Search Results (4,722)

The rising incidence of viral infections demands the creation of innovative, biocompatible antiviral drugs with broad-spectrum effectiveness. This study combines the green synthesis, optimization, and characterization of silver nanoparticles (AgNPs) utilizing Dacryodes edulis (D. edulis) extract, assessing their antiviral, and antimicrobial characteristics. AgNPs were synthesized through the bio-reduction of silver nitrate with D. edulis water extract as a reducing, capping and stabilizing agent. The synthesis was refined through a Design of Experiments methodology. The characterization techniques, UV-Vis, Fourier-transform infrared, transmission electron microscopy, and dynamic light scattering, validated the successful synthesis of AgNPs with an average size of 101.56 ± 28.22 nm (TEM) and 156 ± 0.81 nm (DLS), a polydispersity index of 0.34, and a zeta potential of −22 mV. High-resolution liquid chromatography–tandem mass spectrometry analysis identified some bioactive compounds which enhance the antimicrobial and antiviral properties of the samples. Enzyme kinetics experiments revealed substantial inhibitory efficacy against the SARS-CoV-2 papain-like protease (PL-pro), with AgNPs exhibiting a lower IC₅₀ (0.271 ± 0.051 mg/mL) than the D. edulis extract (0.337 ± 0.043 mg/mL). The AgNPs exhibited MIC of 0.063 mg/mL for E. coli, 0.125 mg/mL for S. aureus and 0.08 mg/mL for S. pyrogens. The corresponding MBC values were 0.125 mg/mL, 0.25 mg/mL and 0.31 mg/mL, respectively. The fungal strains C. glabrata and C. albicans displayed MIC of 0.63 mg/mL and 0.31 mg/mL, respectively, and MBC values of 0.63 mg/mL and 0.31 mg/mL, respectively. This study underscores the potential of D. edulis-derived AgNPs as a cost-efficient, environmentally sustainable, and highly bioactive antibacterial and antiviral nanomaterial, facilitating the advancement of nanotechnology-based therapies for viral infections. Full article

Methodological opacity and the omission of environmental variables in forest masks can generate biased estimates. The objective of this study was to validate a reproducible workflow for quantifying forest cover change in the area adjacent to Nevado de Colima over the 2019–2025 period, subdivided into nine assessment areas with standardized sampling based on 3 × 3 pixel kernels (900 m²). An enriched Random Forest model with slope and spectral indices (NDVI, NBR, NDWI-Gao, and BSI) classified six spectral combinations derived from Sentinel-2 L2A bands B2, B3, B4, B8, B11, and B12, together with a new index proposed in this study, Red-Enhanced Normalized Burn Ratio (RE-NBR), used as a conservative classifier and auxiliary classifier output in the probabilistic cross-check estimation. Validation employed thematic and areal metrics. All combinations reached OA values between 89.44% and 92.53% and Kappa values between 0.79 and 0.85, with Shortwave Infrared (B12, B8, B4) as the most consistent configuration across dates. Allocation disagreement systematically exceeded quantity disagreement on all dates. The Seasonal Stability Index increased from 0.73 in 2019 to 0.77 in 2025, with persistent positive asymmetry between February and April. The probabilistic cross-check adjustment produced an adjusted forest loss of 1594.74 ha and an adjusted gain of 802.65 ha over 120,289.70 ha. Within the protected natural areas, expected change was distributed unevenly among vegetation types, with pine–oak forest showing the highest total expected loss, whereas high-mountain meadow showed the highest expected gain and also remained among the covers with the highest expected loss, indicating active spatial reconfiguration in the upper ecological domain where Pinus hartwegii Lindl. is the dominant species, though no species-level classification was performed. These results provide spatial evidence to support field verification, forest-health monitoring, and management decisions in the protected high-mountain study area. Full article

To address the increasingly prominent challenges of “low inertia” and “weak damping” in modern power systems, grid-forming (GFM) control technologies with inertia and damping support capabilities are being extensively adopted. However, distributed generation units interfaced with GFM inverters are highly susceptible to overcurrent phenomena during grid short-circuit faults. Existing research primarily focuses on current-limiting control strategies for virtual synchronous generators (VSGs), while investigations into their fault current characteristics remain insufficient. Given this, this paper proposes a short-circuit current calculation methodology for VSG-based PV-storage grid-connected systems. First, a model of a grid-forming PV-storage grid-connected system based on virtual synchronous control is established. Subsequently, the virtual impedance is solved within the timescale of current inner-loop stabilization, and the virtual internal electromotive force (EMF) equation for the VSG is formulated. This leads to the derivation of an analytical expression for the VSG short-circuit current, accounting for variations in the virtual internal potential. Furthermore, the impacts of diverse control parameters and fault severities on the short-circuit current are investigated based on this expression. Finally, simulations are conducted on the MATLAB/Simulink(R2024b) platform to validate the accuracy of the proposed short-circuit current calculation method and the correctness of the analysis regarding the influencing factors. Full article

This article proposes an intelligent adaptive arbitrary fixed-time sliding mode control (AFxT-SMC) strategy, integrated with an arbitrary fixed-time disturbance observer (AFx-DO), for precise attitude and altitude tracking of quadcopter UAVs. The primary contribution is achieving arbitrary fixed-time convergence of tracking errors and disturbance estimation, allowing designers to freely prescribe any desired settling time, independent of initial conditions and model parameters. In addition, a novel fixed-time reaching law attenuates chattering by driving the discontinuous control component to zero as the sliding surface is approached, while preserving fast fixed-time convergence through adaptive neural network gain tuning. Its coefficients are dynamically tuned by a neural network using backpropagation to handle time-varying dynamics and enhance adaptability. Finally, the arbitrary fixed-time convergence properties of both the proposed arbitrary sliding surface and the AFx-DO are rigorously established through Lyapunov stability analysis. Simulations under external disturbance conditions show that the proposed method outperforms existing adaptive and observer-based controllers in terms of tracking accuracy, transient response, chattering suppression, and energy efficiency. Quantitative analysis results demonstrate that the proposed methodology significantly enhances tracking precision while concurrently reducing control energy expenditure compared to state-of-the-art approaches. Full article

Background: The integrity of the alveolar ridge is compromised by tooth loss or trauma, initiating chronic resorption that alters bone morphology. Vertical bone augmentation (VBA) is a complex procedure required to rehabilitate severe atrophy. Currently, there is debate regarding the effectiveness of autologous bone versus xenografts and their combination to optimize bone regeneration and ensure dimensional stability. Objectives: To synthesize evidence on the efficacy of VBA by comparing autologous bone, xenografts, and their combination. Specifically, to evaluate vertical bone gain, volumetric stability and resorption, implant survival, complications, and whether combined grafts offer clinical advantages. Methods: A systematic review was conducted following PRISMA 2020 guidelines. MEDLINE, Scopus, SciELO, and Web of Science were searched up to January 2026. Risk of bias was assessed, using RoB 2 for randomized studies and ROBINS-I for non-randomized studies. Results: From 1517 initial records, 9 studies were included and 3 showed high risk of bias. Iliac crest grafts achieved the greatest vertical bone gain but also exhibited higher resorption compared to calvarial grafts. Xenografts (Bio-Oss) demonstrated superior volumetric stability, maintaining 10–13% more residual volume than autologous blocks. The most frequent complication was soft tissue dehiscence. Conclusions: VBA is an effective procedure. The combination of autologous bone and xenograft may represent a balanced approach, providing both biological potential and volumetric stability. Graft origin significantly influences outcomes and morbidity. However, current evidence is limited by methodological heterogeneity and small sample sizes, highlighting the need for well-designed clinical trials with standardized protocols and long-term follow-up. Full article

The buildup of non-biodegradable plastic waste and poor management of agro-industrial by-products have caused a major environmental crisis. The present research addresses the development of novel materials supporting the circular bioeconomy. This study aimed to develop and characterize bio-composite films derived from native Oxalis tuberosa starch and keratin microparticles (KMPs) extracted from cattle horn waste. The experimental methodology employed a 2³ factorial design and involved the characterization of the films included the evaluation of physical and optical properties and the identification of functional groups via spectroscopy, mechanical tests, and thermogravimetric analysis (TGA). The results revealed significant interactions (p ≤ 0.05). Higher processing temperatures were the main reason for the drop in water activity (a_w) and moisture content (MC) levels. Concurrently, the incorporation of KMPs reduced water solubility, increased opacity, and enhanced thermal stability. FTIR analysis confirmed the existence of intermolecular interactions between the hydroxyl and amide functional groups. In conclusion, bio-composites composed based on Oxalis tuberosa starch and keratin microparticles represent a sustainable alternative to mitigate the use of conventional plastics in the industry. Full article

Context: Prosthetic rehabilitation of acquired maxillary defects with Maxillary Resection Prostheses (MRPs) remains biomechanically challenging, particularly in partially edentulous patients, where conventional clasp-retained designs often yield suboptimal retention, stability, and functional outcomes. Research Gap: The integration of telescopic crown systems with semi-precision attachments incorporating a rotational latching mechanism has not been previously described as a unified approach to optimise load distribution and prosthesis stability in maxillary defect rehabilitation. Objective: To describe and clinically evaluate a novel prosthetic design combining telescopic crowns and a semi-precision rotational latching attachment to enhance retention, stability, and functional performance of MRPs. Methodology: A 31-year-old patient with a unilateral maxillary defect following partial maxillectomy presented with an unstable interim prosthesis and impaired speech and mastication. A definitive MRP was designed using telescopic crowns on the remaining dentition to establish a controlled path of insertion and improved axial load transfer. A semi-precision attachment with a key–keyway rotational latching mechanism was incorporated into the secondary framework to engage specific undercuts while minimising lateral forces on abutment teeth. A provisional prosthesis was used for 3 months to evaluate base extension, phonetics, and functional parameters before fabrication of the definitive prosthesis. Results: Serial follow-up at 1, 3, and 6 months demonstrate consistent prosthesis stability, precise seating, and favourable retention. Marked improvements were observed in speech intelligibility, masticatory efficiency, and patient-reported comfort. Conclusions: This combined prosthetic strategy represents a novel and biomechanically optimised approach for the rehabilitation of partially edentulous maxillary defects, with promising clinical and functional outcomes. Full article

Soil monitoring is fundamental for maintaining global soil health, ensuring food security, and achieving sustainable development. While satellite platforms provide invaluable tools for this purpose, the accuracy of soil monitoring heavily relies on the appropriate design of their remote sensing payload parameters. This study focuses on enhancing the accuracy of satellite-based global soil monitoring. Key physicochemical soil parameters—including total nitrogen (TN), soil organic matter (SOM), total salt content (TSS), moisture content (MC), and clay fraction (Clay)—were analyzed. A full-chain analytical validation model integrating “instrument–radiative transfer–soil parameter inversion” was developed. Using spectral measurements and soil sample analyses from the black soil region of Northeast China, the spectral response characteristics of core soil parameters were simulated and cross-validated under varying spectral resolutions and integration times. Results indicate that, under specific parameter configurations, the ‘Linshi-1’ satellite achieved robust TN inversion accuracy with R² > 0.65. SOM consistently exhibited good inversion performance, with RMSE ranging between 5.04 and 5.76 g/kg across various spectral treatments (all < 6 g/kg). TSS inversion demonstrated strong stability, maintaining an RMSE of approximately 0.43–0.44 g/kg at resampled spectral resolutions≥10 nm (corresponding to an SNR > 263). MC inversion accuracy was sensitive to both spectral resolution and regional variations, requiring a resampled resolution below 10 nm for consistently high accuracy. Clay inversion required the highest resolution, achieving an RMSE of less than 6 g/kg only at resampled resolutions of 1 nm or 2 nm (SNR approximately 150–210). These findings guided the design of the ‘Linshi-1’ black soil monitoring satellite system and its hyperspectral payload prototype. This effort establishes a solid theoretical and methodological foundation for future deployment, providing crucial space-based support for China’s black soil resource management and sustainable utilization. Full article

The construction sector is undergoing rapid digital transformation, creating opportunities to enhance environmental safety in urban areas. One critical application lies in air pollution forecasting, particularly regarding fine dust (PM₁₀) emissions. While machine learning (ML) models are widely used for city-wide air quality monitoring, a significant research gap exists in the high-resolution (5 min interval) forecasting of dust at localized “point-pattern” development sites. These densely built urban zones present unique challenges due to highly volatile microclimates and intermittent emission sources that directly affect nearby residents. The purpose of this study is to perform a preliminary performance analysis of eight predictive algorithms—ARIMA, EMA, Prophet, NNAR, Random Forest, SVM, and XGBoost—to identify the most robust approach for short-term PM₁₀ forecasting under limited data (N = 1728). Special attention is paid to the non-linear relationship between meteorological conditions and dust concentrations. Unlike previous studies which focused on general urban backgrounds, this work contributes a validated methodological framework for localized monitoring. The results demonstrate that tree-based ensemble models provide the highest stability and accuracy, offering a reliable basis for future real-time environmental management and active pollution mitigation strategies on urban construction sites. Full article

Background/Objectives: The convergence of traditional medicinal practices in Brazil’s vast biodiversity has fueled pharmaceutical interest in advancing plant-derived formulation. Copaiba (Copaifera spp.) and andiroba (Carapa guianensis) are central to both the economic landscape and healing traditions of the Amazon rainforest. Derivatives from these species have diverse applications, with their oils representing important raw materials for therapeutic use. However, the poor aqueous solubility of oils remains a major barrier to developing formulations with optimal bioavailability. Nanotechnology offers a strategic approach to address this limitation, as nanosystems improve stability, solubility, and biological performance. Methods: This narrative review compiles and analyzes contemporary literature on the chemical composition, physicochemical properties, and pharmacological activities of copaiba and andiroba oils, with emphasis on studies involving nanoformulations, aiming to overcome the solubility limitations of these oils. Results: Evidence from the literature indicates that nanoencapsulation enhances the anti-inflammatory, antimicrobial, and wound-healing activity of the oils’ main constituents, such as beta-caryophyllene and limonoids. However, inconsistencies in reported chemical composition and physicochemical properties across studies highlight the lack of standardized characterization and extraction methods, potentially hindering the development of reproducible nanosystems. Conclusions: Nanoencapsulation represents a promising strategy to improve the therapeutic potential of Amazonian oils. Nevertheless, further efforts are required to standardize methodologies and expand clinical studies to confirm the efficacy and safety of nanosystems derived from these natural products. Full article

Ti-6Al-4V ELI (Grade 23) fabricated by Laser Powder Bed Fusion (LPBF) exhibits well-known susceptibility to adhesive wear and tribo-oxidation under dry sliding, yet the tribological consequences of in-process laser remelting remain poorly characterized. This study investigates the influence of an in-layer laser scan strategy (single-scan and double-scan), normal forces in the 5–15 N range, and a sliding speed of 0.10–0.20 m·s⁻¹ on the dry sliding tribological response of additive manufactured Ti-6Al-4V ELI. A full factorial experimental design was carried out and the most significant factors and their contributions to the coefficient of friction, specific wear rate, and contact temperature were identified by a statistical method using a general linear model (GLM). The optimal parameters for both of the scan strategies were predicted using a response surface methodology (RSM). Furthermore, to assess the effect of the laser scan strategy and the in-layer remelting on the local mechanical properties, a microscale and nanoscale indentation was carried out. The results show that the normal load was the dominant factor with a contribution of 89.3% for the coefficient of friction, 54% for the specific wear rate, and 40.5% for the temperature. A significant load–scan strategy interaction that governed the wear behavior was detected. The double-scan strategy exhibited higher wear at 5 N but lower wear at 15 N than the single-scan, a counter-intuitive reversal attributed to the load-threshold tribolayer stabilization promoted by the remelting-induced near-surface microstructural modification. The novelty of this study was the setup of a robust GLM–RSM framework for predictive modeling and optimization of additively manufactured surfaces under tribological loading. Full article

Nature-based solutions (NBSs) involving tree-based interventions deliver multiple community benefits, yet evidence linking these benefits to underlying socio-ecological vulnerabilities remains limited. This study synthesised metadata from 131 European treescape NBS case studies spanning eight biogeographical regions using reverse-logic, thematic qualitative analysis. Case studies were identified via adapted PRISMA guidelines from open-access repositories, with community benefit themes categorised and mapped spatially across bioregions. The analysis revealed eleven principal community benefit categories and distinct region-specific patterns: Mediterranean interventions primarily mitigated extreme heat and drought vulnerabilities, whilst Alpine projects addressed slope stability and hazard reduction. The Continental and Atlantic regions emphasised social cohesion, recreational access, and the preservation of cultural heritage. The reverse-logic methodology successfully identified underlying socio-ecological vulnerabilities through systematic analysis of observed benefit profiles across diverse European contexts. This approach provides evidence-based guidance for designing location-sensitive treescape NBS that advance environmental research and public health objectives. The findings establish a methodological foundation for future assessments of NBS effectiveness and for refining location-specific treescape interventions that address community vulnerabilities and enhance adaptive capacity. Full article

Radiomics enables quantitative medical image analysis by converting imaging data into structured, high-dimensional feature representations for predictive modeling. Despite methodological developments and encouraging retrospective results, radiomics continue to face persistent challenges related to feature instability, limited reproducibility, validation bias, and restricted clinical translation. Existing reviews largely focus on application-specific outcomes or isolated pipeline components, with limited analysis of how interdependent design choices across acquisition, preprocessing, feature engineering, modeling, and evaluation collectively affect robustness and generalizability. This survey provides an end-to-end analysis of radiomics pipelines, examining how methodological decisions at each stage influence feature stability, model reliability, and translational validity. This paper reviews radiomic feature extraction, selection, and dimensionality reduction strategies; classical machine and deep learning–based modeling approaches; and ensemble and hybrid frameworks, with emphasis on validation protocols, data leakage prevention, and statistical reliability. Clinical applications are discussed with a focus on evaluation rigor rather than reported performance metrics. The survey identifies open challenges in standardization, domain shift, and clinical deployment, and outlines future directions such as hybrid radiomics–artificial intelligence models, multimodal fusion, federated learning, and standardized benchmarking. Full article

Urbanization intensifies microclimatic heterogeneity along the urban–forest gradient, where built morphology, vegetation structure, and hydrological processes interact to shape local thermal conditions. This systematic review synthesizes advances in IoT-based microclimate monitoring across open urban environments, urban forests, and peri-urban forest ecosystems. Following PRISMA 2020 guidelines, 426 records were identified, of which 63 met the eligibility criteria, and 34 core studies were analyzed in depth. In open urban environments, air temperature and relative humidity are predominantly governed by urban morphology and radiative properties. In contrast, forest microclimate is regulated through structural and ecohydrological mechanisms, where canopy structure, edge effects, and water availability determine the stability and depth of microclimatic buffering. Structural simplification and disturbance reduce buffering capacity, whereas canopy continuity enhances thermal stability. IoT-based and low-cost sensor networks enable high-resolution, multi-scale monitoring of these dynamics; however, methodological heterogeneity limits cross-site comparability. By integrating urban climate research with forest microclimate ecology, this review proposes a conceptual and methodological framework for designing distributed sensor networks capable of capturing microclimatic variability along the urban–forest gradient and supporting climate adaptation strategies. Full article

Coastal cities are typical regions where economic growth, population agglomeration, and eco-environmental pressures are strongly coupled. Assessing the coordination between urban development and the eco-environment is therefore important for regional sustainability. This study selected seven representative coastal cities in China—Dalian, Qinhuangdao, Qingdao, Shanghai, Fuzhou, Xiamen, and Zhuhai—and integrated multisource remote sensing data with statistical yearbook data to construct a comprehensive evaluation system for urban development level (UDL) and eco-environmental quality (EEQ). An ecologically enhanced indicator system incorporating vegetation condition index (VCI), biological richness index (BRI), normalized difference vegetation index (NDVI), and dynamic habitat index (DHI) was developed. The coupling coordination degree (CCD) model was then used to evaluate urban sustainable development from 2014 to 2023. In addition, an EWM–MLP adaptive weighting strategy was applied to refine entropy-derived weights, and Random Forest was used to identify variables associated with CCD prediction. The results show that CCD values generally increased during the study period, indicating improved coordination between urban development and the eco-environment. However, the evolutionary pathways differed markedly among cities, and UDL and EEQ changes were not fully synchronized. The EWM–MLP strategy introduced adaptive numerical refinements to CCD values while maintaining the overall stability of coordination-level classification. Random Forest analysis showed that CCD prediction was mainly associated with a limited number of high-contribution indicators. For all indicators combined, approximately 7–10 top-ranked variables were generally required to exceed 80% of the total importance, whereas the UDL and EEQ subsystems reached this threshold with fewer indicators. UDL-related variation was mainly associated with land-use structure, population agglomeration, and economic activity, whereas EEQ-related variation was related to ecological conditions, land-cover composition, and environmental pressure. The high-importance indicators exhibited clear inter-city heterogeneity, suggesting the need for differentiated governance strategies. The proposed framework provides methodological support for sustainable development assessment and differentiated governance in coastal cities. Full article