Search Results (19,258)

Lignin, as one of the three primary components of renewable lignocellulosic biomass, can be converted into aromatic platform chemicals and holds significant potential for high-value applications. p-Hydroxybenzaldehyde is a compound derived from lignin. In this study, the mutant Δ60 of the glycosyltransferase UGT_BL1 derived from Bacillus licheniformis was adopted to catalyze the glycosylation reaction of p-hydroxybenzaldehyde, producing a bioactive compound Helicid analogue (p-hydroxybenzaldehyde β-glucoside). Truncation mutations targeting loop regions may reduce local flexibility, thereby facilitating enhanced access of p-hydroxybenzaldehyde to the active site pocket and promoting relative activity. Under optimal conditions (35 °C, pH 7.5, and glucose 200 mM), a high yield of 97.8% for p-hydroxybenzaldehyde β-glucoside was achieved from 2 mM p-hydroxybenzaldehyde within 10 h. The conversion of 3 mM p-hydroxybenzaldehyde (366.4 mg/L) yielded up to 2.7 mM (767.5 mg/L) of p-hydroxybenzaldehyde β-glucoside within 48 h. According to the molecular docking results, the CDOCKER energy value of mutant Δ60 was lower than that of the wild-type, at −16.0 kcal/mol. To our knowledge, this is the first example of an efficient and environmentally sustainable approach for the synthesis of p-hydroxybenzaldehyde β-glucoside, providing a new insight for the valorization of lignin into valuable biobased chemicals. Full article

Fire spotting (FS), the process by which firebrands are lofted, transported, and ignite new fires ahead of the main flame front, plays a critical role in escalating extreme wildfire events. This systematic literature review (SLR) analyzes peer-reviewed articles and book chapters published in English from 2000 to 2023 to assess the evolution of FS models, identify prevailing methodologies, and highlight existing gaps. Following a PRISMA-guided approach, 102 studies were selected from Scopus, Web of Science, and Google Scholar, with searches conducted up to December 2023. The results indicate a marked increase in scientific interest after 2010. Thematic and bibliometric analyses reveal a dominant research focus on integrating the FS model within existing and new fire spread models, as well as empirical research and individual FS phases, particularly firebrand transport and ignition. However, generation and ignition FS phases, physics-based FS models (encompassing all FS phases), and integrated operational models remain underexplored. Modeling strategies have advanced from empirical and semi-empirical approaches to machine learning and physical-mechanistic simulations. Despite advancements, most models still struggle to replicate the stochastic and nonlinear nature of spotting. Geographically, research is concentrated in the United States, Australia, and parts of Europe, with notable gaps in representation across the Global South. This review underscores the need for interdisciplinary, data-driven, and regionally inclusive approaches to improve the predictive accuracy and operational applicability of FS models under future climate scenarios. Full article

The growing demand for radiation-shielded infrastructure highlights the need for materials that balance shielding performance with environmental and economic sustainability. Heavyweight self-compacting concretes (HWSCC), commonly produced with barite (BaSO₄) or magnetite (Fe₃O₄) aggregates, lack systematic life cycle comparisons. The aim of this study is to systematically compare barite- and magnetite-based HWSCC in terms of life cycle environmental impacts, life cycle cost, functional performance (strength and shielding), and end-of-life circularity, in order to identify the more sustainable and cost-effective material for radiation shielding infrastructure. This study applies cradle-to-grave life cycle assessment (LCA) and life cycle cost analysis (LCC), in accordance with ISO 14040/14044 and ISO 15686-5, to evaluate barite- and magnetite-based HWSCC. Results show that magnetite concrete reduces global warming potential by 19% eutrophication by 24%, and fossil resource depletion by 23%, while lowering life cycle costs by ~23%. Both concretes achieve comparable compressive strength (~48 MPa) and shielding efficiency (µ ≈ 0.28–0.30 cm⁻¹), meeting NCRP 147 and IAEA SRS-47 standards. These findings demonstrate that magnetite-based HWSCC offers a more sustainable, cost-effective, and ethically sourced alternative for radiation shielding in healthcare, nuclear, and industrial applications. In addition, the scientific significance of this work lies in establishing a transferable methodological framework that combines LCA, LCC, and performance-normalized indicators. This enables scientists and practitioners worldwide to benchmark heavyweight concretes consistently and to adapt sustainability-informed material choices to their own regional contexts. Full article

The article addresses the challenges of beam position tracking in Free-Space Optical Communication (FSOC) systems. A review of available photodetector technologies is presented, highlighting their operating principles and applications in optical links. The analysis indicates that most current monitoring devices function with the visible and near- or short-infrared ranges. However, due to the propagation characteristics of radiation in terrestrial environments, the mid-wave infrared (MWIR) region offers particularly promising opportunities. To the end, the work introduces a novel detector module based on an MWIR quadrant detector capable of simultaneously performing two essential tasks: monitoring beam position and receiving transmitted data. Such an integrated approach has the potential to significantly simplify the design of mobile FSOC systems, especially those requiring accurate transceivers’ tracking. The concept was validated through laboratory experiments on an MWIR link model, where both the signal bandwidth and position transfer function of the quadrant detector were examined. Full article

In this study, the distribution of the acoustic pressure field and cavitation threshold region in Al alloy casting under two five-source ultrasound arrangements (A and B) is investigated, aiming to optimize the five-source ultrasound configuration to improve casting quality. Numerical simulations were conducted using COMSOL software (COMSOL Multiphysics 6.0) to analyze the propagation characteristics of ultrasound in the Al melt and its influence on the cavitation effect under the two arrangements. The simulation results indicate that the cavitation threshold region for arrangement A is slightly larger than that for arrangement B. Furthermore, arrangement A demonstrates superior performance in terms of the uniformity of sound pressure distribution and the reduction in the cavitation threshold. Based on the simulation results, arrangement A was selected for experimental validation. The experimental results reveal that arrangement A, with a radial rod distance (L) of 200 mm from the center point and an insertion depth (H) of 270 mm, significantly refines the grains and improves the distribution of the second phase, thereby confirming the reliability of the simulation results. This study provides a theoretical foundation and practical guidance for the application of five-source ultrasound in Al alloy casting. Full article

The recruitment of peripheral membrane proteins is tightly regulated by membrane lipid composition and local electrostatic microenvironments. Our experimental observations revealed that Vp54, a viral matrix protein, exhibited preferential binding to lipid bilayers enriched in anionic lipids such as phosphatidylglycerol (PG) and phosphatidylserine (PS), compared to neutral phosphatidylcholine/phosphatidylethanolamine liposomes, and this occurred in a curvature-dependent manner. To elucidate the molecular basis of this selective interaction, we performed a series of computational analyses including helical wheel projection, electrostatic potential calculations, electric field lines simulations, and electrostatic force analysis. Our results showed that the membrane-proximal region of Vp54 adopted an amphipathic α-helical structure with a positively charged interface. In membranes containing PG or PS, electrostatic potentials at the interface were significantly more negative, enhancing attraction with Vp54. Field line and force analyses further confirmed that both the presence and spatial clustering of anionic lipids intensify membrane–Vp54 electrostatic interactions. These computational findings align with experimental binding data, jointly demonstrating that membrane lipid composition and organization critically modulate Vp54 recruitment. Together, our findings highlight the importance of electrostatic complementarity and membrane heterogeneity in peripheral protein targeting and provide a framework applicable to broader classes of membrane-binding proteins. Full article

Vineyard cover crops deliver well-documented ecosystem services, yet consistent establishment, especially of perennial grasses and legumes, remains a primary barrier to adoption. This review reframes “companion (nurse) cropping” not as a new crop class but as a acilitative establishment strategy within the broader cover-/service-crop literature. We (i) position our contribution relative to recent syntheses, (ii) synthesize evidence on companion crops practices that reduce cover cropping early failure risk, and (iii) propose a testable research agenda. A focused scoping review of peer-reviewed and extension literature indexed in Web of Science and Google Scholar was conducted using search terms encompassing cover/service crops and nurse/companion/facilitation in viticulture systems. Across climates, fast-establishing cereals (Avena sativa, Hordeum vulgare, Secale cereale, × Triticosecale Wittmack) and short-cycle legumes (Vicia sativa, Pisum sativum, Trifolium incarnatum) can reliably “nurse” slower perennials and legumes by providing early groundcover, weeds control, and microclimate buffering when sown at reduced rates (≈25–50% of monoculture) and terminated on time to limit vine competition. Evidence gaps persist for in-row applications, water-use penalties under drought, and long-term effects on yield and grape composition. Companion cropping is argued to be a design principle in vineyard cover-crop programs rather than a separate category. A decision framework and research agenda are presented to quantify establishment reliability, resource trade-offs, and wine-relevant outcomes, and it is recommended that future decision tools make the companion-phase logic explicit to de-risk adoption and align with regional guidelines. Full article

Background/Objectives: The recent development of four-dimensional X-ray velocimetry (4DXV) technology (three-dimensional space and time) provides a unique opportunity to obtain preclinical quantitative functional lung images. Only single-scan measurements in non-survival studies have been obtained to date; thus, methodologies enabling animal survival for repeated imaging to be accomplished over weeks or months from the same animal would establish new opportunities for the assessment of pathophysiology drivers and treatment response in advanced preclinical drug-screening efforts. Methods: An anesthesia protocol developed for animal recovery to allow for repetitive, longitudinal scanning of individual animals over time. Test–retest imaging scans from the lungs of healthy mice were performed over 8 weeks to assess the repeatability of scanner-derived quantitative imaging metrics and variability. Results: Using a murine model of fibroproliferative lung disease, this longitudinal scanning approach captured heterogeneous progressive changes in pulmonary function, enabling the visualization and quantitative measurement of averaged whole lung metrics and spatial/regional change. Radiation dosimetry studies evaluated the effects of imaging acquisition protocols on X-ray dosage to further adapt protocols for the minimization of radiation exposure during repeat imaging sessions using these newly developed image acquisition protocols. Conclusions: Overall, we have demonstrated that the 4DXV advanced imaging scanner allows for repeat measurements from the same animal over time to enable the high-resolution, noninvasive mapping of quantitative lung airflow dysfunction in mouse models with heterogeneous pulmonary disease. The animal anesthesia and image acquisition protocols described will serve as the foundation on which further applications of the 4DXV technology can be used to study a diverse array of murine pulmonary disease models. Together, 4DXV provides a novel and significant advancement for the longitudinal, noninvasive interrogation of pulmonary disease to assess spatial/regional disease initiation, progression, and response to therapeutic interventions. Full article

The custard apple (CA) is a noble fruit in tropical regions worldwide. It has attracted a growing interest due to its organoleptic properties and nutritional value. With the expansion of international trade, both its cultivation and consumption have grown significantly in recent years. Previous researchers have sporadically investigated its nutritional composition and health benefits; however, existing information on its processing and utilization is highly fragmented and lacks a comprehensive overview of its constituents, biological activities, and potential applications. This review is a detailed summary of the nutritional and bioactive properties, safety evaluations, and potential applications of CA. Following PRISMA guidelines, peer-reviewed studies published between 2000 and 2025 were systematically searched in PubMed, Scopus, ResearchGate, and Web of Science. Inclusion criteria comprised studies reporting on nutritional composition, phytochemicals, bioactivities, health promotion, and applications of CA. In addition to primary nutrients like carbohydrates, protein, fatty acids, vitamins, and minerals, CA also contains a multitude of bioactive compounds, mainly including phenols, flavonoids, terpenoids, acetogenins, and alkaloids, which are attributed to a range of health benefits, such as antioxidant, anti-microbial, anti-tumor, blood sugar regulation, and cognitive function improvement. However, more clinical and toxicological profiles remain underexplored, and future research should focus on standardized extraction, safety evaluation, and translational applications. Additionally, the challenges and future perspectives in industrial applications are discussed, which are expected to offer comprehensive information for the utilization of CA. Full article

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments. Full article

Passive microwave remote sensing provides indispensable observations for constructing long-term snow depth records, which are critical for climatology, hydrology, and operational applications. Nevertheless, despite decades of snow depth monitoring, systematic evaluations of how inter-sensor brightness temperature differences (TBDs) propagate into retrieval uncertainties are still lacking. In this study, TBDs between DMSP-F18/SSMIS, FY-3D/MWRI, and AMSR2 sensors were quantified, and the sensitivity of seven snow depth retrieval algorithms to these discrepancies was systematically assessed. The results indicate that TBDs between SSMIS and AMSR2 are larger than those between MWRI and AMSR2, likely reflecting variations in sensor specifications such as frequency, observation angle, and overpass time. In terms of algorithm sensitivity, SPD, WESTDC, FY-3B, and FY-3D demonstrate less sensitivity across sensors, with standard deviations of snow depth differences generally below 2 cm. In contrast, the Foster algorithm exhibits pronounced sensitivity to TBDs, with standard deviations exceeding 11 cm and snow depth differences reaching over 20 cm in heavily forested regions (forest fracion >90%). This study provides guidance for SWE virtual constellation design and algorithm selection, supporting long-term, seamless, and consistent snow depth retrievals. Full article

Emotion recognition using electroencephalogram (EEG) signals has gained significant attention due to its potential applications in human–computer interaction (HCI), brain computer interfaces (BCIs), mental health monitoring, etc. Although deep learning (DL) techniques have shown impressive performance in this domain, they often require large datasets and high computational resources and offer limited interpretability, limiting their practical deployment. To address these issues, this paper presents a novel frequency-driven ensemble framework for electroencephalogram-based emotion recognition (FREQ-EER), an ensemble of lightweight machine learning (ML) classifiers with a frequency-based data augmentation strategy tailored for effective emotion recognition in low-data EEG scenarios. Our work focuses on the targeted analysis of specific EEG frequency bands and brain regions, enabling a deeper understanding of how distinct neural components contribute to the emotional states. To validate the robustness of the proposed FREQ-EER, the widely recognized DEAP (database for emotion analysis using physiological signals) dataset, SEED (SJTU emotion EEG dataset), and GAMEEMO (database for an emotion recognition system based on EEG signals and various computer games) were considered for the experiment. On the DEAP dataset, classification accuracies of up to 96% for specific emotion classes were achieved, while on the SEED and GAMEEMO, it maintained 97.04% and 98.6% overall accuracies, respectively, with nearly perfect AUC values confirming the frameworks efficiency, interpretability, and generalizability. Full article

African swine fever (ASF) is a highly contagious viral disease with significant impacts on domestic pigs and wild boar populations. This study applies GIS-based spatial analysis to monitor ASF outbreaks in northwestern Italy (Piedmont and Liguria) and identify areas at increased risk. Key factors considered include pig density, wildlife proximity, and environmental conditions. The spatial analysis revealed that central–western municipalities exhibited higher risk due to favorable environmental conditions and dense wild boar populations, while peripheral areas showed a temporal delay in outbreak emergence. Mapping the spreading rate and habitat interfaces allowed the development of a spatial risk model, which was further analyzed using geostatistical techniques to understand disease dynamics. The results demonstrate the effectiveness of geospatial modeling in identifying high-risk zones, characterizing spatio-temporal patterns, and supporting targeted prevention and surveillance strategies. These findings provide actionable insights for ASF management and resource allocation. Future studies may refine these models by integrating additional datasets and environmental variables, enhancing predictive capacity and applicability across different regions. Full article

Astrocytoma is the most common type of brain glioma and is classified by the World Health Organization into four grades, providing prognostic insights and guiding treatment decisions. The accurate determination of astrocytoma grade is critical for patient management, especially in high-malignancy-grade cases. This study proposes a fully optimized Convolutional Neural Network (CNN) for the classification of astrocytoma MRI slices across the three malignant grades (G2–4). The training dataset consisted of 1284 pre-operative axial 2D MRI slices from T1-weighted contrast-enhanced and FLAIR sequences derived from 69 patients. To provide the best possible model performance, an extensive hyperparameter tuning was carried out through the Hyperband method, a variant of Successive Halving. Training was conducted using Repeated Hold-Out Validation across four randomized data splits, achieving a mean classification accuracy of 98.05%, low loss values, and an AUC of 0.997. Comparative evaluation against state-of-the-art pre-trained models using transfer learning demonstrated superior performance. For validation purposes, the proposed CNN trained on an altered version of the training set yielded 93.34% accuracy on unmodified slices, which confirms the model’s robustness and potential use for clinical deployment. Model interpretability was ensured through the application of two Explainable AI (XAI) techniques, SHAP and LIME, which highlighted the regions of the slices contributing to the decision-making process. Full article

Extreme rainfall events characterized by small catchments with high-velocity flows pose critical challenges to infrastructure resilience, particularly the rail infrastructure, due to its partial location near rivers and in mountainous regions, and the limited availability of alternative routes. This can lead to severe damages, often resulting in long-term route closures. To mitigate flash flood damage, detailed information about affected structures and damage processes is necessary. Therefore, this study presents a newly developed multi-criteria flash flood damage assessment framework for the rail infrastructure and a QGIS-based analysis of the most frequent damages. Applying the framework to Eifel route damages in Western Germany after the July 2021 flood disaster shows that nearly 45% of the damages affected the track superstructure, especially tracks and bedding. Additionally, power supply systems, sealing and drainage systems, as well as railway overpasses or bridges, were impacted. Approximately 30% of the railway section showed washout of ballast, gravel and soil. In addition, deposit of wood or stones occurred. Most damages were classified as minor (47%) or moderate (34%). Furthermore, damaged track sections were predominantly located within a 50 m distance to the Urft river, whereas undamaged track sections are often located at a greater distance to the Urft river. These findings indicate that the proposed framework is highly applicable to assess and classify damages. Critical elements and relations could be identified and can help to adapt standards and regulations, as well as to develop preventive measures in the next step. Full article