Search Results (12,346)

Implementation of novel antiviral coatings and textiles, which can be utilised in the production of personal protective equipment, has the potential to enhance public health security against future pandemic outbreaks. Respiratory viruses, particularly SARS-CoV-2, responsible for COVID-19, have emerged as a major global concern due to their rapid transmission and high mortality rates, leading to nearly seven million deaths worldwide between 2020 and 2025. This statistic underscores the necessity for the development and implementation of advanced antiviral materials to prevent viral infections. This research focused on the in vitro evaluation of the antiviral properties of three antibacterial compounds containing silver (Ag) that were functionalized with coatings. We assessed onsite synthesised Ag powder in comparison to commercially available antibacterial additives, which included nanosilver on colloidal silica (AgSiO₂) and silver sodium hydrogen zirconium phosphate (AgNaOPZr), as potential antiviral agents in coatings against human coronavirus (HCoV). Antiviral assessments revealed that coatings containing Ag at higher concentrations (2.5 and 5%) exhibited limited antiviral effectiveness, with a titer reduction in log < 2. In contrast, the functionalization of AgSiO₂ on coatings significantly suppressed viral replication resulting in a notable reduction in virus titer of log ≥ 2 for all tested concentrations. Full article

The accelerating deterioration of the global environment underscores the urgent need to transition from the conventional fossil fuels to renewable energy, particularly the abundant solar energy. However, large-scale solar power integration could cause the severe grid fluctuations and compromise the operational stability. Existing studies have attempted to address this issue using hydrogen-based energy storage for peak shaving, but most suffer from low system efficiency. To overcome these limitations, this study proposes a novel solar-driven integrated energy system (IES) for hydrogen production and combined heat and power (CHP) generation, in which advanced hydrogen storage technologies are employed to achieve the efficient system operation. The system couples four subsystems: parabolic trough solar collector (PTSC), transcritical CO₂ power cycle (TCPC), Kalina cycle (KC) and proton exchange membrane electrolytic cell (PEMEC). Thermodynamic analysis of the proposed IES was conducted, and the effects of key parameters on system performance were investigated in depth. Simulation results show that under design conditions, the PEMEC produces 0.514 kg/h of hydrogen with an energy efficiency of 54.09% and an exergy efficiency of 51.59%, respectively. When the TCPC evaporator outlet temperature is 430.35 K, the IES achieves maximum energy and exergy efficiencies of 46.52% and 18.62%, respectively, with a hydrogen production rate of 0.51 kg/h. The findings highlight the importance of coordinated parameter optimization to maximize system efficiency and hydrogen productivity, providing theoretical guidance for practical design and operation of solar-based hydrogen integrated energy system. Full article

Urban wetlands are strategic socio-ecological systems that provide diverse cultural ecosystem services, including recreation, environmental education, and spiritual connections with nature. At the same time, they can generate ecosystem disservices, undermine human well-being, and challenge urban sustainability. This study investigates visitors’ perceptions of such disservices in three Ramsar-designated wetlands in Bogotá, Colombia (Santa María del Lago, Juan Amarillo, and Córdoba) to assess their influence on tourist experiences and their potential role in fostering urban peace. A mixed-methods approach was employed, combining structured surveys, quantitative analysis, and qualitative coding. The results reveal that pollution, insecurity, and unpleasant odors significantly reduce visitors’ willingness to return, with notable variations across gender groups and wetland sites. Visitors also emphasized the need to strengthen infrastructure, surveillance, and environmental education. These findings underscore the importance of incorporating disservice analysis into wetland governance as a strategy to advance regenerative tourism, promote environmental justice, and support peacebuilding in Latin American metropolitan contexts, with broader implications for global urban sustainability. Full article

The rapid development of unmanned aerial vehicles (UAVs) and deep learning has accelerated the application of semantic segmentation in precision agriculture (SSPA). A key driver of this progress lies in multimodal fusion, which leverages complementary structural, spectral, and physiological information to enhance the representation of complex agricultural scenes. Despite advancements, the efficacy of multimodal fusion in SSPA is limited by modality heterogeneity and the difficulty of simultaneously retaining fine details and capturing global context. To address these challenges, we propose AgriFusion, a dual-encoder framework based on convolutional and transformer architectures for SSPA tasks. Specifically, convolutional and transformer encoders are first used to extract crop-related local structural details and global contextual features from multimodal inputs. Then, an attention-based fusion module adaptively integrates these complementary features in a modality-aware manner. Finally, a MLP-based decoder aggregates multi-scale representations to generate accurate segmentation results efficiently. Experiments conducted on the Agriculture-Vision dataset demonstrate that AgriFusion achieves a mean Intersection over Union (mIoU) of 49.31%, Pixel Accuracy (PA) of 81.72%, and F1 score of 67.85%, outperforming competitive baselines including SegFormer, DeepLab, and AAFormer. Ablation studies further reveal that unimodal or shallow fusion strategies suffer from limited discriminative capacity, whereas AgriFusion adaptively integrates complementary multimodal features and balances fine-grained local detail with global contextual information, yielding consistent improvements in identifying planting anomalies and crop stresses. These findings validate our central claims that modality-aware spectral fusion and balanced multi-scale representation are critical to advancing agricultural semantic segmentation, and establish AgriFusion as a principled framework for enhancing remote sensing-based monitoring with practical implications for sustainable crop management and precision farming. Full article

Influenza viruses remain a major global public health concern, causing significant morbidity and mortality annually despite widespread vaccination efforts. The limitations of current seasonal vaccines, including strain-specific efficacy and manufacturing delays, have accelerated the development of next-generation candidates aiming for universal protection. This review comprehensively summarizes the recent progress in universal influenza vaccine research. We first outline the key conserved antigenic targets, such as the hemagglutinin (HA) stem, neuraminidase (NA), and matrix proteins (M2e, NP, and M1), which are crucial for eliciting broad cross-reactive immunity. We then delve into advanced antigen design strategies, including immunofocusing, multi-antigen combinations, computationally optimized broadly reactive antigens (COBRA), and nanoparticle-based platforms. Furthermore, we evaluate evolving vaccine delivery systems, from traditional inactivated and live-attenuated vaccines to modern mRNA and viral vector platforms, alongside the critical role of novel adjuvants in enhancing immune responses. The convergence of these disciplines—structural biology, computational design, and nanotechnology—is driving the field toward a transformative goal. We conclude that the successful development of a universal influenza vaccine will likely depend on the strategic integration of these innovative approaches to overcome existing immunological and logistical challenges, ultimately providing durable and broad-spectrum protection against diverse influenza virus strains. Full article

The global transition toward sustainable and intelligent farming has positioned Electrified Agricultural Machinery (EAM) as a central focus in modern equipment development. By integrating advanced electrical subsystems, high-efficiency powertrains, and intelligent Energy Management Strategies (EMSs), EAM offers considerable potential to enhance operational efficiency, reduce greenhouse-gas emissions, and improve adaptability across diverse agricultural environments. Nevertheless, widespread deployment remains constrained by harsh operating conditions, complex duty cycles, and limitations in maintenance capacity and economic feasibility. This review provides a comprehensive synthesis of enabling technologies and application trends in EAM. Performance requirements of electrical subsystems are examined with emphasis on advances in power supply, electric drive, and control systems. The technical characteristics and application scenarios of battery, series hybrid, parallel hybrid, and power-split powertrains are compared. Common EMS approaches (rule-based, optimization-based, and learning-based) are evaluated in terms of design complexity, energy efficiency, adaptability, and computational demand. Representative applications across tillage, seeding, crop management, and harvesting are discussed, underscoring the transformative role of electrification in agricultural production. This review identifies the series hybrid electronic powertrain system and rule-based EMSs as the most mature technologies for practical application in EAM. However, challenges remain concerning operational reliability in harsh agricultural environments and the integration of intelligent control systems for adaptive, real-time operations. The review also highlights key technical bottlenecks and emerging development trends, offering insights to guide future research and support the wider adoption of EAM. Full article

Phosphate ore is essential for global food security and industry. However, the depletion of high-grade deposits necessitates processing complex low-grade ores, posing significant separation challenges. Flotation, the main beneficiation method, exploits minor differences in surface properties, yet conventional approaches offer limited molecular-level insight, resulting in inefficiency, high reagent use, and pollution. Molecular simulation has emerged as a transformative solution, integrating quantum chemistry, molecular dynamics, and mesoscale modeling to accurately predict electronic structures and optimize flotation systems. This review systematically examines its applications in phosphate ore processing, highlighting four key advances: a multi-scale framework linking atomic mechanisms to macro-performance; structure–activity models for rational reagent design; insights into interfacial micro-environments for intelligent control; and machine learning integration for high-throughput screening. Key challenges such as force field accuracy and simulation scalability are addressed, along with emerging directions like in situ dynamic simulation and integration with process engineering. This review aims to support the development of efficient, sustainable, and intelligently optimized phosphate beneficiation technologies. Full article

Breast cancer remains a major global health challenge. Yet, genomic data from Middle Eastern and North African (MENA) populations are limited, restricting insights into disease drivers and therapeutic opportunities in this demographic. To address this gap, we performed whole-exome sequencing (WES) on 52 breast cancer samples, including 51 from the MENA region, to characterize somatic mutations and potential therapeutic targets. Across the cohort, 37,369 somatic variants matched entries in the COSMIC database, and driver prediction tools (BoostDM and OncodriveMUT) identified 2451 predicted driver mutations, including 648 known driver variants in genes such as TP53, PIK3CA, GATA3, PTEN, SF3B1, and KMT2C. In addition, 1803 novel predicted drivers were detected, many affecting DNA repair pathways, including homologous recombination (BRCA2, RAD51C), mismatch repair (MLH1, MSH2), and nucleotide excision repair (ERCC2, ERCC3), as well as regulators such as TP53 and ATM. Mutational signature analysis revealed a predominance of C>T substitutions and subtype-specific patterns, with SBS22 and SBS43 enriched in Luminal A tumors. Therapeutic annotation using OncoKB identified 223 actionable or likely oncogenic variants, highlighting potential targets for precision oncology. This study provides a comprehensive characterization of the breast cancer mutational landscape in MENA patients and offers a valuable resource for advancing genomic and therapeutic research in this demographic. Full article

Shellfish aquaculture is considered a major pillar of the seafood industry for its high market value, which increases the value for global food security and sustainability, often constrained in terms of conventional, labor-intensive practices. This review outlines the importance of automation and its advances in the shellfish value chain, starting from the hatchery operations to harvesting, processing, traceability, and logistics. Emerging technologies such as imaging, computer vision, artificial intelligence, robotics, IoT, blockchain, and RFID provide a major impact in transforming the shellfish sector by improving the efficiency, reducing the labor costs and environmental impacts, enhancing the food safety, and providing transparency throughout the supply chain. The studies involving the bivalves and crustaceans on their automated feeding, harvesting, grading, depuration, non-destructive quality assessments, and smart monitoring in transportation are highlighted in this review to address concerns involved with conventional practices. The review puts forth the need for integrating automated technologies into farm management and post-harvest operations to scale shellfish aquaculture sustainably, meeting the rising global demand while aligning with the Sustainability Development Goals (SDGs). Full article

Global population growth, intensifying climate change, and escalating food security demands are mounting. In response, modern agriculture must transcend the limitations of traditional experience-based cultivation models to address issues such as low resource utilization, poor environmental adaptability, and significant yield fluctuations. As the core technical support of smart agriculture, agricultural sensors have become the key to transformation. This review systematically introduces the classification and working principles of current mainstream agricultural sensors: according to the monitoring parameters, they can be divided into humidity sensors, light sensors, gas sensors, pressure sensors, nutrient sensors, etc. At the same time, breakthroughs in emerging technologies such as microneedle sensing, nanosensing, and wireless sensor networks are being explored, which are breaking the application limitations of traditional sensors in complex agricultural environments. Combined with specific cases, the practical value of sensor technology is improving in agricultural drought monitoring, soil detection, and agricultural product quality assessment. Looking ahead, if agricultural sensors can overcome existing limitations through breakthroughs in material innovation, multi-sensor unit integration, and artificial intelligence algorithm fusion, this will provide stronger technological support for the further advancement of smart agriculture. Full article

Cardiovascular diseases (CVDs) are the leading cause of death globally. Electrocardiograms (ECGs) are crucial diagnostic tools; however, their traditional interpretations exhibit limited sensitivity and reproducibility. This systematic review discusses the recent advances in artificial intelligence (AI), including deep learning and machine learning, applied to ECG analysis for CVD detection. It examines over 100 studies from 2019 to 2025, classifying AI applications by disease type (heart failure, myocardial infarction, and atrial fibrillation), model architecture (convolutional neural networks, long short-term memory, and hybrid models), and methodological innovation (signal denoising, synthetic data generation, and explainable AI). Comparative tables and conceptual figures highlight performance metrics, dataset characteristics, and implementation challenges. Our findings indicated that AI models outperform traditional methods, especially in terms of detecting subclinical conditions and enabling real-time monitoring via wearable technologies. Nonetheless, issues such as demographic bias, lack of dataset diversity, and regulatory hurdles persist. The review concludes by offering actionable recommendations to enhance clinical translation, equity, and transparency in AI-ECG applications. These insights aim to guide interdisciplinary efforts toward the safe and effective adoption of AI in cardiovascular diagnostics. Full article

Hydroxypropyl cellulose (HPC) is a versatile cellulose ether with two standardized forms: highly substituted (H-HPC), which is water-soluble and thermoresponsive, and low-substituted (L-HPC), which is insoluble but swellable. This systematic review with bibliometric analysis aimed to map the global HPC research landscape (2005–2024), focusing on publication trends, research impact, and thematic directions. Original research articles and conference proceedings indexed in Scopus were included, while reviews and non-research items were excluded. The database was searched on 7 July 2025 using predefined strategies and analyzed using Excel for descriptive statistics and VOSviewer for network visualization. Risk of bias assessment was not applicable; data accuracy was ensured through duplicate removal and the use of standardized bibliometric indicators. A total of 1273 H-HPC and 92 L-HPC publications were analyzed. H-HPC research dominates multidisciplinary applications in drug delivery, 3D printing, thermochromic, and energy materials, whereas L-HPC remains focused on pharmaceutical disintegration and binding. Nevertheless, the field is constrained by reliance on commercial grades and a narrow application focus, leaving broader material innovations underexplored. HPC is positioned as a strategic polysaccharide derivative with expanding translational potential. Future studies should emphasize greener synthesis, advanced functionalization, and industrial scale-up. Funding: Supported by BRIN. Systematic review registration: INPLASY202590019. Full article

Reliable global turbidity monitoring is crucial for water resource management, yet existing satellite-based methods face limitations in accuracy, generalization, and scalability across diverse aquatic environments. This study presents a robust, globally applicable turbidity estimation model using Sentinel-2 imagery and a machine-learning approach, developed based on harmonized global open-source datasets (GLORIA and MAGEST; turbidity range: 0–2200 FNU) encompassing 68 lakes, 2 rivers, 2 estuaries, and 11 coastal oceans across 17 countries. Among the evaluated machine-learning models, gradient boosting regression demonstrated the best performance, achieving a high correlation (r: 0.95) with minimal bias (1.32 FNU) and robust generalization across all water types, outperforming existing turbidity models when evaluated on the same test dataset. Shapley Additive exPlanations-based model interpretability identified the Rrs865/Rrs560 ratio as the dominant predictor, with critical contributions from Rrs783, Rrs665, and Rrs865. The model’s performance is evaluated across various optical water types and aquatic systems in diverse geographical settings, showcasing its robustness in sediment-rich and highly turbid environments that underscores its suitability for reliable turbidity monitoring after severe storms or extreme precipitation. Additionally, innovative automated pipelines integrated within a scientific exploitation platform facilitate scalable and near-real-time operational monitoring. This methodological integration provides a significant advancement in satellite-based turbidity monitoring, enabling informed water quality management under diverse environmental and climatic conditions. Full article

Alzheimer’s disease (AD), a neurodegenerative disorder of the aging brain, is associated with behavioral and cognitive issues and poses a huge burden on the global health care system. One of the key features of AD is the deposition of abnormal proteins called amyloid-beta (Aβ) in the brain, causing inflammatory changes, oxidative stress, and neuronal loss. Recent advancements in the anti-Aβ therapies have considerably improved the management of AD, resulting in better clinical outcomes for patients and caregivers. This review offers an inclusive update on current drug discovery efforts, innovative approaches, and ongoing clinical trials targeting Aβ, a key player in AD pathogenesis. We have evaluated the most recent developments in monoclonal antibodies, including aducanumab (discontinued November 2024), lecanemab, and donanemab, emerging therapeutic options, as well as emerging strategies such as tau-targeting therapies, gene therapy, and small molecule inhibitors. Moreover, we highlighted the challenges and opportunities in AD research, including the need for early diagnosis, personalized medicine, and combination therapies. Our review will offer a concise and informative overview of the current landscape and future directions in anti-Aβ therapeutics for AD, shedding light on potential treatments and prospects for improving patient outcomes. Full article

Deep tech is a broad concept encompassing scientifically and technologically advanced innovations, enterprises, and projects based on profound scientific and engineering knowledge. It addresses complex technological challenges while considering environmental, social, and economic sustainability. Ambitious R&D initiatives act as catalysts for innovative solutions and for transforming companies and sectors toward sustainable development. The literature review highlights the multifaceted nature of deep tech, particularly from diverse stakeholder perspectives—both those directly and indirectly engaged in this field. Fully utilizing deep tech’s potential requires strong scientific, infrastructural, regulatory, and financial foundations. Europe, including dynamically developing EU countries such as Poland, increasingly recognizes the need to build an ecosystem that supports the development and commercialization of frontier technologies grounded in scientific progress. This article clarifies key deep tech concepts and outlines current conditions for technological innovation in Europe. Drawing on desk research, participatory observation, and a survey, it presents an initial analysis of Poland’s deep tech ecosystem. The exploratory pilot study serves as a basis for more focused future research on key sectoral challenges. The findings offer a preliminary assessment of the potential and barriers related to science-based innovation and provide a clearer picture of Poland’s emerging deep tech landscape. This enables more accurate interpretation of results and insights into the sector’s future development. For Europe and the EU, enhancing global competitiveness in deep tech will require coordinated actions and stronger connections among local ecosystems at different stages of maturity, such as those in Poland. Full article