Search Results (12,114)

Microencapsulation is a fundamental technology for protecting active compounds from environmental degradation by factors such as light, heat, and oxygen. This process significantly improves their stability, bioavailability, and shelf life by entrapping an active core within a protective matrix. Therefore, a thorough understanding of the physicochemical interactions between these components is essential for developing stable and efficient delivery systems. The composition of the microcapsule and the encapsulation method are key determinants of system stability and the retention of encapsulated materials. Recently, the application of computational tools to predict and optimize microencapsulation processes has emerged as a promising area of research. In this context, molecular dynamics (MD) simulation has become an indispensable computational technique. By solving Newton’s equations of motion, MD simulations enable a detailed study of the dynamic behavior of atoms and molecules in a simulated environment. For example, MD-based analyses have quantitatively demonstrated that optimizing polymer–core interaction energies can enhance encapsulation efficiency by over 20% and improve the thermal stability of active compounds. This approach provides invaluable insights into the molecular interactions between the core material and the matrix, ultimately facilitating the rational design of optimized microstructures for diverse applications, including pharmaceuticals, thereby opening new avenues for innovation in the field. Ultimately, the integration of computational modeling into microencapsulation research not only represents a methodological advancement but also pivotal opportunity to accelerate innovation, optimize processes, and develop more effective and sustainable therapeutic systems. Full article

This study evaluates the feasibility of using a lightweight, off-the-shelf sensing system for short-term stress detection during exergaming. Most existing studies in stress detection compare rest and task conditions, providing limited insight into continuous stress dynamics, and there is no agreement on optimal sensor configurations. To address these limitations, we investigated dynamic stress responses induced by a cognitive–motor task designed to simulate rehabilitation-like scenarios. Twenty-three participants completed the experiment, providing electrodermal activity (EDA), blood volume pulse (BVP), self-report, and in-game data. Features extracted from physiological signals were analyzed statistically, and shallow machine learning classifiers were applied to discriminate among stress levels. EDA-based features reliably differentiated stress conditions, while BVP features showed less consistent behavior. The classification achieved an overall accuracy of 0.70 across four stress levels, with most errors between adjacent levels. Correlations between EDA dynamics and perceived stress scores suggested individual variability possibly linked to chronic stress. These results demonstrate the feasibility of low-cost, unobtrusive stress monitoring in interactive environments, supporting future applications of dynamic stress detection in rehabilitation and personalized health technologies. Full article

Respiratory viral co-infections by viruses such as influenza virus, SARS-CoV-2, and respiratory syncytial virus (RSV) are a significant clinical issue in high-risk populations such as children, elderly patients, and immunocompromised individuals. Sequential and simultaneous co-infections exacerbate disease severity, leading to acute respiratory distress syndrome (ARDS), prolonged hospitalization, and increased mortality. Molecular and immunological interactions are complex, context-dependent, and largely unknown. Experimental models of infection that accurately mimic human respiratory physiology are required for the study of viral dynamics, virus–virus interactions, and virus–host interactions. This review outlines a range of complex in vitro and ex vivo models, including organoids, air–liquid interface cultures, lung-on-a-chip platforms, and in vivo animal models, highlighting their ability to simulate the complexity of respiratory co-infections and their limitations. The field has developed significantly, despite challenges like variability across viral strains, timing of infection, and non-standardization of models. Integration of multi-omics technologies and application of highly translational models such as non-human primates and lung-on-a-chip technology are promising avenues to uncover the molecular determinants of co-infection and guide development of targeted therapeutic strategies. Interrelatedness of experimental models and clinical outcomes is highly critical to improve prevention and treatment of respiratory viral co-infections mainly among high-risk populations. Full article

Glass fiber (GF) reinforced unsaturated polyester resin (UP) composites are used in cured-in-place pipe (CIPP) rehabilitation technology of drainage systems due to their low cost and excellent force chemical properties. However, the weak interfacial compatibility between GF and the polymer matrix limits the stress transfer efficiency. Herein, a strategy of a polyhydric boehmite (AlOOH) layer coated on GF (GF-AlOOH) was developed for improving the mechanical properties of UP composites, and the enhancement effects of the coating process were analyzed. The AlOOH-modified GFs significantly improved the flexural and tensile strengths of the modified composites by 41.21% and 21.05%, respectively. Moreover, the enhancement mechanism was explored by analyzing the surface chemical structure of GF-AlOOHs. The nano-AlOOH was grafted on the GF surface by O=Al–OH. Meanwhile, the increase in the mechanical properties of UP/GF-AlOOH was mainly attributed to the combined effect of mechanical interlocking interaction, covalent bonding and hydrogen bonding, which improved the interfacial adhesion between GF and UP. In summary, this work provides effective guidance for achieving high-quality interfaces in GF composites and offers important insights into designing durable and cost-effective materials for CIPP rehabilitation and broader infrastructure applications. Full article

Plant viral infections continue to pose a significant and ongoing threat to global food security, especially in the context of climatic instability and intensive agricultural practices. The CRISPR/Cas system has emerged as a powerful tool for developing virus-resistant crops by enabling precise modifications to viral genomes or plant susceptibility factors. Nonetheless, the efficacy and dependability of CRISPR-based antiviral approaches are limited by challenges in guide RNA design, off-target effects, insufficiently annotated datasets, and the intricate biological dynamics of plant–virus interactions. This paper summarizes the latest advancements in the incorporation of artificial intelligence (AI) methodologies, including machine learning and deep learning algorithms, into the CRISPR design and optimization framework. It examines how convolutional and recurrent neural networks, transformer architectures, and generative models like AlphaFold2, RoseTTAFold, and ESMFold can be used to predict protein structures, score sgRNAs, and model host–virus interactions. AI-enhanced methods have been proven to improve target specificity, Cas protein performance, and in silico validation. This paper aims to establish a foundation for next-generation genome editing strategies against plant viruses and promote the adoption of AI-powered CRISPR technologies in sustainable agriculture. Full article

Remote sensing foundation models (RSFMs) have demonstrated excellent feature extraction and reasoning capabilities under the self-supervised learning paradigm of “unlabeled datasets—model pre-training—downstream tasks”. These models achieve superior accuracy and performance compared to existing models across numerous open benchmark datasets. However, when confronted with multimodal data, such as optical, LiDAR, SAR, text, video, and audio, the RSFMs exhibit limitations in cross-modal generalization and multi-task learning. Although several reviews have addressed the RSFMs, there is currently no comprehensive survey dedicated to vision–X (vision, language, audio, position) multimodal RSFMs (MM-RSFMs). To tackle this gap, this article provides a systematic review of MM-RSFMs from a novel perspective. Firstly, the key technologies underlying MM-RSFMs are reviewed and analyzed, and the available multimodal RS pre-training datasets are summarized. Then, recent advances in MM-RSFMs are classified according to the development of backbone networks and cross-modal interaction methods of vision–X, such as vision–vision, vision–language, vision–audio, vision–position, and vision–language–audio. Finally, potential challenges are analyzed, and perspectives for MM-RSFMs are outlined. This survey from this paper reveals that current MM-RSFMs face the following key challenges: (1) a scarcity of high-quality multimodal datasets, (2) limited capability for multimodal feature extraction, (3) weak cross-task generalization, (4) absence of unified evaluation criteria, and (5) insufficient security measures. Full article

Diabetic nephropathy (DN) remains the leading cause of end-stage renal disease worldwide, with proximal tubular epithelial cells (PTECs) playing a central role in its pathogenesis. Under hyperglycemic conditions, PTECs drive a pathological triad of inflammation, apoptosis, and fibrosis. Recent advances reveal that these processes interact synergistically to form a self-perpetuating vicious cycle, rather than operating in isolation. This review systematically elucidates the molecular mechanisms underlying this crosstalk in PTECs. Hyperglycemia induces reactive oxygen species (ROS) overproduction, advanced glycation end products (AGEs) accumulation, and endoplasmic reticulum stress (ERS), which collectively activate key inflammatory pathways (NF-κB, NLRP3, cGAS-STING). The resulting inflammatory milieu triggers apoptosis via death receptor and mitochondrial pathways, while apoptotic cells release damage-associated molecular patterns (DAMPs) that further amplify inflammation. Concurrently, fibrogenic signaling (TGF-β1/Smad, Hippo-YAP/TAZ) promotes epithelial–mesenchymal transition (EMT) and extracellular matrix (ECM) deposition. Crucially, the resulting fibrotic microenvironment reciprocally exacerbates inflammation and apoptosis through mechanical stress and hypoxia. Quantitative data from preclinical and clinical studies are integrated to underscore the magnitude of these effects. Current therapeutic strategies are evolving toward multi-target interventions against this pathological network. We contrast the paradigm of monotargeted agents (e.g., Finerenone, SGLT2 inhibitors), which offer high specificity, with that of multi-targeted natural product-based formulations (e.g., Huangkui capsule, Astragaloside IV), which provide synergistic multi-pathway modulation. Emerging approaches (metabolic reprogramming, epigenetic regulation, mechanobiological signaling) hold promise for reversing fibrosis. Future directions include leveraging single-cell technologies to decipher PTEC heterogeneity and developing kidney-targeted drug delivery systems. We conclude that disrupting the inflammation–apoptosis–fibrosis vicious cycle in PTECs is central to developing next-generation therapies for DN. Full article

Gene regulatory networks (GRN) govern cellular identity and function through precise control of gene transcription. Single-cell technologies have provided powerful means to dissect regulatory mechanisms within specific cellular states. However, existing computational approaches for modeling single-cell RNA sequencing (scRNA-seq) data often infer local regulatory interactions independently, which limits their ability to resolve regulatory mechanisms from a global perspective. Here, we propose a deep learning framework (Planet) based on diffusion models for constructing cell-specific GRN, thereby providing a systems-level view of how protein regulators orchestrate transcriptional programs. Planet jointly optimizes local network structures in conjunction with gene expression profiles, thereby enhancing the structural consistency of the resulting networks at the global level. Specifically, Planet decomposes GRN generation into a series of Markovian evolution steps and introduces a Triple Hybrid-Attention Transformer to capture long-range regulatory dependencies across diffusion time-steps. Benchmarks on multiple scRNA-seq datasets demonstrate that Planet achieves competitive performance against state-of-the-art methods and yields only a slight improvement over DigNet under comparable conditions. Compared with conventional diffusion models that rely on fixed sampling schedules, Planet employs a fast-sampling strategy that accelerates inference with only minimal accuracy trade-off. When applied to mouse-lung Cd8⁺Gzmk⁺ T cells, Planet successfully reconstructs a cell-type-specific GRN, recovers both established and previously uncharacterized regulators, and delineates the dynamic immunoregulatory changes that accompany ageing. Overall, Planet provides a practical framework for constructing cell-specific GRNs with improved global consistency, offering a complementary perspective to existing methods and new insights into regulatory dynamics in health and disease. Full article

While robotic technologies have shown great promise in enhancing productivity and safety, their integration into the mining sector, particularly for search and rescue (SAR) missions, remains limited. The success of these systems depends not only on their technical capabilities, but also on the effectiveness of human–robot interaction (HRI) in high-risk, time-sensitive environments. This review synthesizes key human factors, including cognitive load, situational awareness, trust, and attentional control, that critically influence the design and operation of robotic interfaces for mine rescue missions. Drawing on established cognitive theories such as Endsley’s Situational Awareness Model, Wickens’ Multiple Resource Theory, Mental Model and Cognitive Load Theory, we identified core challenges in current SAR interface design for mine rescue missions and mapped them to actionable design principles. We proposed a human-centered framework tailored to underground mine rescue operations, with specific recommendations for layered feedback, multimodal communication, and adaptive interfaces. By contextualizing cognitive science in the domain of mining emergencies, this work offers a structured guide for designing intuitive, resilient, and operator-supportive robotic systems. Full article

The thermal behavior of 3D-printed polyvinylidene fluoride (PVDF)-based composites enhanced with carbon nanotubes (CNTs), graphene nanoplatelets (GNPs), and their hybrid formulations was investigated under Joule heating at applied voltages of 2, 3, and 4 V. The influence of filler type and weight fraction on both electrical and thermal conductivity was systematically assessed using a Design of Experiments (DoE) approach. Response Surface Methodology (RSM) was employed to derive an analytical relationship linking conductivity values to filler loading, revealing clear trends and interaction effects. Among all tested formulations, the composite containing 6 wt% of GNPs exhibited the highest performance in terms of thermal response and electrical conductivity, reaching a steady-state temperature of 88.1 °C under an applied voltage of just 4 V. This optimal formulation was further analyzed through multiphysics simulations, validated against experimental data and theoretical predictions, to evaluate its effectiveness for potential practical applications—particularly in de-icing systems leveraging Joule heating. The integrated experimental–theoretical–numerical workflow proposed herein offers a robust strategy for guiding the development and optimization of next-generation polymer nanocomposites for thermal management technologies. Full article

As global Marine resource development continues to expand into deep-sea and ultra-deep-sea domains, the intelligent and green transformation of deep-sea aquaculture equipment has become a key direction for high-quality development of the Marine economy. Large deep-sea cages are considered essential equipment for deep-sea aquaculture. However, there are significant challenges associated with ensuring their structural integrity and long-term monitoring capabilities in the complex Marine environments characteristic of deep-sea aquaculture. The present study focuses on large deep-sea cages, addressing their dynamic response challenges and long-term monitoring power supply needs in complex Marine environments. The present study investigates the nonlinear vibration characteristics of flexible net structures under complex fluid loads. To this end, a multi-field coupled dynamic model is constructed to reveal vibration response patterns and instability mechanisms. A self-powered sensing system based on triboelectric nanogenerator (TENG) technology has been developed, featuring a curved surface adaptive TENG array for the real-time monitoring of net vibration states. This review aims to focus on the research of optimizing the design of curved surface adaptive TENG arrays and deep-sea cage monitoring. The present study will investigate the mechanisms of energy transfer and cooperative capture within multi-body coupled cage systems. In addition, the biomechanics of fish–cage flow field interactions and micro-energy capture technologies will be examined. By integrating different disciplinary perspectives and adopting innovative approaches, this work aims to break through key technical bottlenecks, thereby laying the necessary theoretical and technical foundations for optimizing the design and safe operation of large deep-sea cages. Full article

Informal settlements, urban areas with substandard housing conditions and inadequate infrastructure, are increasing in Africa’s sub-Saharan cities, fueled by rapid urbanization, economic challenges, and high housing prices. However, developers often ignore the green building (GB) concept when upgrading housing conditions for these communities. This study aims to investigate GB design strategies specifically for residential structures in Akabahizi to identify and propose practical strategies suitable for informal settlements such as Akabahizi and to develop sustainable housing solutions that enhance environmental quality and meet the needs of residents. Simulation software and combined qualitative and quantitative data collection techniques, including field surveys, interviews, and assessments of existing building conditions, constitute the methodology used in this study. The focus was on the influence of climatic factors, including temperature, precipitation, and wind, on design choices, particularly GB design and current residential buildings in Akabahizi. Based on the survey, 82.5% of residents support the GB concept, 87.4% recognize the importance of GB for community well-being, and 97.1% recognize the benefits of integrating energy-efficient technology for residents’ well-being. Questionnaire findings were considered in decision-making for the design of the new proposed structure to address challenges in the area. Optimized energy efficiency, daylight access, and thermal comfort resulting from courtyard design support GB design incorporating a courtyard as a robust and culturally relevant sustainable design framework tailored for Akabahizi. The courtyard provides green space that promotes social interaction, improves air quality, and delivers natural cooling elements that are essential for residential housing. The proposed new design, with green roof and renewable energy devices, improved material usage, and natural ventilation elements, outperformed the existing one in terms of lower levels of carbon emission for environmental protection. In conclusion, a collaborative effort is needed among various stakeholders, including architects, urban planners, and educational institutions, to promote and implement sustainable building practices. The study suggests that enhancing awareness, offering training opportunities, and empowering local professionals and residents alike can pave the way for improved living conditions and sustainable urban development in Akabahizi and similar informal settlements. Full article

The digital transition is a major contemporary challenge that unevenly impacts the life chances of occupational classes and the well-being of individuals. The decline of the working class, driven by skill-based technological change, further provides additional arguments for examining the impact of digitalization on individuals’ chances from a class perspective. The intersections between social class and gender deserve attention in relation to adult education participation. This paper aims to account for both individual-level characteristics—occupational class and gender—and macro-level characteristics including digitalization, measured by the Digital Economy and Society Index (DESI), and inequality, measured by the Gini coefficient. Analyzing data from the European Social Survey, Round 10 (2021/2022), our results show that digital performance in a given country is positively associated with the probability of participation in adult education. Women in countries with higher levels of digital performance are more likely to participate in adult education. We found evidence for a positive interaction between DESI and lower-grade service class for women, whereas in the case of men, we found positive interaction terms between DESI and small business owners, skilled workers, and unskilled workers. Full article

The precise regulation of water content plays a pivotal role in determining several the critical properties of marine fuels, including combustion stability, corrosion resistance, and the mitigation of pollutant emissions. The present study introduces an innovative, additive-free technique for moisture extraction from Marine Gasoil (MGO) utilizing the hydrophilic polymer polyacrylamide, which leverages its polar amino groups to attract water molecules. This process facilitates the physical extraction of moisture without modifying the fuel’s composition, in contrast to traditional drying techniques or chemical additions. Experimental findings indicate a 34.6% decrease in water content in MGO (from 29.3 mg/kg to 19.15 mg/kg) and a 36.5% reduction in MGO–biodiesel blends (from 32.04 mg/kg to 20.34 mg/kg), accomplished within one hour of treatment. The scientific significance of this work lies in its discovery of polyacrylamide’s ability to retain moisture within a nonpolar fuel matrix—a phenomenon not previously investigated in maritime fuel applications. The findings highlight the potential for further research into polymer–fuel interactions and non-chemical strategies for fuel enhancement. Economically, the proposed technology reduces dependence on costly chemical additives and energy-intensive drying processes, while environmentally, it improves combustion efficiency and lowers emissions of hydrocarbons (HC), carbon monoxide (CO), and smoke. Overall, the results introduce a novel, sustainable, and practical process for improving maritime fuel quality, while supporting compliance with increasingly stringent regional and global environmental regulations. Full article

This work comparably investigates the gas sensing potential of noble metal (Pd and Ru)-doped GeS₂ monolayers upon three C₄F₇N decomposed species (FCN, CF₃CN, and C₂F₄) using the first-principles theory, for operation status evaluation in C₄F₇N-insulated devices. The Pd- and Ru-doping effects on the pristine GeS₂ monolayer are analyzed, followed by the adsorption mechanism and sensing performance of two doped monolayers. Our results demonstrate that while Ru doping induces stronger surface interactions with the GeS₂ substrate and consequently exhibits superior adsorption strengths upon the three gases, the Pd-doped monolayer shows remarkable advantages in charge transfer capability that leads to exceptional room-temperature sensitivity responses of −99.6% (FCN), −95.0% (CF₃CN), and −88.0% (C₂F₄), thus significantly outperforming the Ru-doped system. Combined with the instantaneous recovery for gas desorption, the Pd-GeS₂ monolayer holds significance as an ideal room-temperature sensor to monitor the operation status of C₄F₇N-insulated devices in power systems. This research provides promising insights into the application of GeS₂-based materials for gas sensing in power systems and emphasizes the importance of dopant selection in designing high-performance gas sensing materials, especially for developing advanced electrical equipment monitoring technologies. Full article