Search Results (642)

Monitoring water quality is crucial for achieving clean water and sanitation goals, particularly in remote areas. The project “Morbidity vs. Water Quality for Human Consumption in Tonosí: A Pilot Study” aimed to enhance water quality assessments in Panama using advanced analytical techniques to assess volatile organic compounds, heavy metals, and microbiological pathogens. To support this, the Technical Unit for Water Quality (UTECH) was established, featuring a novel mobile laboratory with cutting-edge technology for accurate testing, minimal chemical reagent use, reduced waste generation, and equipped with a solar-powered battery system. The aim of this paper is to explore the design, deployment, and impact of the UTECH. Furthermore, this study presents results from three sampling points in Tonosí, where several parameters exceeded regulatory limits, demonstrating the capabilities of the UTECH and highlighting the need for ongoing monitoring and intervention. The study also assesses the environmental, social, and economic impacts of the UTECH in alignment with the Sustainable Development Goals and national initiatives. Finally, a SWOT analysis illustrates the UTECH’s potential to improve water quality assessments in Panama while identifying areas for sustainable growth. The study showcases the successful integration of advanced mobile laboratory technologies into water quality monitoring, contributing to sustainable development in Panama and offering a replicable model for similar initiatives in other regions. Full article

As the generation of Municipal Solid Waste (MSW) has exponentially increased, this poses a challenge for waste managers, such as municipalities, to effectively control waste streams. If waste streams are not managed correctly, they negatively contribute to climate change, marine plastic pollution and human health effects. Therefore, waste streams need to be identified, categorised and valorised to ensure that the most effective waste management strategy is employed. Research suggests that a more efficient process of identifying and categorising waste at the source can achieve this. Therefore, the aim of the paper is to identify the state of research of AI-powered drones in identifying and categorising waste. This paper will conduct a systematic review and meta-analysis on the application of drone technology integrated with image sensing technology and deep learning methods for waste management. Different systems are explored, and a quantitative meta-analysis of their performance metrics (such as the F1 score) is conducted to determine the best integration of technology. Therefore, the research proposes designing and developing a hybrid deep learning model with integrated architecture (YOLO-Transformer model) that can capture Multispectral imagery data from drones for waste stream identification, categorisation and potential valorisation for waste managers in small-scale environments. Full article

Fibrate pharmaceuticals (fibrates), as a widespread class of emerging contaminants, pose potential risks to both ecological systems and human health. The photocatalytic system based on nitrogen (N) and fluorine (F) co-doped TiO₂ nanotube arrays (NF-TNAs) provides a renewable solution for fibrate pharmaceutical removal from water, powered by inexhaustible sunlight. In this study, the degradation of two typical fibrates, i.e., bezafibrate (BZF) and ciprofibrate (CPF), under simulated sunlight irradiation through NF-TNAs were investigated. The photocatalytic degradation of BZF/CPF was achieved through combined radical and non-radical oxidation processes, while the generation and reaction mechanisms of associated reactive oxygen species (ROS) were examined. Electron paramagnetic resonance detection and quenching tests confirmed the existence of h⁺, •OH, O₂^•−, and ¹O₂, with O₂^•− playing the predominant role. The transformation products (TPs) of BZF/CPF were identified through high-resolution mass spectrometry analysis combined with quantum chemical calculations to elucidate the degradation pathways. The influence of co-existing ions and typical natural organic matters (NOM) on BZF/CPF degradation were also tested. Eventually, the ecological risk of BZF/CPF transformation products was assessed through quantitative structure–activity relationship (QSAR) modeling, and the results showed that the proposed photocatalytic system can largely alleviate fibrate toxicity. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

The development of multifunctional conductive hydrogels with rapid self-healing capabilities and powerful sensing functions is crucial for advancing wearable electronics. This study designed and prepared a polyvinyl alcohol (PVA)–borax hydrogel incorporating carbon nanotubes (CNTs) and biomass carbon nanospheres (CNPs) as dual-carbon fillers. This hydrogel exhibits excellent conductivity, mechanical flexibility, and self-recovery properties. Serving as a highly sensitive piezoresistive sensor, it efficiently converts mechanical stimuli into reliable electrical signals. Sensing tests demonstrate that the CNT/CNP/PVA–borax hydrogel sensor possesses an extremely fast response time (88 ms) and rapid recovery time (88 ms), enabling the detection of subtle and rapid human motions. Furthermore, the hydrogel sensor also exhibits outstanding cyclic stability, maintaining stable signal output throughout continuous loading–unloading cycles exceeding 3200 repetitions. The hydrogel sensor’s characteristics, including rapid self-healing, fast-sensing response/recovery, and high fatigue resistance, make the CNT/CNP/PVA–borax conductive hydrogel an ideal choice for multifunctional wearable sensors. It successfully monitored various human motions. This study provides a promising strategy for high-performance self-healing sensing devices, suitable for next-generation wearable health monitoring and human–machine interaction systems. Full article

The relationship between ecosystem services (ESs) and human well-being (HWB) is a central issue of sustainable development. However, current research often relies on qualitative frameworks or indicator-based assessments, limiting a comprehensive understanding of the relationship between natural environment and human acquisition, which still needs to be strengthened. As an element transferred in the natural–society coupling system, carbon can assist in characterizing the dynamic interactions within coupled human–natural systems. Carbon, as a fundamental element transferred across ecological and social spheres, offers a powerful lens to characterize these linkages. This study develops and applies a novel analytical framework that integrates carbon flow as a unifying metric to quantitatively assess the spatiotemporal dynamics of the land use and land cover change (LUCC)–ESs–HWB nexus in Guizhou Province, China, from 2000 to 2020. The results show that: (1) Ecosystem services in Guizhou showed distinct trends from 2000 to 2020: supporting and regulating services declined and then recovered, and provisioning services steadily increased, while cultural services remained stable but varied across cities. (2) Human well-being generally improved over time, with health remaining stable and the HSI rising across most cities, although security levels fluctuated and remained low in some areas. (3) The contribution of ecosystem services to human well-being peaked in 2010–2015, followed by declines in central and northern regions, while southern and western areas maintained or improved their levels. (4) Supporting and regulating services were positively correlated with HWB security, while cultural services showed mixed effects, with strong synergies between culture and health in cities like Liupanshui and Qiandongnan. Overall, this study quantified the coupled dynamics between ecosystem services and human well-being through a carbon flow framework, which not only offers a unified metric for cross-dimensional analysis but also reduces subjective bias in evaluation. This integrated approach provides critical insights for crafting spatially explicit land management policies in Guizhou and offers a replicable methodology for exploring sustainable development pathways in other ecologically fragile karst regions worldwide. Compared with conventional ecosystem service frameworks, the carbon flow approach provides a process-based, dynamic mediator that quantifies biogeochemical linkages in LUCC–ESs–HWB systems, which is particularly important in fragile karst regions. However, we acknowledge that further empirical comparison with traditional ESs metrics could strengthen the framework’s generalizability. Full article

This paper presents an innovative exam-creation and grading system powered by advanced natural language processing and local large language models. The system automatically generates clear, grammatically accurate questions from both short passages and longer documents across different languages, supports multiple formats and difficulty levels, and ensures semantic diversity while minimizing redundancy, thus maximizing the percentage of the material that is covered in the generated exam paper. For grading, it employs a semantic-similarity model to evaluate essays and open-ended responses, awards partial credit, and mitigates bias from phrasing or syntax via named entity recognition. A major advantage of the proposed approach is its ability to run entirely on standard personal computers, without specialized artificial intelligence hardware, promoting privacy and exam security while maintaining low operational and maintenance costs. Moreover, its modular architecture allows the seamless swapping of models with minimal intervention, ensuring adaptability and the easy integration of future improvements. A requirements–compliance evaluation, combined with established performance metrics, was used to review and compare two popular multilingual LLMs and monolingual alternatives, demonstrating the system’s effectiveness and flexibility. The experimental results show that the system achieves a grading accuracy within a 17% normalized error margin compared to that of human experts, with generated questions reaching up to 89.5% semantic similarity to source content. The full exam generation and grading pipeline runs efficiently on consumer-grade hardware, with average inference times under 30 s. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

As streaming platforms adopt artificial intelligence (AI)-powered subtitle systems to satisfy global demand for instant localization, the cognitive impact of these automated translations on viewers remains largely unexplored. This study used a web-based eye-tracking protocol to compare the cognitive load that GPT-4o-generated Arabic subtitles impose with that of professional human translations among 82 native Arabic speakers who viewed a 10 min episode (“Syria”) from the BBC comedy drama series State of the Union. Participants were randomly assigned to view the same episode with either professionally produced Arabic subtitles (Amazon Prime’s human translations) or machine-generated GPT-4o Arabic subtitles. In a between-subjects design, with English proficiency entered as a moderator, we collected fixation count, mean fixation duration, gaze distribution, and attention concentration (K-coefficient) as indices of cognitive processing. GPT-4o subtitles raised cognitive load on every metric; viewers produced 48% more fixations in the subtitle area, recorded 56% longer fixation durations, and spent 81.5% more time reading the automated subtitles than the professional subtitles. The subtitle area K-coefficient tripled (0.10 to 0.30), a shift from ambient scanning to focal processing. Viewers with advanced English proficiency showed the largest disruptions, which indicates that higher linguistic competence increases sensitivity to subtle translation shortcomings. These results challenge claims that large language models (LLMs) lighten viewer burden; despite fluent surface quality, GPT-4o subtitles demand far more cognitive resources than expert human subtitles and therefore reinforce the need for human oversight in audiovisual translation (AVT) and media accessibility. Full article

Electrospun nanofiber-based triboelectric nanogenerators (TENGs) have emerged as a highly promising class of self-powered sensors for a broad range of applications, particularly in intelligent sensing technologies. By combining the advantages of electrospinning and triboelectric nanogenerators, these sensors offer superior characteristics such as high sensitivity, mechanical flexibility, lightweight structure, and biocompatibility, enabling their integration into wearable electronics and biomedical interfaces. This review presents a comprehensive overview of recent progress in electrospun nanofiber-based TENGs, covering their working principles, operating modes, and material composition. Both pure polymer and composite nanofibers are discussed, along with various electrospinning techniques that enable control over morphology and performance at the nanoscale. We explore their practical implementations in both contact-type and non-contact-type sensing, such as human–machine interaction, physiological signal monitoring, gesture recognition, and voice detection. These applications demonstrate the potential of TENGs to enable intelligent, low-power, and real-time sensing systems. Furthermore, this paper points out critical challenges and future directions, including durability under long-term operation, scalable and cost-effective fabrication, and seamless integration with wireless communication and artificial intelligence technologies. With ongoing advancements in nanomaterials, fabrication techniques, and system-level integration, electrospun nanofiber-based TENGs are expected to play a pivotal role in shaping the next generation of self-powered, intelligent sensing platforms across diverse fields such as healthcare, environmental monitoring, robotics, and smart wearable systems. Full article

Despite remarkable progress in cancer immunotherapy, many agents that show efficacy in murine or in vitro models fail to translate clinically. Zebrafish (Danio rerio) have emerged as a powerful complementary model that addresses several limitations of traditional systems. Their optical transparency, genetic tractability, and conserved immune and oncogenic signaling pathways enable high-resolution, real-time imaging of tumor–immune interactions in vivo. Importantly, zebrafish offer a unique opportunity to study the core mechanisms of health and sickness, complementing other models and expanding our understanding of fundamental processes in vivo. This review provides an overview of zebrafish immune system development, highlighting tools for tracking innate and adaptive responses. We discuss their application in modeling immune evasion, checkpoint molecule expression, and tumor microenvironment dynamics using transgenic and xenograft approaches. Platforms for high-throughput drug screening and personalized therapy assessment using patient-derived xenografts (“zAvatars”) are evaluated, alongside limitations, such as temperature sensitivity, immature adaptive immunity in larvae, and interspecies differences in immune responses, tumor complexity, and pharmacokinetics. Emerging frontiers include humanized zebrafish, testing of next-generation immunotherapies, such as CAR T/CAR NK and novel checkpoint inhibitors (LAG-3, TIM-3, and TIGIT). We conclude by outlining the key challenges and future opportunities for integrating zebrafish into the immuno-oncology pipeline to accelerate clinical translation. Full article

Gamification has emerged as a powerful strategy in digital education, enhancing engagement, motivation, and learning outcomes. However, most research lacks theoretical grounding and often applies multiple and uncontextualized game elements, limiting its impact and replicability. To address these gaps, this study introduces a Systemic Gamification Theory (SGT)—a comprehensive, human-centered model for designing and evaluating inclusive and effective gamified educational environments. Sustained in Education, Human–Computer Interaction, and Psychology, SGT is structured around four core principles, emphasizing the importance of integrating game elements (1—Integration) into cohesive systems that generate emergent outcomes (2—Emergence) aligned synergistically (3—Synergy) with contextual needs (4—Context). The theory supports inclusivity by accounting for individual traits, situational dynamics, spatial settings, and cultural diversity. To operationalize SGT, we developed two tools: i. a set of 10 Heuristics to guide and analyze effective and inclusive gamification; and ii. a Framework for designing and evaluating gamified systems, as well as comparing research methods and outcomes across different contexts. These tools demonstrated how SGT enables robust, adaptive, and equitable gamified learning experiences. By advancing theoretical and practical development, SGT fosters a transformative approach to gamification, enriching multimedia learning through thoughtful system design and reflective evaluation practices. Full article

Staphylococcus aureus (S. aureus) is a major cause of opportunistic infections in humans and animals, leading to severe systemic diseases. The rise of MDR strains associated with animal carriage poses significant health challenges, underscoring the need to investigate animal-derived S. aureus. Objectives: This study examined the genotypic relatedness and phenotypic profiles of antimicrobial resistance in S. aureus, previously sampled from nostril swabs of healthy horses from two geographically distant Brazilian states (Northeast and South), separated by over 3700 km. The study also sought to confirm the presence of methicillin-resistant (MRSA) and borderline oxacillin-resistant (BORSA) strains and to characterize the isolates through molecular typing using PCR. Methods: Among 123 screened staphylococci, 21 isolates were confirmed as S. aureus via biochemical tests and PCR targeting species-specific genes (femA, nuc, coa). Results: REP-PCR analysis generated genotypic profiles, revealing four antimicrobial resistance patterns, with MDR observed in ten isolates. Six isolates exhibited cefoxitin resistance, suggesting methicillin resistance, despite the absence of the mecA gene. REP-PCR demonstrated high discriminatory power, grouping the isolates into five major clusters. Conclusions: The genotyping indicated no clustering by geographical origin, highlighting significant genetic diversity among S. aureus strains colonizing horses’ nostrils in Brazil. These findings highlight the widespread and varied nature of S. aureus among horses, contributing to a deeper understanding of its epidemiology and resistance profiles in animals across diverse regions. Ultimately, this genetic diversity can pose a public health risk that the epidemiological surveillance services must investigate. Full article

Background/Objectives: This paper introduces a pedagogically grounded framework for transforming canonical literary texts in primary education through generative AI. Guided by multiliteracies theory, Vygotskian pedagogy, and epistemic justice, the system aims to enhance interpretive literacy, developmental alignment, and cultural responsiveness among learners aged 7–12. Methods: The proposed system enables educators to perform age-specific text simplification, visual re-narration, lexical reinvention, and multilingual augmentation through a suite of modular tools. Central to the design is the Ethical–Pedagogical Validation Layer (EPVL), a GPT-powered auditing module that evaluates AI-generated content across four normative dimensions: developmental appropriateness, cultural sensitivity, semantic fidelity, and ethical transparency. Results: The framework was fully implemented and piloted with primary educators (N = 8). The pilot demonstrated high usability, curricular alignment, and perceived value for classroom application. Unlike commercial Large Language Models (LLMs), the system requires no prompt engineering and supports editable, policy-aligned controls for normative localization. Conclusions: By embedding ethical evaluation within the generative loop, the framework fosters calibrated trust in human–AI collaboration and mitigates cultural stereotyping and ideological distortion. It advances a scalable, inclusive model for educator-centered AI integration, offering a new pathway for explainable and developmentally appropriate AI use in literary education. Full article

Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information (CSI) enables device-free recognition by capturing variations in signal propagation caused by human motion. This makes Wi-Fi sensing highly attractive for ambient healthcare, security, and elderly care applications. However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. To address these sensor-related challenges, we propose TCN-MAML, a novel framework that integrates temporal convolutional networks (TCN) with model-agnostic meta-learning (MAML) for efficient cross-subject adaptation in data-scarce conditions. We evaluate our approach on a public Wi-Fi CSI dataset using a strict cross-subject protocol, where training and testing subjects do not overlap. The proposed TCN-MAML achieves 99.6% accuracy, demonstrating superior generalization and efficiency over baseline methods. Experimental results confirm the framework’s suitability for low-power, real-time HAR systems embedded in IoT sensor networks. Full article