilitating a decentralized educational ecosystem where various nodes offer hybrid learning methodologies without intermediaries. This system not only adapts to individual learning speeds and styles to enhance engagement and outcomes but also employs an independent assessment mechanism to evaluate learners. Such evaluations promote transparency and maintain the quality and reputation of educational contributions within the network. The BANFES initiative also addresses implementation challenges, including local distrust and integration with existing educational structures, providing a robust model to overcome barriers to education. Furthermore, the paper explores the scalability of BANFES, proposing its application as a global strategy for non-formal education systems facing similar geopolitical and infrastructural challenges. By creating a secure, flexible, and learner-focused environment, BANFES aims to empower Afghan women and girls with essential skills for personal and professional growth, thus fostering socioeconomic advancement within their communities and setting a new standard for informal education worldwide. Full article

Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly. Full article

Credit card fraud detection is a critical issue for financial institutions due to significant financial losses and the erosion of customer trust. Fraud not only impacts the bottom line but also undermines the confidence customers place in financial services, leading to long-term reputational damage. Traditional machine learning methods struggle to improve detection accuracy with limited data, adapt to new fraud techniques, and detect complex fraud patterns. To address these challenges, we present FedGAT-DCNN, a model integrating a Graph Attention Network (GAT) and dilated convolutions within a federated learning framework. FedGAT-DCNN employs federated learning, allowing financial institutions to collaboratively train models using local datasets, enhancing accuracy and robustness while maintaining data privacy. Incorporating a GAT enables continuous model updates across institutions, quickly adapting to new fraud patterns. Dilated convolutions extend the model’s receptive field without extra computational overhead, improving detection of subtle and complex fraudulent activities. Experiments on the 2018CN and 2023EU datasets show that FedGAT-DCNN outperforms traditional models and other federated learning methods, achieving a ROC-AUC of 0.9712 on the 2018CN dataset and 0.9992 on the 2023EU dataset. These results highlight FedGAT-DCNN’s robustness, accuracy, and applicability in real-world fraud detection scenarios. Full article

This paper focuses on the settlement selection strategic analysis for self-operated e-commerce platforms on hybrid e-commerce platforms under market competition. Taking factors such as the market share, price competition, commission, and customer loyalty into account, a multi-leader–follower game model with the platforms as leaders and the manufacturers as followers is established. Then, we solve the model with the help of some mathematical techniques and describe some numerical experiments to analyze settlement strategies for the self-operated platforms and their impact on other members in the network. The numerical results reveal the following revelations: a lower commission rate is more suitable for the self-operated platforms; once the commission rates are determined, the self-operated platforms prefer to settle in the hybrid platforms under lower medium price competition; when the price competition is fierce, as customer loyalty increases, the self-operated platforms should settle with a low market share; if the self-operated platforms settle in the hybrid platforms, then a higher price competition is advantageous for all members and can facilitate supply chain coordination. Full article

Airport shuttle buses, as a specialized form of bus service, serve as an economical, efficient, and sustainable transportation option for air travelers. In contrast to conventional bus services, airport shuttle bus operations exhibit more pronounced market-oriented characteristics, striving to balance extensive public transport coverage with the optimization of corporate profitability. Although these services outperform regular bus transit in terms of efficiency, they incur higher operational costs. However, existing studies on enhancing profitability and optimizing resource allocation for airport shuttle buses are inadequate. This study proposes a differential pricing strategy based on historical ticketing data. Initially, we analyze the characteristics of orders, users, and reservations within the context of customized bus operations. Leveraging the differences among various groups, we employ clustering techniques to classify seat grades and segment users. Based on the clustering outcomes, we determine distinct price elasticity values for each segment. As the strategies are developed based on seat grades, booking time, and user travel patterns, the numerical experiments indicate that the proposed differentiated pricing strategy can increase the revenue of customized public transport services by at least 41%. This strategy not only enhances the efficiency of resource allocation and service accessibility but also makes the service more financially sustainable. Full article

Many manufacturing industries today are adopting sustainable production methods in response to environmental regulations and efforts. One of the typical criteria they consider is the United Nations has set global objectives (Sustainable Development Goals: SDGs) designed to address various social, economic, and environmental challenges. “Ensuring sustainable consumption and production patterns” (Goal 12) is one of these goals. As a result, not only are manufacturers interested in sustainable products, but consumers are also showing increased interest. Consequently, the market size for sustainable products is also on the rise. This study aims to examine the mechanisms of how to improve customer loyalty of South Korean consumers who have experience purchasing sustainable apparel to vitalize the sustainable product market in Korea. Specifically, this study reveals the impact of perceived value (PV) on loyalty (LY), focusing on the mediating effects of satisfaction (SAT) and trust (TR). The analysis finds that functional value (FV), emotional value (EMV), and green value (GV) have significant direct effects on LY. Additionally, SAT and TR have significant mediating effects between PV and LY, and there is no difference in the strength of the indirect effects of SAT and TR in the relationship between FV, EMV, GV, and LY. This study extends the theoretical background of the mechanisms enhancing loyalty to sustainable apparel through the verification of parallel mediating effects. Furthermore, it is expected that these insights will serve as a direction for the operational strategies of sustainable apparel manufacturing companies. Full article