Search Results (20,214)

In order to reduce the impact of the performance degradation of a direct current (DC) distribution network system caused by the access of scaled electric vehicles (EVs), this paper proposes a collaborative optimization method for a DC distribution network based on scaled EVs charging and discharging and soft open points (SOPs) topology reconfiguration. Firstly, based on the normal charging of scaled EVs, the EV discharge power model and the discharge response model were established based on the V2G (vehicle-to-grid) characteristic. Based on the characteristics of SOPs regulating voltage and power distribution, the SOP model and its equivalent model of DC system are established to identify the collaborative optimization of scaled EVs charging and discharging and SOPs topology reconstruction. Secondly, the bi-level model that optimizes multi-objects, including distribution network system loss, total voltage deviation and customer benefits, is established. The upper and lower models use the multi-objective particle swarm optimization (MOPSO) algorithm and simulated annealing algorithm to jointly optimize the optimal EV discharge power and the optimal SOP access configuration simultaneously. Finally, the effectiveness of the proposed collaborative optimization method is verified by a modified IEEE 33-node DC system example. Full article

In recent years, Estonian employers, upper secondary schools, and other stakeholders have devised various new collaborative measures to effectively integrate workplace learning (WPL) and school education. These efforts are aimed at enhancing the key competences of students. However, the educational purpose and effectiveness of the various initiatives are unknown. The main aim of this research is to explore what kinds of practises are applied in Estonian upper secondary schools to integrate formal education and WPL, and the experiences and requirements of schools and employers in this area. The empirical study is based on individual and focus group interviews conducted with upper secondary school teachers, head teachers, and employers. A phenomenological approach and inductive thematic analysis were used to examine current practises. The results of the study show that workplace learning is integrated into school learning mainly for the development of key competences and career competencies. An important part of the learning process is reflecting on experiences implemented via institutional cooperation. In core or foundation subjects, especially STEM subjects, the topics of work life or recognition of work experience are rarely encountered in school. Full article

In a marine environment, the concentration of chlorophyll is an important indicator of quality, which is also considered an indicator used to predict the marine ecological environment, which is further considered an important means of predicting red tide disasters. Although existing methods for predicting chlorophyll concentration have achieved encouraging performance, there are still two limitations: (i) they primarily focus on the correlation between variables while ignoring negative noise from non-predictive variables and (ii) they are unable to distinguish the impact of chlorophyll from that of non-predictive variables on chlorophyll concentration at future time points. In order to overcome these obstacles, we propose a Multi-Attention Collaborative Network (MACN)-based triangle-structured prediction system. In particular, the MACN consists of two branch networks, with one named NP-net, focusing on non-predictive variables, and the other named T-net, applied to the target variable. NP-net incorporates variable-distillation attention to eliminate the negative effects of irrelevant variables, and its outputs are used as auxiliary information for T-net. T-net works on the target variable, and both its encoder and decoder are related to NP-net to use the output of NP-net for assistance in learning and prediction. Two actual datasets are used in the experiments, which show that the MACN performs better than various kinds of state-of-the-art techniques. Full article

This bibliographic analysis explores the evolving landscape of additive manufacturing (AM) in the context of Industry 4.0 and the emerging paradigms of Industry 5.0. This research critically examines the key literature and scholarly works to clarify the evolution, challenges, and opportunities presented by integrating AM technologies with digital transformation and advanced industrial practices. The exploration begins by tracing the foundational concepts of Industry 4.0, emphasizing the role of cyber–physical systems, data analytics, and automation in reshaping manufacturing ecosystems. It then moves to the developments of Industry 5.0, focusing on human-centric approaches, collaborative robotics, and sustainable manufacturing strategies that extend beyond automation. The impact of AM technologies across various sectors, from aerospace and automotive industries to healthcare and consumer goods, is central to this analysis. This article synthesizes empirical studies, case analyses, and theoretical frameworks to discern the synergies, challenges, and prospects of integrating AM into Industry 4.0 and the evolving Industry 5.0. Through this bibliographic journey, readers gain insights into the transformative potential of AM as a catalyst for innovation, agility, and sustainability in the digital age. The findings underscore the need for interdisciplinary collaborations, policy frameworks, and technological advancements to harness AM’s full potential within Industry 4.0 and 5.0. Full article

With the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the spectrum and with high data demand in future wireless networks. However, the efficient coordination of these techniques in complex and changing 3D environments still faces many challenges. To this end, this paper proposes a NOMA-based multi-UAV-assisted D2D communication model in which multiple UAVs are deployed in 3D space to act as airborne base stations to serve ground-based cellular users with D2D clusters. In order to maximize the system throughput, this study constructs an optimization problem of joint channel assignment, trajectory design, and power control, and on the basis of these points, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network (DH-MDQN) algorithm. The dynamic hypergraph method is first used to construct dynamic simple edges and hyperedges and to transform them into directed graphs for efficient dynamic coloring to optimize the channel allocation process; subsequently, in terms of trajectory design and power control, the problem is modeled as a multi-agent Markov Decision Process (MDP), and the Multi-Agent Deep Q Network (MDQN) algorithm is used to collaboratively determine the trajectory design and power control of the UAVs. Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement. Full article

The merging behavior of vehicles at entry ramps and the speed differences between ramps and mainline traffic cause merging traffic bottlenecks. Current research, primarily focusing on single traffic control strategies, fails to achieve the desired outcomes. To address this issue, this paper explores an integrated control strategy combining Variable Speed Limits (VSL) and Lane Change Control (LCC) to optimize traffic efficiency in ramp merging areas. For scenarios involving multiple ramp merges, a multi-agent reinforcement learning approach is introduced to optimize control strategies in these areas. An integrated control system based on the Factored Multi-Agent Centralized Policy Gradients (FACMAC) algorithm is developed. By transforming the control framework into a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), state and action spaces for heterogeneous agents are designed. These agents dynamically adjust control strategies and control area lengths based on real-time traffic conditions, adapting to the changing traffic environment. The proposed Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control in Dynamic Areas (FM-ITC-Darea) control strategy is simulated and tested on a multi-ramp scenario built on a multi-lane Cell Transmission Model (CTM) simulation platform. Comparisons are made with no control and Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control (FM-ITC) strategies, demonstrating the effectiveness of the proposed integrated control strategy in alleviating highway ramp merging bottlenecks. Full article

Numerous concerns associated with the use of synthetic pesticides have led to important initiatives aimed at ensuring eco-friendly practices in pest and disease control. Third-generation pesticides, biopesticides, have become a key factor in those efforts, capturing the attention of researchers worldwide. To evaluate trends in biopesticide research over the past three decades, a bibliometric analysis was performed on bibliographic data sourced from the Web of Science database. This analysis included metrics such as publication dynamics, types of documents, and assessments of research fields and categories. Co-authorship, citation, and co-occurrence networks were visualized using VOSviewer software to identify collaboration patterns, influential publications, emerging topics, and interdisciplinary connections. A total of 5054 publications on the topic of ‘biopesticide’ were found (1994–2024), nearly half of which were published in the last five years. Out of 136 contributing countries, researchers from the USA, People’s Republic of China, and India produced the most publications, while a total of 19,676 authors contributed to at least one paper throughout the entire examined period. The identification of the most frequently used keywords highlighted significant topics within biopesticide-related publications, from the earliest to the most recent, aiding in our understanding of the developmental trajectory and opportunities for future exploration. Full article

This paper explores the application of multidisciplinary design optimization to the blades in horizontal-axis wind turbines. The aerodynamics and structural performance of blades are considered in the optimization framework. In the aerodynamic discipline, class function/shape function transformation-based parameterized modeling is used to express the airfoil. The Wilson method is employed to obtain the aerodynamic shape of the blade. Computational fluid dynamics numerical simulation is performed to analyze the aerodynamics of the blade. In the structural discipline, the materials and ply lay-up design are studied. Finite element method-based modal analysis and static structural analysis are conducted to verify the structural design of the blade. A collaborative optimization framework is set up on the Isight platform, employing a genetic algorithm to find the optimal solution for the blade’s aerodynamics and structural properties. In the optimization framework, the design variables refer to the length of the blade chord, twist angle, and lay-up thickness. Additionally, Kriging surrogate models are constructed to reduce the numerical simulation time required during optimization. An optimal Latin hypercube sampling method-based experimental design is employed to determine the samples used in the surrogate models. The optimized blade exhibits improved performance in both the aerodynamic and the structural disciplines. Full article

Predicting the survival of startups is a complex challenge due to the multifaceted nature of entrepreneurial ecosystems and the dynamic interplay of internal and external factors. Despite advances in empirical research, existing models often lack integration with robust conceptual frameworks. This study addresses these gaps by developing a multivariate AI-driven model for predicting startup survival, leveraging Lipschitz extensions, neural networks, and linear regression. Using a dataset of 20 startups, selected across diverse industries and evaluated on attributes such as team dynamics, market conditions, and financial metrics, the model demonstrated high accuracy and clustering capabilities. Key findings highlight the pivotal role of team dynamics and product differentiation in determining survival probabilities. By integrating conceptual insights with empirical data, the study bridges gaps in existing literature and offers a practical decision-making tool for entrepreneurs, investors, and policymakers. These findings underscore the importance of fostering collaborative, innovative ecosystems to enhance entrepreneurial success and societal well-being. Full article

This study proposes a new approach and explores how pattern recognition enhances collaboration between users and Agile teams in software development, focusing on shared resources and decision-making efficiency. Using domain-specific modeling languages (DSMLs) within a security-by-design framework, the research identifies patterns that support team selection, effort estimation, and Agile risk management for Afghanistan’s ministries. These patterns align software development with governmental needs by clarifying stakeholder roles and fostering cooperation. The study builds on the p-mart-Repository-Programs (P-MARt) repository, integrating data mining, algorithms, and ETL (Extract, Transform, Load) processes to develop innovative methodologies. These approaches enable dynamic knowledge management, refine documentation, and improve project outcomes. Central to this effort is our new Pattern Shared Vision Refinement (PSVR) approach, which emphasizes robust collaboration, data security, and adaptability. By addressing challenges unique to governmental operations, PSVR strengthens Agile practices and ensures high-quality software delivery. By analyzing historical trends and introducing new strategies, the study underscores the critical role of pattern recognition in aligning development processes with organizational goals. It demonstrates how systematic pattern identification can optimize interaction and secure stakeholder consensus, ultimately enhancing software engineering outcomes in Afghanistan’s governmental context. Full article

This study seeks to investigate gender inequality in the Spanish’s official music charts from 2008 to 2020, using a quantitative methodology. The dataset comprises 1300 entries sourced from Promusicae’s official charts, evenly divided between albums and singles. Firstly, this analysis evaluates the frequency of artist representation; secondly, it assesses the success achieved by gender; and finally, it examines disparities in featured collaborations. The findings indicate that male artists appear almost three times more frequently than their female counterparts on both charts, achieve a higher amount of points, and dominate the role of featuring collaborations. Notably, Spanish female artists are predominantly absent from the top positions in the singles chart. Moreover, there is an absence of female bands in the top 50. The observed outcomes align with previous research concerning gender inequality in the United States and the United Kingdom, highlighting a persistent structural gender inequality within Spain’s music industry throughout the reviewed period, which systematically undermines the creativity of female artists. Full article

The article examines the socio-educational barriers and facilitators faced by families of children with cochlear implants (CI) in Andalucía (a region in southern Spain). Through a qualitative approach based on surveys and interviews, three main categories of limitations are identified: access to and quality of services, parental stress related to caregiving and communication, and socioeconomic and individual factors. Among the facilitators, financial support, family-professional collaboration, and inclusive practices in educational settings are highlighted. The findings underscore challenges such as the lack of coordination between medical and educational services, the need for teacher training, and the emotional and financial impact on families. Conversely, effective strategies such as family support networks, specialized hospital coordinators, and inclusive educational practices are emphasized. The study concludes that improving access to comprehensive services and fostering collaboration between educational and healthcare agents is essential to promote inclusive education and the well-being of families and children with CI. This work advocates for a critical and participatory perspective in the development of inclusive policies. Full article

This article offers a concise overview of the best practices for safety in offshore oil and gas operations, focusing on the risks associated with various types of equipment, particularly on the risk of fire. It identifies specific machinery and systems that could pose hazards, assesses their potential impact on safety, and explores conditions that may lead to accidents. Some of the largest accidents were analyzed for their associations with fire hazards and specific equipment. Two primary regulatory approaches to offshore safety are examined: the prescriptive approach in the United States (US) and the goal-oriented approach in Europe. The prescriptive approach mandates strict compliance with specific regulations, while in the goal-oriented approach a failure to adhere to recognized best practices can result in legal accountability for negligence, especially concerning human life and environmental protection. This article also reviews achievements in safety through the efforts of regulatory authorities, industry collaborations, technical standards, and risk assessments, with particular attention given to the status of Mobile Offshore Drilling Units (MODUs). Contrary to common belief, the most frequent types of accidents are not those involving a fire/explosion caused by the failure of the Blowout Preventer (BOP) after a well problem has already started. Following analysis, it can be concluded that the most frequent type of accident typically occurs without fire and is due to material fatigue. This can result in the collapse of the facility, capsizing of the platform, and loss of buoyancy of mobile units, particularly in bad weather or during towing operations. It cannot be concluded that accidents can be more efficiently prevented under a specific type of safety regime, whether prescriptive or goal-oriented. Full article

The Blue School Program is an educational initiative to enhance ocean literacy (OL) by fostering understanding and awareness of the ocean’s importance among students, teachers, and local communities. Through a holistic, interdisciplinary, and intercultural approach, it integrates scientific, cultural, and ethical dimensions of ocean education. This perspective paper examines the program’s implementation in Portugal, emphasizing its contribution to crucial educational domains such as sustainability education, scientific literacy, and citizenship education. The findings highlight notable achievements, including increased school and community engagement and the establishment of robust public-private partnerships, while also addressing challenges such as resource constraints and geographic disparities. By situating the Blue School Program within broader educational frameworks and analyzing its limitations and enablers, this paper offers insights and recommendations to support the collaborative development of Blue School networks and OL initiatives in diverse educational contexts. Full article

Collaborative flight formations represent a promising operational model, but the integration of multi-source information in manned interfaces often results in cognitive overload and reduced situation awareness. This study evaluates the effectiveness of intelligent auxiliary information presentation modes in enhancing personnel capabilities. Using a simulation-based collaborative flight formation system, four presentation modes with varying levels of information and dynamics were experimentally tested and evaluated across subjective dimensions (cognitive workload, situation awareness, and interface design) and objective dimensions (design task flow information load and operational task flow information load). The results indicate that Level 3 and Level 4 modes significantly reduced mental workload and improved practical operational ability compared to the original mode. Level 3 achieved the highest interface evaluation scores, while Level 4 demonstrated the lowest design task flow information load. Both modes significantly enhanced situation awareness. Altogether, Level 3 and Level 4 resulted in the most significant improvements in personnel capabilities. These findings provide valuable insights for optimizing interface design and improving situation awareness in collaborative flight formation tasks. Full article