Search Results (21,257)

Unsupervised Hebbian learning is a biologically inspired algorithm designed to extract representations from input images, which can subsequently support supervised learning. It presents a promising alternative to traditional artificial neural networks (ANNs). Many attempts have focused on enhancing Hebbian learning by incorporating more biologically plausible components. Contrarily, we draw inspiration from recent advances in ANNs to rethink and further improve Hebbian learning in three interconnected aspects. First, we investigate the issue of overfitting in Hebbian learning and emphasize the importance of selecting an optimal number of training epochs, even in unsupervised settings. In addition, we discuss the risks and benefits of anti-Hebbian learning in model performance, and our visualizations reveal that synapses resembling the input images sometimes do not necessarily reflect effective learning. Then, we explore the impact of different activation functions on Hebbian representations, highlighting the benefits of properly utilizing negative values. Furthermore, motivated by the success of large pre-trained language models, we propose a novel approach for leveraging unlabeled data from other datasets. Unlike conventional pre-training in ANNs, experimental results demonstrate that merging trained synapses from different datasets leads to improved performance. Overall, our findings offer fresh perspectives on enhancing the future design of Hebbian learning algorithms. Full article

Urban heat island (UHI), a significant environmental issue caused by urbanization, is a pressing challenge in modern society. To mitigate it, urban thermal policies have been implemented globally. However, despite differences in topographical and environmental characteristics between cities and within the same city, these policies are largely uniform and fail to reflect contexts, creating notable drawbacks. This study analyzed three cities in Korea with high land surface temperatures (LSTs) to identify factors influencing LST by applying Extreme Gradient Boosting (XGBoost) with Shapley Additive explanations (SHAP) and Geographically Weighted Regression (GWR). Each variable was derived by calculating the average values from May to September 2020. LST was the dependent variable, and the independent variables were chosen based on previous studies: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), ALBEDO, Population Density (POP_D), Digital Elevation Model (DEM), and SLOPE. XGBoost-SHAP was used to derive the relative importance of the variables, followed by GWR to assess spatial variation in effects. The results indicate that NDBI, reflecting building density, is the primary factor influencing the thermal environment in all three cities. However, the second most influential factor differed by city: SLOPE had a strong effect in Daegu, characterized by surrounding mountains; POP_D had greater influence in Incheon, where population distribution varies due to clustered islands; and DEM was more influential in Seoul, which contains a mix of plains, mountains, and river landscapes. Furthermore, while NDBI and ALBEDO consistently contributed to LST increases across all regions, the effects of the remaining variables were spatially heterogeneous. These findings highlight that urban areas are not homogeneous and that variations in land use, development patterns, and morphology significantly shape heat environments. Therefore, UHI mitigation strategies should prioritize improving urban form while incorporating localized planning tailored to each region’s physical and socio-environmental characteristics. The results can serve as a foundation for developing strategies and policy decisions to mitigate UHI effects. Full article

The soil of the Trifinio region, the tri-national territory between Guatemala, Honduras, and El Salvador, is damaged by the expansion of monoculture, which decreases fertility and causes problems for local farmers. Furthermore, the region also faces issues of erosion and soil contamination. As an alternative to soil cultivation, soilless systems can be adopted, not requiring fertile soil, and significantly increasing yields and resource use efficiency. To encourage soilless technique application in the region, the aim of this study was to compare 18 different substrate mixes to identify the most suitable for the local cultivation of cucumber (Cucumis sativus L.). The substrates were obtained comparing three rates of peat and compost (0%, 20% and 40%, by volume) in factorial combination, with the remaining being either coir or pumice (filling component). Plant growth, flower setting, physiological status (relative chlorophyll content and leaf temperature), and plant production were evaluated. Highest yield was achieved with 20% peat, while compost (20% and 40%) was able to increase fruit length and improve the relative chlorophyll content, but did not affect total production. However, when focusing on environmental sustainability as an important standpoint, a peat-free substrate should be utilized even though the results favored the 20% peat treatment for production. Considering that the differences in production in favor of 20% peat treatment were of limited practical relevance. In regard to the filling components (coir and pumice) yields were unaffected and only minor parameters were changed. Based on the results obtained, a substrate consisting of 60% coir and 40% compost resulted in the best option for the soilless cultivation of cucumber in the Trifinio region, with both materials being sustainable and easily available for local farmers. Full article

The World Health Organization and the Centers for Disease Control and Prevention recommend seasonal influenza vaccination for all individuals aged 6 months and older. Despite high national immunization rates, the influenza vaccination coverage among Saudi children remains unclear. Parental knowledge and attitudes significantly impact children’s vaccination rates. Purpose: This study aims to evaluate parental knowledge, awareness, and attitudes regarding influenza vaccination and identify barriers to vaccination uptake among children in Al-Madinah City, Saudi Arabia. Methods: The population includes parents having children aged 6 months to 14 years. A cross-sectional survey utilizing a 33-item validated questionnaire was conducted to evaluate parental awareness, knowledge, and attitudes toward the influenza vaccine. Inferential statistics were employed to evaluate demographic factors influencing parental knowledge and attitudes toward vaccination. Results: This study surveyed 407 parents from Al-Madinah, focusing on their awareness, knowledge, and attitudes towards seasonal influenza vaccination. The sample was primarily Saudi (86.7%), with a mean age of 34 years. Most parents (95.6%) were aware of the vaccine, primarily through media and campaigns. Despite this, only 44.5% had vaccinated themselves or their children, citing perceptions of influenza as mild, vaccine ineffectiveness, and availability issues as primary reasons for non-vaccination. Knowledge about influenza varied, with most parents aware of its contagiousness (64.4%) and symptoms, but misconceptions persisted, such as believing the vaccine could cause the flu. Parental attitudes towards vaccination were mostly positive, with high trust in health information sources and a mean attitude score of 22.48 out of 35. Positive attitudes were correlated with better knowledge and more frequent infection control practices. Age, education, and medical profession status significantly influenced knowledge, while vaccine attitudes were most favorable among those vaccinated (p < 0.001). Conclusions: Most parents in Al-Madinah recognize the importance of vaccination; however, misconceptions about vaccine safety, perceived low need, and barriers such as vaccine availability persist. Sociodemographic factors, including education, income, and profession, are linked to better knowledge and more positive attitudes toward vaccination. Full article

Over the 21st century, the confluence between engineering and universal accessibility has emerged as a key research domain, reflecting the growing awareness of the importance of inclusive layout in technological innovation. Despite the growing number of studies on sustainability and inclusion, there is still a lack of comprehensive analyses exploring how engineering contributes to universal accessibility within the framework of the United Nations Sustainable Development Goals. This study addresses this gap by providing the first large-scale mapping of research trends, collaborations, and thematic evolution in this field. The present bibliometric analysis examines the evolution of engineering research in relation to the United Nations Sustainable Development Goals, stressing its role in encouraging universal accessibility. Through a systematic review of scholarly works produced over the last twenty years, this study uncovers dominant issues, evolving research fronts, and the global relevance of engineering-based approaches to improve accessibility for persons with disabilities. Analyzing citation dynamics, publication trajectories, and institutional involvement, this study underlines the contribution of engineering to building inclusive societies and ensuring equitable access to technology and infrastructure. Discoveries underscore that cross-sector collaboration and technological innovation are essential to overcoming accessibility challenges among disfavored populations, directly advancing SDG 10 on reducing disparities and SDG 11 on sustainable urban development. Full article

Today, vaccines play a crucial role in ensuring personal safety and are the most effective method for preventing related diseases. The ages over which vaccines are efficacious, from infancy to the old, is of utmost importance. With the recent outbreak of COVID-19 in 2019, the demand for vaccines and their usage has significantly increased. This surge in demand has led to issues such as vaccine counterfeiting and related problems, which have raised concerns among the public regarding vaccine administration. As a result, this has also resulted in a lack of trust in vaccine manufacturing companies and raised doubts about production processes. To address these concerns, this study proposed a vaccine production supervision system with Internet of Things (IoT) device based on blockchain. By utilizing IoT devices, vaccine-sensitive production data can be collected and encrypted and leaks that could lead to great benefit losses for vaccine manufacturing companies can also be prevented. This system adopts a digital signature technique to import immutable characteristics to the data, offering conclusive evidence in case any issues occur with the vaccine in the future. Finally, the system also integrates with the Inter Planetary File System (IPFS) with a blockchain solution, storing manufacturing plant vaccine production records in a secure, publicly accessible, and decentralized storage space, and also enabling public verification. Full article

End-of-life tires (ELTs) are an important source of energy and materials, with ELT powder (ELTp) being a secondary raw material of increasing industrial interest. However, the complex structure and composition of ELTp rubber pose technological difficulties and scientific challenges in some high-performance applications in the rubber industry. The mechanical recycling of ELTp produces ground tire rubber (GTR) powder, which is used, among other applications in the rubber field, to prepare thermoplastic vulcanizates (TPVs) due to the interest in these materials in the automotive and construction sectors. Over the last few decades, different approaches have been explored to minimize the limitations of these TPVs, including their large particle size and poor compatibility with GTR powder in other polymer matrices. This study applies different recycling procedures to GTR powder, based on thermal, chemical and mechanical methods, and combinations thereof, to minimize interfacial issues with other matrices used in TPV preparation. The effect of the different rubber recycling processes on the performance of the resulting TPVs was evaluated, optimizing the fraction of recycled rubber from ELTp and the vulcanization system to enhance the mechanical properties and obtain industrially competitive products. Full article

Diarrhea in young ruminants is a global issue and causes significant economic losses worldwide. In addition to common pathogens like rotavirus, coronavirus, and astrovirus, new viruses can be identified through unbiased next-generation sequencing (NGS) techniques. Here, we report the initial identification of a hunnivirus from a one-month-old goat with diarrhea using shotgun metagenomic NGS. A complete hunnivirus genome was recovered. Phylogenetic tree analysis revealed that this goat hunnivirus was more closely related to cattle hunnivirus than to small ruminant hunnivirus strains, suggesting a prior cross-species transmission event. The genome was used to design primers/probes for the conserved 3D^pol RdRP gene for real-time RT-PCR to screen banked ruminant fecal samples. Screening of 144 ruminant fecal samples showed that 9 of 38 goat, 22 of 96 cattle, and 0 of 8 sheep samples were positive for hunnivirus. Sequencing of the 3D^po region was performed on selected positive samples and revealed two lineages of hunnivirus circulating in North America. Our study highlights the importance of further investigation and monitoring of fecal samples using unbiased metagenomic tools to identify potential pathogens or co-infections in ruminants. Full article

The recirculating aquaculture system (RAS) provides a sustainable approach to sustaining aquaculture output while reducing environmental pollution and excessive water consumption. Nonetheless, high concentrations of Total Ammonia Nitrogen (TAN) continue to be a significant obstacle in RAS operations. To address this issue, the Moving Bed Biofilm Reactor (MBBR), with bubble aeration, is important for promoting ammonia degradation. Bubble size impacts the effectiveness of bubble aeration, influencing both oxygen transfer and microbial activity. This research involved a 35-day experiment to evaluate the effects of bubble size, produced by nanobubble and coarse bubble aerators, on biofilm development and TAN decrease. The maximum biofilm thickness of 172.88 µm was recorded during nanobubble aeration, which also produced a higher quantity of microbial colonies (293 × 10⁷ CFU) in comparison to coarse bubble aeration (89 × 10⁷ CFU), as validated by Total Plate Count analysis. SEM–EDX imaging additionally demonstrated a more compact and consistent biofilm structure in the presence of nanobubbles. These results align with an increased TAN degradation efficiency, achieving 83.33% with nanobubble aeration, while coarse bubble aeration reached only 50%. The findings indicate that nanobubble aeration enhances biofilm functionality by improving bacterial dispersion and oxygen availability within the biofilm matrix, thereby promoting a more uniform distribution of microorganisms and nutrients. This mechanism represents a promising approach for improving water quality and overall treatment efficiency in recirculating aquaculture systems (RAS). Full article

Developing efficient photocatalysts is essential for sustainable wastewater treatment and tackling global water pollution. Graphitic carbon nitride (g-C₃N₄) is a promising material because it is active under visible light and chemically stable. However, its practical application is limited by fast recombination of charge carriers and a low surface area. In this study, we report a simple hydrothermal method to synthesize exfoliated porous g-C₃N₄ (E-PGCN) combined with Ti₃C₂ MXene to form a heterojunction composite that addresses these issues. Various characterization techniques (FTIR, XRD, XPS, SEM, BET) confirmed that adding MXene improves light absorption, increases surface area (53.7 m²/g for the composite versus 21.4 m²/g for bulk g-C₃N₄ (BGCN)), and enhances charge separation at the interface. Under UV-visible light irradiation with Rhodamine B (RhB) as the model pollutant, the E-PGCN/Ti₃C₂ MXene composite containing 3 wt% MXene demonstrated an impressive degradation efficiency of 93.2%. This performance is superior to BGCN (66.6%), E-PGCN (82.5%), and E-PGCN/Ti₃C₂ MXene-5 wt% composites (81%). This is due to the excess Mxene which caused agglomeration and reduced activity. Scavenger studies identified electron radicals as the dominant reactive species, with optimal activity at pH ~4.5. This enhanced performance, 1.4 times greater than BGCN and 1.13 times higher than E-PGCN, is ascribed to the synergistic interplay between the excellent electrical conductivity of MXene and the porous structural features of E-PGCN. This work highlights the importance of morphological engineering and heterojunction design for advancing metal-free photocatalysts, offering a scalable strategy for sustainable water purification. Full article

With the development of large language model (LLM) technology, AI-assisted education systems are gradually being widely used. Learning Path Recommendation (LPR) is an important task in personalized instructional scenarios. AI-assisted LPR is gaining traction for its ability to generate learning content based on a student’s personalized needs. However, the native-LLM has the problem of hallucination, which may lead to the inability to generate learning content; in addition, the evaluation results of the LLM on students’ knowledge status are usually conservative and have a large margin of error. To address these issues, this work proposes a novel approach for LPR enhanced by knowledge tracing (KT) and LLM. Our method operates in a “generate-and-retrieve” manner: the LLM acts as a pedagogical planner that generates contextual reference exercises based on the student’s needs. Subsequently, a retrieval mechanism constructs the concrete learning path by retrieving the top-N most semantically similar exercises from an established exercise bank, ensuring the recommendations are both pedagogically sound and practically available. The KT plays the role of an evaluator in the iterative process. Rather than generating semantic instructions directly, it provides a quantitative and structured performance metric. Specifically, given a candidate learning path generated by the LLM, the KT model simulates the student’s knowledge state after completing the path and computes a knowledge promotion score. This score quantitatively measures the effectiveness of the proposed path for the current student, thereby guiding the refinement of subsequent recommendations. This iterative interaction between the KT and the LLM continuously refines the candidate learning items until an optimal learning path is generated. Experimental validations on public datasets demonstrate that our model surpasses baseline methods. Full article

Three-dimensional-printed concrete (3DPC) is an additive manufacturing technology that forms 3D solids via layer-by-layer printing based on 3D model data, but it consumes large amounts of river sand (RS) and has poor frost resistance. To address these issues, this study used industrial waste electric arc furnace slag (EAFS) as an aggregate at 0–100% replacement ratios to test the workability, mechanical properties, frost resistance, and microstructures of 3DPC specimens. The results show that EAFS improves mortar flowability and extends the printing window, but full replacement increases slump and reduces constructability. The stress dispersion and dense packing effects of EAFS ensure excellent mechanical properties of specimens before and after freeze–thaw cycles. At an 80% EAFS replacement ratio, compressive and flexural strengths increase by 2.52%/13.8% and 10.6%/18.2%, respectively; after freeze–thaw cycles, the specimens exhibit the best frost resistance. The interfacial transition zone between EAFS and cement matrix is only 2 μm, with 1.8% lower porosity and 20.14% fewer harmful pores than the 100% RS specimen after freeze–thaw cycles. In conclusion, 80% EAFS replacement balances 3DPC performance and solid waste utilization, providing important references for EAFS’s safe application in 3DPC and its performance improvement mechanism. Full article

The issue of water disasters in the mining floor is extremely severe. Despite significant progress in the on-site monitoring and identification of water inrush channels, research on the spatial development characteristics of cracks and the temporal evolution patterns remains insufficient, resulting in the incomplete development of microseismic-based water disaster early warning theory and practice. Based on this, the present study first derives the expressions for the diameter and length of water inrush channels according to the damage characteristics of microseismic events and the glazed porcelain shape features of the channels. A theoretical model for the correlation between microseismic-water inrush volume is established, and the relationship between microseismic and water level is revealed. Analysis of field monitoring data further indicates that when high-energy microseismic features (such as single high-energy events and higher daily cumulative energy) are detected, the aquifer water level begins to decline, followed by high water inrush events. Therefore, a decrease in water level accompanied by high-energy microseismic features can serve as an important early warning marker for water disasters. Finally, advanced machine learning methods are applied, in which the optimal index combination for floor water inrush prediction is obtained through the genetic algorithm, and the weights of each index are determined by integrating the analytic hierarchy process with the random forest model. Field engineering verification demonstrates that the integrated early warning system performs significantly better than any single monitoring indicator, and all high-water-inrush events are successfully predicted within four days. Full article

Background/Objectives: Machine learning is an extremely important issue, considering the potential to prevent the onset of long-term complications from coronavirus disease or to ensure timely detection and effective treatment. The aim of our study was to develop an algorithm and mathematical model to predict the risk of developing long COVID in children who have had acute SARS-CoV-2 viral infection, taking into account a wide range of demographic, clinical, and laboratory parameters. Methods: We conducted a cross-sectional study involving 305 pediatric patients aged from 1 month to 18 years who had recovered from acute SARS-CoV-2 infection. To perform a detailed analysis of the factors influencing the development of long-term consequences of coronavirus disease in children, two models were created. The first model included basic demographic and clinical characteristics of the acute SARS-CoV-2 infection, as well as serum levels of vitamin D and zinc for all patients from both groups. The second model, in addition to the aforementioned parameters, also incorporated laboratory test results and included only hospitalized patients. Results: Among 265 children, 138 patients (52.0%) developed long COVID, and the remaining 127 (48.0%) fully recovered. We included 36 risk factors of developing long COVID in children (DLCC) in model 1, including non-hospitalized patients, and 58 predictors in model 2, excluding them. These included demographic characteristics of the children, major comorbid conditions, main symptoms and course of acute SARS-CoV-2 infection, and main parameters of complete blood count and coagulation profile. In the first model, which accounted for non-hospitalized patients, multivariate regression analysis identified obesity, a history of allergic disorders, and serum vitamin D deficiency as significant predictors of long COVID development. In the second model, limited to hospitalized patients, significant risk factors for long-term sequelae of acute SARS-CoV-2 infection included fever and the presence of ≥3 symptoms during the acute phase, a history of allergic conditions, thrombocytosis, neutrophilia, and altered prothrombin time, as determined by multivariate regression analysis. To assess the acceptability of the model as a whole, an ANOVA analysis was performed. Based on this method, it can be concluded that the model for predicting the risk of developing long COVID in children is highly acceptable, since the significance level is p < 0.001, and the model itself will perform better than a simple prediction using average values. Conclusions: The results of multivariate regression analysis demonstrated that the presence of a burdened comorbid background—specifically obesity and allergic pathology—fever during the acute phase of the disease or the presence of three or more symptoms, as well as laboratory abnormalities including thrombocytosis, neutrophilia, alterations in prothrombin time (either shortened or prolonged), and reduced serum vitamin D levels, are predictors of long COVID development among pediatric patients. Full article

Background: Oral pemphigus is a rare autoimmune blistering disorder predominantly affecting the mucous membranes, particularly in older adults. Despite therapeutic advances, the chronic, painful, and recurrent nature of oral pemphigus vulgaris substantially impairs patients’ quality of life (QoL). Patient-reported outcomes (PROs) offer valuable insights into the subjective burden of the disease; however, data on PROs in older adults with oral pemphigus are scarce. Objective: To assess QoL in older adults diagnosed with oral pemphigus using validated PRO measures and to identify key clinical factors associated with QoL deterioration. Methods: A cross-sectional pilot study was conducted involving 10 participants aged 60 years or older with confirmed oral pemphigus vulgaris. Participants completed the Oral Pemphigus–Specific Quality of Life Questionnaire (OP-QoLQ) and the Dermatology Life Quality Index (DLQI). Clinical severity was evaluated using the Autoimmune Bullous Skin Disorder Intensity Score (ABSIS). Statistical analyses explored correlations between disease severity, treatment regimens, and QoL outcomes. Results: Most participants reported moderate to severe QoL impairment, with eating difficulties and emotional distress being the most frequently mentioned issues. Higher ABSISs and longer disease duration were significantly correlated with poorer OP-QoLQ and DLQI outcomes (Spearman’s ρ up to 0.80; p ≤ 0.021). Systemic corticosteroid therapy was more frequently reported among those with advanced disease, although treatment-related adverse effects may contribute to reduced QoL. Conclusion: Oral pemphigus substantially compromises QoL in older adults, with both disease- and treatment-related factors playing important roles. These findings support the integration of PROs into the multidisciplinary management of older adults with oral pemphigus vulgaris. Full article