Search Results (768)

Achieving the construction sector’s dual carbon objectives necessitates scaling green energy adoption in new residential buildings. The current literature critically overlooks four unresolved problems: oversimplified penalty mechanisms, ignoring escalating regulatory costs; static subsidies misaligned with market maturity evolution; systematic exclusion of innovation feedback from energy suppliers; and underexplored behavioral evolution of building owners. This study establishes a government–suppliers–owners evolutionary game framework with dynamically calibrated policies, simulated using MATLAB multi-scenario analysis. Novel findings demonstrate: (1) A dual-threshold penalty effect where excessive fines diminish policy returns due to regulatory costs, requiring dynamic calibration distinct from fixed-penalty approaches; (2) Market-maturity-phased subsidies increasing owner adoption probability by 30% through staged progression; (3) Energy suppliers’ cost-reducing innovations as pivotal feedback drivers resolving coordination failures, overlooked in prior tripartite models; (4) Owners’ adoption motivation shifts from short-term economic incentives to environmentally driven decisions under policy guidance. The framework resolves these gaps through integrated dynamic mechanisms, providing policymakers with evidence-based regulatory thresholds, energy suppliers with cost-reduction targets, and academia with replicable modeling tools. Full article

In high-dynamic bipedal locomotion control, robotic systems are often constrained by motor torque limitations, particularly during explosive tasks such as jumping. One of the key challenges in reinforcement learning lies in bridging the sim-to-real gap, which mainly stems from both inaccuracies in simulation models and the limitations of motor torque output, ultimately leading to the failure of deploying learned policies in real-world systems. Traditional RL methods usually focus on peak torque limits but ignore that motor torque changes with speed. By only limiting peak torque, they prevent the torque from adjusting dynamically based on velocity, which can reduce the system’s efficiency and performance in high-speed tasks. To address these issues, this paper proposes a reinforcement learning jump-control framework tailored for tendon-driven bipedal robots, which integrates dynamic torque boundary constraints and torque error-compensation modeling. First, we developed a torque transmission coefficient model based on the tendon-driven mechanism, taking into account tendon elasticity and motor-control errors, which significantly improves the modeling accuracy. Building on this, we derived a dynamic joint torque limit that adapts to joint velocity, and designed a torque-aware reward function within the reinforcement learning environment, aimed at encouraging the policy to implicitly learn and comply with physical constraints during training, effectively bridging the gap between simulation and real-world performance. Hardware experimental results demonstrate that the proposed method effectively satisfies actuator safety limits while achieving more efficient and stable jumping behavior. This work provides a general and scalable modeling and control framework for learning high-dynamic bipedal motion under complex physical constraints. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

Acaricide usage has led to the growing problem of resistance in ticks. A heavy tick burden and the presence of ticks on animals throughout the year, despite the monthly application of acaricides, in farms in the United Arab Emirates formed the motivation for this study. The objectives of this research were as follows: (a) to assess the acaricide resistance status of the most prevalent tick Hyalomma spp. to widely used acaricides Cypermethrin and Deltamethrin; (b) to identify the association of farm management practices and farm-level risk factors with the failure of tick treatment (acaracide resistance). A total of 1600 ticks were collected from 20 farms located in three different regions of Abu Dhabi Emirate including Al Ain (n = 10), Al Dhafra (n = 5), and Abu Dhabi (n = 5). The ticks were subjected to an in vitro bioassay adult immersion test (AIT) modified with a discriminating dose (AIT-DD) against commercial preparations of Cypermethrin and Deltamethrin. A questionnaire was designed to collect metadata and information on farm management and the farm-level risk factors associated with routine farm practices relating to the treatment and control of tick and blood parasite infections in camels and small ruminant populations. Hyalomma anatolicum and Hyalomma dromedarii were identified among the collected ticks, with H. anatolicum being the most prevalent tick species (70%) in the present study. The test results of the in vitro bioassay revealed varied emerging resistance to both of the acaricides in the majority of the three regions; fully susceptible tick isolates with zero resistance to Deltamethrin were recorded in one farm at Al Ain and two farms in the Abu Dhabi region. A questionnaire analysis showed that the failure of tick treatment in farms varied with the presence or absence of vegetation areas, types of animal breeds, and management practices. This study reports the emergence of resistance in ticks to Cypermethrin and Deltamethrin across the Abu Dhabi Emirate, indicating a strict warning for the cautious use of acaricides. There is also a need to improve awareness about sound tick management and control practices among farm owners through a multidisciplinary approach adopting integrated pest management strategies that engage farmers, veterinarians, and policy makers. Full article

Vision-based end-to-end navigation systems have shown impressive capabilities, especially when combined with Imitation Learning (IL) and advanced Deep Learning architectures, such as Transformers. One such example is CIL++, a Transformer-based architecture that learns to map navigation states to vehicle controls based on expert demonstrations only. Nevertheless, reliance on experts’ datasets limits generalization and can lead to failures in unknown circumstances. Deep Reinforcement Learning (DRL) can address this issue by fine-tuning the pretrained policy, using a reward function that aims to improve its weaknesses through interaction with the environment. However, fine-tuning with DRL can lead to the Catastrophic Forgetting (CF) problem, where a policy forgets the expert behaviors learned from the demonstrations as it learns to optimize the new reward function. In this paper, we present CILRLv3, a DRL-based training method that is immune to CF, enabling pretrained navigation agents to improve their driving skills across new scenarios. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas $ϵ$-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments. Full article

This paper explores the complex power dynamics of UK social work higher education through an autoethnographic account of a Black woman course leader’s experiences over a period of two years, focusing on issues related to race, internalized oppression, and class. Drawing on Critical Race Theory (CRT), narrative analysis, and lived experience, it examines how systemic inequities manifest through three interlinked themes: (a) academic contrapower harassment (ACPH), (b) internalized oppression and toxic team dynamics, and (c) the interplay of harassment, institutional failure, managerial inaction, and the marginalization of social work as a discipline. This study illustrates how the intersectionality of multiple identities—namely, race, gender, and professional identity—impacts career progression, well-being, and institutional inclusion. This study examines the tensions between social work’s ethical foundations and performance-driven academic environments, advocating for systemic and policy interventions to stimulate institutional reform and cultivate a more equitable culture that enhances educational outcomes and, ultimately, improves social work practice. Full article

Healthcare abuse is a critical human rights and public health issue, particularly impacting medical interns and trainees who are vulnerable to mistreatment during their formative professional years. This cross-sectional study, conducted from February to June 2024, evaluated the prevalence and psychological impact of harassment and discrimination among 463 healthcare interns in Saudi Arabia from various specialties, including medicine, nursing, pharmacy, and dentistry. Using a self-administered online questionnaire, we found that mistreatment was widely reported, with female interns experiencing significantly higher rates of sexual harassment and gender-based discrimination. Common perpetrators included residents, lecturers, professors, nurses, and patients, with incidents most frequently occurring in surgical and internal medicine departments. Despite high prevalence, only 9% of interns reported the abuse due to mistrust in reporting systems or failure to recognize the behavior as abuse. These experiences were associated with significant psychological distress, including frustration, reduced motivation to learn, and higher DASS scores, particularly among female interns. The study underscores the need for institutional reforms, including policy development, cultural change, and effective reporting systems to ensure a safe and supportive learning environment for future healthcare professionals. Addressing abuse in medical training is essential for individual well-being and the sustainability and integrity of healthcare systems. Full article

This study investigates farmers’ knowledge, perceptions, and adaptation strategies to climate change in Rwanda’s Eastern Province, integrating social and physical science approaches. Analyzing meteorological data (1981–2021) and surveys from 204 farmers across five districts, we assessed climate trends and adaptation behaviors using statistical methods (descriptive statistics, Chi-square, logistic regression, Regional Kendall test, dynamic linear state-space model). Results show that 85% of farmers acknowledge climate change, with 54% observing temperature increases and 37% noting rainfall declines. Climate data confirm significant rises in annual minimum (+0.76 °C/decade) and mean temperatures (+0.48 °C/decade), with the largest seasonal increase (+0.86 °C/decade) in June–August. Rainfall trends indicate a non-significant decrease in March–May and a slight increase in September–December. Farmers report crop failures, yield reductions, and food shortages as major climate impacts. Common adaptations include agroforestry, crop diversification, and fertilizer use, though financial limitations, information gaps, and input scarcity impede adoption. Despite limited formal education (53.9% primary, 22.3% no formal education), indigenous knowledge aids seasonal prediction. Farm location, group membership, and farming goal are key adaptation enablers. These findings emphasize the need for targeted policies and climate communication to enhance rural resilience by strengthening smallholder farmer support systems for effective climate adaptation. Full article

This article investigates the political cost of poverty in democracies marked by deep social divisions. We develop a probabilistic voting model that incorporates clientelism as a strategic tool employed by elite political parties to secure electoral support from non-elite voters. Unlike models based on ideological proximity, our framework conceptualizes party competition as structured by the socioeconomic composition of their constituencies. We demonstrate that in contexts of high inequality and widespread poverty, elite parties face structural incentives to deploy clientelistic strategies rather than universalistic policy agendas. Our model predicts that clientelistic expenditures by elite parties increase proportionally with both inequality (GINI index) and poverty levels, rendering clientelism a rational and cost-effective mechanism of political control. Empirical evidence from a cross-national panel (2013–2019) confirms the theoretical predictions: an increase of the 1 percent in the GINI index increase a 1.3 percent in the clientelism, even after accounting for endogeneity and dynamic effects. These findings suggest that in divided democracies, poverty is not merely a condition to be alleviated, but a political resource that elites strategically exploit. Consequently, clientelism persists not as a cultural residue or institutional failure, but as a rational response to inequality-driven constraints within democratic competition. Full article

Global low-carbon innovation networks face increasing vulnerabilities amid growing geopolitical tensions and technological competition. The interdependent structure of low-carbon innovation networks and the risk propagation dynamics within them remain poorly understood. This study investigates vulnerability patterns by constructing a two-layer interdependent network model based on Chinese low-carbon patent data, comprising a low-carbon collaboration network of innovation entities and a low-carbon knowledge network of technological components. Applying dynamic shock propagation modeling, we analyze how risks spread within and between network layers under various shocks. Our findings reveal significant differences in vulnerability distribution: the knowledge network consistently demonstrates greater susceptibility to cascading failures than the collaboration network, reaching complete system failure, while the latter maintains partial resilience, with resilience levels stabilizing at approximately 0.64. Critical node analysis identifies State Grid Corporation as a vulnerability point in the collaboration network, while multiple critical knowledge elements can independently trigger system-wide failures. Cross-network propagation follows distinct patterns, with knowledge-network failures consistently preceding collaboration network disruptions. In addition, propagation from the collaboration network to the knowledge network showed sharp transitions at specific threshold values, while propagation in the reverse direction displayed more gradual responses. These insights suggest tailored resilience strategies, including policy decentralization approaches, ensuring technological redundancy across critical knowledge domains and strengthening cross-network coordination mechanisms to enhance low-carbon innovation system stability. Full article

Background: Influenza is a significant global respiratory infection, and vaccinating reproductive-age women, particularly in densely populated countries like China, cannot be overlooked. In this study, we aimed to determine influenza vaccination coverage, vaccine hesitancy, as well as associated factors among Chinese women aged 18–49 years old. Methods: A cross-sectional survey among women aged 18–49 years was conducted in China from 15 to 30 March 2023. We collected information such as past-year influenza vaccination, demographic characteristics, health-related factors, COVID-19-related factors, and perceived susceptibility and severity of influenza. Influenza vaccine acceptance among participants who did not receive influenza vaccination in the past year was also investigated. Multivariable logistic regression analyses were employed to investigate the influencing factors of vaccine coverage and vaccine hesitancy. Results: A total of 1742 reproductive-aged women were included in the final analysis. The past-year influenza vaccine coverage among women aged 18–49 years old was only 39.32% in China. Age ≥ 35 years (aOR = 0.72, 95% CI: 0.56–0.94), renting accommodation (aOR = 0.57, 95% CI: 0.44–0.75), and history of COVID-19 infection (aOR = 0.65, 95% CI: 0.47–0.89) and COVID-19 vaccine hesitancy (aOR = 0.39, 95% CI: 0.29–0.54) were all identified as negative correlates of influenza vaccine coverage among Chinese reproductive-aged women, while participants with a history of chronic diseases (aOR = 1.57, 95% CI: 1.23–2.01) and noticeable pandemic fatigue due to COVID-19 (aOR = 1.45, 95% CI: 1.05–2.00) were prone to have higher vaccination rates. Among reproductive-aged women who did not receive influenza vaccination in the past year, the hesitancy rate regarding future influenza vaccination was 31.79%. Factors such as older age, urban residence, living with others, poor self-rated health status, absence of chronic diseases, completion of full COVID-19 vaccination, COVID-19 vaccine hesitancy, pandemic fatigue, and failure to perceive the susceptibility and severity of influenza might increase influenza vaccine hesitancy. Discussion: Overall, a lower coverage rate of influenza vaccine was notably observed among Chinese reproductive-age women, as well as the hesitancy regarding future vaccination. To effectively mitigate the impact of influenza and reduce the incidence of associated diseases, it is imperative to devise targeted intervention strategies and policies tailored to reproductive-age women. Full article

Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article

As Artificial Intelligence systems increasingly surpass or replace traditional human roles, institutions founded on beliefs in human cognitive superiority, moral authority, and procedural oversight encounter a more profound challenge than mere disruption: expiration. This paper posits that, instead of being outperformed, many legacy institutions are becoming epistemically misaligned with the realities of AI-driven environments. To clarify this change, the paper presents the Expiration Theory. This conceptual model interprets institutional collapse not as a market failure but as the erosion of fundamental assumptions amid technological shifts. In addition, the paper introduces the AI Pressure Clock, a diagnostic tool that categorizes institutions based on their vulnerability to AI disruption and their capacity to adapt to it. Through an analysis across various sectors, including law, healthcare, education, finance, and the creative industries, the paper illustrates how specific systems are nearing functional obsolescence while others are actively restructuring their foundational norms. As a conceptual study, the paper concludes by highlighting the theoretical, policy, and leadership ramifications, asserting that institutional survival in the age of AI relies not solely on digital capabilities but also on the capacity to redefine the core principles of legitimacy, authority, and decision-making. Full article

Tropical peatlands store approximately 105 gigatons of carbon (GtC), serving as vital long-term carbon sinks, yet remain critically underrepresented in climate policy. Indonesia peatlands contain 57GtC—the largest tropical peatland carbon stock in the Asia–Pacific. However, decades of drainage, fires, and lax enforcement practices have degraded vast peatland areas, turning them from carbon sinks into emission sources—as evidenced by the 1997 and 2015 peatland fires which emitted 2.57 Gt CO₂eq and 1.75 Gt CO₂eq, respectively. Using system theory validated against historical data (1997–2023), we develop a causal loop model revealing three interconnected feedback loops driving irreversible collapse: (1) drainage–desiccation–oxidation, where water table below −40 cm triggers peat oxidation (2–5 cm subsistence) and fires; (2) fire–climate–permafrost, wherein emissions intensify radiative forcing, destabilizing monsoons and accelerating Arctic permafrost thaw (+15% since 2000); and (2) economy–governance failure, perpetuated by palm oil’s economic dominance and slack regulatory oversight. To break these vicious cycles, we propose a precautionary framework featuring IoT-enforced water table (≤40 cm), reducing emissions by 34%, legally protected “Global Climate Stabilization Zones” for peat domes (>3 m depth), safeguarding 57 GtC, and ASEAN transboundary enforcement funded by a 1–3% palm oil levy. Without intervention, annual emissions may reach 2.869 GtCO₂e by 2030 (Nationally Determined Contribution’s business-as-usual scenario). Conversely, rewetting 590 km²/year aligns with Indonesia’s FOLU Net Sink 2030 target (−140 Mt CO₂e) and mitigates 1.4–1.6 MtCO₂ annually. We conclude that integrating peatlands as irreplaceable climate infrastructure into global policy is essential for achieving Paris Agreement goals and SDGs 13–15. Full article