Search Results (7,936)

Municipal sludge generation has increased rapidly with urbanization, creating significant challenges for sustainable waste management. This review proposes a system-oriented framework for sludge resource utilization by linking sludge characteristics, conversion technologies, environmental risks, and product applications. Major treatment pathways, including anaerobic digestion, pyrolysis, ozonation, and hydrothermal carbonization, are critically compared, with emphasis on their inherent trade-offs between resource recovery, energy consumption, and contaminant control. Particular attention is given to emerging contaminants, such as microplastics, per- and polyfluoroalkyl substances (PFAS), and antibiotic resistance genes, where the distinction between pollutant removal and actual risk reduction remains insufficiently addressed. The review highlights that no single technology can achieve optimal performance under all conditions, and integrated treatment trains are generally required for sustainable sludge management. Among these pathways, pyrolysis shows considerable potential for applications requiring enhanced contaminant control and value-added biochar production due to its ability to promote organic contaminant degradation, heavy metal immobilization, and carbon storage. However, the feasibility of pyrolysis and other technologies depends strongly on site-specific factors, including sludge properties, energy availability, economic conditions, and regulatory requirements. Future research should focus on integrated process optimization, comprehensive pollutant fate assessment, and standardized evaluation frameworks to advance sludge management toward a circular economy. Full article

In the present work, an alternative methodology was developed for the rapid prediction of pressure distributions over wings of low-speed aircraft. A hybrid neural network architecture, named “MARTHA” (Model for Airloads Reconstruction using a Trained Hybrid Architecture), was presented, which is composed of a Multilayer Perceptron and the decoder of an Autoencoder. Three compact representation models—Principal Component Analysis (PCA), Autoencoder (AE), and Variational Autoencoder (VAE)—were systematically evaluated to determine the optimal dimensionality reduction architecture; the AE was selected based on its superior reconstruction accuracy and training stability. The main feature of MARTHA is that it provides predictions of the differential pressure coefficient field in the form of monochrome images, where the pixel intensity directly represents the normalized pressure value. One of the main objectives of developing MARTHA was to create a rapid surrogate model that can approximate vortex lattice method (VLM) simulations in preliminary design and optimization tasks, particularly when thousands of wing configurations need to be evaluated. The key feature of the proposed model is its ability to predict the pressure distribution for trapezoidal wings of various geometries 10¹–10⁴ times faster than numerical models, while maintaining accuracy (R² = 0.9998). The data obtained are presented in a convenient format for their further use in CAE systems of strength analysis. To assess the practical utility of the proposed model, implementation cases were carried out using the finite element software ANSYS 18.2 for three wing configurations not present in the training dataset. The pressure fields predicted by MARTHA were mapped onto the wing meshes, and linear static structural analyses were performed. The obtained Von Mises stress distributions showed good agreement with the corresponding distributions obtained using numerical models. Full article

Super-large aggregate asphalt mixtures (SLAM-50) are expected to improve rutting resistance while reducing asphalt demand, but the quantitative relationships between volumetric parameters and key performance indicators remain unclear, limiting performance-oriented mixture design. This study experimentally evaluated the volumetric properties and key performance indices of SLAM-50 across four aggregate gradations and seven asphalt contents. The measured performance indices included compressive strength, splitting strength, compressive resilient modulus, fracture energy, and dynamic stability. With increasing asphalt content, the air voids (VV) decreased, the voids in mineral aggregate (VMA) decreased initially and then increased, and the voids filled with asphalt (VFA) increased monotonically. All performance indices exhibited a non-monotonic trend, increasing first and then decreasing, with a balanced overall performance at 3.0–3.2% asphalt content. Linear regression models showed limited predictive capability (R² = 0.5944–0.8845). To address this gap, a Backpropagation (BP) neural network was developed using asphalt content, volumetric parameters, mixture density, and gradation type as inputs, and the measured performance indices as outputs. This framework enables simultaneous multi-output prediction and captures the nonlinear, coupled relationships among variables. The model achieved R² > 0.91 for both training and testing datasets, demonstrating its ability to accurately predict SLAM-50 performance. These findings provide a practical, data-driven basis for performance-oriented mixture design and optimization, addressing the current scientific gap and offering guidance for engineering practice. Full article

The main challenge in long-text image–text retrieval lies in the fact that some existing vision–language models mainly rely on global image and text features for matching. Without explicit local alignment constraints, these models may struggle to fully capture complex semantic information in long textual descriptions, such as local objects, fine-grained attributes, and spatial relationships. To address the insufficient modeling of local details in global semantic matching, this paper proposes a patch–word fine-grained alignment retrieval model, named PWFAR. Built upon the Long-CLIP framework, the proposed method introduces an explicit fine-grained alignment mechanism between image patches and text tokens. By using textual words to guide the matching of local image regions, PWFAR enhances the model’s ability to capture local semantic correspondences. Specifically, PWFAR consists of three complementary training objectives: long-text global contrastive learning, short-text compact semantic supervision, and patch–word fine-grained alignment. These objectives are jointly optimized to constrain overall semantic consistency, core semantic stability, and local detail matching relationships. The model is trained on the 888k subset of the ShareGPT4V dataset and evaluated on the COCO2017 and Urban1k datasets. Experimental results show that PWFAR achieves competitive performance across different backbone networks and retrieval directions. Its clearest and most consistent gains are observed on the Urban1k long-text retrieval task, where it outperforms Long-CLIP and Long-CLIP-888k in both retrieval directions. These results indicate that the proposed fine-grained alignment mechanism can effectively improve the model’s ability to capture local semantic relationships in long texts, thereby verifying the effectiveness of PWFAR for long-text image–text retrieval. Full article

Machine learning (ML) algorithms can be used to extract clinically meaningful information from movement data captured by inertial measurement units (IMUs), but many human activity recognition (HAR) pipelines are developed on large laboratory datasets that may not reflect small, heterogeneous, real-world samples. The purpose of this study is to systematically compare the accuracy of multiple ML models, feature sets (both simple and expanded), class balancing strategies, null and transition period handling techniques, and sensor configurations for recognizing a set of four everyday activities extracted from IMU time series data from an age-diverse population. Six ML classifiers were trained and tested: multilayer perceptron, random forest, k-nearest neighbors, logistic regressor, CatBoost, and gaussian naive bayes. These approaches were used in a pipeline with differing sampling techniques including the synthetic minority oversampling technique or random undersampling, and feature handling steps including principal component analysis or a Select-From-Model metatransformer. Additionally, two deep learning methods, DeepConvLSTM and ResGCNN, were trained and tested. Accuracy, precision, recall, and area under the receiver operating characteristic curve were compared to a dummy classifier as a benchmark approximation to chance performance. All pipelines performed better than the dummy classifier, with model accuracy ranging between 0.427 and 0.644. This study demonstrated the ability of several ML algorithms to properly recognize a set of functional activities using limited IMU data from both children and adults. Full article

RNA large language models (RNA LLMs) show potential for predicting viral RNA–RNA interactions (RRIs), but their ability to transfer between viruses remains unclear. Here, we evaluated three nucleotide language models using vRIC-seq-derived RRI datasets from two members of the Coronaviridae family, SARS-CoV-2 and porcine deltacoronavirus (PDCoV). We compared within-virus five-fold cross-validation with bidirectional cross-virus testing. DNABERT achieved within-virus AUC values of 0.9595 for PDCoV and 0.9741 for SARS-CoV-2. Under cross-virus transfer, its AUC decreased to 0.8372 when trained on PDCoV and tested on SARS-CoV-2 and to 0.8116 in the reverse direction, with corresponding F1 scores of 0.7847 and 0.7617. RNAErnie retained similar but slightly lower cross-virus discrimination, whereas the Nucleotide Transformer approached random discrimination. These results provide a two-virus proof of concept that sequence-based models can retain partially shared discriminative signals across coronavirus genera. They do not establish functional, tropism-related, or receptor-dependent transferability, and broader evaluation across additional viruses and orthogonal structural validation will be required before application to emerging-virus screening. Full article

Large classes in Chinese College English programmes make frequent analytic assessment of student writing difficult. Large language models (LLMs) may support more frequent formative assessment, but their scores may vary across queries and be systematically harsher or more lenient than local teacher ratings. Using a corpus-based, five-fold cross-validated comparative rater-evaluation design, this study examined whether statistical calibration could make LLM-assisted scores more interpretable for College English writing assessment and where their use should remain limited. Data comprised 414 timed argumentative essays written by Chinese non-English majors at one applied undergraduate institution. Two trained College English teachers independently rated the essays on a seven-dimension analytic rubric informed by China’s Standards of English Language Ability, providing the local reference standard. Three LLMs rated each essay–dimension pair on five occasions. Under five-fold cross-validation, uncalibrated scores were compared with location–scale correction, isotonic calibration, and equipercentile linking, using quadratic weighted kappa, Spearman correlation, mean absolute error, signed bias, and half-point tolerance accuracy. Agreement between models did not imply agreement with teachers: two models showed inter-model kappa values of 0.70–0.78 but an average kappa of only 0.15 with teacher ratings while rating the essays about one band more severely. Calibration removed most of this severity difference and raised pooled kappa to 0.61–0.70 depending on the method (0.63–0.64 under equipercentile linking), compared with a teacher–teacher agreement benchmark of 0.747. The three methods differed little, and the improvement mainly reflected closer alignment of score distributions rather than better judgement of writing quality. Agreement was higher for vocabulary, syntax, and grammar but remained low for cohesion and conventions. The findings suggest that LLM-assisted scoring may support low-stakes formative feedback when calibrated to local teacher standards and used under teacher supervision, while teachers retain responsibility for judging content, coherence, argumentation, and communicative quality. Full article

Traditional rule-based intrusion detection systems generally fail in identifying unknown or evolving threats; thus, automated and adaptive kinds of methods are crucial. Deep learning models provide promising solutions, but many recent studies depend on hybrid architecture, which increase the computational cost and reduce deploying ability on real-time or resource-limited systems. In this paper, we present and test a standalone LSTM model for multiclass cyberattack detection based on a CIC_IoT_Dataset2023, a recent labeled dataset that mirrors the actual network environment containing 33 attack categories. The dataset was extremely imbalanced as benign traffic accounted for most of the classes. To detect such attacks, we used the Synthetic Minority Oversampling Technique (SMOTE) to increase the frequency of less common types of address. The pre-processed dataset was then employed to train four models (RNN, CNN, DNN and the proposed LSTM) for performance analysis with sequential data. The proposed LSTM model achieved an accuracy between 2% and 7%. LSTM had good detection for frequent attacks and slow-changing patterns, which shows its capacity in learning long-lasting dependencies. The results demonstrate that a simple, lightweight standalone LSTM model can be used for effective and realistic intrusion detection without the need for complex hybrid architecture. Full article

Background/Objectives: Older adults residing in long-term care facilities (LTCFs) are at increased risk of poor oral health because of functional dependence and limited access to professional oral healthcare. Although oral healthcare education programs for care workers improve knowledge and performance, evidence of their effects on residents’ biological oral health outcomes remains limited. This study evaluated the effects of a care worker-centered oral healthcare program on oral inflammatory status in LTCF residents using salivary interleukin-1 beta (IL-1β) as an objective biomarker. Methods: A single-group pretest–posttest quasi-experimental study was conducted in two LTCFs in the Republic of Korea. Forty care workers completed a structured oral healthcare education program and provided daily oral healthcare to residents for 4 weeks. Saliva samples were collected before and after the intervention, and salivary IL-1β concentrations were measured using enzyme-linked immunosorbent assay. Changes in IL-1β concentrations were analyzed using the Wilcoxon signed-rank, Mann–Whitney U, and Kruskal–Wallis tests. Results: Thirty-seven residents completed the study. Median salivary IL-1β concentrations significantly decreased from 19.33 pg/mL (interquartile range [IQR], 11.49–34.66) at baseline to 10.78 pg/mL (IQR, 0.65–33.64) after the intervention (Z = −3.764, p = 0.001). Greater reductions were observed among residents with greater functional dependence, whereas limited communication ability or oral care refusal was associated with minimal improvement. Conclusions: A care worker-centered oral healthcare program significantly reduced salivary IL-1β concentrations among older adults residing in LTCFs. Findings support care worker training and highlight the use of salivary IL-1β as an objective biomarker for evaluating oral healthcare interventions. Full article

Skeletal muscle mitochondria possess the ability to autoregulate their health and functioning by the orchestration of mitochondrial quality control (MQC) pathways. This plasticity allows them to adapt to various stimuli, such as exercise. However, under pathological conditions, mitochondria can become dysfunctional, generating damage-associated molecular patterns (DAMPs), such as reactive oxygen species (ROS) and oxidized mitochondrial DNA (mtDNA). These DAMPs can launch an innate immune response, with consequences of widespread inflammation and atrophy. Integral to this is the NLRP3 inflammasome complex. Activation of the NLRP3 inflammasome results in maturation of caspase-1, which processes pro-inflammatory cytokines IL-1β and IL-18, as well as GSDMD. Consequently, the pore-forming GSDMD-N fragment induces pyroptosis, releasing mature IL-1β and IL-18. Exercise training is widely accepted as a potent mechanism to promote skeletal muscle health, particularly by remodeling the mitochondrial network and reducing the production of DAMPs. It has also been shown promote an anti-inflammatory milieu with the release of various myokines. Indeed, the potential of exercise to mitigate NLRP3 inflammasome-mediated inflammation and atrophy is promising. This review will examine the mechanisms underpinning inflammasome priming and activation, as well the effects of exercise, with an emphasis on the skeletal muscle. Full article

This paper examines the emergence of generative Artificial Intelligence and its increasing integration into educational processes, highlighting the significant impact this transformation has had on teaching practices. The rapid spread of these technologies has contributed to a reconfiguration of teaching and learning methods. It has also highlighted the need for teachers to develop specific digital skills, particularly regarding the critical, ethical and pedagogical use of Artificial Intelligence. Reflecting this concern regarding teacher training for emerging areas in the educational environment, such as Artificial Intelligence, this paper presents the results of a study conducted in Portugal and Brazil based on the Pedagogical DigCompEdu Reloaded framework, specifically regarding digital competences related to Artificial Intelligence, as present in each of the four areas of the pedagogical dimension considered in that framework. The results obtained highlight significant training gaps and critical areas for intervention, clearly indicating that teachers’ proficiency levels in pedagogical competences associated with Artificial Intelligence are generally low. These findings are particularly relevant for educational sustainability, as insufficient AI-related competences may limit teachers’ ability to create inclusive, resilient, and equitable digital learning environments. Although Portugal and Brazil have different educational frameworks and policies, the findings reveal very similar patterns. This suggests the existence of common constraints. These findings reinforce the need for educational policies and training strategies geared towards the critical and pedagogical integration of Artificial Intelligence into education systems. The findings also suggest that strengthening teachers’ AI-related competences is a necessary condition for fostering more inclusive and sustainable educational ecosystems, capable of supporting the ethical and pedagogical integration of Artificial Intelligence while mitigating emerging forms of digital inequality. Full article

Background/Objectives: Regular participation in physical activity is essential for maintaining health and well-being and plays a key role in disease prevention and quality of life, both in the general population and among people with disabilities, who represent approximately 16% of the global population. Martial arts-based physical activity programs incorporating safe-falling techniques may contribute to improving physical fitness, reducing fall-related injuries, and supporting adherence to the World Health Organization physical activity recommendations. This systematic review primarily aimed to identify martial arts-based fall-training programs, particularly judo-based interventions, and examine their effects on health and well-being in individuals with intellectual disability. As a secondary objective, evidence from other populations and martial arts disciplines was considered to identify relevant fall-training protocols and transferability. Methods: The research question was formulated using the PEO framework. Eligibility criteria included: people with intellectual and developmental disabilities or special educational needs; exposure to exercise programs involving martial arts, combat sports, or judo; and outcomes related to safe falling, falls, balance, motor skills, and physical condition. A systematic literature search was conducted between December 2025 and April 2026 on the Web of Science Core Collection, Scopus, PubMed, and SPORTDiscus in accordance with PRISMA guidelines. Results: From a total of 658 records identified, 27 studies met the inclusion criteria, encompassing 1892 participants with various disabilities, including intellectual disability, autism spectrum disorder, visual impairment, and deafness. The interventions involved different martial arts modalities, such as judo, taekwondo, karate, tai chi, and capoeira. Overall, the available evidence suggests beneficial effects on physical fitness, motor abilities, balance, psychosocial outcomes, and safe-falling skills across different populations; however, substantial clinical and methodological heterogeneity was observed among the included studies. Conclusions: No studies investigating structured martial arts-based fall-training programs in individuals with intellectual disability were identified. Although evidence from other populations is promising, it remains heterogeneous. Further high-quality studies are needed to evaluate these interventions in individuals with intellectual disabilities. Full article

Background: Transitional age youth aged 18–25 years are more likely to engage in risky behaviors, displaying significantly higher alcohol and illicit drug use levels than adolescents and older adults. College students are particularly vulnerable at this critical life juncture as transitioning to adulthood may impact emotional health and well-being, contributing to anxiety, depression, and feelings of hopelessness, all of which are linked to increased risk of substance use and disorders. Objective: This study characterizes the design and status of the MAPme Project, a prospective project centered on substance use and emotional wellness during and beyond college. Herein, we describe the 2018–2024 pilot cohort that was followed during the first two years of college, remotely during the COVID-19 pandemic, and upon the return to campus. We also describe our latest 2025+ cohort with ongoing data collection. Participants: The 2018–2024 cohort comprised 301 college freshmen who completed online assessments at baseline (Wave 1). The 2025+ cohort has currently enrolled 618 students and counting. Methods: We provide descriptive information on both cohorts, along with preliminary associations of pre- and early collegiate substance use outcomes, psychopathology, sleep patterns, and personality traits. Results: Approximately 64% of first-year students reported use of a substance in the 90 days leading up to college in 2018. Alcohol and cannabis were the most prevalent substances used in both cohorts with males consuming more alcohol than females. Females reported greater levels of daily stressors and mood symptoms. Other health and personality characteristics and observed associations between psychosocial risk and protective factors are described. Prior studies demonstrate the ability to test research hypotheses in the social, health, and clinical areas of psychology with robust statistical power. Conclusions: Substance use and emotional well-being vary considerably upon entry into college/university and at any particular moment in time. The MAPme Project encourages the engagement of students to address challenges associated with lack of awareness, inclusivity, and acceptance of scientific research. Overcoming barriers of inclusivity and empowering the community is key to the successful adoption and implementation of campus health and community-oriented research programs and systematically enhancing research training. Full article

Background: Cognitive control is the ability to adapt to varying cognitive demands. Alpha neurofeedback (α-NFB) training is reported to affect neural activity, but little is known about whether neurophysiological responses to auditory α-NFB differ across cognitive tasks with different cognitive load. This exploratory pilot study examined the neurophysiological effects of a single session of auditory α-NFB during cognitive tasks with different cognitive loads. Method: Fifteen healthy young adults participated in the study. Participants completed Tasks 1 and 3 of the New Stroop Test I before and after the intervention. Neural oscillations and task-performance measures were assessed before and after the intervention. Pre–post changes were compared between the intervention and control groups. Results: For Task 1, the intervention group exhibited a significantly greater pre–post intervention change in the α/β ratio than the control group. However, the increase in alpha oscillatory power did not remain statistically significant after Holm correction for multiple comparisons. For Task 3, no significant between-group differences were observed in any neurophysiological or behavioral measure. Conclusions: These exploratory findings suggest that auditory αNFB may influence neurophysiological responses during Task 1, whereas no significant neurophysiological effects were observed during Task 3. This pattern is consistent with the possibility that neurophysiological responses to auditory αNFB may differ according to cognitive load; however, this interpretation remains preliminary and requires confirmation in larger studies. However, no corresponding differences in task performance were observed, and the findings should be interpreted as preliminary and hypothesis-generating. Full article

Background/Objectives: Basic sciences form the conceptual foundation of dentistry, yet their delivery in dental curricula varies worldwide. As demographic shifts, growing healthcare complexity and scientific advances continue to reshape the dental profession, renewed attention to how these subjects are taught has become essential. This comparative narrative review aims to examine different approaches to basic science education in dentistry, exploring variation in curricular structure, depth, timing and pedagogical strategies across diverse dental schools. Particular emphasis is placed on models of vertical, horizontal and spiral integration, the alignment of basic sciences with clinical training and the implications of instructional design for long-term knowledge retention and professional competence. Methods: PubMed and Google Scholar were searched from November 2025 to June 2026 using terms related to dental education, curriculum integration and geographic regions. Sources were selected based on predefined relevance criteria rather than through systematic screening. Published literature was supplemented by structured outreach to faculty representatives using a standardised set of questions. Of the 25 dental schools contacted, nine responded, and five were included. Results: Across the programs examined, a structure of approximately two preclinical years followed by clinical training remained the most common. Early clinical exposure appeared to strengthen student confidence and professional identity. Heavy workload and curricular overload were recurring challenges in the dental curricula examined. Conclusions: The findings suggest that although curriculum integration is widely recognised as essential, no single model appears to be universally superior. Its effectiveness likely depends on the quality of implementation and faculty coordination. However, the lack of independent evaluation studies limits the ability to draw conclusions about the long-term effectiveness of different educational approaches. The review highlights the need for independent and systematic evaluation of dental curricula. Full article