Search Results (10,439)

The nonogram is a logic puzzle where each cell should be colored or left blank according to row and column clues to reveal a hidden picture. This puzzle is known as an NP-complete combinatorial problem characterized by an exponential increase in the number of candidate solutions with increasing puzzle size. So far, some methods have been investigated to address these challenges, including conventional line-solving techniques, integer programming, and neural networks. This study introduces a novel Harmony Search (HS)-based approach for solving nonogram puzzles, incorporating problem-specific operators designed to effectively reduce the solution search space and accelerate convergence. Experimental results obtained from benchmark puzzles demonstrate that the proposed HS model utilizing a clue-constrained random-generation operator significantly reduces the average number of iterations and enhances the solution-finding success rate. Additionally, the HS model integrating an initially confirmed cell-scanning operator exhibited promising performance on specific benchmark problems. The authors think that the nonogram puzzle can be a good benchmark problem for quantum computing-based optimization in the future, and the proposed HS algorithm can also be combined with quantum computing mechanisms. Full article

The intervention of distributed loads, propelled by the swift advancement of distributed energy sources and the escalating demand for diverse load types encompassing electricity and cooling within virtual power plants (VPPs), has exerted an influence on the symmetry of the grid. Consequently, a quantitative assessment of the annual peak-shaving capability of a VPP is instrumental in mitigating the peak-to-valley difference in the grid, enhancing the operational safety of the grid, and reducing grid asymmetry. This paper presents a peak-shaving optimization method for VPPs, which takes into account renewable energy uncertainty and flexible load demand response. Firstly, wind power (WP), photovoltaic (PV) generation, and demand-side response (DR) are integrated into the VPP framework. Uncertainties related to WP and PV generation are incorporated through the scenario method within deterministic constraints. Secondly, a stochastic programming (SP) model is established for the VPP, with the objective of maximizing the peak-regulation effect and minimizing electricity loss for demand-side users. The case study results indicate that the proposed model effectively tackles peak-regulation optimization across diverse new energy output scenarios and accurately assesses the peak-regulation potential of the power system. Specifically, the proportion of load decrease during peak hours is 18.61%, while the proportion of load increase during off-peak hours is 17.92%. The electricity loss degrees for users are merely 0.209 in summer and 0.167 in winter, respectively. Full article

Within the framework of the SOCIETAL CHALLENGES—Secure, Clean, and Efficient Energy objective under the European Horizon 2020 research and innovation funding program, the SO-FREE project has developed a future-ready solid oxide fuel cell (SOFC) system with high-efficiency heat recovery. The system concept prioritizes low emissions, fuel flexibility, modular power production, and efficient thermal management. A key design feature is the interchangeability of two different SOFC stack types, allowing for operation under different temperature conditions. The system was developed with a strong emphasis on simplicity, minimizing the number of components to reduce overall plant costs while maintaining high performance. This paper presents the simulation results of the proposed flexible SOFC system, conducted using Aspen Plus^® software version 11 to establish a baseline architecture for real plant development. The simulated layout consists of an autothermal reformer (ATR), a high-temperature blower, an SOFC stack, a burner, and a heat recovery system incorporating four heat exchangers. Simulations were performed for two different anodic inlet temperatures (600 °C and 700 °C) and three fuel compositions (100% CH₄, 100% H₂, and 50% H₂ + 50% CH₄), resulting in six distinct operating scenarios. The results demonstrate a system utilization factor (UF_F) exceeding 90%, electrical efficiency ranging from 60% to 77%, and an effective heat recovery rate above 60%. These findings were instrumental in the development of the Piping and Instrumentation Diagram (P&ID) required for the design and implementation of the real system. The proposed SOFC system represents a cost-effective and adaptable energy conversion solution, contributing to the advancement of high-efficiency and low-emission power generation technologies. Full article

Due to range of factors in the development stage, generative artificial intelligence (AI) models cannot be completely free from bias. Some biases are introduced by the quality of training data, and developer influence during both design and training of the large language models (LLMs), while others are introduced in the text-to-image (T2I) visualization programs. The bias and initialization at the interface between LLMs and T2I applications has not been examined to date. This study analyzes 770 images of librarians and curators generated by DALL-E from ChatGPT-4o prompts to investigate the source of gender, ethnicity, and age biases in these visualizations. Comparing prompts generated by ChatGPT-4o with DALL-E’s visual interpretations, the research demonstrates that DALL-E primarily introduces biases when ChatGPT-4o provides non-specific prompts. This highlights the potential for generative AI to perpetuate and amplify harmful stereotypes related to gender, age, and ethnicity in professional roles. Full article

Viruses exhibit significantly greater diversity than cellular organisms, posing a complex challenge to their taxonomic classification. While primary sequences may diverge considerably, protein functional domains can maintain conserved 3D structures throughout evolution. Consequently, structural homology of viral proteins can reveal deep taxonomic relationships, overcoming limitations inherent in sequence-based methods. In this work, we introduce MPACT (Multimetric Pairwise Comparison Tool), an integrated tool that utilizes both sequence- and structure-based metrics. The program incorporates five metrics: sequence identity, similarity, maximum likelihood distance, TM-score, and 3Di-character similarity. MPACT generates heatmaps and distance trees to visualize viral relationships across multiple levels, enabling users to substantiate viral taxa demarcation. Taxa delineation can be achieved by specifying appropriate score cutoffs for each metric, facilitating the definition of viral groups, and storing their corresponding sequence data. By analyzing diverse viral datasets spanning various levels of divergence, we demonstrate MPACT’s capability to reveal viral relationships, even among distantly related taxa. This tool provides a comprehensive approach to assist viral classification, exceeding the current methods by integrating multiple metrics and uncovering deeper evolutionary connections. Full article

Access to healthcare services in rural areas of Peru remains a challenge, with marked differences compared to urban areas. Despite the importance of primary healthcare (PHC) in these communities, the rural population has a negative perception of the role of nursing staff. This study aimed to assess the perceptions of residents in the highland communities of Huata and Ichu, Province of Puno, located in southern Peru, regarding the role of nursing professionals. The general perception of the residents was first analyzed, followed by an evaluation of two dimensions (Fieldwork and Health Education), which allowed for the development of the REFCO (Role of the Nursing professional in the community) scale. The sample included 329 rural adults, mainly between 30 and 59, predominantly female, with incomplete secondary education. The results showed an unfavorable perception (54%) of nursing staff performance. The fieldwork dimension reported low visibility of nursing activities in the community, such as home visits and health programs. The second dimension demonstrated that educational interventions were perceived as infrequent and of low impact, with insufficient adaptation to local needs. These results highlight the need to strengthen the presence and educational strategies of nursing staff in alignment with the cultural and demographic realities of rural communities. Furthermore, it suggests a greater need for interinstitutional collaboration and a more personalized approach to community activities to improve the perception and effectiveness of healthcare services in these areas. Full article

Background/Objectives: Bariatric surgery is a crucial intervention for the treatment of morbid obesity, necessitating that nursing staff possess a thorough understanding of expected outcomes to ensure successful patient care and promote healthier lifestyles. Recognizing this need, the present study aimed to develop and implement a reference guide tailored to nursing staff providing bariatric surgery care. Methods: A quasi-experimental design was employed, utilizing a convenience sample of 78 nurses and 156 patients—comprising 78 participants before the administration of the reference guide and another 78 participants after its provision—from the general surgery units at Mansoura University Hospital. Four tools were employed for data collection: a sociodemographic datasheet, a knowledge questionnaire assessing nurses’ understanding of care for bariatric surgery patients, a self-reported practice questionnaire, and a patients’ satisfaction measurement tool. A reference guide for bariatric surgery care was constructed. Descriptive and inferential statistics were conducted with adherence to all ethical considerations. Results: After implementing the reference guide, the percentage of nurses who exhibited good knowledge of bariatric surgery increased from 10.3% to 80.8%. Additionally, the proportion of nurses reporting poor self-reported practices related to necessary procedures decreased from 80.8% to 5.1%. Surprisingly, the improvement in knowledge and self-reported practices was sustained for a long time, though reduced. Furthermore, patient satisfaction levels showed a marked improvement following the intervention. Conclusion: Implementing a reference guide significantly improved nurses’ knowledge, self-reported practices, and patient satisfaction. To sustain these improvements, it is recommended that the reference guide be made available and educational programs be provided to nursing staff. Full article

Help-seeking of mental health professionals remains low, even in countries where mental health care is highly accessible. Therefore, it is necessary to continue research on the sociodemographic and psychological factors of attitudes toward seeking mental health services (ATSMHS) in different countries. The purpose of the present study was to identify the associations of the ATSMHS with psychological well-being and time perspective in the Russian Y and Z generations. The Y-Generation sample included 217 (69.9% female) respondents aged 22 to 39 years, and the Z-Generation sample included 256 (82.8% female) respondents aged 17 to 21 years. Russian versions of the Psychological Well-Being Scale (PWBS) by Ryff, Zimbardo Time Perspective Inventory (ZTPI), and Inventory of Attitudes toward Seeking Mental Health Services (IASMHS) by Mackenzie and colleagues were used for diagnostics. The main research findings show that (1) Personal growth is a positive predictor and the Fatalistic present is a negative predictor of ATSMHS in both generations, and (2) additional positive predictors of ATSMHS in the Y–Generation are Positive relations and Hedonistic present, and in the Z–Generation it is Future. These findings are important for developing programs to maintain, preserve, and prevent mental health across generations. Full article

This study numerically investigated free vibration characteristics of functionally graded material (FGM) beams on Winkler–Pasternak three-parameter elastic foundations using the modified generalized differential quadrature (MGDQ) method. To compare the effects of different beam theories on the predicted frequency responses, an nth order generalized beam theory was employed to establish the governing equations of the system’s dynamic model within the Hamilton framework. As a pioneering effort, a MATLAB (version 2021a) computational program implementing the MGDQ method was developed to obtain the free vibration responses of foundation-supported FGM beams. Parametric analyses were conducted through numerical simulations to systematically examine the influences of various factors, including beam theories, damping coefficients, foundation stiffness parameters, boundary conditions, gradient indices, and span-to-thickness ratios, on the natural frequencies and damping ratios of FGM beams. The findings provide an essential theoretical foundation for dynamic characteristic analysis and functional design of foundation-supported FGM beam structures. Full article

Discrete-event simulation (DES) is a powerful tool for modeling and analyzing complex systems where state changes occur at discrete points in time. This paper presents a practical and sustainable approach to implementing DES using free mathematical and programming software, making it accessible to a wider audience including educators, students, and practitioners. This study explores the use of open-source tools, such as Python and Octave, highlighting their capabilities in building and optimizing DES models without the need for expensive and unaffordable software. In the context of Industry 4.0 and smart manufacturing, the ability to simulate and optimize discrete processes with open tools contributes to the development of digital twins, the integration of cyberphysical systems, and data-driven decision-making. Through detailed case studies in industrial fields, including manufacturing, maintenance, and logistics, this study demonstrates the effectiveness of these tools in simulating real processes and promoting their sustainability. Case studies are also re-examined to emphasize their relevance to smart manufacturing, particularly in terms of predictive maintenance, process optimization, and operational flexibility. Several challenges were encountered during the research process, such as adapting DES methodologies to the limitations of general-purpose mathematical software, ensuring accurate time management and event scheduling in environments not specifically designed for simulation, and balancing model complexity with accessibility for nonexpert users. The integration of free software not only reduces costs but also promotes collaborative learning and innovation. Additionally, the paper discusses the best practices for model validation and experimentation, providing a comprehensive guide for those new to DES. By linking open-source DES tools to the objectives of Industry 4.0, we aim to reinforce the applicability of our approach to modern, connected industrial environments. By leveraging free mathematical and programming software, this approach aims to democratize the use of DES, fostering a deeper understanding and broader application of simulation techniques across diverse fields and various regions of the world. Full article

Evidence appears to be building that direct exposure to natural landscapes characterized by significant green cover, such as forests, can help to reduce chronic health conditions such as obesity, stress, hypertension, chronic cardiovascular conditions, depression, anxiety, cancer, and diabetes. One way to encourage greater exposure to nature may be through the use of nature prescriptions, whereby clinicians formally recommend (or prescribe) time in nature to their patients. Based on self-reported data, we describe the implementation and lessons learned from a pilot field experiment examining the clinical use of nature-based versus conventional exercise recommendations in rural Oregon. We discuss the potential benefits of such recommendations, as well as identify several challenges and opportunities associated with field experiments seeking to evaluate whether nature prescriptions, offered as one part of patients’ overall treatment plans, meaningfully improve human health outcomes in clinical settings. We conclude with several recommendations for practitioners and researchers interested in implementing and evaluating nature-based exercise programs to improve public health. Full article

This paper addresses the critical issue of selecting the optimal solar tracking configuration for maximum energy generation, given the increasing demand for sustainable energy solutions in information and communication technology (ICT) facilities. The main goal is to thoroughly evaluate and compare seven different solar tracking configurations across technical, economic, and environmental dimensions: No Tracking (NT), Monthly Adjusted Horizontal Axis (MAHA), Weekly Adjusted Horizontal Axis (WAHA), Daily Adjusted Horizontal Axis (DAHA), Continuously Adjusted Horizontal Axis (CAHA), Continuously Adjusted Vertical Axis (CAVA), and Dual Axis with Continuous Adjustment (DACA). This study utilizes the HOMER simulation program to evaluate its energy and hydrogen production, emissions, and cost-effectiveness performance. Key findings indicate solar tracking improves energy efficiency, with optimal capacity factors of 18.2% and 17.7% for CAHA and DAHA configurations, respectively. Although load-following strategies increase reliability, there is a trade-off between capital costs and energy costs. In addition, an MCDM approach helps to consolidate the evaluation, resulting in CAVA being ranked as the most preferable option. The study contributes to informed decision-making for energy systems in ICT facilities by emphasizing the significance of considering a variety of criteria and evaluation techniques to address complex energy challenges. Full article

Food safety is a fundamental component of public health, economic stability, and consumer confidence. In Portugal, the National Integrated Multiannual Control Plan (NIMCP) serves as a strategic framework for ensuring food safety and compliance with European Union food regulations. However, challenges persist in policy implementation and enforcement, as well as in stakeholder engagement, which impact the effectiveness of food safety governance. This study employs a mixed-methods approach to assess stakeholder perceptions of the NIMCP, focusing on levels of compliance, barriers to its implementation, and potential improvement measures. A structured online survey was conducted with 217 stakeholders, including representatives of public institutions, private entities, associations, and consumer groups. The survey assessed the perceived importance of the NIMCP objectives and levels of compliance and identified barriers, such as a lack of communication between public entities, the dispersion of responsible agencies, and insufficient dissemination of information. The results indicate that stakeholders perceive a satisfactory level of compliance with the NIMCP objectives, especially in areas such as animal health and risk control. However, challenges persist in ensuring plant health and implementing official controls. Furthermore, stakeholders highlight systemic inefficiencies and resource constraints. The main barriers include fragmented governance structures, limited inter-agency collaboration, and insufficient professional training. Stakeholders proposed various improvement measures, emphasizing the need for better coordination, planning, and communication, including awareness campaigns for operators, the creation of an integrated IT network, and the development of training programs. The Analytical Hierarchy Process (AHP) revealed that risk control and consumer protection are top priorities for all stakeholder groups, while plant and animal health receive lower priority. The study concludes that while the NIMCP is generally perceived as effective, addressing systemic issues such as coordination, communication, and resource allocation is essential to improving food safety governance. Policymakers are encouraged to adopt a more structured and integrated approach to improve implementation of the NIMCP, ultimately strengthening public health protection and consumer confidence in the food supply chain. Full article

Liangshan Black pigs are a new Chinese indigenous breed discovered during the Third National Survey of Livestock and Plant Genetic Resources. To uncover genetic diversity, population structure, and potential exotic introgression in this breed, we sampled 191 Liangshan Black pigs from the conservation population and genotyped these individuals using the “Zhongxin-I” porcine chip, then conducted in-depth population genetic analyses in the context of pigs from five introduced breeds. The results revealed that the tested individuals exhibited significant genetic diversity, displayed uneven kinship relationships, and were assigned to nine families according to their clustering patterns in the phylogenetic tree. Further relationship analyses with the five introduced breeds demonstrated that Liangshan Black pigs were clustered separately from the introduced breeds, had larger evolutionary distances with the introduced breeds, and possessed certain genetic components of the introduced breeds, especially those of Duroc. These findings demonstrate that Liangshan Black pigs are generally an indigenous breed independent of the introduced breeds but are slightly affected by the introduced breeds. In summary, the results of our study not only contribute to an in-depth understanding of the population genetic characteristics of Liangshan Black pigs but also provide the necessary data for the implementation of conservation programs. Full article

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events. Full article