Search Results (200)

The decarbonization of electricity generation by 2030 and the realization of a net-zero economy by 2050 are central to the United States’ climate strategy. However, large-scale renewable integration introduces operational challenges, including extreme ramping, unsafe dispatch, and price volatility. This review investigates how demand–response (DR) aggregators and distributed loads can support these climate goals while addressing critical operational challenges. We hypothesize that current DR aggregator frameworks fall short in the areas of distributed load operational flexibility, scalability with the number of distributed loads (prosumers), prosumer privacy preservation, DR aggregator and prosumer competition, and uncertainty management, limiting their potential to enable large-scale prosumer participation. Using a systematic review methodology, we evaluate existing DR aggregator and prosumer frameworks through the proposed FCUPS criteria—flexibility, competition, uncertainty quantification, privacy, and scalability. The main results highlight significant gaps in current frameworks: limited support for decentralized operations; inadequate privacy protections for prosumers; and insufficient capabilities for managing competition, uncertainty, and flexibility at scale. We conclude by identifying open research directions, including the need for game-theoretic and machine learning approaches that ensure privacy, scalability, and robust market participation. Addressing these gaps is essential to shape future research agendas and to enable DR aggregators to contribute meaningfully to US climate targets. Full article

Background: Fever and pain are among the most frequent symptoms in pediatric care, requiring timely and appropriate management. While evidence-based guidelines are available, adherence in real-world practice remains variable. This study aimed to explore the attitudes and prescribing behaviors of Italian Primary Care Pediatricians (PCPs) in the management of fever and pain, and to assess their alignment with current clinical recommendations. Materials and Methods: An anonymous, cross-sectional survey consisting of 30 multiple-choice questions was administered to 900 PCPs between 1 July and 30 October 2024. The questionnaire assessed therapeutic preferences, dosing strategies, and perceived knowledge gaps. Invitations were distributed via pediatric scientific societies and regional professional networks. Results: A total of 244 PCPs completed the survey (response rate 27.1%). The majority were aged over 55 years (72.1%), worked in urban settings (71.3%), and had more than 20 years of clinical experience (74.6%). Most respondents (77%) reported managing pediatric fever or pain on a daily basis. Paracetamol was the preferred first-line treatment for fever (95.9%), primarily due to its perceived safety (82.4%). Ibuprofen was favored by 51.6% of those who selected it for its greater effectiveness. The alternating use of paracetamol and ibuprofen for fever was never adopted by 49.6%, while 31.6% employed this strategy, believing it to be more effective. For pain, 67.6% used paracetamol and 26.2% used ibuprofen as first-line treatments; 15.2% reported alternating the two drugs. Correct dosage practices were followed by 63.9% for both medications, although 40.2% did not differentiate dosages between fever and pain management. Conclusions: While general trends showed alignment with current guidelines, notable inconsistencies were observed in drug selection, dosage, and the use of alternating therapies. These findings highlight a pressing need to improve the dissemination and implementation of pediatric fever and pain management guidelines among PCPs in order to reduce unsafe practices, avoid therapeutic errors, and prevent unnecessary strain on emergency care services. Full article

Plateau hypoxia represents a type of hypobaric hypoxia caused by reduced atmospheric pressure at high altitudes. Pressurization therapy is one of the most effective methods for alleviating acute high-altitude sickness. This study focuses on the development of an advanced control system for a vehicle-mounted mild hyperbaric chamber (MHBC) designed for the prevention and treatment of plateau hypoxia. Conventional control methods struggle to cope with the high complexity and inherent uncertainties associated with MHBC control tasks, thereby motivating the exploration of sequential decision-making approaches such as reinforcement learning. Nevertheless, the application of sequential decision-making in MHBC control encounters several challenges, including data inefficiency and non-stationary dynamics. The system’s low tolerance for trial-and-error may lead to component damage or unsafe operating conditions, and anomalies such as valve failure can emerge during long-term operation, compromising system stability. To address these challenges, this study proposes a decision-time learning and planning integrated framework for MHBC control. Specifically, an innovative latent model embedding decision-time learning is designed for system identification, separately managing system uncertainties to fine-tune the model output. Furthermore, a decision-time planning algorithm is developed and the planning process is further guided by incorporating a value network and an enhanced online policy. Experimental results demonstrate that the proposed decision-time learning and planning integrated approaches achieve notable performance in MHBC control. Full article

Surface roughness is a key factor influencing the safety, comfort, and overall quality of bicycle lanes, which are increasingly integrated into urban transportation systems worldwide. This study aims to assess and quantify the roughness of bicycle lanes in Sejong City, Republic of Korea, by utilizing accelerometer-based sensor technologies. Five study sections (A–E) were selected to represent a range of road surface conditions, from newly constructed roads to severely deteriorated surfaces. These sections were chosen based on bicycle traffic volume and prior reports of pavement degradation. The evaluation of road surface roughness was conducted using a smartphone-mounted accelerometer to measure the vertical, lateral, and longitudinal accelerations. The data collected were used to calculate the Bicycle Road Roughness Index (BRI) and Faulting Impact Index (FII), which provide a quantitative measure of road conditions and the impact of surface defects on cyclists. Field surveys, conducted in 2022, identified significant variation in roughness across the study sections, with values of BRI ranging from 0.2 to 0.8. Sections with a BRI greater than 0.5 were considered unsafe for cyclists. The FII showed a clear relationship between bump size and cycling speed, with higher bump sizes and faster cycling speeds leading to significantly increased impact forces on cyclists. These findings highlight the importance of using quantitative metrics to assess bicycle lane conditions and provide actionable data for maintenance planning. The results suggest that the proposed methodology could serve as a reliable tool for the evaluation and management of bicycle lane infrastructure, contributing to the improvement of cycling safety and comfort. Full article

Construction workers’ unsafe behaviors (CWUBs) are a primary cause of construction safety accidents in railway tunnel engineering (RTE). Understanding the causation mechanism between construction safety accidents, CWUBs, and their influencing factors is crucial for improving safety management. However, research in this area remains insufficient. This study systematically identifies 9 types of construction safety accidents, 11 types of CWUBs, and 35 influencing factors, covering three core dimensions: organizational management, individual safety capacity, and safety environment. Using the 24model, this study qualitatively elucidates the causation mechanism and identifies the primary and secondary causation relationships among 55 factors. On this basis, a network model of CWUBs in RTE is developed and quantitatively analyzed using social network analysis from the perspectives of the overall network, block network, and individual network, resulting in the identification of a critical network comprising 27 key factors. Based on the findings, nine targeted intervention measures are proposed, encompassing pre-emptive prevention, on-site control, and emergency management. This study innovatively integrates the 24model and social network analysis, systematically analyzing the causation mechanism of CWUBs in RTE from both qualitative and quantitative perspectives. This research not only provides a systematic and innovative analytical framework for CWUBs in RTE, addressing a critical gap in the study of unsafe behaviors and accident causation in complex systems, but also offers practical guidance for safety risk management. Additionally, it enriches the theoretical framework of unsafe behavior research, providing valuable insights for further studies in related fields. Full article

This investigation analyses driving behaviors that lead to accidents on overly sharp mountain road curves in Nanjing Province, China. We collected information through field observations and driving simulations while analyzing key indicators like the mean speed of vehicles and spacing between vehicles. The FP-Growth algorithm was used to identify frequent behavioral patterns and measure their relationship with traffic conflicts. The findings showed that unsafe driver behavior on sharp turns was common, while the combination of “speeding–tailgating–frequent lane changing” behavior increased conflict risk by 3.7 times. A predictive LSTM neural network model was developed with driver, vehicle, road, and environmental factors. Testing on 4795 samples achieved 83.7% accuracy in foreseeing conflict risk levels. The model, which distinguishes between safety conditions and three severity levels of potential conflict, can provide the most fundamental level of safety needed. The research provides quantitative tools for improved road safety management aimed at supporting real evidence-based “safe roads” approaches. Full article

In order to explore the causative factors of falling accidents at high-rise building construction sites, this study collected 207 reports of these accidents from 2014 to 2024. We used the Human Factor Analysis and Classification System (HFACS) during sample collection, from the four perspectives of organizational impact, unsafe supervision, prerequisites for unsafe behavior, and unsafe behavior. In total, 21 important causal factors were identified, and the samples were classified according to these factors. Descriptive statistics, chi-square testing, and limit matrix analysis were mainly used. SPSS 27.01 was used to analyze the samples, and Super Decisions software was used to normalize the limit supermatrix and calculate the weight. Subsequently, innovative and comprehensive application of chi-square testing and correlation coefficients was applied to determine the correlation of factors, and ANP was used to determine the weight of the factors. According to the weight, we determined the key factors, levels, and paths, and the relationship between the causes of falling accidents in building construction was systematically studied. Finally, based on the key causal path and key factors, a corresponding pre-control strategy was proposed. The results show that the key factors are a lack of awareness of personnel safety, safety education and training, and on-site safety management and an absence of safety inspections and routine maintenance. The key causes are that labor companies are not qualified, there is a lack of on-site safety oversight, and personnel do not have a permit to work at significant heights and do not wear safety protection equipment properly. This study explores the shortcomings of safety management in the construction industry. In order to reduce the accident rate, it is very important to improve the level of decision-making regarding safety management by the government and construction industry. This study has the following limitations: firstly, the information obtained from the investigation report of high-rise building construction accidents is not adequate to fully reflect the situation of workers on-site, which inevitably leads to some deviations. Secondly, due to the high mobility of construction workers, it is very difficult to investigate psychological or physiological states that may have a potential impact on unsafe behavior. Full article

E-waste, or electronic waste, refers to discarded electronic devices and components, and the management of e-waste has become a newly arising and challenging issue both in India and globally. Due to the increase in population, urbanization, global demand, and expansion of the digital infrastructure, generation of electronic waste is increasing annually. This study provides a comprehensive and thoroughly reviewed qualitative study on electronic waste management practice. This study highlights an outline of the amount of electronic waste generation in India and the world and examines prevailing approaches in the treatment and management of electronic waste, including unsafe informal recycling and inadequate inventory control. This article focuses on major problems such as child labor, illegal dumping, poor infrastructure, limited knowledge and awareness among the public inadequate legal regulation, and spillage of various toxic heavy metals such as arsenic (As), mercury (Hg), and barium (Ba) via electronic waste. This study analyzes the harmful effects of toxic heavy metals, such as arsenic and mercury, on environmental quality and human well-being. To address these issues, this study outlines various sustainable recommendations, such as technology improvement proper collection; handling, management, and eradication of waste generated by electrical equipment in formal recycling practices; the 3Rs (reduce, reuse, and recycle) following circular economy practice, including collaboration between governmental, non-governmental, business, industries, and civil society; better legislative measures such as extended producer responsibility (EPR) and a single approach method, where collecting, sorting, and dismantling electronic waste is handled by the informal sector, while the formal sector manages extraction of metal, disposal, and recycling. Full article

Questions arise about links between the use of substances and violence, especially when parents misuse substances and there is potential to expose children to family violence. Background. The review has four aims: identify research into the harmful impacts of parental substance use on children; identify policies in Australia about the risks from parental substance misuse; identify interventions to manage unsafe parental substance use; and review practices involving confidentiality and collaboration when a parent has multiple complex needs. Method. The paper provides a rapid review of the literature, linking parental substance misuse, family violence, and parenting capacity and covering both policies and empirical evidence. Results. The main finding is that parental substance misuse does affect parenting capacity and is associated with family violence. The concept of the cumulative risk of harm to vulnerable children is supported by research but is not yet implemented in policy. Reports indicate that some parents who misuse substances have multiple complex needs including comorbid mental health issues, domestic violence, and difficulty in managing their children’s behavior. Early intervention therapies designed to address this cluster of issues are reviewed. Conclusion. There is a need to establish objective assessment instruments that are relevant to the cohort of parents who misuse substances and engage in family violence and to improve policy to ensure vulnerable children and families in which parents misuse substances are referred to appropriate therapies. Full article

The increasing adoption of electric vehicles (EVs) needs efficient and cost-effective charging solutions. This study presents a smart on-board charging system using low-cost materials while ensuring safe and optimized battery management. The proposed system is controlled by an Arduino MEGA 2560 microcontroller, integrating Pulse-Width Modulation (PWM) for precise voltage regulation and real-time monitoring of charging parameters, including voltage, current, and state of charge (SoC). The charging process is structured into three states (connection, standby, and charging) and follows a multi-stage strategy to prevent overcharging and prolong battery lifespan. A relay system and safety mechanisms detect disconnections and voltage mismatches, automatically halting charging when unsafe conditions arise. Experimental validation with a 12 V lead-acid battery verifies that the system follows standard charging profiles, ensuring optimal energy management and charging efficiency. The proposed charger demonstrates significant cost savings (~94.82 €) compared to commercial alternatives (1200 €–2000 €), making it a viable low-power solution for EV charging research and a valuable learning tool in academic environments. Future improvements include a printed circuit board (PCB) redesign to enhance system reliability and expand compatibility with higher voltage batteries. This work proves that affordable smart charging solutions can be effectively implemented using embedded control and modulation techniques. Full article

The primary strategy for managing Nacobbus aberrans has traditionally relied on synthetic chemicals. However, increasing regulatory pressure on unsafe products has led to a growing research focus on nematicides. Despite this, chemical nematicides remain more effective than other control methods. Consequently, there is a pressing need to develop novel nematicides that are both effective and environmentally safer. This study aimed to evaluate the nematocidal efficacy of various synthetic molecules against the second-stage juveniles of N. aberrans, the false root-knot nematode. A total of fifty-eight synthetic derivatives were obtained and tested in vitro at a concentration of 500 µg/mL. The results identified the AGAz family as the most promising, with AGAz-3 (LC₅₀: 52.7 µg/mL) and AGAz-4 (LC₅₀: 103.22 µg/mL) surpassing the efficacy of chitosan. Our findings emphasize the strong potential of AGAz-3 and AGAz-4 as nematocidal agents, particularly for in situ applications in agricultural settings. Additionally, AGAz-3 demonstrates potential not only as a nematocidal agent but also as an incentive for related research exploring its analogs as effective ovicidal compounds and investigating its efficacy against other phytonematodes. Furthermore, compounds from the N-Sulfonyl-hydrazone and N-acyl-hydrazone series showed efficacy (>50%), warranting additional experiments to assess their effectiveness across the most important pest phytonematodes. Full article

Mpox (formerly known as monkeypox), the major public health concern of 2022, has elicited much attention globally. In addition to the usual symptoms observed in smallpox virus infections, infected mothers were found to hold a possible risk of transmission to newborns during delivery. This review aimed to summarize recent clinical trials that involved antiviral therapy, vaccines, immunoglobulin therapy, and other pharmacological interventions specifically for treating infected pregnant women. A comprehensive search was performed using databases such as PubMed, Google Scholar, and Medline to find appropriate disease management strategies. Amongst the vaccines and antivirals being used for treatment, vaccines such as Modified Vaccinia Ankara (MVA/MVA-BN) and Lister clone 16-medium pocket size-8 (LC16m8), while prophylactically effective, have been deemed unsafe for pregnant and lactating females. Antivirals like Tecovirimat, on the other hand, are considered to be a better alternative, but they are not without risks that may outweigh the potential benefits. Additionally, efforts to reduce maternal and fetal complications include administering the MVA-BN vaccine and awareness campaigns regarding herd immunity. Therefore, necessary precautions, prophylactic vaccinations in high-risk outbreak regions, and symptomatic treatment in pregnant and lactating females currently appear to be more feasible approaches against the mpox virus. Full article

Prefabricated building construction is a crucial approach to achieving “green” building goals, yet it differs significantly from traditional cast-in-place construction methods. Due to complex cross-operations, high mechanization requirements, insufficient worker experience, and inadequate safety measures, prefabricated construction faces greater safety management challenges than traditional methods. This study identifies 17 key indicators influencing unsafe behaviors among prefabricated building construction workers across four dimensions: physical environment, individual factors, team factors, and safety management. A combined weighting method, integrating the entropy weight method and the coefficient of variation method, was employed to determine the weight of each factor. Based on the weight analysis results, a system dynamics intervention model for unsafe behaviors of prefabricated building construction workers was developed and simulated using the Vensim platform. The results indicate that, among single intervention measures, improving individual comprehensive skills has the most significant effect, reducing unsafe behaviors by 37.78%, though this still falls short of the desired target. In contrast, combined interventions yield superior outcomes, with reductions of 68.87% for individual factor interventions, 59.93% for safety management interventions, 46.32% for team factor interventions, and 20.71% for physical environment interventions. These findings highlight the significant advantages of combined interventions. Based on the study results and the characteristics of prefabricated construction, this paper proposes specific intervention strategies, emphasizing the importance of training and institutional development to systematically enhance workers’ safety behaviors. Full article

Employee behavior is a key factor affecting the safe construction of nuclear power plants and is directly related to whether construction work can be carried out smoothly. However, employee behaviors are difficult to quantify, which brings significant challenges to safety management and control. To this end, this paper constructs an SEM-SD model to explore in depth the intrinsic relationship between burnout, unsafe behaviors, and safety climate, and simulates and analyzes the dynamic evolution process of these factors. Based on the analysis results, this paper proposes a joint intervention strategy system for burnout and unsafe behaviors of employees in nuclear power plants under construction. In addition, this paper designs and develops an intervention system to effectively deal with burnout and unsafe behaviors. The research results not only clarify the system of intervention methods for the safety behavior of employees in nuclear power plants under construction, but also provide a theoretical basis and practical reference for the management and control of employees’ unsafe behaviors, thus providing strong support for the safe construction of nuclear power plant projects. Full article

With the development of drone and image recognition technologies, using drones to capture images for engineering structural damage detection can replace inefficient and unsafe manual maintenance inspections. This paper focuses on the pairing method between drone devices and the BIM components of large buildings, with Revit’s secondary development serving as the technical approach. A plugin for pairing drone images with BIM components is developed. The research first establishes the technical scheme for pairing drone images with BIM models. Then, the positional and directional information of the drone images are extracted, and a reference coordinate system for the drone’s position and image capture orientation is introduced. The transformation method and path from the real-world coordinate system to the Revit 2023 software coordinate system are explored. To validate the interactive logic of the transformation path, a pairing plugin is developed in Revit. By employing coordinate conversion and Revit family loading procedures, the relative position and capture orientation of the drone are visualized in the 3D BIM model. The plugin uses techniques such as family element filtering and ray tracing to automatically identify and verify BIM components, ensuring the precise matching of drone images and BIM components. Finally, the plugin’s functionality is verified using a high-rise building in Wuhan as a case study. The results demonstrate that this technological approach not only improves the efficiency of pairing drone images with models in building smart maintenance but also provides a fast and reliable method for pairing drones with BIM systems in building management and operations. This contributes to the intelligent and automated development of building maintenance. Full article