Collaborative Strategies Research Articles

Sequential Task Allocation of More Scalable Artificial Dragonfly Swarms Considering Dubins Trajectory

With the rapid advancement of UAV technology and the increasing complexity of tasks, multi-UAV systems face growing challenges in task execution. Traditional task allocation algorithms often perform poorly when dealing with issues such as local optima, slow convergence speed, and low convergence accuracy, making it difficult to meet the demands for efficiency and practicality in real-world applications. To address these problems, this paper focuses on collaborative task allocation technology for multi-UAV. It proposes a collaborative task allocation strategy for multi-UAV in a multi-target environment, which comprehensively considers various complex constraints in practical application scenarios. The strategy utilizes Dubins curves for trajectory planning and constructs a multi-UAV collaborative task allocation model, with targets including the shortest total distance index, the minimum time index, and the trajectory coordination index. Each UAV is set as an artificial dragonfly by modifying the traditional dragonfly algorithm, incorporating differential evolution algorithms and their crossover, mutation, and selection operations to bring UAV swarms closer to the characteristics of biological dragonflies. The modifications can enhance the global scalability of artificial dragonfly swarms (ADSs), including wider search capacity, wider speed range, and more diverse search accuracy. Meanwhile, potential solutions with global convergence properties are stored to better support real-time adjustments to task allocation. The simulation results show that the proposed strategy can generate a conflict-free task execution scheme and plan the trajectory, which has advantages in changing the data scale of the UAV and the target and improves the reliability of the system to a certain extent.

Assessing the potential of collaborative innovation strategies to drive green development: Threshold effect of market competition

Environmental management literature suggests that elevating the total factor productivity (TFP) of the renewable energy (RE) industry is pivotal for achieving green development. With intensifying market competition in the RE sector, collaborative innovation (COI) has emerged as a crucial strategic choice for enterprises to address developmental bottlenecks. To assess the potential of COI in driving green development, we leveraged data from 114 Chinese RE enterprises from 2011 to 2022. The research findings indicate: (1) COI significantly enhances the TFP of RE enterprises; this enhancement primarily stems from the positive influence of COI on innovation capabilities. (2) The positive effects of COI on TFP manifest only when market competition surpasses a certain threshold level. (3) COI significantly impacts the TFP of small and medium-sized RE enterprises in the growth stage.

Sewage Treatment Equipment Supply Chain Collaboration and Resilience Improvement Path Analysis: Collaborative Decision-Making, Information Sharing, Risk Management

Since the dawn of the 21st century, supply chains (SCs) have faced an array of unprecedented challenges, encompassing economic volatility, escalating geopolitical conflicts, intensified trade disputes, and persistent environmental degradation. These challenges have imposed immense pressures on SCs, amplifying both the risks and occurrences of disruptions. In response to these critical demands, there has been a substantial increase in academic, industrial, and governmental focus on the resilience and recovery capabilities of SCs. In this context, the supply chain (SC) for sewage treatment equipment has been significantly affected. This study aims to systematically investigate collaborative strategies and mechanisms for bolstering the resilience of manufacturing SCs within the sewage treatment sector, with particular attention to collaborative decision-making, information sharing, and risk mitigation throughout the SC lifecycle. Following a comparative analysis of various methodologies, this paper employed the structural equation model (SEM) and subsequently conducted a comprehensive survey of manufacturers specializing in glass fiber sewage treatment systems, yielding 385 valid responses. Through this data, a structural equation model was developed to analyze pathways for resilience enhancement. A thorough analysis of the results indicates that Supply Chain Collaboration (SCC) and collaborative decision-making are instrumental in strengthening Glass Fiber Sewage Treatment Supply Chain Resilience (SCR) through the implementation of effective information sharing and risk management practices. These findings contribute to the theoretical framework of SCC and SCR by clarifying the influence of collaborative practices while also providing practical guidance for the governance and strategic management of sewage treatment equipment enterprises and their associated SCs. The conclusions drawn from this study can guide us in establishing a more resilient and sustainable SC ecosystem, making it more directive and capable of mitigating complex disruptions.

Balancing Bytes and Ethics: Stakeholder Implications of Private LLMs

This research explores the ethical implications of private large language models (PLLMs) through the lens of stakeholder theory. Private LLMs, tailored for specific organizational needs, present unique privacy and data protection challenges. We examine the historical development of LLMs and their impact on stakeholders, including shareholders, employees, customers, and society. Our proposed framework balances stakeholder interests with ethical considerations, offering a comprehensive approach to the ethical development and deployment of PLLMs. This framework emphasizes transparency, accountability, and sustainable practices to ensure long-term value creation. Future research directions include developing regulatory frameworks, conducting detailed social impact assessments, and exploring strategies for effective human-AI collaboration. This study contributes to academic discourse by providing a multi-faceted approach to managing the ethical challenges posed by PLLMs, fostering best practices, and mitigating potential conflicts among stakeholders.

Adopting Covid-19 and Flu Testing Strategies In An Underserved Community

Abstract Introduction/Objective The COVID-19 pandemic has had a significant impact on communities worldwide, while little is known about how medically underserved minority communities perceive and experience COVID-19 and/or COVID- 19/Flu combined testing. This research target at a medically underserved community in east Harris County, Texas, and was designed to 1) understand the perception of the single COVID-19 test as well as the COVID-19 and flu combined test, 2) implement different testing strategies in collaboration with the local community. Methods/Case Report Residents in the eligible communities who are age ≥ 18 years or older are recruited through community engagement groups and community originations. Participant’s demographic information, perception and histories of vaccination and testing were collected through a cross-sectional questionnaire. Participants willing to undergo testing were assisted by community health workers, who then collected feedback on test acceptability via follow-up questionnaires. Results (if a Case Study enter NA) A total of 325 individuals participated in the study from Sept 2022-June 2023. 52% of participants are minority ethnicity, including 35% African American and 17% Latino. 11% of participants are uninsured and 32% of participants are from households with income lower than 25,000 a year. We surveyed our participants on their opinion of COVID-19 and Flu testing and vaccination, majority of our participants have had more experience in COVID-19 (92% had been tested and 93% had been vaccinated) than Flu (64% had been tested and 85% had been vaccinated). 96% consider COVID-19 testing tolerable, with major barrier of being tested is fear of painful sampling (49%). PCR testing is the dominant testing method in the community (> 40%) for COVID-19, while only 16% received antigen testing. Conclusion Our findings suggest that while participants recognized the role of COVID-19 testing, there were significant barriers to access and uptake of testing, including concerns about painful sampling, inconvenience, and a lack of trust in the healthcare system. Participants also expressed a desire for clearer information about testing and more accessible testing sites. Participants have less experience with Flu testing and many of whom had never been tested in the past. Our study highlights the need for targeted community education and outreach efforts to increase testing uptake. Development and implementation of these efforts would better prepare us for combating other commutable diseases of public health significance in the future.

Unlocking the efficiency potential of all-perovskite tandem solar cells: Insights from photoelectric coupling simulations

All-perovskite tandem solar cells (TSCs) have garnered widespread attention due to their high-efficiency potential and low-cost fabrication processes. However, a significant efficiency gap remains between all-perovskite TSCs (30.1%) and their Shockley-Queisser limit (∼44%), primarily due to a lack of comprehensive understanding of the working mechanisms and design principles for TSCs. Here, we develop rigorous photoelectric coupling simulation models and systematically explore the device physics and working mechanisms of all-perovskite TSCs. Through in-depth simulations, we find that: 1) TSC performance is more sensitive to the interface defects in current-limited sub-cells, while bulk defects in both wide-bandgap and narrow-bandgap perovskites are crucial in limiting TSC performance, regardless of current-matching conditions. 2) The interface defects of perovskite/functional layers (FLs) near intermediate recombination layers (IRLs) have a substantial effect on TSC efficiency, which, however, can be mitigated by a double-layer FL design. 3) The work function of the IRL and the energy levels of adjacent FLs should be well-controlled to balance carrier transport barriers at FL/perovskite and FL/IRL interfaces. Based on these principles, we propose a detailed roadmap to enhance the efficiency of all-perovskite TSCs to 34.15% through collaborative optimization strategies. This study provides valuable guidance for designing high-efficiency all-perovskite TSCs.

Collaborative Governance Strategy in Increasing Production and Strengthening Food Diversification in Rokan Hulu Regency, Riau Province, Indonesia

This research aims to analyze Collaborative Governance strategies in food diversification efforts in Rokan Hulu Regency, focusing on the regional food policy context. Utilizing qualitative analysis with the Nvivo 12 Plus tool, it systematically processes interview and online media data, revealing key factors such as coordination (47.06%), trust (29.41%), and commitment to common goals (23.53%) that influence collaborative governance. The novelty lies in highlighting the low level of stakeholder commitment, linked to a lack of participation forums, which impacts local food consumption patterns still dependent on rice. While leadership and institutional design are identified as critical aspects of collaborative governance, this study advances the discourse by emphasizing the dominance of the government sector and the need for more balanced stakeholder involvement in program implementation. It also underscores the importance of developing alternative commodities like corn and cassava to enhance food diversification. This research contributes to the state of the art by offering timely insights into the practical challenges of collaboration, advancing both theoretical and applied aspects of governance in rural food security strategies.

Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis.

Flooding is a global threat causing significant economic and environmental damage, necessitating a policy response and collaborative strategy. This study assessed global research trends and advances in geospatial and meteorological flood risk assessment (G_MFRA), considering the ongoing debate on flood risk management and adaptation strategies. A total of 1872 original articles were downloaded in BibTex format using the Web of Science (WOS) and Scopus databases to retrieve G_MFRA studies published from 1985 to 2023. The annual growth rate of 15.48% implies that the field of G_MFRA has been increasing over time during the study period. The analysis of global trends in flood risk research and practice highlights the key themes, methodologies, and emerging directions. There exists a notable gap in data and methodologies for flood risk assessment studies between developed and developing countries, particularly in Africa and South America, highlighting the urgency of coordinated research efforts and cohesive policy actions. The challenges identified in the body of extant literature include technical expertise, complex communication networks, and resource constraints associated with the application gaps of the study methodologies. This study advocates for a holistic research approach to flood disaster management through ecosystem-based adaptation that underpins the Sustainable Development Goals to develop innovative flood techniques and models with the potential to influence global decision-making in the G_MFRA domain. Addressing these global challenges requires a networked partnership between the research community, institutions, and countries.

Uncovering perspectives on physical activity in nursing homes: a qualitative exploration of the experiences of healthcare professionals and family caregivers

BackgroundThe ageing population has increased the demand for healthcare services. In Norway, community-based long-term care are prioritised, leading to fewer nursing home places. As a result, nursing home residents are now older and have more complex needs. Nearly 92% of nursing home residents are affected by cognitive impairments accompanied by neuropsychiatric symptoms (NPS) that affect their daily activity, physical function, cognition, and behaviour. Traditionally, pharmacological therapy has been the prevailing treatment for NPS. However, emerging evidence suggests that physical activity can serve as an alternative treatment approach. Physical activity has the potential to maintain physical independence and enhance the quality of life (QoL) for the residents. Despite these benefits, institutionalisation in a nursing home often restricts activity levels of residents. This study explores facilitators and barriers to physical activity in nursing homes through the experiences of healthcare professionals and family caregivers. The goal is to enhance our understanding of how to promote and support physical activity for nursing home residents by identifying essential factors for successfully implementing daily physical activity initiatives.MethodsSeven focus groups were conducted with a total of 31 participants. Participants included healthcare professionals (physiotherapists, nurses, unit- and department managers, assistant occupational therapists, and assistant nurses) and family caregivers of residents at nursing homes. Data were analysed using Braun and Clarke’s reflexive thematic analysis, underpinned by hermeneutic phenomenology.ResultsThree main themes related to facilitators and barriers to physical activity in nursing homes were identified: inconsistency in task prioritisation; need for improved interprofessional collaboration; and need for improved utilisation of external resources. The participants experienced task prioritisation and lack of interdisciplinary collaboration as barriers to physical activity. The involvement of external societal resources was considered as both a facilitator and a necessity for obtaining physical activity in nursing homes.ConclusionsThis study highlights the need for a consensus in task prioritisation, enhanced competence among healthcare professionals, and better interdisciplinary collaboration to facilitate physical activity in nursing homes. Involving external societal resources could be a strategic approach to address barriers and support physical activity initiatives. Future research should focus on developing effective strategies for interdisciplinary collaboration that prioritises and promotes physical activity in nursing homes.

Collaborative strategies for adolescent suicide prevention: insights from Slovakia and Kyrgyzstan

ObjectiveThe study aimed to gain insights about the collaborative efforts of various professionals involved in preventing adolescent suicide within multidisciplinary teams in Slovakia and Kyrgyzstan.Materials and methodsA qualitative research method was employed, involving semi-structured interviews with 10 specialists from Slovakia and Kyrgyzstan.ResultsDespite differing levels of development and implementation of multidisciplinary team concepts, findings revealed a generally positive impact of collaborative efforts in both countries (more structured in Slovakia and less developed in Kyrgyzstan). Multidisciplinary teams in educational settings strive to create supportive environments for adolescents, contributing significantly to mitigating various issues during their formative years.ConclusionsPrevention efforts require coordinated action among multiple social institutions with unified strategies. While progress has been made, there remains potential for further development, particularly in improving cooperation among specialists, national recognition of youth suicide as a critical issue, and institutionalizing procedures to elevate the status and education of multidisciplinary teams.SignificanceThis research provides innovative insights into the effectiveness of multidisciplinary teams in suicide prevention among adolescents. By suggesting comprehensive strategies for team collaboration, it offers valuable contributions to reducing youth suicide rates in both Slovakia and Kyrgyzstan.

Collaborative Models for Community-Driven Industrial Education: Enhancing Workforce Development Through Partnership Programs in Malaysia

This study analyses the value of collaborative education strategies for improving workforce development in Malaysia, focusing on collaborations between educational institutions and industry. In recent years, the Malaysian job market has experienced major challenges, especially in terms of graduate preparation for employment. Many graduates struggle due to inadequate skills and limited employment opportunities, emphasizing the critical need for comprehensive educational changes. These coordinated activities aim to bridge the education-industry divide by developing connections between academia and the business sector. Moreover, the study uses a qualitative approach, conducting in-depth interviews with industry managers to understand better the impact of partnership programs, government support, and community participation on workforce readiness. Through these interviews, the study emphasizes the importance of collaborative education in providing graduates with the skills and competencies they need to succeed in a competitive work market. The findings show that, while these collaborations significantly boost graduates' employability and skill sets, issues such as unequal access to opportunities and an overreliance on government assistance remain. Furthermore, the analysis discovers gaps in the available literature, particularly in the context of Malaysia, emphasizing the need for further research targeted to this environment. Future research directions will include empirical investigations to quantify the impact of collaborative education initiatives, quantitative analyses to evaluate the efficacy of various programs, and policy recommendations to improve Malaysia's workforce development and foster long-term economic growth. By addressing these issues, the study hopes to contribute to a more comprehensive understanding of how collaborative education may effectively bridge Malaysia’s skills gap.

Fatigue strength reliability assessment of turbofan blades subjected to intake disturbances based on the improved kriging model

This paper aims to develop an efficient and precise reliability analysis method to enhance the numerical prediction accuracy for complex structures. Kriging, an implicit surrogate model, used to address highly nonlinear and complex problems. In this study, genetic algorithms (GA) are utilized to optimize the parameters of the Kriging model, which is then integrated with a distributed collaborative strategy to introduce the Genetic Algorithm Optimized Distributed Collaborative Kriging Model (DCGAK). Using the CFM56-fan blade as a case study, the impact of intake disturbances at the engine inlet is evaluated to assess the fatigue strength reliability of the blade. Comparison with different mathematical models demonstrates that the prediction accuracy of DCGAK closely aligns with the Monte Carlo sampling results, suggesting promising prospects for its application in numerical prediction and reliability analysis. This approach enriches the current methods for structural reliability analysis of complex mechanical systems.

Understanding Career Adapt Abilities, Career Stress, Work Stress, Career Regret, Job Satisfaction Among Nursing Professionals.

This study aims to conduct a comprehensive analysis of the interrelationships among career stress, work stress, career regret, job satisfaction and career adapt abilities within the nursing profession. The research involved a sample of 576 nurses from ten urban hospitals, distributed across five distinct cities in Turkiye, between December 2023 and January 2024. The study employed regression models to systematically test hypotheses, providing nuanced insights into the complex dynamics of the variables under investigation. Regression models were utilized to test the hypotheses systematically, considering the moderating role of career adapt abilities. The study included a sample of 576 nurses from 10 urban hospitals in Turkey, distributed across five distinct cities. This approach allowed for a detailed examination of the intricate relationships between career stress, work stress, career regret, job satisfaction and career adapt abilities within the nursing profession. The findings underscore the delicate balance between the positive gains of job satisfaction and the challenges posed by career stress, work stress and career regret in the multifaceted landscape of nursing. In nursing, the relationship between career stress, work stress, career regret and job satisfaction include progress, resilience and obstacles. Developing strategies requires a cooperative effort from health care organizations, nursing leaders and individual nurses. The research provides valuable insights into the interplay of these factors and emphasizes the moderating role played by career adapt abilities. The implications of the study extend to healthcare organizations, nursing leaders and individual nurses. The research advocates for collaborative strategies aimed at enhancing job satisfaction, alleviating stressors and addressing career regret. The ultimate goal is to contribute to the retention of a resilient nursing workforce, emphasizing the importance of strategies that support the well-being and satisfaction of nurses in their careers. No patient or public contribution.

Technical Innovations and Social Implications: Mapping Global Research Focus in AI, Blockchain, Cybersecurity, and Privacy

This study examines the balance between technical and social focus in artificial intelligence, blockchain, cybersecurity, and privacy publications in Web of Science across countries, exploring the social factors that influence these research priorities. We use regression analysis to identify predictors of research focus and cluster analysis to reveal patterns across countries, combining these methods to provide a broader view of global research priorities. Our findings reveal that liberal democracy index, life expectancy, and happiness are significant predictors of research focus, while traditional indicators like education and income show weaker relationships. This unexpected result challenges conventional assumptions about the drivers of research priorities in digital technologies. The study identifies distinct clusters of countries with similar patterns of research focus across the four technologies, revealing previously unrecognized global typologies. Notably, more democratic societies tend to emphasize social implications of technologies, while some rapidly developing countries focus more on technical aspects. These findings suggest that political and social factors may play a larger role in shaping research agendas than previously thought, necessitating a re-evaluation of how we understand and predict research focus in rapidly evolving technological fields. The study provides valuable information for policymakers and researchers, informing strategies for technological development and international collaboration in an increasingly digital world.

Assessing Clinical Outcomes and Treatment Efficacy in a Patient with Crescentic Glomerulonephritis (CGN)- A Case Report

Crescentic glomerulonephritis (CGN) is a rapidly progressive renal condition marked by crescent formation in the glomeruli, often resulting in end-stage renal disease (ESRD) if not promptly addressed. This case report examines a 54-year-old female with a sudden onset of generalized edema and reduced urine output, which led to her hospitalization. Laboratory evaluations revealed acute renal failure with markedly elevated serum creatinine levels. Despite a background of Antineutrophil Cytoplasmic Antibodies (ANCA)-associated vasculitis, her management was complicated by inconsistent adherence to immunosuppressive therapy. Notably, severe hypertension exacerbated her renal impairment, necessitating immediate intervention. The clinical progression of CGN highlights the critical importance of early diagnosis, continuous monitoring, and a multidisciplinary approach for effective management. Emphasizing patient education and lifestyle modifications, particularly in dietary habits and blood pressure control, can significantly influence treatment outcomes. This case illustrates the urgency of addressing CGN and the necessity of a collaborative healthcare strategy to improve prognosis and enhance the patient's quality of life.

Edge-device collaborative computing for multi-view classification

Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the cloud, to the edge of the network to deliver faster responses to end users, reduce bandwidth consumption to the cloud, and address privacy concerns. However, to fully realize deep learning at the edge, two main challenges still need to be addressed: (i) how to meet the high resource requirements of deep learning on resource-constrained devices, and (ii) how to leverage the availability of multiple streams of spatially correlated data, to increase the effectiveness of deep learning and improve application-level performance. To address the above challenges, we explore collaborative inference at the edge, in which edge nodes and end devices share correlated data and the inference computational burden by leveraging different ways to split computation and fuse data. Besides traditional centralized and distributed schemes for edge-end device collaborative inference, we introduce selective schemes that decrease bandwidth resource consumption by effectively reducing data redundancy. As a reference scenario, we focus on multi-view classification in a networked system in which sensing nodes can capture overlapping fields of view. The proposed schemes are compared in terms of accuracy, computational expenditure at the nodes, communication overhead, inference latency, robustness, and noise sensitivity. Experimental results highlight that selective collaborative schemes can achieve different trade-offs between the above performance metrics, with some of them bringing substantial communication savings (from 18% to 74% of the transmitted data with respect to centralized inference) while still keeping the inference accuracy well above 90%.

Enhancing Collaboration and Interdisciplinary Strategies for Navigating Innovative Technologies and Regulatory Approvals in the Cosmetic Industry

Abstract: The cosmetic industry, driven by innovation, constantly seeks safer, more effective, and sustainable products. However, regulatory hurdles often impede the introduction of innovative cosmetics. This paper explores the convergence of advanced technologies and regulatory conformity, emphasizing the need for collaborative, interdisciplinary approaches. Through an extensive review of literature and case studies, we reveal the multifaceted challenges within the industry. These encompass shifting consumer expectations, ingredient safety concerns, and intricate global regulations. Cross-functional collaborations, uniting researchers, regulators, industry experts, and consumers, are pivotal, driving innovation while ensuring product safety and compliance. Our research stresses the importance of a proactive, all-encompassing approach to product development. This approach should integrate cutting-edge technologies, rigorous safety assessments, and smart regulatory strategies from project inception. Additionally, it emphasizes engaging stakeholders, sharing knowledge, and practicing transparency to earn trust from consumers and regulators. This paper provides a comprehensive overview of the balance between innovation and regulation in the cosmetic industry, advocating for a paradigm shift characterized by collaboration and interdisciplinary efforts. By aligning innovation with regulatory compliance, the industry can effectively navigate the complex landscape of modern cosmetics, fostering the creation of safer, more sustainable, and consumer-centric products.

Praktik Ekonomi Lokal Masyarakat Melalui Bisnis Mangga dalam Perspektif Fikih Muamalah

This research aims to examine community economic empowerment through the mango business from the perspective of muamalah fiqh in Oro-Oro Ombo Kulon Village, Rembang District, Pasuruan Regency. Using a descriptive qualitative approach, this research identifies the economic potential of the mango business in the village and analyzes the application of muamalah fiqh principles in every aspect of the business. The research results show that Oro-Oro Ombo Kulon Village has great potential in mango cultivation and economic empowerment through this business has had a positive impact on the local community. The application of the principles of muamalah fiqh, such as justice, transparency and honesty, in the mango business also creates an ethical and sustainable business environment. This research provides recommendations for infrastructure development, training, product diversification, collaboration, and marketing strategies that can increase the effectiveness of economic empowerment through the mango business and create a business model that is in accordance with Islamic principles.

Synergistic enhancement for energy-saving, emission reduction and profit improvement in iron and steel manufacturing system: Strategies for parameter regulation and technologies integration

The steel industry, characterized by high energy consumption and carbon emission, poses a significant threat to global energy and environmental security. In response to the energy conservation and dual-carbon targets, the steel industry must urgently find sustainable pathways for transformation and development. Steel production structure optimization and the application of cutting-edge technologies have been proven to be highly effective. However, the complexity and variability of actual production conditions highlight the absence of a methodology capable of adapting to real production scenarios, elucidating influencing principles across multiple factors, analyzing synergistic mechanisms of multiple indicators, and proposing collaborative control strategies. Furthermore, the potential for technology applications that are closely integrated with the company’s resource endowment remains uncertain, leading to an unclear overall strategy for energy saving and carbon reduction. This study develops a multi-scale optimization and evaluation model for ISMP, incorporating interconnected and matching processes, as well as nested multi-indicators, in response to the context. Subsequently, the disturbance mechanisms of various factors are accurately identified and a multi-indicator synergistic control strategy and framework are proposed. Following optimization, the exergy loss, energy consumption, carbon emission intensity, cost, and pollutant emissions of ISMP per tonne of steel are reduced by 1418.70 MJ, 42.93 kgce, 154.79 kg, 171.21 CNY, and 0.0168 kg, respectively. Additionally, measures such as reducing moisture content in coking coal can effectively regulate multiple indicators. Based on the proposed sensitivity quantification method, the temperature of hot blast is identified as the most effective controllable factor. Technologies like hydrogen-rich fuel injection, integrated waste heat recovery, and solar photovoltaic & energy storage have shown favorable prospects, according to analysis conducted from various angles regarding economic viability and potential for energy-saving and carbon reduction. However, the application of aforementioned technologies may be limited by financial consideration, necessitating cautious and judicious investment choices.

An on-demand collaborative edge caching strategy for edge–fog–cloud environment

In this paper, we tackle the critical challenges of content edge caching, such as limited storage capacity, content popularity prediction, dynamic user demand, and user privacy, issues that most existing studies only address partially. We present an innovative Genetic Algorithm-based On-demand Collaborative Edge Caching mechanism (GAOCEC), which introduces a multi-tiered caching architecture integrating cloud, fog, and edge computing. To enhance caching efficiency and minimize system cost, a novel on-demand caching quota mechanism is proposed that dynamically allocates cache resources to edge servers. To strengthen user privacy protection during content popularity prediction, a CNN-BiLSTM-based Federated Learning algorithm (CBFL) is presented that ensures high prediction accuracy without the need to upload local data to the cloud. We also refine the genetic algorithm for content placement by fine-tuning various parameter sets to identify the optimal balance between latency reduction and caching cost. Our experimental results validate the effectiveness of our approach, demonstrating increased cache hit rates, decreased content response times, and an overall improvement in system efficiency. This work provides a comprehensive, adaptive, and privacy-preserving solution for the edge–fog–cloud environment.