Search Results (41,214)

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

Conventional multispectral photodetectors typically rely on multiple electrical terminals to discriminate different wavelengths, which inevitably increases structural complexity. Here, we break this paradigm by demonstrating a dual-color visible–infrared photodetector based on a simple two-terminal Au/MoS₂/Te heterostructure. The device operates through a bias-switching mechanism: reversing the voltage polarity selectively activates either the MoS₂/Au Schottky junction for visible-light detection (520 nm) or the Te/MoS₂ heterojunction for infrared detection (1550 nm). This bias-controlled wavelength selectivity is unambiguously verified by scanning photocurrent mapping. Beyond dual-color discrimination, an adaptive convolutional neural network is employed to decode the nonlinear current–voltage characteristics and enable precise spectral identification, achieving a reconstruction error of approximately 4.5%. Furthermore, high-fidelity dual-color imaging is demonstrated at room temperature. These results establish a hardware–algorithm co-design strategy based on a minimalist two-terminal architecture, providing a viable route toward compact and intelligent spectral-sensing systems. Full article

The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction costs for residential building envelopes in Chengdu’s hot summer and cold winter climate. The framework uses the NSGA-II genetic algorithm within DesignBuilder to explore trade-offs between energy efficiency and economic cost. Key design parameters (wall insulation thickness, roof insulation thickness, and window glazing type) are optimized to obtain a Pareto-optimal front. A subsequent global incremental cost analysis of the non-dominated solutions identifies the optimal balance where significant energy savings are achieved before diminishing returns set in. The research results show that by combining the NSGA-II algorithm with the global incremental cost method in the Chengdu area, the parameters of the enclosure structure can be systematically optimized, and the optimal balance point between energy conservation and cost can be effectively identified. Based on this, an “energy-saving optimal—trade-off optimal—cost optimal” template set design path based on dual objectives of energy consumption and cost can be obtained, which is applicable to different demand-oriented engineering scenarios. This research provides a quantifiable decision-making basis for the design of buildings with passive design strategies that achieve near-zero energy consumption in hot summer and cold winter regions, helping to achieve the coordinated optimization of energy efficiency goals and economic feasibility, and promoting the reliable promotion and application of near-zero energy buildings. Full article

Accurate prediction of syngas composition is essential for process design, optimization, and scale-up, yet it remains challenging due to interactions among operating conditions, biomass properties, and chemical reactions. This study used a database of 450 experimental observations spanning a wide range of biomass feedstocks and operating conditions to compare the predictive performance of Aspen Plus simulations and Machine Learning models in estimating the concentrations of CO, CO₂, H₂, and CH₄ in syngas. Aspen Plus was used to simulate the 4 stages of the biomass gasification process under different operating conditions, with special focus on the three reactor modules (RPlug, RGibbs, and REquil) modeling the last two stages. In parallel, Machine Learning models using four regression algorithms (XGBoost, Support Vector Machines, Random Forest and Artificial Neural Networks), with different preprocessing and data-splitting strategies, were evaluated for predicting syngas composition. The best Machine Learning models achieved R² values of 0.753 (CO), 0.866 (CO₂), 0.879 (H₂) and 0.734 (CH₄) on the test set. These results outperformed the Aspen Plus approach and highlight the potential of Machine Learning models as complementary or alternative tools for modelling biomass gasification. Shapley Additive Explanation analysis identified the most influential input variables, revealing key roles for the steam-to-biomass ratio and the equivalence ratio in predicting syngas composition. This study demonstrates that existing Aspen Plus simulation models require further development to improve performance metrics across a wide range of biomass feedstocks and operating conditions. Full article

Renewable energy technologies are becoming more and more relevant in a variety of engineering fields as a result of the move toward low-carbon, sustainable energy systems. Although research has historically concentrated on power generation, it now covers a broad range of applications, including precision agriculture, smart grids, energy storage, healthcare devices, and sustainable buildings. However, existing review studies are often limited to single disciplines or specific technologies, lacking a unified cross-disciplinary perspective that captures the interconnected nature of modern renewable energy systems. This gap motivates the need for a comprehensive review that bridges multiple engineering domains. This review provides a comprehensive synthesis of literature on renewable energy applications in electrical and electronics, computer, environmental, biomedical, architectural, and agricultural engineering. In electrical and electronics engineering, the use of renewable energy sources is largely based on the efficient generation of electricity from natural resources such as solar, wind, and ocean energy. Computer engineering contributes through artificial intelligence (AI), Internet of Things (IoT) architectures, digital twins, and cybersecurity solutions, optimizing energy management. Environmental engineering emphasizes life cycle assessment, carbon footprint reduction, and circular economy strategies. In biomedical engineering, energy harvesting and self-powered devices illustrate micro-scale applications of renewable energy. Architectural engineering integrates renewable systems through building-integrated photovoltaics, net-zero energy designs, and smart building management, while agricultural engineering uses solar-powered irrigation, biomass utilization, agrivoltaic systems, and other sustainable practices. To support a low-carbon future with integrated and sustainable engineering solutions, this study not only highlights innovations within individual fields but also showcases how different disciplines can connect and work together. Overall, the review offers a novel cross-disciplinary framework that advances the understanding of renewable energy systems beyond isolated applications and provides direction for future integrative research. Full article

A polymer electrolyte is developed by integrating a poly(methyl methacrylate) (PMMA)/eutectic electrolyte (EE) phase into a porous polyethylene (PE) scaffold via a solution-casting strategy. In this rigid–flexible coupled architecture, the PMMA matrix serves as a solid host that coordinates with Li⁺ through its polar carbonyl groups, thereby promoting lithium salt dissociation and establishing a stable ion transport network. The incorporated EE, composed of ethylene carbonate and LiTFSI, effectively reduces the glassy rigidity of PMMA and provides continuous pathways for fast ionic conduction. Meanwhile, the porous PE scaffold reinforces mechanical strength and resists lithium dendrite penetration, enabling a thin electrolyte membrane with excellent flexibility. The resulting electrolyte achieves an ionic conductivity of 1.59 × 10⁻⁴ S cm⁻¹ at 30 °C, a lithium-ion transference number of 0.45, and an electrochemical stability window up to 4.75 V. In Li||LiFePO₄ cells, it delivers stable cycling at 3 C for 1000 cycles with 76.8% capacity retention and a Coulombic efficiency exceeding 99.9%. The monomer-free design eliminates residual reactive species that commonly compromise interfacial stability, offering a reliable pathway toward high-voltage solid-state lithium-metal batteries. Full article

Infrared and visible image fusion aims to combine the complementary advantages of thermal radiation information and rich texture details to generate more informative images for downstream perception tasks. However, existing deep learning-based methods usually assume ideal imaging conditions and often suffer from performance degradation in complex environments such as low illumination, rain interference, and strong lighting disturbances. To address this problem, this paper proposes an adaptive prompting-driven degradation-aware fusion framework. Specifically, a degradation-aware prompt generation module is introduced to automatically perceive degradation patterns from the input images and generate structured conditional prompts. These prompts guide the network to adaptively adjust feature representations through learnable affine modulation. Furthermore, a semantic-aligned feature learning strategy is designed to ensure consistent cross-modal representation in the latent space. Extensive experiments demonstrate that the proposed method achieves superior performance compared with several state-of-the-art fusion approaches under both normal and degraded conditions. Full article

The scarcity and incompleteness of well-log data pose a critical challenge to deep learning-based reservoir prediction. To address this small-sample problem and improve prediction quality, we propose a novel semi-supervised asymmetric deep co-training framework integrated with a shape context descriptor. This method leverages abundant unlabeled seismic data as well as complementary information on related physical properties. Specifically, we introduce a shape context descriptor to encode seismic waveform morphology and spatial context, thereby improving the lateral continuity and interpretability of predictions while mitigating issues inherent in the sequence-to-point paradigm, wherein three-dimensional seismic data are used as input and a single target point is predicted. To overcome data limitations, a sliding-window resampling strategy is employed to expand the training samples. For co-training, we design an asymmetric dual-task architecture wherein one model performs porosity regression while the other conducts reservoir type classification, thereby enabling synergistic learning. The proposed framework is validated using real three-dimensional seismic data from the Yinggehai Basin in the South China Sea through ablation experiments. The results demonstrate superior performance in prediction accuracy, spatial consistency, and training stability compared to baseline methods. Full article

Autoimmune diseases arise from the failure of self-tolerance. The recognition of self-antigen peptide–MHC (pMHC) complexes by the T-cell receptor (TCR) is the fundamental event triggering autoimmune pathogenesis. While traditional immunosuppressants provide broad systemic effects, they often compromise global immunity. Emerging molecular strategies aim to selectively disrupt the trimolecular complex—comprising the TCR, the antigenic peptide, and the MHC molecule—to induce antigen-specific tolerance. This review highlights the pMHC–TCR interaction as the primary molecular checkpoint for antigen-specific intervention. We discuss the structural basis of these interactions and their potential to redefine the therapeutic landscape for autoimmune diseases (ADs). We examine the molecular drivers of tolerance breakdown—including genetic susceptibility, molecular mimicry, post-translational modifications (PTMs), and ectopic MHC II expression—that shape the autoreactive T-cell landscape. This review examines current advancements in biological and pharmacological interventions, such as pMHC-decorated nanoparticles and soluble pMHC, to reprogram pathogenic T-cell response. We also explored CAR-T therapy strategies for autoimmune diseases, such as CAR-Treg, designed to precisely modulate pMHC-TCR signaling. Collectively, these precision interventions in immunological synapse assembly during autoimmune response are considered the basis for safer, antigen-specific immunotherapy capable of restoring self-tolerance without global immunosuppression. Full article

Building upon previously developed thin-film modular soft actuators for elongation and deflection, this study develops a modular soft robot for pipeline locomotion, addressing insufficient anchoring capability in confined environments. Conventional inflatable airbags typically expand into spindle-shaped geometries, resulting in limited contact length and reduced effective gripping stability. To overcome this issue, a corrugated thin-film gripping actuator is proposed, in which two high-aspect-ratio sub-airbags are arranged above a compression structure to regulate deformation through geometric constraints. Numerical simulation and experimental evaluation were conducted to investigate contact behavior and locomotion performance. Under an input pressure of 30 kPa, the proposed design achieves a contact length of 46 mm, compared to 37 mm for a conventional three-layer airbag configuration under the same conditions, corresponding to a 24.33% increase in a 10 mm plate-spacing environment. The gripping module is integrated into the modular framework to extend the motion primitives of the soft robot to include anchoring functionality. The results indicate that the corrugated structure effectively suppresses the spindle effect and improves contact effectiveness under compression. These findings demonstrate that structural regulation of thin-film pneumatic actuators provides a feasible strategy for enhancing anchoring performance and locomotion capability of soft robots in confined pipeline environments. Full article

Planar microwave (MW) sensors offer high-resolution, non-invasive technology for monitoring critical soil properties, serving as a support for modern precision agriculture. While laboratory studies confirm their exceptional sensitivity, the widespread adoption of these sensors is severely impeded by critical translational challenges that constitute a defining “lab-to-field gap”. These barriers include high sensor-to-sensor variability, debilitating thermal cross-sensitivity, soil heterogeneity necessitating unique site-specific calibration, and the enduring tension between high-performance and cost-effective scaling. This review systematically synthesizes the current state of planar permittivity MW technology, moving beyond technical mechanisms to critically assess these operational limitations. We detail advanced architectural strategies designed to bridge this gap, focusing particularly on the transition toward more robust solutions. The key strategies analyzed include the adoption of differential sensor designs using microstrip patch antennas to mitigate common-mode environmental errors, the integration of ultra-compact metamaterial structures such as split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) for enhanced field robustness and deep soil sensing, and the necessity of multi-parameter sensing capabilities (moisture, pH, and salinity). By establishing a comprehensive roadmap that prioritizes field stability, cost efficiency, and seamless IoT integration, this review demonstrates that planar MW sensors are poised to become reliable and scalable tools. Addressing these critical translational hurdles will ensure optimal resource management, significantly enhance crop productivity, and enable sustainable practices within smart farming ecosystems. Full article

Microbial electrochemical technologies (METs) have emerged as promising approaches for coupling wastewater treatment with energy and resource recovery. Considerable progress has been made in elucidating extracellular electron transfer, biofilm behavior, and electrode development, enabling laboratory systems to achieve high removal efficiencies under controlled conditions. Despite these advances, implementation in real treatment infrastructure remains limited. This review evaluates the progression of METs from laboratory studies to pilot-scale and field applications within the wider landscape of electrochemical wastewater treatment. The effects of reactor setup, material strength, and operational difficulty on performance at different scales are emphasized. Evidence from recent pilots consistently shows reduced energy recovery, along with challenges such as internal resistance, mass-transfer constraints, fouling, and cathode degradation. Laboratory-scale MFC systems have reported peak power densities of up to 23,000 mW/m² and normalized energy recoveries of up to 1.2 kWh/kg COD removed under optimized, controlled conditions; however, pilot-scale systems typically recover only 0.01–0.05 kWh/kg COD removed, representing one to two orders of magnitude below laboratory-reported values. This contrast underscores the persistent gap between controlled experimental performance and operational reality. Proposed solutions, such as modular scale-out, membrane simplification, and the use of low-cost, replaceable materials, are assessed based on their maturity and practical applicability. Techno-economic and life-cycle analyses indicate that component longevity and integration strategy are often more decisive than peak electrochemical output. METs are therefore most likely to provide near-term benefits in hybrid or niche applications rather than as standalone replacements. Advancement toward wider implementation will require standardized metrics, long-term demonstrations, and engineering designs prioritizing robustness and maintainability. Full article

The earthquake of 23 June 2001, Mw 8.4, caused catastrophic damage in the city of Moquegua (Peru), especially in reinforced-concrete educational buildings. In this research, advanced procedures have been used and compared to assess the seismic performance of a new educational building designed under the current Peruvian construction regulations. Two nonlinear static procedures, the capacity spectrum method and an improved procedure based on the equivalent linearization method, have been applied and compared. Damage probabilities for a 475-year-return-period earthquake for the city of Moquegua evidence that the improved procedure based on the equivalent linearization method turns out to be slightly more conservative than the capacity spectrum method. Incremental dynamic analyses, based on 15 seismic events selected according to specific criteria, are taken as reference and complete the building damage assessment. Probabilistic damage matrices are proposed to assess damage using a probabilistic approach, which makes it possible to determine the levels of risk to be assumed in likely post-seismic scenarios and to carry out probabilistic estimates of the impacted population, the expected damage to structures, and the ranges of economic (social and material) costs. These tools assist stakeholders, civil protection and fire departments and the administrations involved in risk management and contingency planning in developing prevention strategies and improving preparedness for natural disasters such as earthquakes. Full article

Background/Objectives: With the acceleration of global industrialization and continuous population growth, the world is increasingly confronted with the dual challenges of food insecurity and cultivated land contamination. The screening and breeding of rice varieties with superior agronomic traits and low heavy metal accumulation have therefore become important strategies for ensuring food safety and sustainable agricultural production. Methods: In this study, rice varieties carrying the Gn1a-i gene and exhibiting specific cadmium (Cd) accumulation characteristics were screened using a combination of molecular marker detection and cadmium accumulation evaluation. Specific loop-mediated isothermal amplification (LAMP) primers targeting the Gn1a-i gene were designed and combined with a lateral flow dipstick (LFD) assay to enable rapid genetic screening of rice varieties. A six-day hydroponic experiment under cadmium stress was conducted across three temperature ranges (15–20 °C, 22–27 °C, and 30–35 °C), and cadmium accumulation in different plant organs (roots, stem sheath, and leaves) was analyzed. Results: Seven varieties carrying the Gn1a-i gene, including Xiangwanxian 12, were identified among ten tested rice varieties. Xiangwanxian 12 was subsequently selected for further evaluation, with the high-cadmium-accumulating variety Yuzhenxiang used as a control. At 144 h, the total Cd content in the measured organs of Xiangwanxian 12 was 9.6%, 4.0%, and 23.2% lower than that of Yuzhenxiang under low, medium, and high temperatures, respectively (one-tailed t-test, p < 0.01 for all three temperatures). Conclusions: The integration of LAMP-based genotyping and physiological evaluation provides a novel and reliable strategy for identifying low-Cd rice germplasm. Xiangwanxian 12, which carries the Gn1a-i allele and exhibits consistently lower Cd accumulation than Yuzhenxiang, suggests potential as a candidate for breeding high-yield, low-Cd rice cultivars. Full article