Search Results (10,847)

This narrative review examines topical anti-inflammatory therapies in veterinary medicine through the lens of the One Health framework, integrating pharmacology, dermatology, ecotoxicology, food safety, and regulatory science. It discusses the interconnected roles of veterinarians, pharmacists, environmental scientists, public health authorities, and regulatory bodies in addressing antimicrobial resistance, environmental contamination, zoonotic transmission, and drug residues in food-producing animals. By emphasising cross-sector collaboration, the review highlights how coordinated strategies can enhance animal welfare, safeguard human health, and reduce ecological burden. The article analyses inflammatory conditions in companion and farm animals and compares systemic versus topical anti-inflammatory approaches. Particular attention is given to corticosteroids, NSAIDs, immunomodulators, pro-resolving lipid mediators, and plant-derived bioactives, alongside advances in vehicles such as lipid nanocarriers and biodegradable film-forming systems designed to minimise systemic absorption and environmental dispersion. Regulatory considerations, residue control, pharmacovigilance gaps, and sustainability-oriented formulation strategies are critically addressed. Topical anti-inflammatory therapies, when rationally designed and monitored under One Health principles, represent a strategic opportunity to improve therapeutic precision while limiting systemic toxicity and ecological impact. Future directions should prioritise translational research, eco-compatible formulation design, and harmonised regulatory frameworks. Full article

With the advent of AI and the smart systems era, neuromorphic computing will be imperative to support next-generation AI-related applications. Existing intelligent systems, (such as smart cities, robotics), face many challenges and requirements including, high performance, adaptability, scalability, dynamic decision-making, and low power. Neuromorphic computing is emerging as a complementary solution to address these challenges and requirements of next-gen intelligent systems. Neuromorphic computing comprises many traits, such as adaptive, low-power, scalable, parallel computing, that satisfies the requirements of future intelligent systems. There is a need for innovative solutions (in terms of models, architectures, techniques) for neuromorphic computing to support next-gen intelligent systems to overcome several challenges hindering the advancement of neuromorphic computing. In this research work, we introduce a novel and efficient FPGA-HLS-based hardware accelerator for the Generalized Hebbian learning algorithm (GHA) for neuromorphic computing applications. We decided to focus on GHA, since it was demonstrated that GHA enables online and incremental learning, and provides a hardware-efficient unsupervised learning framework that aligns closely with the principles of biological adaptation—traits that are vital for neuromorphic computing applications. In addition, our previous work showed that FPGAs have many features, such as low power, customized circuits, parallel computing capabilities, low latency, and especially adaptive nature, which make FPGAs suitable for neuromorphic computing applications. We propose two different hardware versions of FPGA-HLS-based GHA hardware accelerators: one is memory-mapped interface-based and another one is streaming interface-based. Our streaming interface-based FPGA-HLS-based GHA hardware IP achieves up to 51.13× speedup compared to its embedded software counterpart, while maintaining small area and low power requirements of neuromorphic computing applications. Our experimental results show great potential in utilizing FPGA-based architectures to support neuromorphic computing applications. Full article

This research paper demonstrates how to manage Caputo fractional neutral integro-differential equations which include both integral and nonlinear elements through a unified framework that models dynamic systems with memory-based dynamics. The research establishes sufficient conditions for controllability through fixed point theory in a Banach space framework which requires particular assumptions while the study focuses on the ${\mathfrak{K}}_1<1$ condition which leads to the existence of a controllable solution. The proposed criteria are demonstrated through a numerical example which tests the theoretical results. The real-world case study uses artificial neural network (ANN) technology to predict Litecoin prices through the application of the fractional controllability model which analyzes historical financial data. The hybrid framework enables precise forecasting of nonlinear time series because it combines fractional calculus mathematical principles with ANN learning abilities. The proposed method demonstrates its predictive efficiency. The method shows robust performance through experimental results using cross-validation and performance metrics. The proposed model demonstrates competitive performance while providing additional advantages such as incorporation of memory effects and theoretical controllability. The research establishes a novel connection between fractional dynamical systems and machine learning which serves as an essential tool for studying complicated systems in theoretical research and practical applications. Full article

The construction sector remains a major contributor to global energy consumption and greenhouse gas emissions. Heat loss through building envelopes plays a key role, especially in regions with long heating seasons. Hollow building blocks are widely used due to their low cost and structural simplicity, but their inadequate thermal insulation requires additional layers of insulation, increasing costs and complicating installation. The production of cement and traditional insulation materials is associated with a high carbon footprint and disposal issues, which conflict with sustainable development principles and decarbonization goals. In contrast to previous reviews that primarily address bio-based insulation in general building envelopes or focus on bioaggregates in concrete mixes, this paper specifically targets the application of biomaterials in hollow building blocks. It emphasizes how bio-based loose-fill and bound fillers interact with the peculiar thermo-fluid behavior of hollow cavities, including natural convection, conduction and radiation. The effects on thermal performance (thermal conductivity, U-value of walls) are analyzed, along with selected aspects of mechanical strength and durability. Gaps in long-term data on biodegradation are identified. Recommendations for selecting strategies depending on climate and design are offered, as well as directions for future research, including numerical modeling of thermal conditions. The results highlight the potential of biomodified blocks for creating energy-efficient and environmentally friendly wall systems. Full article

Diameter at Breast Height (DBH) is a key parameter in forest measurement. However, existing research has mostly focused on improving the accuracy of individual technologies, lacking a systematic synthesis of the evolutionary logic of measurement techniques and a standardized selection framework for forestry applications. To this end, this paper constructs a multi-level classification framework based on measurement platforms and technical principles, establishes for the first time a five-dimensional comprehensive evaluation system (covering accuracy, efficiency, cost, environmental adaptability, and automation) along with a hierarchical technology decision tree, and systematically analyzes the application logic of multi-source fusion technologies across three levels: ground-based, near-ground mobile, and aerial. The review indicates that traditional contact-based measurement has limited efficiency; modern remote sensing technologies (photogrammetry and LiDAR) offer significant advantages in automation and accuracy, but still face challenges such as high equipment costs, complex data processing, and poor environmental adaptability. Multi-source fusion and machine learning are key methods to overcome the limitations of single sensors and improve the robustness of DBH estimation. Finally, it is anticipated that with decreasing sensor costs and the advancement of intelligent algorithms, DBH measurement will continue to evolve toward automation, intelligence, and engineering practicality, providing technical support for large-scale, long-term, and repeatable forest monitoring. Full article

Contemporary emergency systems operate at the intersection of climate volatility, digital interdependence, and cascading institutional disruptions. Despite growing research on resilience, adaptive governance, and digital transformation, these fields remain largely disconnected, leaving a theoretical gap in explaining how emergency systems perform under compound uncertainty. This integrative review synthesizes 32 peer-reviewed articles (post-2020) using structured narrative methodology and VOSviewer bibliometric analysis to map the field’s intellectual architecture and identify its structural gaps. The analysis reveals six thematic clusters organized around resilience as the central construct, yet characterized by three recurring disconnections: the weak integration between digital transformation and governance theory, the operational underdevelopment of polycentric governance frameworks, and the temporal separation between emergency response and climate adaptation. Drawing on this structural diagnosis, the study advances the Complex Adaptive Governance (CAG) model—a three-layer framework encompassing systemic architecture, adaptive mechanisms, and operational resilience—in which digital interoperability functions as a cross-cutting accelerator. The CAG model reconceptualizes resilience as a relational property of governance ecosystems, enhanced by digital interoperability, and offers design principles for climate-resilient emergency systems aligned with SDG 9, SDG 11, SDG 13, and SDG 16. Full article

Although extensive research has been conducted on peristaltic robots, early designs are often constrained by mechanical configurations and material constraints, which restrict kinematic capability, particularly steering control. In contrast, snails steer by modulating mucus secretion to redistribute ventral friction along the foot. Inspired by this strategy, we propose a friction-differential steering mechanism and develop a novel crawler that implements it. The crawler is integrated with a peristaltic robot, and three experiments are conducted to evaluate steering performance. We further establish a physical model of friction-differential steering, including cases identified from the experiments. The proposed model captures the experimentally observed trend that the steering response increases with the friction differential and provides a qualitative physical interpretation of the steering mechanism. Finally, the method is generalized by analyzing its limiting behavior, thereby clarifying the operating bounds of the proposed approach. This work provides a principled framework for steering control in peristaltic robots and offers a promising direction for improving their motion controllability. Full article

Flexible acceleration sensors demonstrate revolutionary potential in healthcare, structural vibration monitoring, and consumer electronics owing to their unique conformal adhesion capability and mechanical adaptability. However, current academic research presents two distinct paradigms for realizing flexibility: one is the hybridly flexible sensor, which incorporates traditional micro-electro-mechanical System (MEMS) acceleration sensor chips with flexible packaging/substrates; the other is the intrinsically flexible sensor, whose sensing unit and substrate are entirely composed of flexible materials enabled by microstructural design. This review first analyzes the fundamental differences and design challenges between these two flexible architectures. It then systematically elucidates five core sensing mechanisms—capacitive, piezoresistive, triboelectric, piezoelectric, and electromagnetic—comparing their working principles, material systems, structural designs, and performance metrics. Among these, piezoelectric and triboelectric types exhibit distinctive advantages in self-powering capability, whereas resistive and capacitive approaches offer greater ease of integration. Furthermore, the applications of intrinsically flexible acceleration sensors in structural health monitoring, wearable devices, automotive safety, and other fields are discussed, with particular emphasis on their unique strengths in real-time vibration monitoring. Finally, the review summarizes existing challenges, such as the trade-off between sensitivity and flexibility, and provides theoretical insights to guide future innovations in intrinsically flexible acceleration sensor technology. Full article

Water scarcity and pollution from anthropogenic activities are major challenges, increasing the need for sustainable wastewater treatment solutions. Constructed wetlands mimic natural wetland ecosystems using macrophytes and substrates, representing a possible nature-based solution aligned with circular economy principles and the United Nations Sustainable Development Goals. So, this revision integrates recent literature, providing an overview of natural wetlands and examining the design and operation of constructed wetland systems. Also, incorporates a case study that focuses on a constructed wetland implemented at an eco-friendly dog shelter in Portugal—a unique example globally—demonstrating practical wastewater treatment and small-scale water reuse, and offering insights for sustainable management. Performance assessment based on previous work indicates that the system effectively reduces most water quality parameters to levels compliant with national and European irrigation standards. Removal efficiencies exceeded 97% for chemical oxygen demand, total suspended solids, and turbidity, while maintaining low energy consumption and minimal maintenance. Overall, constructed wetlands emerge as a sustainable alternative to conventional wastewater treatment systems; however, several challenges remain to be addressed. Future research should focus on improved aeration strategies, optimized substrate–macrophyte combinations, and long-term monitoring under climate variability, with floating wetlands offering promising opportunities to further enhance treatment efficiency. Full article

Background: Migraine in older adults represents an increasingly relevant yet underrecognized clinical challenge in aging societies, where multimorbidity, frailty, and polypharmacy complicate both diagnosis and management. Although traditionally considered a disorder of younger individuals, migraine frequently persists or presents after the age of 60 with atypical features, contributing to diagnostic uncertainty. Methods: This narrative review, conducted in accordance with the SANRA principles, aims to provide a comprehensive overview of the epidemiology, clinical presentation, pathophysiology, and management of migraine in older adults, with particular emphasis on age-related complexities, therapeutic challenges, and unmet clinical needs. Results: Migraine in this population often presents with atypical or misleading features, such as aura without headache, vestibular symptoms, or overlap with cerebrovascular conditions, leading to delayed or incorrect diagnoses. The burden of disease is substantial, affecting physical function, mobility, cognition, emotional well-being, and social participation, and is further amplified by comorbid conditions including cardiovascular and metabolic disorders, mood disturbances, and chronic pain syndromes. Aging-related neurobiological changes, such as impaired pain modulation, endothelial dysfunction, and neuroinflammation, may influence disease expression and treatment response. Therapeutic management is challenged by contraindications, increased susceptibility to adverse drug effects, and the complexity of polypharmacy, highlighting the importance of individualized and non-pharmacological approaches. Conclusions: Migraine in older adults is a significant but often overlooked contributor to disability and reduced quality of life. Improved recognition of its unique clinical features and age-specific vulnerabilities is essential to optimize patient-centered care. Future research should prioritize the inclusion of older populations and the development of tailored, safe, and effective management strategies. Full article

Artificial intelligence has been considered as a transformative element capable of reshaping STEM education into equitable, resource-efficient, and scalable learning environments. However, realizing this potential requires striking a careful balance between technological innovation, pedagogical considerations, and ethical concerns. This study sought to examine the implementation of artificial intelligence (AI) tools by STEM university faculty members in Saudi Arabia to promote Sustainable Development Goal 4 (quality education). While doing so, the study attempted to explore how Saudi STEM university faculty members integrated AI tools in their instructional practices and analyze their perceptions towards these tools. To achieve these goals, the study employed an explanatory sequential mixed-methods design. In the first phase of data collection, a close-ended questionnaire was applied to a random sample of (324) STEM university faculty members. The second phase involved gathering qualitative data using a semi-structured interview administered to 12 purposively selected experts. Key quantitative findings revealed an overall AI integration at a medium level with a mean of (2.71) and standard deviation of (0.36) across three instructional practices, namely planning, implementation, and assessment. The highest integration level was in assessment (M = 2.93, medium) while the lowest was in planning (M = 2.61, medium). The results also revealed that the participants’ perceptions towards integrating AI tools were highly positive (M = 4.00, high), albeit with some concerns regarding the effect of excessive and unguided use of AI tools on students’ higher-order thinking skills, particularly the risk of AI functioning merely as an information delivery mechanism rather than serving its more pedagogically valuable role as a brainstorming scaffold. Furthermore, the study unveiled a number of barriers to integrating AI tools, including the weakness of digital infrastructure, lack of professional development, the limited credibility of AI-generated content, and ethical concerns related to academic integrity and copyrights. The research suggests the establishment of a sustainable digital environment by improving the infrastructure, providing specific training in accordance with the principles of sustainability, and implementing policies that promote equitable, transparent, and responsible integration of AI. These strategies can coordinate the growth of technology with the larger needs of the quality of education, inclusion, and sustainability of STEM education in the long term. Full article

The Somyne lake-mire system is a unique wetland landscape complex in the Polissia region of Ukraine and forms part of the Rivne Nature Reserve. Its ecological importance is internationally recognised through its designation as the Ramsar wetland “Somyne Peatland Massif”. Effective conservation of this wetland requires an understanding of the factors controlling the functioning of the lake and its drainage basin, considered in this study as a lake-basin system (LBS). The aim of this study is to assess the geoecological condition of the Somyne LBS using the principles of landscape limnology and the basin approach. The research integrates morphological, morphometric, hydrological, landscape-metric, hydrochemical and geochemical analyses. These are complemented by bathymetric modelling, landscape mapping, and analysis of long-term meteorological observations. The results identify key natural and anthropogenic drivers shaping the functioning of the system, characterise the hydrochemical state of lake waters and the geochemical properties of bottom sediments, and describe the spatial distribution of bottom sediments and the bathymetric structure of the lake basin. A multivariate algorithm for the geoecological assessment of lake-basin systems is proposed, providing a framework for comparative analysis of small lakes in the Polissian lake region under climate change and increasing anthropogenic pressure. Full article

Across the globe, companies are facing significant pressure to reduce waste, improve resource efficiency, and report their sustainability efforts transparently. ESG frameworks have become essential tools for sustainability transformation. However, traditional business models, based on a linear “take–make–dispose” approach, continue to dominate industries, limiting the impact of ESG efforts. The circular economy offers a compelling alternative: it encourages designing products for reuse, recycling, and regeneration, thus aligning closely with ESG principles. When businesses transition to circular models, they reduce their environmental footprint, create new green jobs and social inclusion opportunities, and strengthen accountability across business value chains. This study explores how selected firms in the mining, energy, consumer cyclical, technology, and healthcare sectors are aligning circular principles with ESG practices. Using a longitudinal, multi-sector comparative analysis of ESG indicators spanning 2014–2024, the research examines sector-level ESG evolution, firm-level ESG leadership, and the alignment of ESG performance with circular business model pathways. Rather than directly measuring circular transformation, ESG indicators are interpreted as signals of emerging circular business model pathways. This study identifies ESG-based ways and enabling conditions through which circularity may be increasingly embedded across different sectors. Full article

This study introduces a novel Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL) for comprehensive optimization of conical solar distillers equipped with sand-filled copper conical fins. The proposed algorithm synergistically combines quantum computing principles (superposition and entanglement), bio-inspired metaheuristics (Bald Eagle Search and Ukari Algorithm), and reinforcement learning mechanisms to achieve unprecedented optimization performance in complex thermal-hydraulic systems. The QI-HBEUA-RL framework employs quantum-encoded population representation, enabling simultaneous exploration of multiple solution states, while reinforcement learning dynamically adjusts algorithmic parameters based on search landscape characteristics and historical performance data. Experimental validation tested seven distiller configurations in El-Oued, Algeria, under controlled conditions (7.85 kWh/m²/day solar radiation, 42.2 °C ambient temperature). The optimal configuration of copper conical fins with 14 g sand at 0 cm spacing achieved: daily productivity of 7.75 L/m²/day (+61.46% improvement over conventional design), thermal efficiency of 61.9%, exergy efficiency of 4.02%, and economic payback period of 5.8 days. Comprehensive algorithm comparison against six state-of-the-art multi-objective optimizers (NSGA-II, MOEA/D, MOPSO, MOGWO, MOHHO) across 30 independent runs demonstrated statistically significant superiority (p < 0.001, Wilcoxon test). QI-HBEUA-RL achieved 7.42% improvement in hypervolume indicator, 29.35% reduction in inverted generational distance, and 19.49% better solution spacing. Generalization validation on seven benchmark problems (ZDT1-6, DTLZ2, DTLZ7) and three renewable energy applications confirmed algorithm robustness across diverse problem types. Three real-world case studies, remote village water supply (238:1 benefit–cost), industrial facility (100% energy reduction), and emergency relief (740× cost savings) validate practical implementation viability. This research advances solar thermal desalination technology and multi-objective optimization methodologies, providing validated solutions for sustainable freshwater production in water-scarce regions. Full article

Hybrid metal–polymer components are used in many industries, such as in aerospace, automotives, and electronics, due to the possibility of reducing the weight of the final part while maintaining mechanical properties comparable to components made entirely of metal. Conventional 3D printing processes do not enable the direct fabrication of hybrid structures consisting of solid metal and polymer parts due to the significant differences in the processing temperatures of both materials. A solution to this problem is the integration of two processes, electrodeposition and photopolymerization, which allow fabrication to be carried out at room temperature. This paper presents preparatory studies aimed at developing a new 3D printing technology that uses the simultaneous application of electrodeposition and photopolymerization to manufacture hybrid metal–polymer elements in a single, integrated 3D printing process. Here, a hybrid metal–polymer element is defined as a component composed of at least two bonded parts, including at least one metal part fabricated by electrodeposition and at least one polymer part produced by photopolymerization. Thus, it is not a polymer component merely coated with an electrodeposited metal layer, but a true hybrid structure consisting of functional metallic and polymeric parts. Such components can be manufactured using the world’s first hybrid 3D printer, which integrates electrodeposition and photopolymerization to produce metal–polymer hybrid parts within a single 3D printing process (the device has been submitted to the Polish Patent Office). However, its design and operating principle are beyond the scope of this paper. The presented research focuses on initial study of selected feedstock materials for this printer, namely photocurable resins and electroplating baths. Since the entire hybrid printing process occurs in an electroplating bath environment, studies of these materials for 3D printing under such conditions were essential. This work includes a screening study of photocurable formulations with respect to rheological properties, 3D printing tests in a model copper electroplating bath, and selection of a suitable bath brightener to maximize the quality (fine grain size, homogeneous grain distribution) of additively deposited copper layers. The study was conducted using copper electrodeposition and acrylate resin photopolymerization as model processes for evaluating the proposed hybrid metal–polymer 3D printing technology. Finally, the most suitable feedstock materials for producing metal–polymer hybrid parts via the proposed 3D printing method were selected. Full article