Search Results (2,200)

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carrier consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carrier consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs and HEVs, and BEVs are the least. This study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection. Full article

Paralytic shellfish toxins (PSTs) are produced by several toxic species of the dinoflagellate genera Alexandrium and Gymnodinium, and they pose significant threats to marine ecosystems and public health. Rapid and accurate detection of harmful algal blooms (HABs) is essential for effective management. In this study, we developed a multiplex quantitative real-time PCR (qPCR) assay targeting the 28S ribosomal DNA region to simultaneously detect three PST-producing dinoflagellates, Alexandrium catenella, A. pacificum, and Gymnodinium catenatum, in the East China Sea off southern Korea. Species-specific primers and hydrolysis probes labeled with distinct fluorophores were validated for simultaneous detection. The standard curves showed strong linearity (R² > 0.99) and high amplification efficiencies (95.268–99.325%). No cross-reactivity was observed among the 20 non-target microalgal species. Field application of the assay using environmental DNA (eDNA) samples collected during spring successfully detected A. catenella and A. pacificum, whereas G. catenatum was not detected during the survey period. This multiplex qPCR assay provides a rapid and reliable molecular tool for early detection and spatial monitoring of potentially PST-producing dinoflagellates, supporting sustainable HAB management in East Asian coastal ecosystems. Full article

The noise generated by wind turbines is a critical issue that impacts both operational efficiency and public health, necessitating a comprehensive investigation into its sources and propagation. This study investigates the near-wake noise of an S-airfoil horizontal-axis wind turbine using statistically optimized near-field acoustic holography (SONAH) with a 60-channel rotating microphone array in an open-jet wind tunnel. The results show that the noise in the wake is predominantly caused by the rotation of the rotor. The position of the highest sound pressure level concentration is at 0.78R of the blade under different operating conditions within the rotor’s rotation plane. The sound pressure level radiates outward in a spiral pattern across eleven identified sections, progressively decreasing with distance. The most significant attenuation occurs between 0.04 m and 0.06 m from the rotating surface. This study provides foundational insights into the near-field acoustic characteristics of wind turbines, serving as a valuable reference for noise reduction strategies and environmental impact assessments in wind energy projects. 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

This report describes the development and deployment of the Public Health Quality Tool (PHQTool), an online resource designed to help mobile health clinics (MHCs) assess and improve the quality of their public health services. MHCs provide essential clinical and public health services to underserved populations but have historically lacked tools to assess and improve the quality of their work. To address this gap, the PHQTool was developed as an online, evidence-based, self-assessment resource for MHCs, hosted on the Mobile Health Map (MHMap) platform. This report documents the collaborative development process of the PHQTool and presents preliminary evaluation findings related to usability and relevance among mobile health clinics. Drawing from national public health frameworks and Honore et al.’s established public health quality aims, the PHQTool focuses on six aims most relevant to mobile care: Equitable, Health Promoting, Proactive, Transparent, Effective, and Efficient. Selection of the six quality aims was guided by explicit criteria developed through pilot testing and stakeholder feedback. The six aims were those that could be directly implemented through mobile clinic practices and were feasible to assess within diverse mobile clinic contexts. The remaining three aims (“population-centered,” “risk-reducing,” and “vigilant”) were determined to be less directly actionable at the program level or required system-wide or data infrastructure beyond the scope of individual mobile clinics. Development included expert consultation, pilot testing, and iterative refinement informed by user feedback. The tool allows clinics to evaluate practices, identify improvement goals, and track progress over time. Since implementation, 82 MHCs representing diverse organizational types have used the PHQTool, reporting high usability and identifying common improvement areas such as outreach, efficiency, and equity-driven service delivery. Across pilot and post-pilot implementation phases, a majority of respondents agreed or strongly agreed that the tool was user-friendly, relevant to their work, and appropriately scoped for mobile clinic practice. Usability and acceptance were assessed using descriptive statistics, including percentage agreement across Likert-scale items as well as qualitative feedback collected during structured debriefs. Reported findings reflect self-reported perceptions of feasibility, clarity, and relevance rather than inferential statistical comparisons. The PHQTool facilitates systematic quality assessment within the mobile clinic sector and supports consistent documentation of public health efforts. By providing a standardized, accessible framework for evaluation, it contributes to broader efforts to strengthen evidence-based quality improvement and promote accountability in MHCs. Full article

The extensive consumption of freshwater resources and the continuous discharge of pharmaceutical residues pose serious risks to aquatic ecosystems and public health. In this study, pristine ZnO, TiO₂, Zn@TiO₂, and Ti@ZnO nanocomposites were synthesized via a precipitation-assisted solid–liquid interference method and systematically evaluated for the photocatalytic degradation of the antibiotic levofloxacin under UV and visible light irradiation. The structural, optical, and surface properties of the synthesized materials were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), UV–visible diffuse reflectance spectroscopy (UV–DRS), and X-ray photoelectron spectroscopy (XPS). XRD analysis confirmed the crystalline nature of all samples, while SEM images revealed spherical and agglomerated morphologies. Photocatalytic experiments were conducted using a 50-ppm levofloxacin solution with a catalyst dosage of 1 g L⁻¹. Pristine ZnO exhibited limited visible-light activity (33.81%) but high UV-driven degradation (92.98%), whereas TiO₂ showed comparable degradation efficiencies under UV (78.6%) and visible light (78.9%). Notably, Zn@TiO₂ nanocomposites demonstrated superior photocatalytic performance, achieving over 90% and near 70% degradation under both UV and visible light, respectively, while Ti@ZnO composites exhibited less than 60% degradation. The enhanced activity of Zn@TiO₂ is attributed to improved interfacial charge transfer, suppressed electron–hole recombination, and extended light absorption. These findings highlight Zn@TiO₂ nanocomposites as promising photocatalysts for efficient treatment of pharmaceutical wastewater under dual-light irradiation. Full article

Introduction: The World Health Organisation (WHO) and health advocates have called on governments across the globe to introduce a sugar tax to reduce the intake of sugar-sweetened beverages (SSBs), to prevent obesity and type 2 diabetes. Despite efforts to introduce a sugar tax, there are limited data on the efficiency and sustainability of the sugar tax in the African continent. Methods: We conducted a systematic literature review to identify studies from Africa and selected countries across the world from 2014 to 2024, to determine the efficiency and sustainability of the sugar tax regarding its impact on beverage intake in the African context. Studies were selected according to their report of the impact of sugar tax on consumption, the decline in beverage products high in sugar content, the reformulation of sugary beverages, and the public acceptability of the tax. Conclusions: There is evidence that the introduction of a sugar tax has resulted in mixed reactions but has generated increased revenue in some African countries: for example, South Africa. The majority of countries in Africa have not introduced the tax. The failure or absence of the tax in Africa has commonalities with some countries elsewhere across the globe. In some developed economies, the tax was introduced but withdrawn one year after its implementation. In addition, limited studies have reported on the sustainability of the tax in Africa. Full article

Food fraud represents a growing global challenge with significant implications for public health, market integrity, sustainability, and consumer trust. Beyond economic losses, fraudulent practices undermine the environmental and social sustainability of food systems by distorting markets, misusing natural resources, and weakening incentives for authentic and responsible production. Despite the establishment of harmonized frameworks of the European Union for official controls, the increasing complexity of food supply chains has exposed persistent gaps in fraud detection, particularly for high-value products such as those with PDO (Protected Designation of Origin) and PGI (Protected Geographical Ιndication) Certification. This study investigates the perceptions, attitudes, and experiences of frontline inspectors in Greece to assess current challenges and opportunities for strengthening official food fraud controls. Data were collected through a structured questionnaire, validated by experts and administered nationwide, involving 122 participants representing all major national food inspection authorities. Statistical analysis revealed significant institutional differences in perceptions of fraud prevalence, with mislabeling of origin, misleading organic claims, ingredient substitution, and documentation irregularities identified as the most common fraudulent practices. Olive oil, honey, meat, and dairy emerged as the most vulnerable product categories. Inspectors reported relying primarily on consumer complaints and institutional databases as key tools for identifying fraud risks. Food fraud was perceived to contribute strongly to losses in consumer trust in food safety and product authenticity, as well as to the erosion of sustainable production models that depend on transparency, fair competition, and responsible resource use. Overall, the findings highlight detection gaps, uneven resources across authorities, and the need for improved coordination and capacity-building to support more efficient, transparent, and sustainability-oriented food fraud control in Greece. Full article

Nanotechnology has emerged as a transformative tool in animal production, offering novel strategies to enhance productivity, health, and product quality. Among trace elements, selenium (Se) plays an essential role in antioxidant defence, immune regulation, and redox balance through its incorporation into selenoproteins. Selenium nanoparticles (SeNPs), synthesized via chemical, physical, or biological methods, have shown superior bioavailability, stability, and lower toxicity compared to traditional organic and inorganic selenium forms. This review explores the synthesis, physicochemical properties, and metabolic fate of SeNPs, emphasizing their advantages in poultry production systems. In poultry, SeNPs exhibit potent antioxidant and anti-stress effects by enhancing the activity of glutathione peroxidase, superoxide dismutase, and thioredoxin reductase, thereby mitigating lipid peroxidation and oxidative tissue damage. Their immunomodulatory effects are linked to improved lymphocyte proliferation, cytokine regulation, and increased immunoglobulin levels under normal and stress conditions. SeNP supplementation has been associated with enhanced growth performance, feed efficiency, carcass quality, and reproductive outcomes in broilers, layers, and quails. Furthermore, selenium nanoparticles have demonstrated therapeutic potential in preventing or alleviating chronic diseases such as cancer, diabetes, cardiovascular dysfunction, and neurodegenerative disorders. SeNPs also serve as biofortification agents, increasing selenium deposition in poultry meat and eggs, thus improving their nutritional value for human consumption. However, selenium’s narrow safety margin requires careful dose optimization to avoid potential toxicity. This review highlights the multifaceted benefits of selenium nanoparticles in poultry nutrition and health, while underscoring the need for further studies on grey SeNPs, long-term safety, and regulatory frameworks. Integrating SeNPs into poultry production represents a promising strategy to bridge animal health, food security, and public nutrition. Full article

The objective of this research was to optimize a technological package for the biosorption of heavy metals in water, using agricultural waste activated with lemon juice, as a sustainable development alternative. Heavy metals such as lead, cadmium, copper, and chromium were characterized in two stages (field and laboratory conditions) using the American Public Health Association (APHA) method, and morphological characterization was performed using electron scanning techniques. Cocoa pod husk (CPH) and banana stem (BS) waste was collected with the informed consent of the native communities to obtain charcoal activated with lemon juice (LJ). In addition, a portable filter was designed that could be adapted to the native communities. The efficiency and validation of the filter were also calculated in the field. Statistical analysis was performed using Student’s t-test and Pearson’s correlation. The results show a significant reduction in lead from 0.209 mg/L to 0.02 mg/L. With regard to morphological characterization, more compact structures were observed after activation with BS, favoring the absorption of heavy metals. The correlations were positive for copper and lead (1.000), evidently due to the alteration of anthropic factors. The efficiency of the cocoa filter reached 87.48% and that of the banana stem reached 88.77%. For the cadmium, copper, and chromium parameters, the values obtained were within the maximum permissible limit (LMP). The validation of the filters showed that 80% of the population agrees with using the filters and hopes for their large-scale implementation. These findings represent a new alternative for native communities and a solution to the problem of heavy metals in drinking water. Full article

Vehicle exhaust gases remain one of the key sources of atmospheric air pollution and pose a serious threat to ecosystems and public health. This study presents an experimental investigation into reducing the toxicity of gasoline internal combustion engine exhaust using ultrasonic waves and infrared (IR) laser exposure. An original hybrid system integrating an ultrasonic emitter and an IR laser module was developed. Four operating modes were examined: no treatment, ultrasound only, laser only, and combined ultrasound–laser treatment. The concentrations of CH, CO, CO₂, and O₂, as well as exhaust gas temperature, were measured at idle and under operating engine speeds. The experimental results show that ultrasound provides a substantial reduction in CO concentration (up to 40%), while IR laser exposure effectively decreases unburned hydrocarbons CH (by 35–40%). The combined treatment produces a synergistic effect, reducing CH and CO by 38% and 43%, respectively, while increasing the CO₂ fraction and decreasing O₂ content, indicating more complete post-oxidation of combustion products. The underlying physical mechanisms responsible for the purification were identified as acoustic coagulation of particulates, oxidation, and photodissociation of harmful molecules. The findings support the hypothesis that combined ultrasonic and laser treatment can enhance real-time exhaust gas purification efficiency. It is demonstrated that physical treatment of the gas phase not only lowers the persistence of by-products but also promotes more complete oxidation processes within the flow. Full article

Contamination of heavy metals, particularly chromium from industrial activities, represents a critical challenge for public health and the environment. The aim of this study is to evaluate the adsorption performance of green mesoporous silica (GMS-24 h), synthesized through a sustainable process from industrial sodium silicate, and its derivative functionalized with amino groups (GMS-24 h–NH₂) for the removal of Cr(III) in aqueous systems. FTIR and CP–MAS NMR characterization confirmed the surface modification and textural property improvement of green mesoporous silica. The adsorption experiments, carried out under varying pH and Cr(III) concentration conditions, were fitted to the Langmuir and Freundlich models. The results showed that GMS-24 h reached a maximum capacity of 303 mg·g⁻¹ at pH 3, while GMS-24 h–NH₂ achieved 370 mg·g⁻¹ at pH 5. The evaluated adsorbents showed up to a 44% increase in efficiency. Preliminary kinetic studies indicated that the pseudo-second-order model accurately describes the process (R² > 0.99), with the rapid stabilization of the system. Diffusion analysis indicated combined mechanisms, with an additional chelation step (N → Cr) in GMS-24 h–NH₂. These findings suggest that GMS–NH₂ has the potential to be a sustainable and economical adsorbent for the remediation of wastewater from the tanning industry in León, Guanajuato, Mexico. Full article

Brain tumors, encompassing subtypes with distinct progression and risk profiles, are a serious public health concern. Magnetic resonance imaging (MRI) is the primary imaging modality for non-invasive assessment, providing the contrast and detail necessary for diagnosis, subtype classification, and individualized care planning. In this paper, we evaluate the capability of modern deep learning models to classify gliomas as high-grade (HGG) or low-grade (LGG) using reduced training data from MRI scans. Utilizing the BraTS 2019 best-slice dataset (2185 images in two classes, HGG and LGG) divided in two folders, training and testing, with different images obtained from different patients, we created subsets including 10%, 25%, 50%, 75%, and 100% of the dataset. Six deep learning architectures, DeiT3_base_patch16_224, Inception_v4, Xception41, ConvNextV2_tiny, swin_tiny_patch4_window7_224, and EfficientNet_B0, were evaluated utilizing three-fold cross-validation (k = 3) and increasingly large training datasets. Explainability was assessed using Grad-CAM. With 25% of the training data, DeiT3_base_patch16_224 achieved an accuracy of 99.401% and an F1-Score of 99.403%. Under the same conditions, Inception_v4 achieved an accuracy of 99.212% and a F1-Score of 99.222%. Considering how the models performed across both data subsets and their compute demands, Inception_v4 struck the best balance for MRI-based glioma classification. Both convolutional networks and vision transformers achieved superior discrimination between HGGs and LGGs, even under data-limited conditions. Architectural disparities became increasingly apparent as training data diminished, highlighting unique inductive biases and efficiency characteristics. Even with a relatively limited amount of training data, current deep learning (DL) methods can achieve reliable performance in classifying gliomas from MRI scans. Among the architectures evaluated, Inception_v4 offered the most consistent balance between accuracy, F1-Score, and computational cost, making it a strong candidate for integration into MRI-based clinical workflows. Full article

Crack detection plays a pivotal role in ensuring the safety and stability of infrastructure. Despite advancements in deep learning-based image analysis, accurately capturing multiscale crack features in complex environments remains challenging. These challenges arise from several factors, including the presence of cracks with varying sizes, shapes, and orientations, as well as the influence of environmental conditions such as lighting variations, surface textures, and noise. This study introduces DAH-YOLO (Dynamic-Attention-Haar-YOLO), an innovative model that integrates dynamic convolution, an attention-enhanced dynamic detection head, and Haar wavelet down-sampling to address these challenges. First, dynamic convolution is integrated into the YOLOv8 framework to adaptively capture complex crack features while simultaneously reducing computational complexity. Second, an attention-enhanced dynamic detection head is introduced to refine the model’s ability to focus on crack regions, facilitating the detection of cracks with varying scales and morphologies. Third, a Haar wavelet down-sampling layer is employed to preserve fine-grained crack details, enhancing the recognition of subtle and intricate cracks. Experimental results on three public datasets demonstrate that DAH-YOLO outperforms baseline models and state-of-the-art crack detection methods in terms of precision, recall, and mean average precision, while maintaining low computational complexity. Our findings provide a robust, efficient solution for automated crack detection, which has been successfully applied in real-world engineering scenarios with favorable outcomes, advancing the development of intelligent structural health monitoring. Full article