Search Results (3,924)

(1) Background: Dioxins and dioxin-like polychlorinated biphenyls (dl-PCBs) are persistent organic pollutants (POPs), characterized by high toxicity and strong lipophilicity, which promote their bioaccumulation in human tissues. Their detection in breast milk raises concerns about early-life exposure during lactation. Although dietary intake is the primary route of maternal exposure, environmental pathways—including inhalation, dermal absorption, and residential proximity to contaminated sites—may also significantly contribute to the maternal body burden. (2) Methods: This narrative review examined peer-reviewed studies investigating maternal and environmental determinants of dioxin and dl-PCB concentrations in human breast milk. A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science (2000–2024), identifying a total of 325 records. Following eligibility screening and full-text assessment, 20 studies met the inclusion criteria. (3) Results: The included studies consistently identified key exposure determinants, such as high consumption of animal-based foods (e.g., meat, fish, dairy), living near industrial facilities or waste sites, and maternal characteristics including age, parity, and body mass index (BMI). Substantial geographic variability was observed, with higher concentrations reported in regions affected by industrial activity, military pollution, or inadequate waste management. One longitudinal study from Japan demonstrated a declining trend in dioxin levels in breast milk, suggesting the potential effectiveness of regulatory interventions. (4) Conclusions: These findings highlight that maternal exposure to dioxins is influenced by identifiable environmental and behavioral factors, which can be mitigated through public health policies, targeted dietary guidance, and environmental remediation. Breast milk remains a critical bioindicator of human exposure. Harmonized, long-term research is needed to clarify health implications and minimize contaminant transfer to infants, particularly among vulnerable populations. Full article

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

Background and Aim: Type 2 diabetes (T2D) continues to pose a significant public health challenge worldwide. Among therapeutic options, glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have proven effective in optimizing glycemic control and improving cardiometabolic profiles. Semaglutide, now available in an oral formulation, represents a modern strategy to improve patient adherence while supporting glucose and weight regulation. This study primarily investigated the effects of oral semaglutide on key metabolic indicators and secondary endpoints included cardiovascular risk markers (blood pressure and lipid profile) and patient-reported quality of life (QoL). Study Design and Methods: A longitudinal, prospective observational study was conducted involving patients with T2D across two Italian healthcare facilities. Participants were assessed at baseline (T0) and at three subsequent intervals—6 months (T1), 12 months (T2), and 18 months (T3)—following the initiation of oral semaglutide use. Key Findings: Out of 116 participants enrolled, 97 had complete and analyzable data. Across the 18-month follow-up, significant improvements were observed in glycemic parameters, with a notable reduction in HbA1c levels (T0 vs. T3, p = 0.0028; p ≤ 0.05, statistically significant). Self-reported outcomes showed enhanced quality of life, especially in treatment satisfaction and perceived flexibility (T0 vs. T3, p < 0.001). Conclusions: Daily administration of 14 mg oral semaglutide in individuals with T2D resulted in substantial benefits in glycemic regulation, weight reduction, cardiovascular risk management, and overall patient satisfaction. These findings reinforce its potential role as a sustainable and effective option in long-term diabetes care from both a clinical and public health perspective. Full article

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, and facility location and allocation. Real-world case studies from cities like Braga, Lisbon, Uppsala, and Cyprus demonstrate how MOEAs can enhance operational efficiency, boost energy recovery, and reduce environmental impacts. While these algorithms offer significant advantages, challenges remain in computational complexity, adapting to dynamic environments, and integrating with emerging technologies. Future research directions highlight the potential of combining MOEAs with machine learning and real-time data to create more flexible and responsive waste management strategies. By leveraging these advancements, MOEAs can play a pivotal role in developing sustainable, efficient, and adaptive waste disposal systems capable of meeting the growing demands of urbanization and stricter environmental regulations. Full article

With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due to low traffic flow, resulting in a waste of resources. Areas with high traffic flow may have fewer charging stations, resulting in long queues and road congestion. The purpose of this study is to optimize the location of charging stations and the number of charging piles in the stations based on the distribution of traffic flow, and to construct a bi-level programming model by analyzing the distribution of traffic flow. The upper-level planning model is the user-balanced flow allocation model, which is solved to obtain the optimal traffic flow allocation of the road network, and the output of the upper-level planning model is used as the input of the lower-layer model. The lower-level planning model is a generalized minimum cost model with driving time, charging waiting time, charging time, and the cost of electricity consumed to reach the destination of the trip as objective functions. In this study, an empirical simulation is conducted on the road network of Hefei City, Anhui Province, utilizing three algorithms—GA, GWO, and PSO—for optimization and sensitivity analysis. The optimized results are compared with the existing charging station deployment scheme in the road network to demonstrate the effectiveness of the proposed methodology. Full article

Furunculosis caused by Aeromonas salmonicida poses a significant challenge to the sustainable production of maraena whitefish (Coregonus maraena). This case report outlines a multi-year disease management strategy at a European whitefish facility with two production departments, each specialising in different life-cycle stages. Recurrent outbreaks of A. salmonicida necessitated the development of effective vaccination protocols. Herd-specific immersion vaccines failed to confer protection, while injectable formulations with plant-based adjuvants caused severe adverse reactions and mortality rates exceeding 30%. In contrast, the bivalent vaccine Alpha Ject 3000, containing inactivated A. salmonicida and Vibrio anguillarum with a mineral oil adjuvant, yielded high tolerability and durable protection in over one million whitefish. Post-vaccination mortality remained low (3.3%), aligning with industry benchmarks, and furunculosis-related losses were fully prevented in both departments. Transcriptomic profiling of immune-relevant tissues revealed distinct gene expression signatures depending on vaccine type and time post-vaccination. Both the herd-specific vaccine and Alpha Ject 3000 induced the expression of immunoglobulin and inflammatory markers in the spleen, contrasted by reduced immunoglobulin transcript levels in the gills and head kidney together with the downregulated expression of B-cell markers. These results demonstrate that an optimised injectable vaccination strategy can significantly improve health outcomes and disease resilience in maraena whitefish aquaculture. Full article

Urban forests, as a form of green infrastructure, play a vital role in enhancing urban resilience, environmental health, and quality of life. However, users perceive and utilize these spaces in diverse ways. This study aims to identify latent perception types among urban forest visitors and analyze their behavioral, demographic, and policy-related characteristics in Incheon Metropolitan City (Republic of Korea). Using latent class analysis, four distinct visitor types were identified: multipurpose recreationists, balanced relaxation seekers, casual forest users, and passive forest visitors. Multipurpose recreationists preferred active physical use and sports facilities, while balanced relaxation seekers emphasized emotional well-being and cultural experiences. Casual users engaged lightly with forest settings, and passive forest visitors exhibited minimal recreational interest. Satisfaction with forest elements such as vegetation, facilities, and management conditions varied across visitor types and age groups, especially among older adults. These findings highlight the need for perception-based green infrastructure planning. Policy recommendations include expanding accessible neighborhood green spaces for aging populations, promoting community-oriented events, and offering participatory forest programs for youth engagement. By integrating user segmentation into urban forest planning and governance, this study contributes to more inclusive, adaptive, and sustainable management of urban green infrastructure. Full article

This study assessed the effectiveness of stress-based cognitive-behavioral training compared to standard training in firefighters, emphasizing their ability to distribute extinguishing water and cool environments evenly during enclosure fires. Experiments took place at the Zbiroh training facility with two firefighter teams (Team A with stress-based training and Team B with standard training) under realistic conditions. Using 58 thermocouples and 4 radiometers, temperature distribution and radiant heat flux were measured to evaluate water distribution efficiency and cooling performance during interventions. Team A consistently achieved temperature reductions of approximately 320 °C in the upper layers and 250–400 °C in the middle layers, maintaining stable conditions, whereas Team B only achieved partial cooling, with upper-layer temperatures remaining at 750–800 °C. Additionally, Team A recorded lower radiant heat flux densities (e.g., 20.74 kW/m² at 0°) compared to Team B (21.81 kW/m²), indicating more effective water application and adaptability. The findings confirm that stress-based training enhances firefighters’ operational readiness and their ability to distribute water effectively during interventions. This skill is essential for safer and effective management of indoor fires under extreme conditions. This study supports the inclusion of stress-based and scenario-based training in firefighter education to enhance safety and operational performance. Full article

Forecasting the energy consumption of different consumers became an important procedure with the creation of the European Electricity Market. This study presents a methodology for 24-hour ahead prediction of the energy consumption, which is suitable for application in animal husbandry facilities, such as pig farms. To achieve this, 24 individual models are trained using artificial neural networks that forecast the energy production 1 to 24 h ahead. The selected features include power consumption over the last 72 h, time-based data, average, minimum, and maximum daily temperatures, relative humidities, and wind speeds. The models’ Normalized mean absolute error (NMAE), Normalized root mean square error (NRMSE), and Mean absolute percentage error (MAPE) vary between 16.59% and 19.00%, 22.19% and 24.73%, and 9.49% and 11.49%, respectively. Furthermore, the case studies showed that in most situations, the forecasting error does not exceed 10% with several cases up to 25%. The proposed methodology can be useful for energy managers of animal farm facilities, and help them provide a better prognosis of their energy consumption for the Energy Market. The proposed methodology could be improved by selecting additional features, such as the variation of the controlled meteorological parameters over the last couple of days and the schedule of technological processes. Full article

Housing sustainability is a cornerstone element of sustainable economic and social development. This is particularly true for China, where high-rise residential buildings are the primary form of housing. In recent years, China has experienced frequent housing-related accidents, resulting in a significant loss of life and property damage. This study aims to identify the key factors influencing housing sustainability and provide a basis for the prevention and control of housing-related safety risks. This study has developed a housing sustainability evaluation indicator system comprising three primary indicators and 16 secondary indicators. This system is based on an analysis of the causes of over 500 typical housing accidents that occurred in China over the past 10 years, employing research methods such as literature reviews and expert consultations, and drawing on the analytical frameworks of risk management theory and system safety theory. Subsequently, the NK-SNA model, which significantly outperforms traditional models in terms of adaptive learning and optimization, as well as the explicit modeling of complex nonlinear relationships, was used to identify the key risk factors affecting housing sustainability. The empirical results indicate that the risk coupling value is correlated with the number of risk coupling factors; the greater the number of risk coupling factors, the larger the coupling value. Human misconduct is prone to forming two-factor risk coupling with housing, and the physical risk factors are prone to coupling with other factors. The environmental factors easily trigger ‘physical–environmental’ two-factor risk coupling. The key factors influencing housing sustainability are poor supervision, building facilities, the main structure, the housing height, foundation settlement, and natural disasters. On this basis, recommendations are made to make full use of modern information technologies such as the Internet of Things, big data, and artificial intelligence to strengthen the supervision of housing safety and avoid multi-factor coupling, and to improve upon early warnings of natural disasters and the design of emergency response programs to control the coupling between physical and environmental factors. Full article

Introduction: Falls are the leading cause of Emergency Department (ED) presentations among residents from residential aged care facilities (RACFs). While most current studies focus on post-fall evaluations and fall prevention, limited research has been conducted on decision-making in post-fall management. Objective: To develop and internally validate a model that can predict the likelihood of RACF residents being discharged from the ED after being presented for a fall. Methods: The study sample was obtained from a previous study conducted in Shepparton, Victoria, Australia. Consecutive samples were selected from January 2023 to November 2023. Participants aged 65 and over were included in this study. Results: A total of 261 fall presentations were initially identified. One patient with Australasian Triage Scale category 1 was excluded to avoid overfitting, leaving 260 presentations for analysis. Two logistic regression models were developed using prehospital and ED variables. The ED predictor model variables included duration of ED stay, injury severity, and the presence of an advance care directive (ACD). It demonstrated excellent discrimination (AUROC = 0.83; 95% CI: 0.79–0.89) compared to the prehospital model (AUROC = 0.77, 95% CI: 0.72–0.83). A simplified four-variable Discharge Eligibility after Fall in Elderly Residents (DEFER) score was derived from the prehospital model. The score achieved an AUROC of 0.76 (95% CI: 0.71–0.82). At a cut-off score of ≥5, the DEFER score exhibited a sensitivity of 79.7%, a specificity of 60.3%, a diagnostic odds ratio of 5.96, and a positive predictive value of 85.0%. Conclusions: The DEFER score is the first validated discharge prediction model for residents of RACFs who present to the ED after a fall. Importantly, the DEFER score advances personalised medicine in emergency care by integrating patient-specific factors, such as ACDs, to guide individualised discharge decisions for post-fall residents from RACFs. Full article

Rapid industrialization and urbanization have progressed in Korea, yet public attention to hazardous pollutants emitted from industrial complexes remains limited. With the increasing coexistence of industrial and residential areas, there is a growing need for real-time monitoring and management plans that account for the rapid dispersion of hazardous air pollutants (HAPs). In this study, we conducted spatiotemporal data collection and analysis for the first time in Korea using real-time measurements obtained through mobile extractive differential optical absorption spectroscopy (Me-DOAS) mounted on a solar occultation flux (SOF) vehicle. The measurements were conducted in the Saha Sinpyeong–Janglim Industrial Complex in Busan, which comprises the Sasang Industrial Complex and the Sinpyeong–Janglim Industrial Complex. BTEX compounds were selected as target volatile organic compounds (VOCs), and real-time measurements of both BTEX and fine particulate matter (PM) were conducted simultaneously. Correlation analysis revealed a strong relationship between PM₁₀ and PM_2.5 (r = 0.848–0.894), indicating shared sources. In Sasang, BTEX levels were associated with traffic and localized facilities, while in Saha Sinpyeong–Janglim, the concentrations were more influenced by industrial zoning and wind patterns. Notably, inter-compound correlations such as benzene–m-xylene and p-xylene–toluene suggested possible co-emission sources. This study proposes a GIS-based, three-dimensional air quality management approach that integrates variables such as traffic volume, wind direction, and speed through real-time measurements. The findings are expected to inform effective pollution control strategies and future environmental management plans for industrial complexes. Full article

Educational institutions are facing a crisis characterized by the need to address diverse learning styles and vocational aspirations, exacerbated by ongoing financial pressures. To navigate these challenges effectively, there is an urgent need to innovate educational practices and learning environments, ensuring they are adaptable and responsive to the evolving needs of students and the workforce. The adoption of the Industry 5.0 framework offers a promising solution, providing a holistic approach that emphasizes the integration of human creativity and advanced technologies to transform educational institutions into resilient, human-centric, and sustainable learning environments. In this context, this article presents a transdisciplinary methodology that integrates Asset Management (AM) with Social Innovation (SI) through Design Thinking (DT) to co-design Educational Facilities 5.0 with stakeholders. The application of the proposed approach in an AgroLab case study—a food and agricultural laboratory—demonstrates how the methodology enables the definition of an Educational Facility 5.0 and generates AM Design Knowledge to support informed decision-making in the subsequent design, implementation, and operation phases. Following DT principles—where knowledge emerges through iterative experimentation and insights from practical applications—this article also discusses the role of SI and DT in AM, the role of Large Language Models in convergent processes, and a vision for Educational Facilities 5.0. Full article

Background/Objectives: Pulmonary embolism (PE) remains a major cause of morbidity and mortality. Despite advances in care, its nonspecific symptoms pose diagnostic and therapeutic challenges. Emerging evidence suggests sex-based differences in PE presentation, management, and outcomes, yet real-world data from European settings remain scarce. This study aimed to investigate sex differences in clinical presentation, diagnostic workup, therapeutic interventions, and outcomes among hospitalized PE patients. Methods: We conducted a retrospective cohort study including all adult patients (≥18 years) admitted with a main diagnosis of acute PE at the Cantonal Hospital Baselland between January 2018 and December 2020. Data were extracted from electronic medical records and included demographics, comorbidities, symptoms, diagnostics, treatments, and outcomes. Sex-based comparisons were performed using univariate analyses. Results: Among 197 patients, 54% were women. Compared to men, women were more often admitted by ambulance (42% n = 45 vs. 24% n = 22, p = 0.009), had more frequent tachycardia (38% n = 41 vs. 23% n = 21, p = 0.024), and received lysis therapy more often (10% n = 11 vs. 2% n = 2, p = 0.023). DVT was more frequently diagnosed in women when sonography was performed (82% n = 49 vs. 64% n = 34, p = 0.035). Men had higher rates of B symptoms, smoking, and family history of PE. Women had longer hospital stays and were more frequently discharged to rehabilitation facilities. No sex differences were found in in-hospital mortality, 6-month rehospitalization, or adherence to diagnostic guidelines. Conclusions: This study reveals sex-based differences in PE presentation and management, suggesting potential disparities in care pathways. Further research is needed to promote equitable, personalized treatment strategies. Full article

A particularly important activity carried out by railway infrastructure managers to maintain railway devices in full working order is the diagnostic process. It increases the level of railway safety. The diagnostic process involves collecting information about the equipment through inspections, tests, functional trials, parameter measurements, and analysis of the working environment, followed by comparing the obtained information with the required parameters or permissible conditions. This activity also enables the formulation of a technical diagnosis regarding the current ability of the devices to perform its intended functions, taking into account the impact of its technical condition on railway traffic safety. This is especially important in the case of railway traffic control devices, as these devices are largely responsible for ensuring railway traffic safety. The collection of data on the condition of railway traffic control devices in the form of Big Data sets and diagnostic inference is an effective factor in making operational decisions for such devices. It enables the acquisition of complete information about the actual course of the exploitation process and allows for obtaining reliable information necessary to manage this process, particularly in the areas of diagnostics forecasting of devices conditions, renewal, and organization of maintenance and repair facilities. To support this, a service data acquisition and analysis system for railway traffic control devices (SADEK) was developed. This system can serve as a software platform for maintenance needs in the railway sector. Full article