Search Results (24,171)

Conventional hydrometallurgical processes typically employ inorganic acids as leaching agents; however, these processes are frequently associated with significant environmental pollution and suffer from poor metal selectivity. Oxalic acid, as a green alternative leaching agent, demonstrates considerable application potential owing to its mild acidity, strong reducing capability, and superior complexing properties. This paper presents a systematic review of recent advances in the application of oxalic acid in hydrometallurgy, encompassing the coordination chemistry between oxalic acid and metal ions, its role as a selective leaching agent, and strategies for handling multicomponent oxalate-rich solutions. Furthermore, the industrial prospects of oxalic acid-based leaching technologies are discussed. Research indicates that oxalic acid exhibits high selectivity and efficient leaching performance for critical metals—including vanadium, lithium, cobalt, nickel, and gallium—from both primary ores and solid secondary resources. The underlying leaching mechanism primarily involves the formation of stable chelation complexes between oxalate anions and high charge-density metal ions, or valence state modulation via reduction, enabling selective dissolution and separation of target metals. In multicomponent oxalate systems, where metals predominantly exist as anionic complexes, established enrichment and purification approaches include anion exchange extraction, as well as precipitation techniques based on valence adjustment and double salt crystallization. To advance the industrial implementation of oxalic acid leaching technologies, further in-depth investigation is required into the recycling mechanisms of oxalic acid and the fundamental reaction pathways governing leaching and metal recovery processes. Full article

As the global energy transition accelerates toward low-emission and sustainable industrial energy systems, green hydrogen produced from renewable sources has emerged as a promising alternative to natural gas in energy-intensive sectors. This study presents the design, implementation, and experimental validation of a rooftop photovoltaic–proton exchange membrane (PV–PEM) hydrogen energy system developed as a proof-of-concept for textile industry applications. The proposed system integrates monocrystalline photovoltaic panels with east–west solar tracking, a 4 kW inverter, and a PEM electrolyzer with a hydrogen production capacity of 3.6 L/h, enabling on-site solar-to-hydrogen conversion. Produced hydrogen is stored in a high-pressure metal tank and utilized for downstream energy applications, demonstrating a complete renewable energy pathway. System performance is monitored in real time and evaluated using an experimental methodology supported by GUM-based and Monte Carlo uncertainty analysis. A carbon reduction assessment is conducted under representative industrial operating scenarios, including uncertainty quantification. The results indicate that the prototype system achieves an energy output corresponding to an average monthly emission reduction of approximately 222 kg CO₂e. The modular and scalable architecture allows flexible expansion to support gradual natural gas substitution in textile processes such as drying, heating, and steam generation. Overall, the study demonstrates the technical feasibility and environmental potential of integrating rooftop PV–PEM hydrogen systems into textile manufacturing, providing a transferable framework for industrial decarbonization. Full article

In emergency scenarios where visibility is compromised, rapid and accurate object detection becomes critical. This study addresses this challenge by proposing an IoT-enabled robotic solution capable of operating in low-visibility environments, with a focus on supporting search and rescue missions through autonomous sensing and real-time data communication. This research presents the development and implementation of an IoT-based sensorized system designed to detect objects in low-visibility environments. The system aims to enhance search and rescue operations by identifying potential human presence in areas with limited access due to smoke, darkness, or hazardous conditions. The platform integrates distance sensors, a thermal camera (AMG8833), a PIR motion sensor, and wireless communication through the Arduino MKR1000 and ESP32-CAM boards. The mobile robot is equipped with obstacle avoidance, person detection, and IoT communication modules, allowing data to be sent to the cloud via ThingSpeak and enabling remote commands through TalkBack. A structured methodology was followed, including technology selection, hardware/software design, and testing under various lighting and opacity conditions. Experimental results showed the effectiveness of the system in identifying obstacles and detecting heat signatures representing human body, with optimal performance observed at a 15 cm detection threshold. The system demonstrated robust operation in simulated rescue environments, providing real-time data transmission and remote-control capabilities. Full article

Microbial contamination of table eggs remains an important food safety concern, largely due to the presence of Salmonella spp. on eggshell surfaces and the potential for cross-contamination along the collection, grading, and packing chain. Conventional sanitation practices, including chlorinated-water washing, can reduce surface microbial loads but may also present limitations related to cuticle alteration, process variability, water use, and the risk of recontamination when operational conditions are not tightly controlled. This review synthesizes evidence on non-thermal and selected mild thermal technologies for the surface decontamination of intact table eggs, including ultraviolet-C (UV-C) irradiation, pulsed light, ozone-based treatments (gas and microbubble systems), non-thermal plasma, plasma-activated water, and gas-phase hydroxyl radical processes. For each approach, antimicrobial performance is discussed alongside effects on eggshell integrity, cuticle preservation, and key quality indicators (e.g., Haugh unit, albumen pH, yolk color, and shell strength). Particular attention is given to industrial constraints that influence real-world performance, such as treatment uniformity and shading effects, humidity dependence, line speed, equipment integration, and validation criteria. A shared limitation of surface treatments is their inability to inactivate pathogens that have penetrated shell membranes or contaminated egg contents, underscoring the need to align technology selection with the targeted hazard and the regulatory context. Thus, available data indicate that non-thermal technologies can contribute to reducing eggshell contamination when properly optimized, although broader implementation will depend on standardized operating parameters, robust process validation, and regulatory acceptance within existing egg processing systems. Full article

Educational tourism in protected natural areas (PNAs) has established itself as a valuable strategy for promoting sustainability by integrating learning experiences with conservation and territorial development objectives. However, the international literature still offers limited evidence on the conditions that facilitate its implementation in non-formal contexts, particularly regarding the availability, consistency, and quality of interpretive infrastructure for public use that supports educational processes. This study addresses this gap through an assessment aimed at examining the relationship between educational tourism and landscape characteristics, as well as identifying which PNAs are most suitable for the development of educational tourism initiatives. The province of Ciudad Real (Castilla-La Mancha, Spain) is used as a case study, as it presents the highest regional concentration of PNAs and a remarkable diversity of landscapes. Using a standardised field form, 35 PNAs were inventoried and evaluated according to three key dimensions: interpretive provision, accessibility, and informational materials, including the incorporation of landscape content as a pedagogical resource. The variables used were subjected to a critical comparative analysis for the characterisation and categorical organisation of the spaces studied. The results reveal marked territorial heterogeneity in terms of facility availability, state of conservation, and effective integration into the interpretive offering, with comprehensive approaches remaining scarce. Only a limited number of sites adequately combine these dimensions, establishing themselves as enclaves with high tourism and educational potential. Overall, the findings highlight the importance of developing coherent infrastructure and considering the landscape as a key interpretive element in educational tourism in PNAs. Full article

Background: Nurse teletriage has emerged as a component of modern healthcare delivery, utilizing telecommunication technologies to assess patient conditions remotely and guide appropriate care decisions. As healthcare systems face increasing demand and the need for cost-effective care delivery, teletriage services have expanded, particularly following the COVID-19 pandemic. Objective: This narrative review examines the current state of nurse teletriage practice, its effectiveness, safety outcomes, and implementation considerations. A comparative analysis with physician-led teletriage models is provided, and the emerging role of artificial intelligence is explored. Methods: A narrative review of the literature was conducted through searches of multiple databases including PubMed/MEDLINE, CINAHL, Cochrane Library, Embase, Web of Science, and Google Scholar. This approach was selected due to the heterogeneous nature of the teletriage literature, which spans diverse study designs, populations, and outcomes that are not amenable to formal systematic synthesis. Peer-reviewed articles published between 1970 and 2024 examining safety outcomes, effectiveness, and implementation frameworks were reviewed. Results: The available evidence suggests that nurse-led teletriage systems, particularly when supported by computerized decision support systems, can improve patient access to care while maintaining safety standards. Studies indicate that telephone triage nursing does not increase mortality, hospitalization rates, or emergency department referrals when properly implemented. One well-documented physician-led model in Israel reported diagnosis accuracy rates of 98.5% and decision reasonableness rates of 92%, though generalizability across settings requires caution. Key success factors appear to include the use of evidence-based protocols, staff training, technology infrastructure, and quality assurance programs. While these findings are promising, the heterogeneous nature of the included studies and absence of formal quality assessment warrant cautious interpretation. Conclusions: Nurse teletriage appears to be an effective and safe approach to healthcare delivery that addresses challenges in modern healthcare systems. The choice between nurse-led and physician-led models should consider population complexity, case types, available resources, and economic factors. Artificial intelligence technologies offer potential opportunities to enhance teletriage, though careful validation is essential. Future research should focus on long-term outcomes, comparative effectiveness across healthcare systems, and rigorous evaluation of AI applications. Highlights: Telephone triage services, where nurses or physicians assess patients remotely and guide them to appropriate care, have become increasingly important in modern healthcare. This narrative review examines the evidence on nurse-led telephone triage, comparing it with physician-led models and exploring emerging technologies like artificial intelligence. The available evidence suggests that nurse-led systems, when supported by appropriate protocols and training, can safely improve patient access to care while reducing healthcare costs. Physician-led models may offer advantages for complex cases but at higher costs. While artificial intelligence shows promise for enhancing triage accuracy, current evidence specific to telephone triage remains limited. Healthcare organizations should carefully consider their population needs, available resources, and local context when implementing teletriage services. Full article

This study investigates the development of advanced protective gloves by applying novel 3D-printed PET-G mesh overlay structures onto three textile substrates—polyamide (PA), polyester (PES), and cotton—using ultrasonic welding and contact welding. The focus was on assessing weld quality, thickness uniformity, and functional durability. Weld morphology and bonding integrity were evaluated using X-ray microtomography (micro-CT), while bending fatigue tests assessed mechanical performance under cyclic loading. The results show that ultrasonic welding produces more uniform welds, enhancing fatigue resistance, particularly on cotton and polyamide substrates. Non-uniform welds with thicker or uneven areas, typical of contact welding, correlated with reduced mechanical durability. These findings highlight the potential of additively manufactured overlay structures for hybrid protective gloves, demonstrating that weld thickness uniformity and substrate compatibility are key factors in optimizing mechanical performance. This work extends our previous research by introducing new 3D-printed overlay architectures and provides valuable insights into the practical implementation of additively manufactured polymeric structures in PPE development. Full article

We describe a comprehensive methodology for the application of game theory to omics data analysis, with a particular focus on coalitional games and Shapley values. This approach evaluates the cooperative distribution of genes within high-dimensional transcriptomics datasets, providing a complementary perspective to conventional statistical methods. We present the mathematical framework, implementation details, and references for applications that demonstrate its ability to improve the detection of biologically meaningful signals that may not be explicitly modeled by many conventional statistical methods. Our results highlight the potential of coalitional game theory as a powerful tool for enhancing reproducibility and interpretability in omics research, opening new perspectives in systems biology and precision medicine. Full article

Avian influenza (AI), particularly highly pathogenic avian influenza (HPAI), represents a serious and growing threat to global poultry production, international trade, and human health security. Control of AI is complicated by the high evolutionary rate of influenza A viruses, which drives antigenic diversity and ongoing emergence of novel strains. Effective surveillance and disease management therefore depend on timely and accurate diagnostics. While conventional methods—including virus isolation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assays (ELISAs)—remain effective and widely used, they are limited by long turnaround times, the need for specialized equipment, and reliance on highly trained personnel. In addition, strict state and federal regulatory requirements restrict testing to a limited number of authorized laboratories. Although these regulations are essential for maintaining diagnostic accuracy and quality assurance, they place substantial strain on laboratory capacity during outbreaks and delay actionable results. The need for rapid, on-site decision making has driven interest in alternative diagnostic approaches, including biosensor technologies. A major limitation of current diagnostic strategies is the lack of robust DIVA (Differentiating Infected from Vaccinated Animals) capability. In countries such as the United States, where poultry vaccination against AI is not routinely practiced, the absence of DIVA-compatible diagnostics has hindered adoption of vaccination as a disease management tool, as seropositive birds and products face significant trade restrictions. Biosensor platforms capable of enabling DIVA strategies offer a potential pathway to support vaccination while preserving surveillance integrity. This review examines the current landscape of AI and HPAI diagnostics, emphasizing the limitations of traditional approaches and the opportunities presented by biosensor platforms. We evaluate electrochemical, optical, piezoelectric, and nucleic-acid-based biosensors, with particular attention to biorecognition strategies, performance metrics, field deployability, and applications supporting subtype discrimination, DIVA implementation, and One Health surveillance. Full article

Digital transformation offers significant potential to reshape supply chains; however, implementation efforts remain fragmented, technology-centric, and insufficiently aligned with strategic, organizational, and sustainability goals. Existing frameworks and maturity models tend to emphasize the technological dimension, offering limited guidance on how digital transformation should be integrated with people, processes, culture, and sustainability at the supply network level. Building on evidence synthesized through an umbrella review of the state of the art, this paper proposes the Agile and Sustainable Supply Network Compass, a holistic and actionable framework designed to support organizations in advancing toward agile and sustainable supply networks. The Compass incorporates three structural dimensions—Strategy, Processes, and Capabilities (related to digitalization and sustainability)—as foundational pillars for transformation. We hypothesize that an effective transformation requires the joint alignment of strategy, cross-functional processes, and capabilities, as well as the explicit identification of a reduced supply network, a focal firm, and its critical linkages. The results show that positioning agility and sustainability as shared strategic objectives at the supply network level enables coherent decision-making, targeted capability development and improved coordination across interconnected actors. Rather than prescribing specific technologies, the proposed framework provides a guiding methodological logic that explains how digitalization and sustainability can co-evolve within supply networks. This work contributes to both theory and practice by bridging conceptual gaps in the literature and establishing the groundwork for future maturity models and empirical applications. Full article

(1) Background: Virtual environments (VEs) significantly influence human emotions through various elements such as lighting, color, and terrain. While the effects of lighting and color on emotions within VEs have been extensively studied, the impact of the terrain remains underexplored. This paper addresses this gap by investigating the correlation between terrain characteristics in VEs and users’ emotional states. (2) Methods: We conducted a user study in which participants were exposed to various 3D terrains and used the Self-Assessment Manikin (SAM) to rate their emotional responses (valence, arousal, and dominance). Building on these insights, we propose MoodScape, an automated framework for emotion-informed terrain generation that significantly reduces the need for extensive expertise and manual effort. In the current implementation, continuous SAM valence–arousal targets are discretised into four quadrant-based affect/terrain classes, and this discrete class label conditions DH-CVAE-GAN terrain synthesis. MoodScape designs a generative adversarial network (GAN) architecture called DH-CVAE-GAN, which integrates a dual-head conditional variational autoencoder as the generator alongside a discriminator network to ensure effective and realistic terrain generation. The DH-CVAE-GAN is trained on a satellite-derived digital elevation model (DEM) dataset, which helps the generated terrains reflect realistic geographic patterns. (3) Results: Quantitative and qualitative evaluations on our study sample suggest that MoodScape can generate terrains whose perceived affective tone is broadly consistent with the specified affect-class inputs, indicating potential applications in gaming and exploratory therapeutic Virtual Reality, while formal clinical efficacy remains in future work. Full article

Given the concurrent challenges of declining participation rates in STEM education and the growing societal demand for STEM expertise, understanding and implementing motivation-enhancing interventions is essential for safeguarding the future STEM workforce and enabling societies to respond to technological and societal challenges. This narrative literature review synthesized studies published between 2014 and 2025 and aimed to elucidate the underlying rationales and drivers of motivation-focused STEM research, as well as to identify and evaluate interventions designed to increase students’ motivation for STEM. The synthesis identified four recurring drivers of motivation-focused STEM research: increasing demand for the STEM workforce, inequities in STEM participation, the strategic socioeconomic importance of STEM and declining student motivation over time. Interventions were analytically grouped into six categories: motivational STEM interventions/programs, community engagement initiatives, hands-on learning approaches, supportive instructional materials and educational technologies, extracurricular interventions, and interventions leveraging social support. Overall, the synthesis indicated that motivational effects were shaped less by the setting of an intervention and more by its implementation characteristics, including duration, intensity, pedagogical integration and alignment with students’ motivational needs. Interventions that were sustained and well-integrated tended to have more positive effects, whereas short or weakly embedded approaches yielded mixed or transient outcomes. The insights presented here provide structured guidance for educators and policymakers seeking to foster more motivated STEM learners, with potential implications for improving retention in STEM pathways and addressing the growing societal demand for STEM professionals. Full article

Edge computing has garnered significant attention in recent years due to its potential in distributed systems. However, the dynamic and heterogeneous nature of edge environments introduces substantial challenges for task scheduling. Conventional rule-based scheduling algorithms often fail to adapt to rapid load fluctuations, resulting in cluster load imbalance and suboptimal resource utilization. To address this issue, we propose a container-based edge cluster scheduling framework designed to enhance load balancing. Within this framework, we introduce an Experience-Prioritized Reinforcement Scheduler (EPRS), which leverages a priority-driven sample selection mechanism to facilitate focused learning of high-value samples. The EPRS dynamically monitors node resource states via a real-time resource monitor and optimizes multi-dimensional resource allocation by jointly considering node-level metrics (e.g., computational resources, memory pressure, storage performance, and container density) and task-specific resource requirements. To validate our approach, we implemented a system prototype integrated with the proposed framework and EPRS in a Kubernetes-based edge cluster. Experimental results demonstrate that the proposed method significantly improves multi-dimensional load balancing performance, achieving an average gain of 28.25% over existing reinforcement learning-based scheduling approaches and a 29.78% improvement compared with the traditional scheduling algorithm. Full article

Background: Children’s development is dependent on a range of factors influencing their life course outcomes. Protective and challenging social and cultural determinants impact how Indigenous families support their children’s developmental foundations. However, there is a lack of international evidence investigating Indigenous child development interventions. To gain a perspective across nations with comparable settler-colonial histories, this scoping review summarised studies on family and community-centred approaches among Indigenous populations in Australia, Canada, New Zealand, and the United States, focusing on outcomes and evidence gaps. Methods: A scoping review followed PRISMA-ScR guidelines. Medline, CINAHL, and PsycINFO (Ovid) were searched from their inception to October 2025, including grey literature sources from Aboriginal HealthInfoNet, the Lowitja Institute and the Secretariat of National Aboriginal and Islander Child Care. Empirical studies, including quantitative, mixed-methods, evaluation studies, and descriptive or case-study designs, were included provided they reported empirical data on intervention outcomes. Due to study heterogeneity, data were synthesised narratively. Results: Following screening of 2355 records, eight from 2013 to 2020 met the inclusion criteria. These were mostly small-scale, non-randomising designs evaluating different interventions, with the behavioural and emotional domain being the most frequently assessed outcome, alongside developmental vulnerability and academic/educational areas. There was limited consideration of protective cultural determinants of health in the study design and implementation. Six studies reported positive associations between interventions or programmes and early childhood development outcomes. Conclusions: While the number and rigour of identified interventions were limited, several demonstrated potential benefits for Indigenous children’s early childhood development. However, strengthening the evidence base requires culturally grounded, adequately powered evaluations using rigorous study designs that include culturally co-designed adaptations conducted with Indigenous families and communities. Support is recommended for capacity building and funding. Full article

The Philippines, which is rich in natural resources, has significant biomass potential. Among the country’s renewable energy sources, biomass is currently the slowest-growing in terms of power generation. Various types of biomass resources with full or partial use in Laguna Province include bagasse, sweet sorghum, coconut, rice husk, corn cobs, and municipal solid waste. Additionally, the adoption and implementation of HRESs (hybrid renewable energy systems) are mainly achieved through large-scale projects. This paper intentionally showcases highly optimized hybrid configurations for off-grid microgrids to promote rural electrification in Laguna, with a focus on various technoeconomic parameters, specifically the minimization of net present costs and the levelized cost of electricity across all simulations. Each off-grid scenario was compared with scenarios featuring hybrid renewable energy systems incorporating a biomass generator. Laguna, one of the few provinces in the Philippines with all forms of renewable energy systems present, each with high renewable energy potential and renewable fraction values, was selected as the primary study site in this paper. After optimizing and analyzing technoeconomic parameters such as the net present cost and the levelized cost of electricity, a hybrid biomass-solar-wind energy system is proposed to power off-grid areas in Laguna, thereby supporting rural electrification and decarbonization goals. Scenario simulations and comparisons using hybrid optimization demonstrate that adding battery backup systems improves both economic and environmental performance. This paper highlights two key benefits of including a biomass generator: (1) a 17.0% reduction in long-term carbon emissions for the entire system and (2) approximately 9.4% savings in operation and maintenance costs after seven years. The optimization results support the goal of providing Laguna with power through off-grid, decentralized, community-based hybrid renewable energy systems. Full article