Advanced Management Strategies Research Articles

Enhanced soil moisture–temperature coupling could exacerbate drought under net-negative emissions

Our understanding of drought evolution and land-atmosphere interactions under climate mitigation scenarios remains limited. Here, we analyzed future drought under net-zero and net-negative emission scenarios using the Community Earth System Model version 2, particularly focusing on three atmospheric CO2 states: linearly increases, decreases, and a return to the initial state. Interestingly, results revealed that net-zero emissions are more effective for drought mitigation than net-negative targets. Drying trends and drought characteristics — such as the duration, frequency, intensity, and area expansion are prominently increased under net-negative emissions due to higher potential evapotranspiration (PET). This is because the soil moisture and temperature couplings are stronger over drought regions and years, especially under net-negative forcing, with notable impacts in Central Africa and South Asia. Nevertheless, both target scenarios offer regional benefits, such as weakened dryness. This suggests that mitigating CO2 alone may not be sufficient to manage future droughts, highlighting the need for advanced water management strategies.

Identifying contextual content-based risk drivers for advanced risk management strategies

This research proposes a profound contextual topic identifier that incorporates topic modelling and a word embedding technique to discover and quantify corporate risks from its self-identified risk disclosures and examines the association between each risk type and operating performance via artificial intelligence (AI) technique. Via topic modelling adoption, we are able to discover the most essential risks confronted by corporates in the near future and evaluate how they respond to these risks. To gain deeper insight, the study performs a bidirectional encoder representation from transformers (BERT) (one type of word embedding approach) to extract and quantify the semantic features embedded into each risk disclosure. The results show that operating performance significantly and positively relates to corporate-specific risks. This study offers solid and direct support for authorities that set accounting principles to encourage firm managers to add a new section on risk factors in annual reports.

Innovative hybrid energy storage systems with sustainable integration of green hydrogen and energy management solutions for standalone PV microgrids based on reduced fractional gradient descent algorithm

This paper investigates innovative solutions to enhance the performance and lifespan of standalone photovoltaic (PV)-based microgrids, with a particular emphasis on off-grid communities. A major challenge in these systems is the limited lifespan of batteries. To overcome this issue, researchers have created hybrid energy storage systems (HESS) along with advanced power management strategies. This study introduces innovative multi-level HESS approaches and a related energy management strategy designed to alleviate the charge/discharge stress on batteries. Comprehensive Matlab Simulink models of various HESS topologies within standalone PV microgrids are utilized to evaluate system performance under diverse weather conditions and load profiles for rural site. The findings reveal that the proposed HESS significantly extends battery life expectancy compared to existing solutions. Furthermore, the paper presents a novel energy management strategy based on the Reduced Fractional Gradient Descent (RFGD) algorithm optimization, tailored for hybrid systems that include photovoltaic, fuel cell, battery, and supercapacitor components. This strategy aims to minimize hydrogen consumption of Fuel Cells (FCs), thereby supporting the production of green ammonia for local industrial use. The RFGD algorithm is selected for its minimal user-defined parameters and high convergence efficiency. The proposed method is compared with other algorithms, such as the Lyrebird Optimization Algorithm (LOA) and Osprey Optimization Algorithm (OOA). The RFGD algorithm exhibits superior accuracy in optimizing energy management, achieving a 15 % reduction in hydrogen consumption. Its efficiency is evident from the reduced computational time compared to conventional algorithms. Although minor losses in computational resources were observed, they were substantially lower than those associated with traditional optimization techniques. Overall, the RFGD algorithm offers a robust and efficient solution for enhancing the performance of hybrid energy systems.

Challenges, Opportunities and Countermeasures of Education and Teaching Management in the Digital Age

In the digital age, education and teaching management is facing many challenges and opportunities. The challenges mainly include difficulties for teachers and students to adapt, information security issues and the digital divide. In addition, the rise of online learning platforms has also brought about the quality of education and the difficulty of management. However, digitization also provides rich opportunities, such as personalized learning, sharing and optimization of educational resources, innovation of teaching methods, etc. Addressing these challenges requires enhanced teacher training, improved information infrastructure, data security, and relevant policies to bridge the digital divide. By actively adopting advanced technology and scientific management strategies, the quality of education can be effectively improved and the fairness and efficiency of education can be realized.

Coordination of smart inverter-enabled distributed energy resources for optimal PV-BESS integration and voltage stability in modern power distribution networks: A systematic review and bibliometric analysis

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

Towards Good Governance in the Management of Educational Institutions in Cameroon: Importance, Challenges and Opportunities

Purpose: This conceptual paper explored the path toward institutionalisation of good governance in the management of educational institutions in Cameroon. Aim was to identify key principles, assess challenges to effective governance, and explore opportunities for improvement, with the view to offer actionable recommendations that could help enhance educational management in Cameroon. Materials and Methods: A qualitative approach that combines personal observations with a comprehensive textual review was employed. The review examined national and international policy documents, strategic plans, reports and academic articles to clarify theoretical constructs and practical applications of good governance in education. Recommendations were based on personal reflections and literature analysis. Findings: The education sector has evolved from being simpler and less competitive to a complex, competitive and more demanding landscape, requiring technology and advanced management strategies centered on governance principles like transparency and accountability. In Cameroon, historical, socio-economic, political, and structural challenges hinder the establishment of good governance in education. However, there is room for educational managers to navigate these realities and promote effective governance, which is essential for unlocking education’s transformative potential and achieving socioeconomic development goals. Implications to Theory, Practice and Policy: The various ministries of education should work in synergy with the other stakeholders to implement a comprehensive training programme for educational managers; invest more resources in educational technology for better administrative efficiency; strengthen monitoring and auditing of expenditures; and enhance the capacity of decentralised management structures to effectively address local needs. These strategies should help create a more accountable and responsive education management framework for Cameroon.

Smart Grid Energy Reduction and QoS Maximization through Renewable Energy and Data Buffering

The integration of renewable energy sources and advanced data management strategies is pivotal for the evolution of smart grids. This research focuses on optimizing energy consumption and enhancing Quality of Service (QoS) in smart grids by leveraging renewable energy and implementing data buffering techniques. The proposed data buffering integrated renewable smart grid framework reduces the dependance on conventional energy sources, minimizes energy costs, and improves service consistency. Through a detailed analysis and experimental validation, the proposed study demonstrates significant improvements in energy efficiency and QoS metrics, showcasing the potential of the proposed approach in modern smart grid environments.

Calculating Premiums for Extreme-Tail Risks with Liability Claims and Nuclear Verdicts

Purpose: When insurance claims, particularly liability claims and nuclear verdicts, are governed by fat-tailed distributions, considerable uncertainty exists about the value of the tail index. Material and Methods: Using the theory of risk aversion, this paper establishes a new premium principle (the power principle – analogous to the exponential principle for thin-tailed claims) and investigates its properties. Findings: Applied to claims arising from generalized Pareto distributions, the resultant premium is shown to be the ratio of the two largest expected claims. This structure provides a natural model for incorporating tail-index uncertainty into premiums. The theory is illustrated through possible ‘premiums’ for liability claims and nuclear verdicts, utilizing the consistent pattern of observed extremes. Implications to Theory, Practice and Policy: By integrating statistical methods for tail index estimation and addressing the inherent uncertainty, the power principle offers a robust framework for determining premiums in high-risk environments. The paper concludes with practical implications for insurers, highlighting the need for advanced risk management strategies and regulatory considerations in dealing with extreme liability claims.

Insights into hydro thermal gasification process of microplastic polyethylene via reactive molecular dynamics simulations

Microplastics have become a pressing environmental issue due to their widespread presence in our ecosystems. Among various plastic components, polyethylene (PE) is a prevalent and persistent contaminant. Hydrothermal gasification (HTG), a promising technology for converting PE into syngas, holds great promise for mitigating the microplastic problem. In this study, we employ ReaxFF molecular dynamics simulations to investigate the HTG process of PE, shedding light on the intricate relationships between temperature, water content, carbon conversion efficiency, and product distributions. The results reveal that hydrothermal gasification of PE is a complex process involving multiple reaction pathways. Consistently with experimental findings, the calculations indicate that the gas phase exhibits a substantial hydrogen fraction, reaching up to 70%. Interestingly, our simulations reveal a dual role of water content in the HTG process. On one hand, water enhances hydrogen production by promoting the gas formation. On the other hand, it elevates the activation energy required for PE decomposition. Depending on the water content, the calculated activation energies range from 176 to 268 kJ/mol, which are significantly lower than those reported for thermal gasification (TG). This suggests that HTG may be a more efficient route for PE conversion. Furthermore, this study highlights the importance of optimizing both temperature and water content in HTG systems to achieve high yields of hydrogen-rich syngas. The results obtained from our ReaxFF MD simulations demonstrate the robustness of this computational methodology in elucidating complex chemical reactions under extreme conditions. Our findings offer critical insights into the design of advanced waste management strategies for microplastics and contribute to the development of sustainable practices for resource recovery. This work underscores the potential of HTG as a key technology for addressing the global challenge of plastic pollution.

Energy Transition and Resilient Control for Enhancing Power Availability in Microgrids Based on North African Countries: A Review

The ambition of making North Africa a hub for renewable energies and green hydrogen has prompted local governments and the private sector to work together towards boosting the growth of locally available, sustainable energy resources. Numerous climate and energy challenges can be addressed by microgrid technologies, which enable cost-effective incorporation of renewable energy resources and energy storage systems through smart management and control infrastructures. This paper discusses the ongoing energy transition in the countries of North Africa, highlighting the potential for renewable energy sources as well as regional obstacles and challenges. Additionally, it explores how robust and stable controls and advanced management strategies can improve microgrids’ performances. Special attention is given to assessing the advantages and disadvantages of conventional and advanced controllers, with an emphasis on resilience needed within the harsh North African environment.

Practice of a Load Shifting Algorithm for Enhancing Community-Scale RES Utilization

Amidst the recent energy crisis, the pivotal roles of resource efficiency and renewable energy sources (RES) for sustainable development have become apparent. The transition to sustainability involves decentralized energy solutions empowering local communities to generate, store, and utilize their energy, diminishing the reliance on centralized systems and potentially transforming them into resources for power flexibility. Addressing the above necessitates, amongst other elements, the adoption of advanced demand-side management (DSM) strategies. In response, we introduce a versatile algorithm investigating the impact of DSM on the community scale, designed to maximize the utilization of renewable energy produced from local installations. Integrated as an ancillary module in a research data management platform, the algorithm underwent testing using historical datasets collected from end-consumers and a small-scale RES installation. This study not only offers insights for energy stakeholders, but also establishes theoretical parameters that can inform subsequent decision-making processes in the field.

Unraveling the enigma of root-knot nematodes: from origins to advanced management strategies in agriculture.

Integrated management strategies, including novel nematicides and resilient cultivars, offer sustainable solutions to combat root-knot nematodes, crucial for safeguarding global agriculture against persistent threats. Root-knot nematodes (RKN) pose a significant threat to a diverse range of host plants, with their obligatory endoparasitic nature leading to substantial agricultural losses. RKN spend much of their lives inside or in contact by secreting plant cell wall-modifying enzymes resulting in the giant cell development for establishing host-parasite relationships. Additionally, inflicting physical harm to host plants, RKN also contributes to disease complexes creation with fungi and bacteria. This review comprehensively explores the origin, history, distribution, and physiological races of RKN, emphasizing their economic impact on plants through gall formation. Management strategies, ranging from cultural and physical to biological and chemical controls, along with resistance mechanisms and marker-assisted selection, are explored. While recognizing the limitations of traditional nematicides, recent breakthroughs in non-fumigant alternatives like fluensulfone, spirotetramat, and fluopyram offer promising avenues for sustainable RKN management. Despite the success of resistance mechanisms like the Mi gene, challenges persist, prompting the need for integrative approaches to tackle Mi-virulent isolates. In conclusion, the review stresses the importance of innovative and resilient control measures for sustainable agriculture, emphasizing ongoing research to address evolving challenges posed by RKN. The integration of botanicals, resistant cultivars, and biological controls, alongside advancements in non-fumigant nematicides, contributes novel insights to the field, laying the ground work for future research directions to ensure the long-term sustainability of agriculture in the face of persistent RKN threats.

An adaptive control strategy for integration of wind farm using a VSC-HVDC transmission system

This study addresses the issue of integrating renewable energy sources into the power grid by proposing advanced power management strategies based on an adaptive observer. The system comprises a wind turbine farm (WTF) connected to an energy storage system via a Vienna rectifier, which serves as the voltage source converter (VSC), and is linked to the DC grid through a high-voltage direct current (HVDC) transmission line. The main objective of this study is to design a nonlinear controller to improve energy extraction from the wind power frame and enhance the stability of the power grid. The system controller is designed to operate in two modes. The main feature of this controller is its ability to achieve multi-objective control using different references. In this study, we have four references but only two control laws. Furthermore, an adaptive observer is designed to estimate the power grid requirements, allowing us to produce depending on the grid state. Finally, simulation results using MATLAB/Simulink will be displayed to demonstrate the stability and efficiency of the proposed implementation.

Analysis of voltage limit‐induced barrier for connecting inverter‐based distributed generators to medium voltage networks: Australian case studies

AbstractInverter‐based distributed generators (IBDGs), mainly solar photovoltaic, connected in medium‐voltage (MV) networks cause challenges, such as voltage limit violations, for distribution network service providers (DNSPs), and require advanced network management strategies to mitigate these challenges. A theoretical analysis of the voltage limit‐induced barrier to IBDG connection and their export limits due to the change in network characteristics is imperative for developing new strategies. The authors formulated a relationship between the network equivalent impedance and the IBDG's connection point in the network and further explored the link between the network equivalent impedance and voltage magnitude due to the IBDG connection point. The authors also assessed the voltage limit‐induced barrier to IBDG connections in MV networks and proposed solutions to overcome issues with the dynamic export limit of IBDGs. Four representative Australian MV networks are analysed in DIgSILENT PowerFactory under different scenarios, such as variation in IBDG location and static and dynamic export limits. The authors found that an IBDG connected at the end of the network can achieve better performance in supporting the network voltage. An IBDG with a dynamic export limit can export three times more energy than the static export limit, which benefits both the DNSPs and IBDG owners.

Optimising Urban Waste Management Through Smart Monitoring Systems: A Case Study in Enhancing Efficiency and Sustainability

Abstract This paper illustrates the significant potential of advanced waste management strategies, particularly the implementation of a trash level monitoring system, to enhance urban sustainability and operational efficiency. This research rigorously evaluates the superiority of real-time data-driven systems over conventional waste management practices by leveraging a dynamic model to simulate trash fill levels and employing algorithms to identify optimal collection routes.

Insurance Premium Prediction Using Machine Learning

RiskInsight represents an innovative company poised to transform the world of insurance by applying state-of-the-art machine learning techniques. The focus of this project is the Insurance Risk Assessment System, a high-level framework carefully designed using the Python programming language and the sklearn library. Anchored by unprecedented accuracy and operational efficiency, RiskInsight enters the field of risk assessment with unwavering determination. Using the power of advanced algorithms inspired by artificial neural networks, the system navigates huge historical data and evaluates real-time risk dynamics. The cornerstone of RiskInsight is its in-depth analysis of various risk factors carefully curated in a robust database. Combining machine learning capabilities and domain expertise, RiskInsight seeks to redefine the boundaries of risk assessment by transcending traditional paradigms. The goal is to bring about a paradigm shift in super forecasting and provide stakeholders with insights that promote fairer payment structures and informed decision-making. With its innovative design and flexible functionality, RiskInsight promises to catalyze major changes in the insurance ecosystem. By providing accurate payment forecasts and advanced risk management strategies, the platform aims to enhance the efficiency of insurance operations. A testament to its transformative potential, RiskInsight is poised to usher in a new era of efficiency and equity in the insurance industry and sets a precedent for technological innovation in risk assessment and predictive systems. Keywords: Machine Learning, Fairer Payment Structure, Efficiency, Transformative Potential Acronyms: sklearn - Scikit-learn

Multiphysics integrated model of NMC111 battery module for micro-mobility applications using PCM as intercell material

The widespread use of e-bikes and e-scooters powered by lithium-ion batteries in urban settings has heightened safety concerns, particularly the risk of thermal runaway, which can result in dangerous incidents. The surge in battery-related fire incidents highlights the critical need for a deep understanding of thermal runaway phenomena. This study addresses this imperative by introducing a Multiphysics model combining pseudo-bidimensional electrochemical for simulating electrical performance, lumped thermal model for simulating the heat transfer behavior inside the module, and a thermal runaway kinetic model for propagation evaluation. This approach facilitates precise predictions of battery performance and proactively addresses safety concerns by incorporating thermal behavior simulations and predicting potential thermal runaway events. The model proposed allows the evaluation of different strategies for improving battery safety and performance, such as phase change materials and thermal insulators. Simulations reveal that implementing sodium hydrogen phosphate dodecahydrate (Na2HPO4··12H2O) as an intercell material substantially enhances thermal management. This material reduced the maximum temperature of the battery module by up to 30% during critical thermal runaway events. Additionally, it slowed the rate of temperature increase by 50%, significantly decreasing the possibility of adjacent cells reaching thermal runaway conditions. The research findings demonstrate the effectiveness of advanced thermal management strategies using PCMs to prevent potentially catastrophic thermal events in battery modules typical of micro-mobility devices.

Enhancing Musculoskeletal Injection Safety: Evaluating Checklists Generated by Artificial Intelligence and Revising the Preformed Checklist.

Background Musculoskeletal disorders are a significant global health issue, necessitating advanced management strategies such as intra-articular and extra-articular injections to alleviate pain, inflammation, and mobility challenges. As the adoption of these interventions by physicians grows, the importance of robust safety protocols becomes paramount. This study evaluates the effectiveness of conversational artificial intelligence (AI), particularly versions 3.5 and 4 of Chat Generative Pre-trained Transformer (ChatGPT), in creating patient safety checklists for managing musculoskeletal injections to enhance the preparation of safety documentation. Methodology A quantitative analysis was conducted to evaluate AI-generated safety checklists against a preformed checklist adapted from reputable medical sources. Adherence of the generated checklists to the preformed checklist was calculated and classified. The Wilcoxon signed-rank test was used to assess the performance differences between ChatGPT versions 3.5 and 4. Results ChatGPT-4 showed superior adherence to the preformed checklist compared to ChatGPT-3.5, with both versions classified as very good in safety protocol creation. Although no significant differences were present in the sign-in and sign-out parts of the checklists of both versions, ChatGPT-4 had significantly higher scores in the procedure planning part (p = 0.007), and its overall performance was also higher (p < 0.001). Subsequently, the preformed checklist was revised to incorporate new contributions from ChatGPT. Conclusions ChatGPT, especially version 4, proved effective in generating patient safety checklists for musculoskeletal injections, highlighting the potential of AI to streamline clinical practices. Further enhancements are necessary to fully meet the medical standards.

Online Education Big Data Management and Mining Based on Intelligent Technology

The advent of online education platforms has revolutionized traditional learning paradigms, providing unprecedented access to educational resources worldwide. However, the vast amounts of data generated by these platforms present significant challenges in terms of effective management and utilization. This paper proposes a comprehensive approach to address these challenges through the integration of big data management and mining techniques, supported by intelligent technologies. By harnessing the power of advanced data management strategies and cutting-edge machine learning algorithms, our approach aims to unlock valuable insights from online education data, thereby enhancing the learning experience and improving educational results. This paper presents a systematic framework for online education big data management and mining, encompassing data collection, storage, preprocessing, analysis, and visualization. Furthermore, we discuss the role of intelligent technologies such as artificial intelligence, natural language processing, and predictive analytics in optimizing the process of data mining and knowledge discovery. Through case studies and experimental evaluations, we demonstrate the effectiveness and applicability of our approach in real-world online education scenarios. Our research contributes to the growing body of knowledge in the field of educational data mining and underscores the potential of intelligent technology in shaping the future of online learning.

Patterned PCL/PGS Nanofibrous Hyaluronic Acid-Coated Scaffolds Promote Cellular Response and Modulate Gene Expression Profiles.

Chronic wounds impose a significant burden on individuals and healthcare systems, necessitating the development of advanced wound management strategies. Tissue engineering, with its ability to create scaffolds that mimic native tissue structures and promote cellular responses, offers a promising approach. Electrospinning, a widely used technique, can fabricate nanofibrous scaffolds for tissue regeneration. In this study, we developed patterned nanofibrous scaffolds using a blend of poly(ε-caprolactone) (PCL) and poly(glycerol sebacate) (PGS), known for their biocompatibility and biodegradability. By employing a mesh collector, we achieved a unique fiber orientation pattern that emulated the natural tissue architecture. The average fiber diameter of PGS/PCL collected on aluminum foil and on mesh was found to be 665.2 ± 4 and 404.8 ± 16 nm, respectively. To enhance the scaffolds' bioactivity and surface properties, it was coated with hyaluronic acid (HA), a key component of the extracellular matrix known for its wound-healing properties. The HA coating improved the scaffold hydrophilicity and surface wettability, facilitating cell attachment, spreading, and migration. Furthermore, the HA-coated scaffold exhibited enhanced biocompatibility, promoting cell viability and proliferation. High-throughput RNA sequencing was performed to analyze the influence of the fabricated scaffold on the gene expression levels of endothelial cells. The top-upregulated biological processes and pathways include cell cycle regulation and cell proliferation. The results revealed significant alterations in gene expression profiles, indicating the scaffold's ability to modulate cellular functions and promote wound healing processes. The developed scaffold holds great promise for advanced wound management and tissue regeneration applications. By harnessing the advantages of aligned nanofibers, biocompatible polymers, and HA coating, this scaffold represents a potential solution for improving wound healing outcomes and improving the quality of life for individuals suffering from chronic wounds.