Search Results (6,996)

This practice-based implementation report describes the adoption of the My Abilities First (MAF) initiative for children with developmental delays in Taiwan. Grounded in the International Classification of Functioning, Disability and Health (ICF) framework, MAF emphasizes a strengths-based, participatory, and human rights-oriented approach to early childhood intervention. The purpose of this report is to describe the development of the MAF framework and the details of its innovative, culturally sensitive implementation in Taiwan, using implementation science principles to support the national adoption of My Abilities ID Cards (ABIDs). Central to the MAF initiative is the ABID, a tool that empowers children to express their abilities, preferences, and support needs using their own voice or preferred mode of communication. Guided by implementation science, the MAF team in Taiwan engaged stakeholders in urban and rural centers, developed training programs, and integrated ABID into early intervention and special education systems. Preliminary outcomes indicate that from 2021 to 2025, 140 training sessions reached a total attendance of 6961. Notably, satisfaction with training was high (>95%), and practitioner subjective competence adopting positive language improved. The number of children under age 12 creating ABIDs grew to approximately 700. Preliminary evidence suggests that ABIDs might increase systematic adoption of children’s opinions in assessments and interventions. Qualitative feedback from parents and professionals highlights the contribution of ABIDs, ensuring self-expression, motivation, and meaningful participation. The pioneering Taiwanese experience demonstrates the feasibility and impact of MAF and ABIDs in promoting children’s rights and participation, offering practical insights for global adaptation in diverse contexts. Full article

Ensuring that students are motivated in and beyond middle school is an important educational objective. Based on achievement goal theory, research has demonstrated that the classroom motivational climate can influence students’ motivation and achievement. Instructional practices that foster mastery goal structures have been proposed within the TARGET framework. However, relatively few studies have explored students’ views of these, and thus, the aim of the current study was to explore which instructional practices students perceive as conducive to the classroom motivational climate. In the current qualitative study, 13 students (Grades 6 to 9) in Germany were asked about their past experiences and expressed their desires in semi-structured interviews. A deductive content analysis was utilized to identify instructional practices under the TARGET dimensions. The results provide insights into students’ preferred instructional practices, which are compared with those previously suggested for fostering mastery goal structures in the Discussion. Preliminary implications for practice and future research are suggested. Full article

The continuous expansion of the Internet of Things (IoT) has intensified the need to evaluate and guarantee the quality of entropy sources used in random number generation, an essential element in securing communications used in IoT ecosystems. This work presents an automated and web-based framework designed to execute and analyze the results of statistical tests defined in the NIST SP 800-22 standard, enabling systematic assessment of entropy sources and random numbers generators in IoT devices and environments. The proposed system integrates a Python-based backend built upon an optimized implementation of the original NIST suite, along with an intuitive web interface that facilitates configuration, monitoring, and parallel execution of tests through Representational State Transfer (REST) endpoints. Session management based on Redis ensures reliable and concurrent operation of multiple users or devices while maintaining isolation and data integrity. To demonstrate its applicability, an emulated IoT ecosystem was implemented in which multiple virtual devices periodically and asynchronously request real-time validation of their local random numbers generators. The obtained results confirm the system’s capability to detect deficiencies in pseudo random generators and validate true random number sources, highlighting its potential as a diagnostic and verification tool for distributed IoT security systems. The tool developed in this work is fully accessible to the public, allowing researchers, engineers, and practitioners to evaluate random number generators without requiring specialized hardware or proprietary software. Full article

The short service life of oil fields and limited oil deposits in shallow waters requires a constant search for new oil fields in deeper waters. Compliant towers are one of the most suitable structures for water depths between 300 m and 600 m, where fixed structures are economically unfeasible. The principal characteristics of compliant towers include a minimal number of cross sections in their main structural elements throughout their height, combined with significant flexibility and buoyancy. Due to their flexibility and buoyancy, gravitational loads at the deck do not significantly impact the foundation. Moreover, compliant towers do not need advanced building systems, installation processes or special maintenance. Additionally, the large height of compliant towers reduces their natural frequencies, which prevents them from being within the frequency range of environmental forces capable of producing structural resonance. For this reason, efforts are made to design compliant towers to be as flexible as possible. Hence, this research is focused on examining a methodology for the structural analysis of compliant towers at ultimate and serviceability limit states for a water depth of 550 m in the Mexican waters of the Gulf of Mexico. Full article

Machine translation for low-resource languages such as Kazakh remains a complex task due to the scarcity of training data, intricate morphological structures, and culturally specific linguistic characteristics. This study presents the first extensive exploration of fine-tuning large language models for automated post-editing of Kazakh translations. We introduce KazPE, a carefully curated and annotated dataset that includes 10,008 training sentences and 311 test sentences spanning six domains: the medical, scientific, journalistic, oral, fiction, and legal. The dataset features detailed error classifications across 9 linguistic categories. Our method fine-tunes GPT-4.1 mini using supervised learning to enhance translation quality by systematically correcting targeted errors. According to human evaluations, conducted on a continuous 0–1 scale, the fine-tuned model achieves an average quality score of 0.84, surpassing the baseline score of 0.80, corresponding to a 5% relative improvement. The greatest improvements are observed in handling morphological and lexical errors, as well as in domain-specific texts—particularly in legal (+17%) and medical (+22%) domains. In addition, the translations were evaluated using the automatic metrics: BLEU, TER and METEOR. The fine-tuned model shows improvements across all automatic metrics (BLEU, TER, METEOR), which confirms better n-gram overlap with reference texts, fewer edits needed, and enhanced lexical and semantic alignment with the reference texts. Comprehensive error analysis shows that the fine-tuning process effectively mitigates challenges related to Kazakh’s agglutinative morphology and specialized terminology, while preserving accuracy on already correct sentences. This research establishes the first structured evaluation framework for Kazakh translation post-editing and offers valuable guidance for enhancing machine translation in morphologically rich, low-resource languages. To facilitate further progress in Turkic language processing, we publicly release the KazPE dataset, trained models, and evaluation framework. Full article

Globally, diverse agricultural production strategies have been implemented to address the impacts of climate change, with sustainable farming models emerging as key approaches, particularly in regions affected by environmental degradation. Latin America is especially vulnerable due to its strong dependence on agriculture, pressure on natural resources, and persistent socioeconomic inequalities in rural areas. This study presents a review of sustainable agricultural practices, with particular attention to evidence from Latin America on sustainable agricultural practices as effective strategies for climate change adaptation and mitigation, natural resource conservation, and food security enhancement. Special emphasis is placed on the role of the bioeconomy and the integration of traditional knowledge with modern agricultural management, highlighting their combined contribution to agroecosystem resilience. The review critically examines how sustainable agricultural practices influence soil health, agroecosystem resilience, and the long-term sustainability of agricultural production within a circular economy framework. The findings indicate that practices such as no-till farming, crop rotation, organic fertilization, and integrated soil management significantly improve soil structure, nutrient retention, organic matter content, and soil biodiversity. These practices also reduce soil degradation, enhance resource-use efficiency, and promote carbon sequestration, thereby contributing directly to climate change mitigation. Overall, the results underscore the importance of holistic approaches that integrate traditional practices with technological innovations and highlight the need for further applied research across diverse environmental and socioeconomic contexts, particularly to address adoption barriers among smallholder farmers and to optimize sustainable agricultural strategies at local and regional scales. Full article

Parents of children with special educational needs (SEN) experience elevated stress, anxiety, and depression, a challenge compounded by insufficient emotional support services. This study developed and evaluated a culturally adapted online counselling programme for Hong Kong Chinese parents of adolescents with SEN, integrating Solution-Focused Brief Therapy (SFBT) and Mindfulness Training. The 8-week programme aimed to reduce parental distress and improve family dynamics by emphasising strengths, fostering self-compassion, and enhancing empathetic interactions. A mixed-methods approach was used, combining standardised self-report measures such as the Parental Stress Scale (PSS), Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), and Child Behaviour Checklist (CBCL), with qualitative interviews and behavioural observations. Quantitative analysis of pre–post data via paired samples t-tests indicated significant within-group reductions in anxiety across all groups and in depression for the active control group. However, between-group comparisons of post-test scores did not show clear superiority of the experimental intervention. Qualitative findings highlighted perceived benefits, including increased emotional regulation, a shift towards a strengths-based perspective, and enhanced self-compassion, with the programme’s cultural adaptation deemed crucial for engagement. The study addresses a significant service gap and provides preliminary evidence for the acceptability and potential mechanisms of an integrative online model, while highlighting the need for further research with larger samples. Full article

Background: Smart mining is rapidly evolving through the integration of automation, advanced sensing technologies, and Machine Learning (ML). Methods: This Systematic Literature Review (SLR), based on 99 peer-reviewed studies published between 2021 and 2025 and synthesized following PRISMA 2020 guidelines, analyzes the current role of ML algorithms in the mining sector, focusing on their applications, algorithmic prevalence, benefits, and challenges. Results: Machine Learning algorithms are primarily applied to equipment failure prediction, ore classification, grade and flotation control, transportation optimization, and environmental monitoring. The most frequently adopted algorithms include Decision Tree-based models, Artificial Neural Networks, Deep Learning architectures, Support Vector Machines, K-means clustering, and Gradient Boosting methods, reflecting different trade-offs between interpretability, computational complexity, and predictive performance. Reported benefits include improved operational efficiency, cost reduction, enhanced predictive maintenance, improved decision-making, and increased safety and environmental performance. However, widespread adoption remains constrained by limited availability of high-quality data, data heterogeneity, high implementation costs, shortages of specialized personnel, algorithm interpretability issues, and cybersecurity risks. Conclusions: Overall, ML algorithms emerge as key enablers of intelligent and sustainable mining. The review highlights the need for explainable and robust algorithms, improved multimodal data integration, and large-scale real-world validation to support the next generation of smart mining systems. Full article

Antimicrobial resistance (AMR) poses a severe threat to global health by limiting treatment options and increasing clinical and economic burdens. This review synthesizes evidence showing that resistance evolution is strongly shaped by antibiotic pressure, leading to the accumulation of adaptive mutations, activation of efflux systems, and widespread dissemination of resistance determinants across clinical, animal, and environmental settings. We highlight recent genomic, metagenomic, and structural findings that elucidate the molecular basis of AMR, with particular emphasis on horizontal gene transfer mediated by mobile genetic elements such as plasmids, integrons, and transposons. Analyses across One Health interfaces reveal extensive sharing of antimicrobial resistance genes among humans, livestock, and environmental reservoirs, identifying Enterobacteriaceae and ESKAPE pathogens as key hubs of resistance dissemination. Special focus is placed on Acinetobacter baumannii, where phylogenetic and three-dimensional structural analyses of class D β-lactamases OXA-23 and OXA-24/40 demonstrate a conserved catalytic framework coupled with substantial sequence and conformational variability. These structural differences likely influence carbapenem specificity and resistance levels. Collectively, the findings underscore how genetic diversity, mobile elements, and structural adaptation converge to drive AMR, reinforcing the need for integrated genomic and structural approaches to guide surveillance and antimicrobial development. Full article

Background/Objectives: Pregnancy-associated breast cancer (PABC) is uncommon but increasingly encountered as more women delay childbearing. Its prognostic impact remains controversial, particularly for cancers diagnosed in the early postpartum period. We aimed to synthesize contemporary evidence on the prognosis of breast cancer diagnosed during pregnancy or within 12 months after delivery compared with breast cancer in other young women, with a specific focus on differences between pregnancy time and postpartum disease. Methods: We performed a systematic review of observational studies published from 2005 onwards that reported oncologic outcomes for women with invasive PABC versus non-PABC comparators or PABC-only cohorts with internal timing comparisons. PubMed, Cochrane Library, Scopus and ClinicalTrials.gov were systematically searched using predefined strategies. Two reviewers independently screened records, extracted data and assessed risk of bias using the ROBINS-E tool, treating PABC status as the exposure. Because of substantial heterogeneity in PABC definitions, outcomes and adjustment sets, no meta-analysis was performed. Results: Twenty-one observational studies (single-center, multicenter and population-based) were included. PABC cases more often presented with larger tumors, higher nodal burden, high-grade and hormone receptor-negative/HER2-positive phenotypes and worse survival compared to non-PABC controls. In most contemporary cohorts that delivered guideline-oriented therapy and adjusted for stage and tumor biology, a diagnosis during pregnancy was not an independent predictor of poorer disease-free or overall survival. In contrast, multiple large registry and institutional studies reported significantly higher risks of recurrence and death for cancers diagnosed in the early postpartum period, even after multivariable adjustment. Conclusions: Current evidence suggests that pregnancy itself does not inevitably worsen breast cancer prognosis when treatment is not compromised. However, breast cancers diagnosed soon after childbirth represent a distinct high-risk subgroup. These findings support full-intensity, guideline-based therapy during pregnancy and highlight the need for special attention and further research focused on postpartum breast cancer. Full article

Generative Artificial Intelligence (GAI) is rapidly reshaping pedagogical practices and offering new opportunities to advance sustainability within higher education. This study investigates the extent to which postgraduate students utilize GAI to support Sustainable and Environmentally Responsible Teaching Practices (SERTPs), and examines whether this use varies across demographic, academic, and technological characteristics. A descriptive quantitative design was employed, involving 310 postgraduate students from the College of Education at King Khalid University. Data were collected using a validated and highly reliable instrument measuring five dimensions of GAI-supported sustainable teaching. Descriptive and inferential analyses, including t tests, one-way ANOVA, and LSD post hoc comparisons, were conducted. The findings reveal that postgraduate students demonstrate a moderate overall level of GAI use in advancing SERTPs, with the highest engagement occurring in the promotion of sustainable educational practices. Significant differences were only found in relation to students’ levels of technology use and students’ levels of GAI use, indicating that frequent and sophisticated engagement with AI tools is the strongest predictor of sustainable teaching practices. No significant differences emerged across gender, age, academic department, program level, or specialization. The study highlights the need for targeted training and institutional strategies that enhance students’ AI proficiency, thereby enabling GAI to serve as a catalyst for environmentally responsible and sustainable teaching practices in higher education. Full article

The growing occurrence of emerging contaminants, particularly pharmaceutical residues, in aquatic environments represents a major environmental concern worldwide. While pharmaceutical contamination has been increasingly studied in marine systems, port environments remain largely understudied despite their complex anthropogenic pressures. This study investigates the occurrence, spatial distribution, and potential environmental risk of pharmaceutical residues in surface waters of the port of Cartagena, a multifunctional port on the Spanish Mediterranean coast. Fifteen pharmaceuticals were analysed across nine sampling sites, of which six were not detected. Diclofenac and several antibiotics (erythromycin, azithromycin, clindamycin, and trimethoprim) were the most frequently detected compounds, reaching maximum concentrations of up to 12,294.1 ng/L. Elevated concentrations were observed at sites associated with intense human activity, while the detection of multiple pharmaceuticals at a designated Special Area of Conservation suggests additional diffuse pollution sources, likely linked to insufficient wastewater management in nearby informal settlements. Most detected concentrations exceeded established environmental-quality or risk-threshold values, indicating a potential threat to marine ecosystems. These findings highlight the vulnerability of port environments to pharmaceutical pollution and underscore the need for continuous monitoring programs to support effective environmental management and biodiversity protection in coastal port areas. Full article

The turning point that sparked the initiation of quantum theory was the Planck–Einstein postulate that the energy of a monochromatic radiation field is quantized in terms of photons, and this was followed by the development of the principles of quantum mechanics. Although some conceptual issues remain to be resolved, quantum mechanics is regarded as a well-established discipline which may lead to the unraveling of the nature of matter in general. Today, the influence of quantum mechanics is evident in its applications, with remarkable technological advances involving diverse aspects of the physical world. What appears to need particular attention, however, (after a hundred years have elapsed since the birth of quantum mechanics) is the impact that the concept of the ‘quantum’ has had beyond traditional quantum mechanics. The paper describes how the ‘quantum’ concept has influenced and continues to influence developments in physical systems, which are essentially classical, in that they are basically governed, entirely, or in part, by non-quantum laws, but in which, the physics is distinguished by its own special quantum—the photon analogue. The paper illustrates this by considering, as prototype examples, bulk plasmons and phonons. The study outlines the systematic quantization of plasmons and phonons, both of the polariton (transverse) forms and their longitudinal forms, and discusseshow these interact with quantum systems such as electrons, atoms, and condensed matter. It is demonstarted using one case, namely, involving longitudinal plasmons, how utilizing quantum concepts and techniques facilitate their interaction with matter, as in electron energy loss spectroscopy. Full article

The purpose of this study is to explore how adolescents in self-contained classrooms or schools for students with autism or ADHD, with no prior involvement in criminality, perceive and interpret different forms of early offending behavior through fictional case stories. The study specifically aims to examine their ability to discern what constitutes offending behavior, based on the double empathy problem. In total, 13 participants currently receiving secondary-level education (grades 10–12, aged 16–20 years) in self-contained classes at schools for adolescents with autism or ADHD participated. No student had cognitive disabilities or had been involved in any criminal act or criminal justice issues. The students were individually given three fictional written cases of offending behavior (theft, physical assault, and sexual assault). Audio-recorded stimulated recall interviews were obtained while the students solved tasks in relation to the cases, and these were analyzed to capture whether and what aspects of early offending were discerned. Overall, the results indicated limited awareness and enhanced social vulnerability, risking unwitting engagement in early offending behavior. Adapting social science education to students’ special educational needs to understand social interactions might be used to prevent and enhance their awareness of early offending behavior. Full article

Dissecting cellulitis of the scalp (DCS) is a chronic, inflammatory follicular occlusion disorder characterized by painful nodules, abscesses, and sinus tracts that lead to scarring alopecia. The therapeutic goal is to limit disease progression and the extent of scarring. Although DCS is traditionally managed with systemic retinoids, antibiotics, and surgical interventions, therapeutic responses are variable and long-term remission remains challenging. Recent insights into the immunological overlap between DCS, hidradenitis suppurativa (HS), and other autoinflammatory follicular disorders have expanded therapeutic options, particularly with biologic agents targeting tumor necrosis factor alpha (TNF-α), interleukin (IL)-17, and IL-23 pathways, as well as Janus kinase (JAK) inhibitors. This review synthesizes the current evidence on medical, procedural, and emerging targeted therapies for DCS, incorporating data from case reports, case series, retrospective cohorts, and recent systematic reviews up to 2025. Special emphasis is placed on the evolving role of biologics and small-molecule inhibitors, which show growing promise for refractory or syndromic presentations. Current evidence supports a stepwise, phenotype-driven approach in which systemic retinoids remain first-line systemic therapy, while biologics represent a rational and increasingly evidence-supported option for moderate-to-severe, treatment-resistant, or syndromic disease. Further controlled studies are needed to define optimal sequencing, duration, and combination strategies for long-term management. Full article