Search Results (307)

Hand hygiene (HH) is essential for preventing disease transmission, particularly in schools where children are in close contact with other children. This study evaluated a school-based intervention on observed HH practices and hand cleanliness in six primary schools in Guatemala. Hand cleanliness was measured using the Quantitative Personal Hygiene Assessment Tool. The intervention included (1) HH behavior change promotion through Handwashing Festivals, and (2) increased access to HH materials at HH stations. Handwashing Festivals were day-long events featuring creative student presentations on HH topics. Schools were provided with soap and alcohol-based hand rub throughout the project to support HH practices. Appropriate HH practices declined from 51.2% pre-intervention to 33.1% post-intervention, despite an improvement in median Quantitative Personal Hygiene Assessment Tool scores from 6 to 8, indicating cleaner hands. Logistic regression showed higher odds of proper HH when an assistant was present. The decline in HH adherence was likely influenced by fewer assistants and changes in COVID-19 policies, while improvements in hand cleanliness may reflect observational bias. These findings emphasize the importance of sustained behavior change strategies, reliable HH material access, and targeted interventions to address gaps in HH practices, guiding school health policy and resource allocation. Full article

The generation of memory CD8⁺ T cells is essential for establishing protective T cell immunity against pathogens and cancers. However, the cellular and molecular mechanisms underlying memory CD8⁺ T cell formation remain incompletely understood. Reliance on specific pathogen-free (SPF) models, characterized by restricted microbial exposure, may limit our understanding of physiologically relevant immune memory development. This study reveals that CD1d-restricted NKT cells regulate central memory T cell (TCM) generation exclusively in a microbe-rich (“dirty”) environment. Under non-SPF housing, CD1d⁺/⁻ and Ja18⁺/⁻ mice exhibited enhanced TCM formation compared to NKT-deficient controls (CD1d⁻/⁻/Ja18⁻/⁻), demonstrating that microbial experience is required for NKT-mediated TCM regulation. Mechanistically, CD1d-restricted NKT cells increased IL-15Rα expression on CD4⁺ T cells in CD1d⁺/⁻ mice, potentiating IL-15 trans-presentation and thereby activating the IL-15/pSTAT5/Eomes axis critical for TCM maintenance. Functional validation through adoptive transfer of CFSE-labeled OT-1 memory cells revealed an NKT cell-dependent survival advantage in CD1d⁺/⁻ hosts. This provides direct evidence that microbiota-experienced niches shape immune memory. Collectively, these findings establish CD1d-restricted NKT cells as physiological regulators of TCM generation and suggest their potential utility as vaccine adjuvants to enhance protective immunity. Full article

Federated learning (FL) enables collaborative model building among a large number of participants without sharing sensitive data to the central server. Because of its distributed nature, FL has limited control over local data and the corresponding training process. Therefore, it is susceptible to data poisoning attacks where malicious workers use malicious training data to train the model. Furthermore, attackers on the worker side can easily manipulate local data by swapping the labels of training instances, adding noise to training instances, and adding out-of-distribution training instances in the local data to initiate data poisoning attacks. And local workers under such attacks carry incorrect information to the server, poison the global model, and cause misclassifications. So, the prevention and detection of such data poisoning attacks is crucial to build a robust federated training framework. To address this, we propose a prevention strategy in federated learning, namely confident federated learning, to protect workers from such data poisoning attacks. Our proposed prevention strategy at first validates the label quality of local training samples by characterizing and identifying label errors in the local training data, and then excludes the detected mislabeled samples from the local training. To this aim, we experiment with our proposed approach on both the image and audio domains, and our experimental results validated the robustness of our proposed confident federated learning in preventing the data poisoning attacks. Our proposed method can successfully detect the mislabeled training samples with above 85% accuracy and exclude those detected samples from the training set to prevent data poisoning attacks on the local workers. However, our prevention strategy can successfully prevent the attack locally in the presence of a certain percentage of poisonous samples. Beyond that percentage, the prevention strategy may not be effective in preventing attacks. In such cases, detection of the attacked workers is needed. So, in addition to the prevention strategy, we propose a novel detection strategy in the federated learning framework to detect the malicious workers under attack. We propose to create a class-wise cluster representation for every participating worker by utilizing the neuron activation maps of local models and analyze the resulting clusters to filter out the workers under attack before model aggregation. We experimentally demonstrated the efficacy of our proposed detection strategy in detecting workers affected by data poisoning attacks, along with the attack types, e.g., label-flipping or dirty labeling. In addition, our experimental results suggest that the global model could not converge even after a large number of training rounds in the presence of malicious workers, whereas after detecting the malicious workers with our proposed detection method and discarding them from model aggregation, we ensured that the global model achieved convergence within very few training rounds. Furthermore, our proposed approach stays robust under different data distributions and model sizes and does not require prior knowledge about the number of attackers in the system. Full article

A 14-day study was conducted to evaluate the effect of litter type (dirty litter, DL; fresh litter, FL) and Salmonella Enteritidis SE challenge (no challenge, NC; challenge, SE) on the growth performance and cecal microbial composition of neonate chicks. Day-old chicks (n = 240, Ross 708 male) were allocated to a 2 × 2 factorial design consisting of four treatments: chicks raised on dirty litter (CONDL), chicks raised on fresh litter (CONFL); and chicks raised on litter types similar to CONDL and CONFL but inoculated with 7.46 × 108 CFU SE/mL at d 1 (CONDLSE and CONFLSE). The performance indices measured included body weight (BW), body weight gain (BWG), feed intake (FI), mortality, and feed conversion ratio (FCR). Cecal SE concentration was assessed on d 3 and 14, and ceca were collected from chicks on day 14 for DNA extraction. The Illumina Miseq platform was used for microbiome analysis of the V3–V4 region of the 16S rRNA gene. The interaction of litter type and SE influenced FCR and FI. CONDL recorded the poorest FCR (1.832). FI was highest and similar in CONFLSE, CONDL, and CONDLSE (0.655, 0.692, and 0.677, respectively). Cecal SE concentration was significantly reduced in CONDLSE at d 3 and 14. Alpha diversity was higher (p < 0.05) in the DL compared to that in NC. Beta diversity showed a separation (p < 0.05) between the DL and the FL. Comparative tree analysis revealed 21 differential significant genera, with 14 prevalent in the DL and 7 in the FL, specifically, bacteria genera such as Lactobacillus, Clostridia_vadinBB60_group, Lachnospira, Oscillospiraceae UCG_005, and Marvinbryantia, which play significant roles relating to improved growth performance, metabolic homeostasis within the gut, energy metabolism, and short-chain fatty acid (SCFA) utilization. Our results concluded that litter management regimen differentially alters the microbiome of chicks, which accounts for the improved performance and exclusion of pathogens in the study. Full article

The plasma reactor and cylindrical-type electrostatic precipitator (PRESP), combined operation in one device, made in the metallic chimney of low-thermal power boilers (up to 50 kW) that burn wood, can be used in home applications. The discharge electrode is stretched and supported by two groups of medium-voltage insulators. The sensitive elements of PRESP are medium-voltage insulators. This article analyses the design, use, and effect of dirty gases on the medium-voltage insulators that support the discharge electrode under real operating conditions for a PRESP installed in a 20 kW thermal power boiler that burns wood (there are no studies on the performance of PRESP). The electrical properties of the medium-voltage insulators (isolation resistance, dielectric absorption ratio, and polarisation index) and the chemical analysis of the dust layer deposited on the medium-voltage insulators are analysed. Of the two types of insulators analysed, a longer length of the electrical insulators determines a safer and better operation of PRESP. After a period of operation of the PRESP, the insulation resistance decreases by more than 10 times. The polarisation index (values greater than 1.1–1.2) provides better information (compared to the dielectric absorption ratio) on the insulation quality. Full article

Laser marking on wafers can introduce various defects such as inconsistent mark quality; under- or over-etching, and misalignment. Excessive laser power and inadequate cooling can cause burning or warping. These defects were inspected using machine vision, confocal microscopy, optical and scanning electron microscopy, acoustic/ultrasonic methods, and inline monitoring and coaxial vision. Machine learning has been successfully applied to improve the classification accuracy, and we propose a random forest algorithm with a training database to not only detect the defect but also trace its cause. Four causes have been identified as follows: unstable laser power, a dirty laser head, platform shaking, and voltage fluctuation of the electrical power. The object-matching technique ensures that a visible image can be utilized without a precise location. All inspected images were compared to the standard (qualified) product image pixel-by-pixel, and then the 2D matrix pattern for each type of defect was gathered. There were 10 photos for each type of defect included in the training to build the model with various labels, and the synthetic testing images altered by the defect cause model for laser marking defect inspection had accuracies of 97.0% and 91.6% in sorting the error cause, respectively Full article

Challenge, adventure, and risky play have repeatedly been found to be learning environments that positively shape childhood well-being and development. Extant evidence-based research conveys the physical, cognitive, and socio-emotional growth associated with risky play provision. However, understanding the interplay of risky play, injury, and safety is more nuanced and complex. The goal of this paper is to introduce a tool which allows educators, parents, health practitioners, urban planners, playground designers, certifiers, manufacturers, and inspectors to estimate both the benefit and risk of outdoor play and learning settings, such as playgrounds, adventure parks, or risk-taking activities. One of the key challenges associated with societal risk appetite or risk tolerance has been the inability to quantify the inherent benefits of risk taking in playgrounds and educational settings. Historically, the assessment of ‘benefit–risk’ has been dominated by a zero tolerance of incidents, whether in the workplace or road safety settings. Against this backdrop, if playgrounds and outdoor learning settings are boring, children will go elsewhere to seek thrills and adventure, which may often be manifested in antisocial behaviour. In 2023, ‘benefit–risk’ was formally recognised in the area of sport and recreation by the International Organisation for Standardisation, when it published the ISO 4980:2023 benefit–risk assessment for sport and recreational facilities, activities, and equipment. ISO 4980:2023 is a departure from the traditional view of risk management, in that it presents a perspective of risk which is not limited to framing risk as negative, but rather balances the calculation of positive benefits as well as the risks associated with the activity. Correspondingly, hazardous situations which have zero benefit should be eliminated or mitigated. The tool introduced in this paper offers playground inspectors and educators the ability to measure and assess both the benefit and risk of outdoor playgrounds and educational settings where children play, learn, and flourish. Full article

Selenium sulfide, the active ingredient of traditional antidandruff shampoos, is industrially produced from selenium dioxide (SeO₂) and hydrogen sulfide (H₂S) under acidic conditions. This reaction can also be carried out with natural H₂S and H₂S generated by sulfate-reducing bacteria (SRB). These bacteria are robust and, by relying on their conventional growth medium, also thrive in “waste” materials, such as a mixture of cabbage juice and compost on the one side, and a mixture of spoiled milk and mineral water on the other. In these mixtures, SRB are able to utilize the DL-lactate and sulfate (SO₄²⁻) present naturally and produce up to 4.1 mM concentrations of H₂S in the gas phase above a standard culture medium. This gas subsequently escapes the fermentation vessel and can be collected and reacted with SeO₂ in a separate compartment, where it yields, for instance, pure selenium sulfide, therefore avoiding the need for any cumbersome workup or purification procedures. Thus “harvesting” H₂S and similar (bio-)gases produced by the fermentation of organic waste materials by suitable microorganisms provides an elegant avenue to turn dirty waste into valuable clean chemical products of considerable industrial and pharmaceutical interest. Full article

Salmonella spp. is a pathogen detected in fish, although it is not part of its microbiota; the production and processing environment is the main source of contamination. Brazilian legislation recommends 5 ppm of free residual chlorine for fish washing, but Salmonella can still be present. The objective of this study was to evaluate flaws in the processing flowchart and propose adjustments to reduce Salmonella spp. on the fish surface. Ninety samples were analyzed in a fish processing plant in Mato Grosso, divided into three treatments: (1) conventional processing, (2) modified flowchart, and (3) modified flowchart adapted to the specific plant conditions. Treatment 2 completely eliminated Salmonella spp., while treatment 3 reduced contamination to 3.3%, compared to 56.7% in conventional processing. The success of the modified treatment was only possible due to the main changes implemented in the process, which included the separation of dirty areas (responsible for gill and scale removal) and clean areas (designated for the careful removal of viscera without rupture and for filleting). No statistical difference was found between treatments 2 and 3 (p = 1, CI 0.00000–39.00055), suggesting that the adjusted flowchart can be implemented on a large scale to ensure food safety (OR = ∞, CI = [7.655, ∞], p < 0.001). This study highlights the effectiveness of the adjusted flowchart in reducing Salmonella spp. contamination in fish, with treatment 2 resulting in a complete absence of contamination. Treatment 3 maintained low contamination levels, demonstrating practical applicability in meatpacking plants. Full article

Biogas is once again emerging as a potential household cooking option that can help developing countries achieve energy targets. However, the adoption of biogas remains relatively slow, necessitating a diagnosis of the problem the review of literature identified. The review identified key factors influencing the adoption of household biogas technology, including policy and regulatory frameworks, financing mechanisms, public awareness, and socio-economic factors. Therefore, this study involved undertaking a survey where heads of 385 households were interviewed. The study found that low income of households, averaging USD 67/month, is a major constraint to biogas adoption, especially when dirty fuels cost little or nothing. In addition, a lack of awareness of the benefits of biogas over the available dirty fuels has the potential to limit its adoption. This explains why 99% of the households interviewed indicated firewood and charcoal were their first option, and 52% believed that these dirty fuels were dependable. Regardless of these bottlenecks, the study found that households are ready to try better cooking options. About 99% of households were interested in using biogas, and 94% wanted to learn more about biogas. Therefore, there is a need for increased awareness, and suppliers must adopt innovations that make biogas more accessible and competitive against traditional cooking fuels. Full article

Solar panels are critical for renewable electricity generation, yet defects significantly reduce power output and risk grid instability, necessitating reliable AI-driven defect detection. We propose the SPHERE (Solar Panel Hidden-Defect Evaluation for Renewable Energy) method for such cases. This study compares deep learning models for classifying solar panel images (broken, clean, and dirty) using a novel, proprietary dataset of 6079 images augmented to enhance performance. The following three models were evaluated: YOLOv8-m, YOLOv9-e, and a custom CNN with 9-fold cross-validation. Pre-trained models (e.g., VGG16 and ResNet) were assessed but outperformed by YOLO variants. Metrics included accuracy, precision–recall, F1-score, sensitivity, and specificity. YOLOv8-m achieved the highest accuracy (97.26%) and specificity (95.94%) with 100% sensitivity, excelling in defect identification. YOLOv9-e showed slightly lower accuracy (95.18%) but maintained high sensitivity. The CNN model demonstrated robust generalization (92.86% accuracy) via cross-validation, though it underperformed relative to YOLO architectures. Results highlight YOLO-based models’ superiority, particularly YOLOv8-m, in balancing precision and robustness for this classification task. This study underscores the potential of YOLO frameworks in automated solar panel inspection systems, offering enhanced maintenance and grid stability reliability. This contributes to advancing AI applications in renewable energy infrastructure, ensuring efficient defect detection and sustained power output. The dataset’s novelty and the models’ comparative analysis provide a foundation for future research in autonomous maintenance solutions. Full article

Background: The dirty utility room (DUR) plays a vital role in maintaining and optimizing the safety of patients and healthcare staff. A substantial gap exists in the literature concerning the current topic in terms of empirical studies and reviews. Therefore, this study aims to shed light on the subject and provide reliable evaluations. Methods: A qualitative case study design (observational) was used. We included the DURs of hospitals in multiple regions of the Kingdom of Saudi Arabia/in wards and units of each hospital. To achieve data saturation, visits across wards and ICUs were conducted until no new information was retrieved. NVivo Software version 14 was used for management and analysis of the data. We used our notes to initiate codes and then created themes involving the six steps of thematic analysis for the observational study. Results: Among several main hospitals in the central, western, eastern, southern, and northern geographical locations in Saudi Arabia that included DURs, a total of 24 DURs were explored to capture all relevant aspects related to the observations. Considering the range of items presented in DURs, the majority of hospitals exhibited a substantial lack of equipment. There were disagreements regarding the definition of DURs and the name of DURs. The observers agreed with the practice of urine disposal, which is performed by hand. The observers from all regions mutually agreed that stool disposal methods for patients involved diapers and the cleaning of patients manually with bed sheets. Several risks of infection control were observed related to DUR design and protocols. Conclusions: This national observational study of DURs in Saudi Arabian hospitals revealed major inadequacies in the design, equipment, and processes that are critical for infection control and healthcare quality, emphasizing the critical necessity for standardized methods and appropriate equipment. Full article

This study examines restroom cleanliness in public facilities, department stores, supermarkets, and schools by using water droplet volumes around washbowls as an indicator of usage. Rising cleaning costs due to labour shortages necessitate more efficient restroom maintenance. Quantifying water droplet accumulation and predicting cleaning schedules can help optimise cleaning frequency. To achieve this, water droplet volumes were measured at specific time intervals, with significant variations indicating increased restroom usage and potential dirt buildup. For real-world assessment, acrylic plates were placed on both sides of washbowls in public restrooms. These plates were collected every hour over five days and analysed using near-infrared photography to track changes in water droplet areas. The collected data informed the development of a prediction system based on the decision tree method, implemented via the LightGBM framework. This paper presents the developed prediction system, which utilises in situ water droplet volume measurements, and evaluates its accuracy in forecasting restroom cleaning needs. Full article

This study investigates the multifractal characteristics of the tanker freight market from 1998 to 2024. Using multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrending moving average (MF-DMA), we analyze temporal correlations and volatility, revealing subtle differences in multifractal features before and after 2010. We further examine the influence of key external factors—including economic disturbances (the 2008 financial crisis), technological innovations (the 2014 Shale Oil Revolution), supply chain disruptions (the COVID-19 pandemic), and geopolitical uncertainties (the Russia–Ukraine conflict)—on market complexity. Building on this, a predictive framework is introduced, leveraging the Baltic Dirty Tanker Index (BDTI) to forecast Brent oil prices. By integrating multifractal analysis with machine learning models (e.g., XGBoost, LightGBM, and CatBoost), our framework fully exploits the predictability from the freight index to oil prices across the above four major global events. The results demonstrate the potential of combining multifractal analysis with advanced machine learning models to improve forecasting accuracy and provide actionable insights during periods of heightened market volatility. On average, the coefficient of determination (R²) increases by approximately 62.65% to 182.54% for training and 55.20% to 167.62% for testing, while the mean squared error (MSE) reduces by 60.83% to 92.71%. This highlights the effectiveness of multifractal analysis in enhancing model performance, especially in more complex market conditions post-2010. Full article

Thermoelectric materials have considerable potential in the mitigation of the global energy crisis, through their ability to convert heat into electricity. This study aims to valorize natural resources, and potentially reduce production costs, by incorporating tetrahedrite–tennantite (td) ores from the Portuguese Iberian Pyrite Belt into synthetic samples. The ore samples were collected in a mine waste at Barrigão and as “dirty-copper” pockets of ore from the Neves Corvo mine. Subsequently, high-energy ball milling and hot pressing were employed in the production of thermoelectric materials. These are characterized by XRD, SEM/EDS, and thermoelectrical properties. The complete dissolution of the dump material sulfides with the synthetic tetrahedrite constituents led to an increase in the amount of the tetrahedrite–tennantite phase, which was made up of a tetrahedrite–tennantite–(Fe) solid solution. The thermoelectric characterization of these materials is provided, revealing that most of the combined synthetic ore samples displayed better results than the pristine tetrahedrite, mostly due to higher Seebeck coefficient values. Furthermore, the best thermoelectric performance is achieved with 10% of ore, where a power factor of 268 µW.K⁻².m⁻¹ is reached at room temperature. Full article