Search Results (1,801)

The next frontier in postmenopausal osteoporosis management lies not in novel pharmacological agents, but in the systematic integration of mechanism-guided drug selection, fall prevention, and long-term adherence strategies into a unified patient-centered care model. This review is intended for clinicians and clinical researchers involved in the diagnosis, treatment, and long-term management of postmenopausal osteoporosis. We provide a mechanism-to-practice framework that explicitly maps each therapeutic class to the specific molecular pathway it targets: bisphosphonates inhibit osteoclast function downstream of RANKL activation; denosumab blocks RANKL directly at the cytokine level; romosozumab inhibits sclerostin to restore Wnt-mediated bone formation. This mechanistic foundation supports a risk-stratified treatment paradigm in which antiresorptives address accelerated remodeling in moderate-risk patients, while patients at very high fracture risk—characterized by severe bone deficit or recent fragility fractures—benefit from an anabolic-first approach followed by consolidation. Beyond drug selection, we examine the persistent treatment gap in which fewer than 20% of post-fracture patients receive therapy, arguing that fall prevention—responsible for >90% of hip fractures—and medication adherence deserve equal priority in clinical practice. We further analyze key controversies, including T-score- versus FRAX-based intervention thresholds, limitations of the trabecular bone score, cost-effectiveness constraints on anabolic-first sequencing, and evidence gaps in post-denosumab transition strategies. By synthesizing mechanistic insights, guideline recommendations, and critical appraisal of current limitations, this review offers not only an overview of existing knowledge but a coherent decision-support model aimed at improving fracture prevention through comprehensive, individualized care. Full article

Timely and accurate urban change detection is vital for sustainable urban development, infrastructure management, and disaster response. Traditional two-dimensional approaches often overlook vertical and structural variations in dense urban areas. This study proposes a three-dimensional (3D) change detection framework that integrates high-resolution optical imagery and Digital Surface Models (DSMs) from two time points to capture both horizontal and vertical transformations. The method is based on a deep learning architecture combining a ResNet34 encoder with a UNet++ decoder, enabling the joint learning of spectral and elevation features. The research was carried out in two stages. First, a binary classification model was trained to detect areas of change and no-change, allowing direct comparison with conventional methods such as Principal Component Analysis (PCA), Change Vector Analysis (CVA) with thresholding, K-Means clustering, and Random Forest classification. In the second stage, a multi-class model was developed to categorize the types of structural changes, including new building construction, complete destruction, building height increase, and height decrease. Experiments conducted on a high-resolution urban dataset demonstrated that the proposed CNN-based framework significantly outperformed traditional methods, achieving an overall accuracy of 96.58%, an F1-score of 96.58%, and a recall of 96.7%. Incorporating DSM data notably improved sensitivity to elevation-related changes. Overall, the ResNet34–UNet++ architecture offers a robust and accurate solution for 3D urban change detection, supporting more effective urban monitoring and planning. Full article

Bioinsecticides offer eco-friendly alternatives to chemical insecticides and thereby meet the need for sustainable pest control. Entomopathogenic fungi (EPF) represent one of the core classes of microbial insecticides, distinguished by their advantageous contact-based mode of action. Several products have been successfully commercialized, and with continuing improvements to the technology, the market size for EPF continues to grow. The translation of EPF into reliable field performers relies upon formulation technologies that ensure product quality, stability, virulence, and cost-effectiveness. Current formulations comprise diverse solid and liquid states (e.g., wettable powders, oil dispersions) that deliver a range of propagules (conidia, blastospores, microsclerotia). While advanced approaches like nanoparticle encapsulation show promise, some limitations hinder their widespread use. Major constraints include maintaining fungal viability during storage/transport and protecting propagules from harsh environmental factors post-application. Regulatory requirements also present significant barriers to widespread uptake. Addressing these formulation challenges through continued research is essential for advancing mycoinsecticide technology and increasing their contribution to integrated pest management. This review aims to present the latest scientific advances in EPF formulation technologies and application strategies, alongside an overview of current regulatory frameworks and an up-to-date analysis of registered microbial biopesticide products in some of the world’s largest markets. Full article

Agricultural soils surrounding mining areas are often polluted with heavy metals (HMs) due to long-term mining activities and high geological background values. In this study, we investigated the distribution and transport of Cu, Cr, Zn, Cd, Pb, and As in a soil–rice system near a century-old mining site, evaluated their toxic effects on rice (Oryza sativa L.) throughout the growth period, and assessed the associated health risks using the Nemerow index and potential ecological risk index. The results showed that HM contents in rice grown in contaminated soils were significantly higher than in the control. HMs mainly accumulated in roots, with the lowest contents in grains. Cd exhibited the highest enrichment capacity, with bioconcentration factors of 0.79, 1.04, and 1.95 at the tillering, heading, and maturity stages, respectively, and its accumulation increased with rice growth. Transport from stems to leaves was relatively strong. HM exposure significantly inhibited rice growth, reducing plant height, biomass, tiller number, and panicle emergence. In addition, oxidative stress indicators and antioxidant enzyme activities, as well as root amino acid exudation, were markedly altered under HM stress. According to soil–rice HM contents, the pollution level of agricultural soils reached a high class, with As, Pb, Cd, and Zn as the main contributors. The potential ecological risk reached a moderate level, with Cd identified as the dominant factor. Notably, the health risks to children were substantially higher than those to adults, and Monte Carlo simulation indicated a 100% probability of non-carcinogenic and carcinogenic risks for adults and children. The above results highlighting the urgent need for risk management in mining-affected regions. Full article

Accurate mapping of hurricane-induced damage is essential for guiding rapid disaster response and long-term recovery planning. This study evaluates the Three-Dimensional Multi-Attributes, Multiscale, Multi-Cloud (3DMASC) framework for semantic classification of pre- and post-hurricane Light Detection and Ranging (LiDAR) data, using Mexico Beach, Florida, as a case study following Hurricane Michael. The goal was to assess the framework’s ability to classify stable landscape features and detect damage-specific classes in a highly complex post-disaster environment. Bitemporal topo-bathymetric LiDAR datasets from 2017 (pre-event) and 2018 (post-event) were processed to extract more than 80 geometric, radiometric, and echo-based features at multiple spatial scales. A Random Forest classifier was trained on a 2.37 km² pre-hurricane area (Zone A) and evaluated on an independent 0.95 km² post-hurricane area (Zone B). Pre-hurricane classification achieved an overall accuracy of 0.9711, with stable classes such as ground, water, and buildings achieving precision and recall exceeding 0.95. Post-hurricane classification maintained similar accuracy; however, damage-related classes exhibited lower performance, with debris reaching an F1-score of 0.77, damaged buildings 0.58, and vehicles recording a recall of only 0.13. These results indicate that the workflow is effective for rapid mapping of persistent structures, with additional refinements needed for detailed damage classification. Misclassifications were concentrated along class boundaries and in structurally ambiguous areas, consistent with known LiDAR limitations in disaster contexts. These results demonstrate the robustness and spatial transferability of the 3DMASC–Random Forest approach for disaster mapping. Integrating multispectral data, improving small-object representation, and incorporating automated debris volume estimation could further enhance classification reliability, enabling faster, more informed post-disaster decision-making. By enabling rapid, accurate damage mapping, this approach supports sustainable disaster recovery, resource-efficient debris management, and resilience planning in hurricane-prone regions. Full article

Natural disasters, particularly floods and landslides, can cause severe losses; however, their impacts can be significantly mitigated through proactive planning. In August 2021, a devastating flood in northern Türkiye resulted in major damage, including the displacement of logs from the Ayancık Forest Management Directorate’s depot, which exacerbated the disaster’s effects. This study aims to identify the most suitable location for a new forest depot in Ayancık, considering disaster risk, logistical needs, and environmental factors. A hybrid geospatial approach was employed by integrating Logistic Regression (LR)-based landslide susceptibility modeling and the Analytic Hierarchy Process (AHP). Key conditioning factors such as altitude, slope, aspect, lithology, land cover, plan and profile curvature, topographic wetness index (TWI), distance to drainage networks, roads, and faults were used to produce the LSM. The AHP weights of the factors used in selecting a suitable depot location were determined based on expert opinions. The integration of physical, logistical, and risk-based parameters allowed for a spatial prioritization of suitable areas. Results indicate that approximately 10.69% of the study area is classified as class 1 (very high suitability), 16.59% as class 2 (high), 20.71% as class 3 (moderate), 23.34% as class 4 (low), and 28.67% as class 5 (very low), corresponding to 27.28% of the area in classes 1–2 and 52.01% in classes 4–5. These results indicate that the study area is predominantly characterized by medium-low suitability conditions. Notably, these areas show significantly lower flood and landslide susceptibility compared to the current depot sites. By aligning forest infrastructure planning with disaster resilience principles, this study offers a replicable model for sustainable forest depot site selection. The findings provide valuable guidance for forest managers and policymakers to enhance the safety, functionality, and long-term viability of forestry operations in hazard-prone regions. Full article

Detecting active failures is important for the Industrial Internet of Things (IIoT). The IIoT aims to connect devices and machinery across industries. The devices connect via the Internet and provide large amounts of data which, when processed, can generate information and even make automated decisions on the administration of industries. However, traditional active fault management techniques face significant challenges, including highly imbalanced datasets, a limited availability of failure data, and poor generalization to real-world conditions. These issues hinder the effectiveness of prompt and accurate fault detection in real IIoT environments. To overcome these challenges, this work proposes a data augmentation mechanism which integrates generative adversarial networks (GANs) and the synthetic minority oversampling technique (SMOTE). The integrated GAN-SMOTE method increases minority class data by generating failure patterns that closely resemble industrial conditions, increasing model robustness and mitigating data imbalances. Consequently, the dataset is well balanced and suitable for the robust training and validation of learning models. Then, the data are used to train and evaluate a variety of models, including deep learning architectures, such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), and conventional machine learning models, such as support vector machines (SVMs), K-nearest neighbors (KNN), and decision trees. The proposed mechanism provides an end-to-end framework that is validated on both generated and real-world industrial datasets. In particular, the evaluation is performed using the AI4I, Secom and APS datasets, which enable comprehensive testing in different fault scenarios. The proposed scheme improves the usability of the model and supports its deployment in a real IIoT environment. The improved detection performance of the integrated GAN-SMOTE framework effectively addresses fault classification challenges. This newly proposed mechanism enhances the classification accuracy up to 0.99. The proposed GAN-SMOTE framework effectively overcomes the major limitations of traditional fault detection approaches and proposes a robust, scalable and practical solution for intelligent maintenance systems in the IIoT environment. Full article

Slime moulds (Eumycetozoa) are closely associated with forest structure, moisture and the availability of microhabitats, which together make them promising candidates for bioindication. This study synthesised an integrated, georeferenced resource from Central and Eastern Europe to assess how forest habitat, management intensity, and elevation structure assemblages, and to identify indicator taxa suited to monitoring. Analyses in R (RStudio, version 4.5.2) combined effort-controlled diversity comparisons, models of record intensity, habitat-stratified elevation responses, constrained ordination, and indicator testing at species and higher ranks. The resulting corpus encompassed 624 species from 16 countries and eight consolidated forest habitat classes, enabling quantification of joint assemblage responses to habitat, management intensity, and elevation under effort-controlled sampling, and facilitating the identification of indicator sets that are robust to uneven sampling. At the order and genus levels, Physarales, Trichiales, and Stemonitidales, together with genera such as Trichia, Meriderma, and Polyschismium, exhibited the clearest and most transferable indicator behaviour, while species including Trichia varia, Fuligo septica, and Meriderma carestiae emerged as promising candidates for fine-grained bioindication along habitat and elevation gradients. Habitat exerted clearer contrasts than management; elevation effects were strongly habitat specific, and a compact set of taxa showed stable, interpretable indicator behaviour across gradients. These indicator assemblages, together with an appraisal of cross-country generalisation, provide an operational basis for elevation-aware, habitat-structured bioindication with slime moulds in European forests. Taken together, these results indicate that slime mould assemblages have the potential to complement existing forest bioindication systems, both by tracking broad forest habitat types along management and elevation gradients and by providing indirect information on less conspicuous attributes such as stand naturalness and the availability of dead wood, although such applications remain at a proof-of-concept stage and will require further targeted evaluation before operational deployment. Full article

Landscape fragmentation as a process of landscape transformation affects the structure and composition of plant communities; however, relationships between fragmentation metrics and vegetation characteristics often remain weakly expressed and difficult to interpret, especially under conditions of multiple natural (wildfires, windstorms, pest outbreaks) and anthropogenic stressors (construction, forest management, agriculture). The aim of this study was to identify the sensitivity of forest community characteristics to landscape fragmentation metrics using methods that are effective at low correlation coefficients. The study analyzed 1694 vegetation relevés of forest communities in the center of the Russian Plain in the territory of the Moscow region. Seven uncorrelated metrics were calculated using the moving window method (2000 m) in Fragstats 4.3. The relationships between selected metrics and 20 community characteristics were evaluated using Spearman’s rank correlation method, assessment of statistically significant differences between classes, and testing for non-linear interactions. The species richness and Shannon index showed no correlation with fragmentation for tree and herb layers; however, the composition of ecological–coenotic groups demonstrated high sensitivity. The proportion of boreal and oligotrophic species, as well as the moss layer abundance, increased with increasing patch size, while nemoral and adventive species dominated in small-contrast patches. Results showed that fragmentation leads to asynchronous responses from ecosystem components, reducing correlations between structure and functioning. The conservation of large connected forest patches is critical for preserving the boreal–oligotrophic complex and moss layer, and is a priority task for climate adaptation. The robustness of the findings is supported by the extensive number of analyzed vegetation relevés. The multi-method approach demonstrated effectiveness in identifying significant ecological patterns under conditions of high multifactorial impact, emphasizing the need for a functionally oriented approach to managing fragmented temperate forests. Full article

This study explores the potential of Precision Agriculture (PA) to optimize top-dressing nitrogen (N) fertilization in rainfed barley under drought conditions in Central Catalonia (Spain). Efficient N management is critical in Mediterranean dryland winter cereal systems, where water scarcity and environmental regulations limit fertilization strategies. Two plots (2.93 ha and 1.80 ha) were zoned using soil apparent electrical conductivity (ECa) and elevation data obtained with the VERIS 3100 ECa soil surveyor. An on-farm experimental design tested four N dose rates (0 kg N/ha, 32 kg N/ha, 64 kg N/ha, and 96 kg N/ha) across two management zones per plot. Yield data were collected using a combine harvester equipped with a yield monitor and were mapped using geostatistical methods. A linear model (ANOVA) was used to analyze barley yield (kg/ha at 13% moisture), with nitrogen rate and soil zone (management class) as explanatory factors. Results showed low average yields (~1200 kg/ha–1300 kg/ha) due to severe water stress during the 2022–2023 season. Non-fertilized plots (N0) and those receiving moderate (N64) or high fertilization (N96) achieved the best performance, with the latter likely enhancing crop N uptake during the post-stress recovery period. In contrast, low fertilization (N32) proved less effective. Marginal return analysis supported variable-rate N application only in one plot, whereas under drought conditions, a no-fertilization strategy proved more suitable in the other. Ultimately, additional trials conducted under more favourable climatic scenarios are necessary to assess and validate the effectiveness of Precision Agriculture-based fertilization strategies in rainfed barley. Full article

Background/Objectives: Complete denture rehabilitation in edentulous patients presents functional and biomechanical challenges. Mini-implant-supported overdentures improve retention, stability, function, and comfort, particularly in complex class II or class III mandibulo-maxillary relationships. However, mechanical complications such as ball abutment wear may compromise long-term success. This case report aims to describe the clinical context, methods employed to manage ball abutment wear, and related complications in a patient with a mandibular mini-implant overdenture. Methods: This retrospective case report presents two approaches to managing abutment wear and enhancing overdenture retention: silicone matrices (Retention.Sil, Bredent Medical GmbH & Co.KG, Senden, Germany) and abutment reconstruction using prefabricated cemented spheres (Concave Reconstructive Sphere, Rhein83, Bologna, Italy). Results: A significant mechanical complication associated with mini-implant overdentures is the wear of ball abutments, which may develop over time as a result of continuous interaction between the O-ring system and the abutment surfaces. Both techniques effectively preserved mini-implants while enhancing denture retention, function, and comfort. Conclusions: Mechanical complications, such as ball abutment wear, may compromise the retention and functional performance of mandibular overdentures. Alternatives like silicone matrices and reconstructive spheres address abutment wear in mandibular overdentures, ensuring long-term retention and sustainable, patient-centered care for the elderly. Full article

Road traffic accidents remain a major global challenge, contributing to significant human and economic losses each year. In Portugal, the analysis and prevention of severe accidents are critical for optimizing the allocation of law enforcement resources and improving emergency response strategies. This study aims to develop and evaluate predictive models for accident severity using real-world data collected by the Portuguese Guarda Nacional Republicana (GNR) between 2019 and 2023. Four algorithms, Random Forest, XGBoost, Multilayer Perceptron (MLP), and Deep Neural Networks (DNN), were implemented to capture both linear and non-linear relationships within the dataset. To address the natural class imbalance, class weighting, Synthetic Minority Oversampling Technique (SMOTE), and Random Undersampling were applied. The models were assessed using Recall, F1-score, and G-Mean, with particular emphasis on detecting severe accidents. Results showed that DNNs achieved the best balance between sensitivity and overall performance, especially under SMOTE and class weighting conditions. The findings highlight the potential of classical machine learning and deep learning models to support proactive road safety management and inform resource allocation decisions in high-risk scenarios.This research contributes to sustainability by enabling data-driven road safety management, which reduces human and economic losses associated with traffic accidents and supports more efficient allocation of public resources. By improving the prediction of severe accidents, the study reinforces sustainable development goals related to safe mobility, resilient infrastructure, and effective disaster prevention and response policies. Full article

Mapping Plant Ecological Units (PEUs) support sustainable rangeland management. Yet, distinguishing them from multispectral imagery remains challenging due to high intra-class variability and spectral overlap. This study evaluates the contribution of auxiliary data layers to improve PEU classification from Landsat-8 OLI imagery in semi-arid rangelands of northeastern Iran. A random forest (RF) classifier was trained using field samples and multiple feature combinations, including spectral indices, topographic variables (DEM, slope, aspect), and principal component analysis (PCA) components. Classification performance was assessed using overall accuracy (OA), kappa coefficient, and non-parametric Friedman and post hoc tests to determine significant differences among scenarios. The results show that auxiliary features consistently enhanced classification performance as opposed to spectral bands alone. Integrating DEM and PCA layers yielded the highest accuracy (OA = 79.3%, κ = 0.71), with statistically significant improvement (p < 0.05). The findings demonstrate that incorporating topographic and transformed spectral information can effectively reduce class confusion and improve the separability of PEUs in complex rangeland environments. The proposed workflow provides a transferable approach for ecological unit mapping in other semi-arid regions facing similar environmental and management challenges. Full article

Based on existing models, this paper incorporates some key ecological factors, thereby obtaining a class of eco-epidemiological models that can more objectively reflect natural phenomena. This model simultaneously integrates disease dynamics within the prey population and the Beddington–DeAngelis functional response, thus achieving an organic combination of ecological dynamics, epidemic transmission, and spatial movement under time-varying environmental conditions. The proposed framework significantly enhances ecological realism by simultaneously accounting for spatial dispersal, predator–prey interactions, disease transmission within prey species, and seasonal or temporal variations, providing a comprehensive mathematical tool for analyzing complex eco-epidemiological systems. The theoretical results obtained from this study can be summarized as follows: Firstly, the existence and uniqueness of globally positive solutions for any positive initial data are rigorously established, ensuring the well-posedness and biological feasibility of the model over extended temporal scales. Secondly, analytically tractable sufficient conditions for uniform population persistence are derived, which elucidate the mechanisms of species coexistence and biodiversity preservation even under sustained epidemiological pressure. Thirdly, by employing innovative applications of differential inequalities and fixed point theory, the existence and uniqueness of a positive spatially homogeneous periodic solution in the presence of time-periodic coefficients are conclusively demonstrated, capturing essential rhythmicities inherent in natural systems. Fourthly, through a sophisticated combination of the upper and lower solution method for parabolic partial differential equations and Lyapunov stability theory, the global asymptotic stability of this periodic solution is rigorously established, offering a powerful analytical guarantee for long-term predictive modeling. Beyond theoretical contributions, these research findings provide actionable insights and quantitative analytical tools to tackle pressing ecological and public health challenges. They facilitate the prediction of thresholds for maintaining ecosystem stability using real-world data, enable the analysis and assessment of disease persistence in spatially structured environments, and offer robust theoretical support for the planning and design of wildlife management and conservation strategies. The derived criteria support evidence-based decision-making in areas such as controlling zoonotic disease outbreaks, maintaining ecosystem stability, and mitigating anthropogenic impacts on ecological communities. A representative numerical case study has been integrated into the analysis to verify all of the theoretical findings. In doing so, it effectively highlights the model’s substantial theoretical value in informing policy-making and advancing sustainable ecosystem management practices. Full article

With the launch of an increasing number of satellites to establish complex satellite communication networks, automatic modulation recognition (AMR) plays a crucial role in satellite signal recognition and spectrum management. However, most existing AMR models struggle to handle signals in such complex satellite communication environments. Therefore, this paper proposes an adaptive multi-rate atrous convolution network with residual attentional feature fusion (RAFF-AMACNet) that employs the adaptive multi-rate atrous convolution (AMAC) module to adaptively extract and dynamically join more prominent multi-scale features, enhancing the model’s time-series context awareness and generating robust feature maps. On this basis, the pyramid backbone consists of multiple stacked residual attentional feature fusion (RAFF) modules, featuring a dual-attention collaborative mechanism designed to mitigate feature map shifts and increase the separation between feature clusters of different classes under significant Doppler effects and nonlinear influences. On our independently constructed RML24 dataset, a general-purpose dataset tailored for satellite cognitive radio systems, simulation results indicate that at a signal-to-noise ratio of 0 dB, the modulation recognition accuracy reaches 92.99%. Full article