Search Results (10,901)

Precision Agriculture (PA) is increasingly applied to nutraceutical cropping systems, where agronomic productivity must be integrated with the stabilization of phytochemical quality and environmental sustainability. This structured narrative review synthesizes scientific evidence (primarily 2010–2025) on the use of Unmanned Aerial Vehicle (UAV)-based multispectral and thermal sensing, LiDAR-derived canopy characterization, Internet of Things (IoT) monitoring, and artificial intelligence (AI)-driven analytics in medicinal, aromatic, and functional crops. The literature indicates that PA enhances high-resolution monitoring of crop–environment interactions, supporting site-specific irrigation, nutrient management, and stress detection. Under validated conditions, these interventions are associated with improved yield stability, resource-use efficiency, and modulation of secondary metabolite accumulation. However, reported outcomes vary substantially across species, agroecological contexts, and experimental scales, and most studies remain plot-scale or pilot-scale, limiting large-scale generalization. Moringa oleifera Lam. is examined as a model species for Mediterranean and semi-arid systems. Evidence suggests that integrated spectral, structural, and environmental monitoring can support optimized irrigation scheduling, canopy uniformity, and phytochemical consistency. Nonetheless, genotype-specific calibration, multi-season validation, standardized metabolomic benchmarking, and cross-regional transferability remain significant research gaps. Overall, PA represents a scientifically promising but still maturing framework for nutraceutical agriculture. Future progress will require rigorous multi-site validation, improved model robustness, standardized sustainability metrics, and comprehensive economic assessments to ensure scalability and long-term impact. Full article

Double stator cylindrical linear oscillating generators (DSCLOGs) have been widely used in renewable energy power generation systems due to their higher power density, higher reliability, and low-noise characteristics. However, the detent force of a DSCLOG is an inevitable problem, which causes oscillations in the generator and leads to system instability. Conventionally, auxiliary teeth and skewed pole methods are employed to mitigate detent force, but these approaches often increase the overall machine size and the complexity of the manufacturing process. To solve this issue, an improved DSCLOG with curved teeth (CT-DSCLOG) is proposed to minimize the detent force. First, the structural characteristics and working principle of CT-DSCLOG are introduced. Then, to achieve a rapid and accurate analysis of the magnetic field in the irregular air gap, a 2D magnetic equivalent circuit (MEC) model is established by introducing Schwarz–Christoffel (S-C) mapping. And key structural parameters are identified through variance sensitivity analysis. Subsequently, a multi-objective optimization of the linear generator is performed using the Taguchi method combined with 3D finite element analysis (3D-FEA) to obtain the optimal structural parameters of CT-DSCLOG. Finally, the proposed structure is validated through prototype experiments. The results are provided to validate the effectiveness of the proposed structure. Full article

The anammox technology, as an efficient and energy-saving denitrification method, has been widely used in the field of wastewater treatment. Nevertheless, this process faces two key challenges in actual operation, namely the fluctuation of nitrite substrate supply and the residual nitrate, which greatly limits its promotion and application in a wider range. Although the traditional combined process of partial denitrification/anammox (PD/A) can generate nitrite substances, the coexistence of heterotrophic microorganisms and organic carbon sources in the system may have a significant inhibitory effect on the proliferation of Anammox bacteria. The sulfur-oxidizing bacteria (SOB) involved in the sulfur autotrophic denitrification process (SAD) have overlapping ecological niches with Anammox microorganisms and have stable nitrite enrichment characteristics. In view of this, sulfur-oxidizing bacteria are regarded as a potential candidate for combining with the Anammox process. However, the denitrification efficiency of this process is often restricted by the low solubility and poor bioavailability of substrates. At the same time, there are significant research gaps and data deficiencies regarding the key operating parameters for autotrophic short-range denitrification using elemental sulfur to achieve nitrite accumulation and the coupling application of this process with other wastewater treatment technologies. In view of this, this study is committed to comprehensively sorting out and evaluating the existing optimization methods of the elemental sulfur autotrophic denitrification process, while providing an in-depth analysis of its mechanism of action and environmental control factors. At the same time, this study also carried out innovative exploration on the modification process of the sulfur element from the frontier perspective of materials science and pointed out the key directions for subsequent optimization of the construction path of the elemental sulfur autotrophic denitrification system and for improving the denitrification process efficiency. In summary, this study systematically discusses the mechanism of action, practical application, and improvement scheme of PS⁰AD. Full article

With applications in psychology, security, and human–computer interaction, facial expression recognition (FER) has become an essential tool for non-verbal communication. Current research often categorizes expressions into micro- and macro-types, yet existing datasets suffer from inconsistent labelling for classes, limited diversity of the databases, and insufficient scale for the currently available datasets. To address these gaps, this work proposes a novel framework combining the diffusion model with pre-trained CNNs. Leveraging original images from established datasets, CASME II, we generate synthetic facial expressions to augment training data, mitigating bias and inconsistency. The synthetic dataset is evaluated using ResNet 50, VGG16 and Inception V3 architectures. Inception V3 trained on the proposed AI-generated dataset and tested using CASME II, VGG-16 with data augmentation applied is trained on CASME II and tested on the proposed AI-generated dataset, and Inception V3 with 30% freezing layers method is trained on the proposed AI-generated dataset and tested using CASME II. These all successfully achieved state-of-the-art performance. The data augmentation and freezing layers approaches significantly improved the performance of the models. Our proposed approaches achieved state-of-the-art performance and outperformed most of the existing state-of-the-art approaches benchmarked in this study. Full article

Background/Objectives: The ethical climate in a healthcare institution encompasses the shared perceptions of how ethical issues are managed in everyday practice. Our prior survey at the University Hospital of Split, Croatia, showed a simultaneous predominance of the “Rules” and “Laws and professional codes” ethical climates. Building on these findings, we explored how these climates manifest in everyday practice, how they align with staff values and guide their ethical decision-making, and how they are shaped by external factors. Methods: We conducted seven focus groups with 31 participants: nurses, residents, specialists, and members of the Hospital Ethics Committee (HEC). We identified patterns in the data using Graneheim and Lundman’s qualitative content analysis. Results: Three themes emerged from our analysis. We observed that the ethical climate was shaped predominantly by healthcare professionals themselves based on shared professional values and informal norms, rather than explicit institutional rules. Nurses, positioned as frontline workers, felt particularly exposed to ethical dilemmas, reporting perceived subordination to physicians, increased pressures from patients, and vulnerability in ethically ambiguous situations. The participants generally believed that institutional leadership insufficiently utilised existing tools, bodies, and mechanisms to support ethical behaviour and sanction misdemeanors, resulting in gaps in human resource management, a lack of practical protocols, and a weak HEC. Conclusions: To strengthen the ethical climate, institutional leadership should provide clear and practical guidelines, effectively utilise regulating bodies and support services, establish dedicated mechanisms to support nurses, and consistently enforce sanctions for unethical behaviour. Full article

Since 2025, 45% of the world’s population of 8.2 billion people has lived in cities, and by 2050, that number is expected to increase to 66%. As the number of people living in cities increases, natural landscapes will be transformed into impervious surfaces, leading to serious challenges and resulting in a phenomenon named the urban heat island (UHI) effect. Although urban thermal variation has been studied globally, few studies have examined the impact of land use transitions on local surface temperatures. This study aims to address this gap by investigating the impact of LULC transitions on the land surface temperature (LST) and the urban thermal field variation index (UTFVI) in the five most populated cities in Saudi Arabia between 2000 and 2024: Riyadh, Jeddah, Makkah, Madinah, and Dammam. This study provides not only a comprehensive overview of the cities in Saudi Arabia but also a detailed analysis of each city using a novel approach that integrates thermal land use analysis. In this study, Landsat TM-5, OLI-TIRS-8, and OLI2-TIRS2-9 were used to process the LULC using random forest machine learning and thermal indices. Fifteen LULC maps were generated and assessed based on four classifications across the cities and time periods: urban area, barren land, vegetation, and water. The difference-in-difference (DiD) analytical approach was used to compute the thermal effect size and compare the specified changed pixels (barren-to-urban, vegetation-to-urban) with stable urban. Then, the relationship between the LST and the NDVI–NDBI were investigated. The results show that the overall accuracy of the 15 LULC classifications ranged from 89.00% to 97.00%. The urban area increased across all the cities, with the greatest changes being 448.84, 179.67, 177.96, 126.33, and 95.69 km² in Riyadh, Jeddah, Dammam, Madinah, and Makkah, respectively. Furthermore, the vegetation cover increased in most of the cities over time. The LST of the urban areas increased by 8.31 °C in Riyadh, 5.24 °C in Jeddah, and 1.41 °C in Makkah in 2024 compared to 2000, while those in Dammam and Madinah decreased by 2.67 °C and 0.60 °C, respectively. This study delivers robust insights into two decades of urban surface temperature dynamics across major Saudi Arabian cities, offering critical evidence to inform UHI mitigation strategies and support the long-term sustainability of urban environments. Full article

Low Earth Orbit (LEO) satellite signals of opportunity (SOP) provide a viable positioning alternative in GNSS (Global Navigation Satellite System)-denied environments, yet their accuracy is fundamentally constrained by the low-quality orbital data typically available, such as SGP4 (Simplified General Perturbations model 4) predictions derived from Two-Line Elements (TLEs). To address this limitation, this paper proposes a dynamics-informed non-causal deep learning framework that enhances low-quality orbital data into high-fidelity trajectories for accurate SOP positioning. The proposed Non-Causal Dynamics-Informed Representation Temporal Convolutional Network (Non-Causal DIR-TCN) integrates phase space reconstruction and a Temporal Convolutional Network to explicitly model the chaotic dynamics inherent in LEO orbits, while relaxing the causality constraints of standard temporal convolutions to utilize both past and future context from the available SGP4 stream. Experimental results demonstrate that the framework significantly reduces orbit estimation errors and accelerates model convergence. When applied to LEO-SOP positioning, it achieves approximately 20% improvement in 2D positioning accuracy compared to conventional SGP4-based methods. This work effectively bridges the gap between accessible low-precision orbital data and high-accuracy state estimation, advancing the practical deployment of opportunistic signals for resilient positioning in challenging environments. Full article

This study investigates the relationship between viscosity and manufacturability of two-component silicones in extrusion-based additive manufacturing. A methodology is proposed to adapt commercially available, low-viscosity general-purpose silicones for direct 3D printing using the material extrusion system provided by Lynxter S300X. EcoFlex™ 00-50 silicone was modified through controlled additions of a thixotropic agent (THI-VEX), producing formulations with progressively increased viscosity. After a preliminary qualitative viscosity assessment, formulations were printed using identical process parameters and evaluated through a set of dedicated geometric benchmark specimens targeting critical failure modes, including unsupported thin walls, overhangs, gaps, and slender structures. Print outcomes were assessed via multi-rater visual inspection with inter-rater reliability analysis to ensure consistency. Results reveal a strong correlation between thixotropy and geometric fidelity, identifying the formulation containing 4.0 wt% THI-VEX as optimal under the tested conditions. The study provides practical design and process guidelines for silicone additive manufacturing and highlights the importance of integrated material–process optimization for reliable fabrication of soft, highly deformable materials. Full article

While the generation mechanisms of high-frequency oscillations caused by voltage source converter-based high-voltage direct current (VSC-HVDC) systems have been widely investigated, their propagation paths and spatial influence within the power grid remain largely unexplored. To address this critical gap, this paper proposes a novel oscillation propagation analysis method based on branch high-frequency active power distribution. First, from the perspective of equivalent impedance, the mechanism of high-frequency oscillation caused by the VSC-HVDC system in a single-machine system is elaborated. Then, mathematical modeling and theoretical derivations reveal that synchronous generators primarily act as passive impedances at high frequencies and that transmission lines significantly distort high-frequency voltage and current amplitudes. Crucially, high-frequency active power remains inherently stable and immune to these line distortion effects. Building upon these characteristics, an instantaneous power calculation method using broadband measurement data is derived to trace the propagation path. Comprehensive case studies using a 4-machine 2-area system and the New England 10-machine 39-bus system demonstrate that the proposed method can accurately map actual physical propagation paths, evaluate an oscillation’s influence range, and reliably locate a high-frequency oscillation’s source. Full article

Recent advances in large language models (LLMs) have made automated multiple-choice question (MCQ) generation increasingly feasible; however, reliably producing items that satisfy controlled cognitive demands remains a challenge. To address this gap, we introduce ReQUESTA, a hybrid, multi-agent framework for generating cognitively diverse MCQs that systematically target text-based, inferential, and main idea comprehension. ReQUESTA decomposes MCQ authoring into specialized subtasks and coordinates LLM-powered agents with rule-based components to support planning, controlled generation, iterative evaluation, and post-processing. We evaluated the framework in a large-scale reading comprehension study using academic expository texts, comparing ReQUESTA-generated MCQs with those produced by a single-pass GPT-5 zero-shot baseline. Psychometric analyses of learner responses assessed item difficulty and discrimination, while expert raters evaluated question quality across multiple dimensions, including topic relevance and distractor quality. Results showed that ReQUESTA-generated items were consistently more challenging, more discriminative, and more strongly aligned with overall reading comprehension performance. Expert evaluations further indicated stronger alignment with central concepts and superior distractor linguistic consistency and semantic plausibility, particularly for inferential questions. These findings demonstrate that hybrid, agentic orchestration can systematically improve the reliability and controllability of LLM-based generation, highlighting workflow design as a key lever for structured artifact generation beyond single-pass prompting. Full article

As Unmanned Aerial Vehicles (UAVs) become integral to critical infrastructure, ranging from precision agriculture to emergency disaster recovery, their security becomes a matter of systemic resilience. This paper provides a comprehensive thematic survey of the security landscape for unmanned devices, bridging the gap between low-level hardware vulnerabilities and high-level mission failures. We propose a multidimensional taxonomy that categorizes challenges into hardware roots of trust, swarm intelligence threats, and domain-specific applications. A primary focus is placed on the Resource–Security Paradox, where the energy cost of heavy cryptographic or AI defenses directly reduces flight endurance, creating a trade-off that adversaries exploit through battery-exhaustion attacks. Beyond standard threats, we analyze emerging risks in additive manufacturing supply chains, the “Sim-to-Real” gap in AI-driven perception, and the legal necessity of Digital Forensic Readiness (DFR) for post-incident attribution. Through a systematic review of defensive frameworks, including lightweight encryption, Mamba-KAN anomaly detection, and blockchain-anchored logging, we evaluate the effectiveness of current solutions against complex adversarial models. Finally, we identify critical research gaps, providing a roadmap for security-by-design in the next generation of critical infrastructure swarms. Full article

Cell and Gene Therapy (CGT) represents a paradigm shift in medicine, offering curative potential for previously intractable diseases. However, the complexity, high cost, and manufacturing challenges inherent in developing, producing, and administering these therapies hinder their widespread accessibility. This review examines the critical and increasingly synergistic role of Artificial Intelligence (AI) and Machine Learning (ML) in overcoming these barriers across the entire CGT lifecycle, from discovery and construct design to smart manufacturing, clinical translation, and regulatory applications. We analyze how AI-driven approaches fundamentally differ from conventional methods, facilitating rapid construct optimization, generating highly predictive translational models, enabling the vision of autonomous, digital-twin-driven manufacturing, and establishing new paradigms for pharmacovigilance and regulatory oversight. The integration of AI is not merely an incremental improvement but a foundational transformation, positioning CGT to move from niche, bespoke treatments to scalable, accessible, and highly personalized medical modalities. We conclude by discussing current gaps, particularly data scarcity and regulatory uncertainty, and outlining a roadmap to realize the full potential of AI-enabled CGT. Full article

Clinical discharge summaries contain rich patient information but remain difficult to convert into structured representations for downstream analysis. Recent advances in large language models (LLMs) have introduced new approaches for clinical text extraction, yet their relative strengths compared with supervised methods remain unclear. This study presents a controlled evaluation of three dominant strategies for structured clinical information extraction from electronic health records: prompting-based extraction using LLMs, retrieval-augmented generation for terminology canonicalization, and supervised fine-tuning of domain-specific transformer models. Using discharge summaries from the MIMIC-IV dataset, we compare zero-shot, few-shot, and verification-based prompting across closed-source and open-source LLMs, evaluate retrieval-augmented canonicalization as a post-processing mechanism, and benchmark these methods against a fine-tuned BioClinicalBERT model. Performance is assessed using a multi-level evaluation framework that combines exact matching, fuzzy lexical matching, and semantic assessment via an LLM-based judge. The results reveal clear tradeoffs across approaches: prompting achieves strong semantic correctness with minimal supervision, retrieval augmentation improves terminology consistency without expanding extraction coverage, and supervised fine-tuning yields the highest overall accuracy when labeled data are available. Across all methods, we observe a consistent $40-50\%$ gap between exact-match and semantic correctness, highlighting the limitations of string-based metrics for clinical Natural Language Processing (NLP). These findings provide practical guidance for selecting extraction strategies under varying resource constraints and emphasize the importance of evaluation methodologies that reflect clinical equivalence rather than surface-form similarity. Full article

Food waste in urban households is a critical barrier to sustainable development, often driven by inefficient inventory management and consumer forgetfulness. While institutional interventions exist, effective tools for the domestic pre-consumption stage remain scarce. This paper presents the design, development, and pilot validation of “ZeroWasteAI,” a novel mobile application developed by the authors that integrates Generative AI (Gemini 1.5 Flash) to automate food tracking and expiration monitoring. To evaluate its technical feasibility and impact on household waste, a four-week longitudinal pilot study was conducted with a sample of 11 households in Lima, Peru, employing a quasi-experimental pre-post design. The methodology combined quantitative waste tracking (kg) with qualitative assessments using the uMARS scale. Results validated the primary hypothesis (H1), achieving a 26.5% reduction in household food waste (from 31.3% to 23.0% waste rate). Furthermore, the study revealed a significant behavioral gap between purchasing and consumption, highlighting “overbuying” as a key target for future AI interventions. High usability scores confirm that integrating GenAI reduces the cognitive load of manual tracking, offering a scalable, software-based solution for sustainable consumption in developing economies. Full article

Individuals with intellectual disabilities (IDs) consistently demonstrate lower levels of objectively measured physical activity (PA) compared to the general population, yet limited evidence exists regarding how activity accumulated during physical education (PE) contributes to overall daily movement within structured school contexts. Within the school setting, PE represents one of the primary structured opportunities for engaging students with IDs in PA. Although objective physical activity monitoring approaches are recommended for school-based PA assessment, limited evidence exists on the contribution of PE to total school-day activity in students with intellectual disabilities, a gap addressed in the present study. In this context, the present study objectively recorded PA levels of students with IDs both during PE lessons and across five school days, in order to examine the contribution of PE to overall PA. Potential differences in PA according to gender and severity of the ID were also examined. Twenty students aged 15–25 years with mild and moderate IDs participated in the study. PA was assessed using the YAMAX Power Walker EX-510 pedometer, which automatically recorded step counts. The results indicated that only six participants reached step-count reference values. Students with mild IDs accumulated significantly more steps than those with moderate IDs, while male students were more physically active than female students, both during PE lessons and across the school day. PE lessons contributed approximately 4% to the total PA accumulated across the five monitored school days. These findings highlight the limited contribution of PE to overall PA and underscore the importance of promoting greater movement opportunities within adapted PE lessons. Full article