Search Results (7,862)

Canada’s ageing population continues to grow rapidly and significantly more diverse, which will require unique health and home service needs. The COVID-19 pandemic exacerbated existing challenges in Canada’s healthcare system and demonstrated the need for long-term care (LTC). Semi-structured interviews were conducted with 17 decision makers, managers, and leaders in long-term ethnically focused facility care. Braun & Clarke’s six-stage process of thematic analysis was applied using an iterative, deductive approach to examine the experiences of stakeholders involved in the operational, managerial, financial, and clinical aspects of an ethnicity-focused LTC. Findings highlighted salient characteristics of impactful ethnicity-focused care and factors were uniquely shaped by the delivery of culturally specific care. Key subthemes included social isolation and emotional impact, operational and logistic difficulties during COVID-19, mitigation measures implemented in response, and the social, behavioural, and health benefits observed among seniors living in these LTC homes. Participants identified political and economic constraints (e.g., provincial funding) to establishing ethnicity-focused care homes but proposed several solutions and highlighted potential benefits that could support successful implementation. Analysis of experiences of operational challenges in safely and adequately running ethnicity-focused LTC reinforces the value of ethnicity-focused LTC during times of crisis such as the COVID-19 pandemic, as they provide a culturally safe and familiar space with preventive measures applied in a timely manner for seniors to engage with their peers in an environment that meets their health needs, ensuring safety standards. Full article

Parenteral nutrition (PN) is essential for patients who are unable to tolerate oral or enteral feeding, providing them with necessary nutrients intravenously, including dextrose, amino acids, electrolytes, vitamins, trace elements, and lipid emulsions. Clinical pharmacists (CPs) play a critical role in PN management by ensuring proper formulation, monitoring therapy, preventing complications, and optimizing patient outcomes. In Saudi Arabia, limited literature exists on CPs’ involvement in total parenteral nutrition (TPN) administration, health information management (HIM) systems, and pharmacist staffing ratios. This paper examines the evolving role of CPs in PN management, addressing key challenges such as the optimal patient-to-CP ratio, the impact of HIM systems on PN prescribing, and the advantages and limitations of centralized versus decentralized PN prescription models. It highlights the need for standardized staffing levels, structured pharmacist training, and improved HIM integration to enhance workflow efficiency and prescribing accuracy. Additionally, the study examines how the adoption of advanced HIM systems can streamline documentation, reduce prescribing errors, and enhance interdisciplinary collaboration. This paper provides a framework for optimizing PN delivery, enhancing healthcare quality, and strengthening CPs’ contributions to nutrition support by addressing these factors. Implementing these recommendations will improve patient outcomes and establish a more efficient PN management system in Saudi Arabia, reinforcing the vital role of CPs in multidisciplinary care. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

Hair dyes are widely used across all socioeconomic groups and regions worldwide. However, some studies indicate that these products contain substances known to be toxic to a wide variety of organisms. Moreover, dyeing practices generate effluents that may carry the toxicity of hair dyes into the environment. Due to these facts, there is great concern about the impacts these products may have on the environment, as well as on the health of their users and professionals in the field of cosmetology. This scoping review analyzed 184 publications from major databases (PubMed, SciELO, Scopus, Google Scholar, and MEDLINE). Ultimately, 126 scientific studies published between 1981 and 2024 were included based on methodological rigor and their relevance to the One Health framework. According to the literature, the components of hair dyes can induce adverse responses in biological systems, ranging from reversible topical irritations to severe systemic effects. Among the studies evaluated, more than half reported significant toxicological or genotoxic associations related to oxidative dye components such as p-phenylenediamine and its derivatives. These compounds are frequently associated with various types of human cancers, including breast, prostate, bladder, skin, ocular cancers, and brain tumors. In addition to their effects on humans, hair dyes exhibit ecotoxicity, which may threaten the maintenance of ecosystems exposed to their residues. The reported environmental impacts result from effluent emissions after successive hair washes that release unreacted dye residues. Due to the low biodegradability of these compounds, conventional wastewater treatment methods are often ineffective, leading to environmental accumulation and changes in aquatic ecosystems, soil fertility, and trophic balance. Data on the toxicity of hair dye effluents remain scarce and sometimes contradictory, particularly regarding the effects of their transformation products and metabolites. Overall, the evidence underscores the need for continuous monitoring, updated risk assessments, and the adoption of advanced treatment technologies specific to beauty salon effluents. The information presented in this work may support further studies and guide public management agencies in developing policies for mitigating the impacts of hair dye pollutants within the One Health perspective. Full article

The deposition of airborne microplastics (MPs) poses potential risks to human health and terrestrial ecosystems. Therefore, suitable mitigation efforts are needed, as is knowledge of their deposition patterns in inhabited and remote regions. Currently, there are no standardized protocols for monitoring airborne MPs, and implementing and managing automatic monitoring systems would be costly and feasible only in a few fixed locations. Over the past few decades, several species of cryptogams have proven to be reliable biomonitors of persistent atmospheric contaminants. Due to the lack of standardized methodologies, the results of preliminary biomonitoring surveys for MPs have been inconsistent and difficult to compare. However, they clearly indicate higher MP concentrations in epigeic mosses than in epiphytic lichens (collected at the same site or experimentally exposed in parallel in bags). This review discusses the morphophysiological features that favor the entrapment and retention of intercepted MPs in mosses, as well as the field and laboratory activities necessary to determine whether these organisms progressively accumulate airborne MPs as a function of the exposure time. Steps for future research needed to develop a cost-effective, reliable and easily applicable biomonitoring methodology are suggested. Evaluating the advantages of active moss biomonitoring over sampling atmospheric bulk deposition or exposing suitable commercial materials is recommended. Full article

Accurate prediction of lithium battery remaining useful life (RUL) is crucial for battery management systems to monitor battery health status. However, RUL prediction remains challenging due to capacity non-stationarity caused by capacity regeneration phenomena. Therefore, this study proposes a novel RUL prediction framework that combines a two-stage decomposition strategy with a parallel Informer-LSTM architecture. First, STL decomposition is employed to decompose the capacity sequence into trend, seasonal, and residual components. The VMD method further refines the residual component from STL, extracting the underlying multiscale subsignals. Subsequently, a parallel dual-channel prediction network is constructed: the Informer branch captures global long-range dependencies to prevent trend drift, while the LSTM branch models local nonlinear dynamics to reconstruct fluctuations associated with capacity regeneration. Experiments on the NASA dataset demonstrate that this framework achieves an MAE below 0.0109, an RMSE below 0.0160, and an R² above 0.9950. Additional validation on the Oxford battery dataset confirms the model’s robust generalization capability under dynamic conditions, with an MAE of 0.0017. This further demonstrates that the proposed RUL prediction framework achieves significantly enhanced prediction accuracy and stability, offering a reliable solution for battery health status detection in battery management systems. Full article

Since the COVID-19 pandemic, wastewater monitoring has become an additional tool in the surveillance of infectious diseases. Many EU countries put wastewater surveillance systems (WSS) in place to track SARS-CoV-2 and its variants and other pathogens, such as the influenza virus or Respiratory syncytial virus (RSV). In Germany, several research and pilot projects funded by the EU, the Federal Ministry of Education and Research, the Federal Ministry of Health, and projects at Federal State level have been launched in the last four years. In Germany, wastewater monitoring was not implemented as a public health tool before the COVID-19 pandemic, but in September 2022, it has been legally determined in the German infection protection act (Infektionsschutzgesetz, IfSG). As Germany is a federal state, competencies in epidemic management partly belong to the 16 federal states (“Länder”). In the federal states, the local health authorities at the county (“Kreise”) level also have specific risk management and communication competencies. Furthermore, WSS has been incorporated into the revised Urban Wastewater Treatment Directive (EU) 2024/3019. For this reason, the federal states and local health authorities play a pivotal role in successfully implementing wastewater monitoring as a supplementary component of disease surveillance in Germany. Between November 2021 and August 2022, the federal state of Thuringia, Germany, supported a pilot study to implement a surveillance system for SARS-CoV-2-RNA in wastewater of 23 wastewater treatment plants in 17 counties in Thuringia. Here, we describe the study design and the system behind the logistics and the planning, and we provide an overview of the options for involving the public health service. Furthermore, the possibilities for IT concepts and approaches to innovative AI solutions are shown. We also aim to explore the feasibility and potential barriers to further implementing wastewater surveillance as a supplementary public health tool in Thuringia. Full article

Tea (Camellia sinensis) cultivation is a major global industry that faces sustainability challenges due to soil degradation and greenhouse gas (GHG) emissions from intensive management. Biochar—charcoal designed and used as a soil amendment—has emerged as a potential tool to improve soil health, enhance carbon sequestration, and mitigate GHG fluxes in agroecosystems. However, field-scale evidence of its effects on GHG dynamics in woody crops like tea remains limited, particularly regarding methane (CH₄). Here, we present, to our knowledge, the first field assessment of biochar impacts on CO₂, CH₄, and H₂O vapour fluxes in a subtropical tea agroforestry system with and without shade trees in northeastern Bangladesh. Using a closed dynamic chamber and real-time gas analysis, we found that biochar application (at 7.5 t·ha⁻¹) significantly enhanced average soil methane (CH₄) uptake by 84%, while soil respiration (CO₂ efflux) rose modestly (+18%) and water-vapour fluxes showed a marginal increase. Canopy conditions modulated these effects: biochar strongly enhanced CH₄ uptake under both shaded and open canopies, whereas biochar effects on water-vapour flux were detectable only when biochar was combined with a shade-tree canopy. Structural equation modelling suggests that CH₄ flux was primarily governed by biochar-induced changes in soil pH, moisture, nutrient status, and temperature, while CO₂ and H₂O fluxes were shaped by organic matter availability, temperature, and phosphorus dynamics. These findings demonstrate that biochar can promote CH₄ uptake and alter soil carbon–water interactions during the dry season in tea plantation systems and support operational biochar use in combination with shade-tree agroforestry. Full article

The subject of this research is the exploration of the potential of remote sensing and Geographic Information Systems (GIS) for basin-scale spatio-temporal monitoring of drought and its impacts in the Ukrina River Basin, Bosnia and Herzegovina (BH), during the last decade (2015–2024). The aim is to integrate meteorological, hydrological, agricultural, and socio-economic drought signals and to delineate areas of long-term drought exposure. Meteorological drought was evaluated using CHIRPS precipitation and the Standardized Precipitation Index (SPI) calculated at 1-, 3-, 6-, and 12- month accumulation scales using Gamma fitting and a fixed long term reference period; hydrological drought was examined using available water-level records complemented by the Standardized Water Level Index (SWLI) and supported by correspondence with standardized ERA5-Land runoff anomalies; agricultural drought was mapped using remote sensing indices—the Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI)—calculated from MODIS satellite data; and socio-economic effects were assessed using municipal crop-production statistics (2015–2019). The results indicate that drought conditions were most pronounced in 2015, 2017, 2021, and especially 2022, showing consistent agreement between precipitation deficits, hydrological responses, and vegetation stress, while 2016, 2018–2020, 2023, and 2024 were generally more favorable. As a key novelty, a persistent drought-prone zone was delineated by intersecting drought-affected areas across major episodes, providing a basin-scale identification of chronic drought hotspots for a river basin in BH. The persistent zone covers 40.02% of the basin and spans nine cities and municipalities, with >93% located in Prnjavor, Derventa, Stanari, and Teslić. Hotspots are concentrated mainly in lowlands below 400 m a.s.l., with a statistically significant concentration across lower elevation classes, indicating higher long-term exposure in the central and northern valley sectors, and land use overlay further highlights high relative exposure of productive land. Overall, the integrated remote sensing and GIS framework strengthens drought monitoring by providing spatially explicit and repeatable evidence to support targeted adaptation planning and drought-risk management. Full article

The sustainability of the beef and dairy industry requires a systems approach that integrates environmental stewardship, social responsibility, and economic viability. Over the past two decades, global genetics consortia have advanced data-driven germplasm programs (breeding and conservation programs focusing on genetic resources) to enhance sustainability across cattle systems. These initiatives employ multi-trait selection indices aligned with consumer demands and supply chain trends, targeting production, longevity, health, and reproduction, with outcomes including greenhouse gas mitigation, improved resource efficiency and operational safety, and optimized animal welfare. This study analyzes strategic initiatives, germplasm portfolios, and data platforms from leading genetics companies in the USA, Europe, and Brazil. US programs combine genomic selection with reproductive technologies such as sexed semen and in vitro fertilization to accelerate genetic progress. European efforts emphasize resource efficiency, welfare, and environmental impacts, while Brazilian strategies focus on adaptability to tropical conditions, heat tolerance, and disease resistance. Furthermore, mathematical models and decision support tools are increasingly used to balance profitability with environmental goals, reducing sustainability trade-offs through data-driven resource allocation. Industry-wide collaboration among stakeholders and regulatory bodies underscores a rapid shift toward sustainability-oriented cattle management strategies, positioning genetics and technology as key drivers of genetically resilient and sustainable breeding systems. Full article

Unsafe behaviors remain a major contributor to workplace accidents within broader safety-management systems. Acknowledging the essential influence of organizational and leadership factors, this study focuses on systematically identifying and prioritizing individual-level determinants of unsafe behavior through an integrated qualitative–quantitative methodology to clarify their specific role within the wider safety framework. Grounded Theory analysis of semi-structured interviews with 40 industry professionals yielded a conceptual model encompassing demographic characteristics, general health, individual competencies, personality traits, and psychological factors. Subsequently, the Fuzzy Delphi Method, applied with 20 domain experts, validated and ranked these determinants. The analysis highlighted risk perception as the most influential factor, followed by work experience, skill level, knowledge, and risk-taking propensity, whereas variables such as family welfare, substance use, and self-display exhibited relatively minor effects. These findings reveal the multidimensional nature of unsafe behavior and underscore the importance of focusing on high-impact personal attributes to enhance workplace safety. By recognizing that many individual factors are shaped by organizational and psychosocial conditions, the study provides evidence-based insights for developing integrated safety management and targeted intervention strategies in industrial settings. Full article

Background/Objectives: Water pollution caused by human activities disrupts ecosystems and promotes the spread of antimicrobial resistance genes (ARGs), posing a public health threat. This study investigated the presence of resistant Gram-negative bacteria and resistance genes in water from two sites occasionally exposed to domestic and hospital effluents, the Carioca River (CR) and Rodrigo de Freitas Lagoon (RFL), both used for recreation. Methods: Physicochemical parameters and coliform levels were measured. Bacterial isolates were identified by Matrix-Assisted Laser Desorption Ionization–Time-of-Flight Mass Spectrometry (MALDI-TOF MS) and tested for antimicrobial susceptibility using disk diffusion. The Minimum Inhibitory Concentration (MIC) was determined using the E-test^® and broth microdilution methods. PCR was used to detect carbapenem resistance and other ARGs from the DNA of bacterial isolates obtained from water samples. Results: CR presented signs of environmental degradation, with low dissolved oxygen and high coliform counts. One Citrobacter braakii isolate showed resistance to all tested antimicrobials, raising concern for untreatable infections. Carbapenem-resistant isolates accounted for 49.4% of the total, harboring bla_KPC (20%), bla_TEM (5%), bla_VIM (5%), and bla_SPM (5%). The intl1 gene was found in 10% of isolates, indicating potential horizontal gene transfer. Conclusions: The findings from a one-day sampling reveal the presence of multidrug-resistant bacteria that carry antimicrobial resistance genes in polluted aquatic systems. These highlight the connection between water contamination and antimicrobial resistance. The evidence underscores the urgent need for environmental monitoring and effective management strategies to reduce public health risks. Full article

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems. Full article

Aquaculture has grown rapidly worldwide and has become a key source of food and employment opportunities. However, its expansion faces environmental, health, reproductive, and technological challenges that threaten its long-term sustainability. In this context, biologists play a crucial role in promoting sustainable practices and integrated management of aquaculture systems. This article reviews their main contributions to animal health, genetic improvement, assisted reproduction, and resource conservation. They also highlight their leadership in applying advanced technologies, including biotechnology, nanotechnology, and genetic engineering. Moreover, this study explores emerging research trends and emphasizes the importance of interdisciplinary training to address the evolving demands of the sector. This underscores the need to strengthen collaboration between science, technology, and public policy to ensure sustainable aquaculture. Enhancing the role of biologists is essential for overcoming current challenges and advancing efficient, ethical, and environmentally responsible aquaculture systems that meet global demand. Full article

Reliable monitoring of pesticide residues is essential for ensuring food safety. Conventional chromatographic and spectrometric techniques remain labor-intensive, time-consuming, and costly. Recent progress in Machine Learning (ML) provides computational tools that improve the precision and efficiency of pesticide residue detection in diverse food matrices. This review presents a comprehensive analysis of current ML-based approaches for pesticide analysis, with particular attention to supervised learning algorithms such as support vector machines, random forests, boosting methods, and deep neural networks. These models have been integrated with chromatographic, spectroscopic, and electrochemical platforms to achieve enhanced signal interpretation and more reliable prediction from existing analytical data, and more robust data processing in complex food systems. The review also discusses methodologies for feature extraction, model validation, and the management of heterogeneous datasets, while examining ongoing challenges that include limited training data, matrix variability, and regulatory constraints. Emerging advances in deep learning architectures, transfer learning strategies, and portable sensing technologies are expected to support the development of real-time, field-ready monitoring systems. The findings highlight the potential of ML to advance food quality assurance and strengthen public health protection through more efficient and accurate pesticide residue detection. Full article