Search Results (139)

Groundwater level monitoring is crucial for assessing hydrological responses to climate change and human activities, which pose significant threats to the sustainability of semi-arid aquifers and the cultural heritage they sustain. This study presents an integrated remote sensing and transformer-based deep learning framework that combines diverse geospatial datasets to predict spatiotemporal variations across the plain near the Persepolis and Naqsh-e Rustam archaeological complexes—UNESCO World Heritage Sites situated at the plain’s edge. We assemble 432 synthetic aperture radar (SAR) scenes (2015–2022) and derive vertical ground motion rates greater than −180 mm yr⁻¹, which are co-localized with multisource geoinformation, including hydrometeorological indices, biophysical parameters, and terrain attributes, to train transformer models with traditional deep learning methods. A sparse probabilistic transformer (ConvTransformer) trained on 95 gridded variables achieves an out-of-sample R² = 0.83 and RMSE = 6.15 m, outperforming bidirectional deep learning models by >40%. Scenario analysis indicates that, in the absence of intervention, subsidence may exceed 200 mm per year within a decade, threatening irreplaceable Achaemenid stone reliefs. Our results indicate that attention-based networks, when coupled to synergistic geodetic constraints, enable early-warning quantification of groundwater stress over heritage sites and provide a scalable template for sustainable aquifer governance worldwide. Full article

Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built in MSC Adams View. Three operating scenarios were evaluated—ideal geometry, measured microgeometry without pitch error, and measured microgeometry with pitch error—at a nominal speed of 1000 r min⁻¹. Time domain analysis shows that integrating the pitch table increases the mean transmission error (TE) by almost an order of magnitude and introduces a distinct 16.66 Hz shaft order tone. When the measured tooth topologies are added, peak-to-peak TE nearly doubles, revealing a non-linear interaction between spacing deviation and local flank shape. Frequency domain results reproduce the expected mesh-frequency side bands, validating the mapping of the pitch table into the solver. The combined method therefore provides a more faithful digital twin for predicting tonal noise and demonstrates why indexing tolerances must be considered alongside profile relief during gear design optimization. Full article

Background: Shirakiopsis indica (Willd.) (Family: Euphorbiaceae), a mangrove species found in the Asian region, is a popular folkloric plant. Locally, the plant is traditionally used to treat various types of ailments, especially for pain relief. Therefore, the current study investigates the neuropharmacological, antipyretic, thrombolytic, and anthelmintic properties of the S. indica fruit methanolic extract (SIF-ME). Methods: The neuropharmacological activity was evaluated using several bioactive assays, and the antipyretic effect was investigated using the yeast-induced pyrexia method, both in Swiss albino mice models. Human blood clot lysis was employed to assess thrombolytic activity, while in vitro anthelmintic characteristics were tested on Tubifex tubifex. Insights into phytochemicals from SIF-ME have also been reported from a literature review, which were further subjected to molecular docking, pass prediction, and ADME/T analysis and validated the wet-lab outcomes. Results: In the elevated plus maze test, SIF-ME at 400 mg/kg demonstrated significant anxiolytic effects (200.16 ± 1.76 s in the open arms, p < 0.001). SIF-ME-treated mice exhibited increased head dipping behavior and spent a longer time in the light box, confirming strong anxiolytic activity in the hole board and light–dark box tests, respectively. It (400 mg/kg) also significantly reduced depressive behavior during forced swimming and tail suspension tests (98.2 ± 3.83 s and 126.33 ± 1.20 s, respectively). The extract induced strong locomotor activity, causing mice’s mobility to gradually decrease over time in the open field and hole cross tests. The antipyretic effect of SIF-ME (400 mg/kg) was minimal using the yeast-induced pyrexia method, while it (100 μg/mL) killed T. tubifex in 69.33 ± 2.51 min, indicating a substantial anthelmintic action. SIF-ME significantly reduced blood clots by 67.74% (p < 0.001), compared to the control group’s 5.56%. The above findings have also been predicted by in silico molecular docking studies. According to the molecular docking studies, the extract’s constituents have binding affinities ranging from 0 to −10.2 kcal/mol for a variety of human target receptors, indicating possible pharmacological activity. Conclusions: These findings indicate that SIF-ME could serve as a promising natural source of compounds with neuropharmacological, anthelmintic, thrombolytic, and antipyretic properties. Full article

This study takes the Jinfoping Small Watershed in the Loess Hilly Region as the research area. Through field investigation and laboratory analysis, combined with methods such as spatial autocorrelation analysis, the ordinary least squares method (OLS), and the geographically weighted regression model (GWR), it deeply explores the spatial distribution characteristics and driving factors of Formicidae in the study area. The research results are as follows: (1) Spatial autocorrelation analysis indicates that the distribution of Formicidae is significantly regulated by spatial dependence and has significant spatial autocorrelation (global Moran’s I = 0.332; p < 0.01). (2) The spatial visualization analysis of the GWR model reveals that soil physical and chemical properties and topographic factors have local influences on the spatial distribution of Formicidae. Available phosphorus (AP) and slope (SLP) were significantly positively correlated with the number of ants. Hydrogen peroxidase (HP) and topographic relief (TR) were significantly negatively correlated with the number of ants. This study reveals the spatial distribution pattern of Formicidae in the Loess Hilly Region and its complex relationship with environmental factors, and clarifies the importance of considering spatial heterogeneity when analyzing ecosystem processes. The research results provide a scientific basis for the protection and management of soil ecosystems, and also offer new methods and ideas for future related research. Full article

The rapid advancement of unmanned aerial vehicle (UAV) technology has enabled the coordinated operation of multi-UAV systems, offering significant applications in agriculture, logistics, environmental monitoring, and disaster relief. In agriculture, UAVs are widely utilized for tasks such as ecological restoration, crop monitoring, and fertilization, providing efficient and cost-effective solutions for improved productivity and sustainability. This study addresses the collaborative task allocation problem for multi-UAV systems, using ecological grassland restoration as a case study. A multi-objective, multi-constraint collaborative task allocation problem (MOMCCTAP) model was developed, incorporating constraints such as UAV collaboration, task completion priorities, and maximum range restrictions. The optimization objectives include minimizing the maximum task completion time for any UAV and minimizing the total time for all UAVs. To solve this model, a deep reinforcement learning-based seagull optimization algorithm (DRL-SOA) is proposed, which integrates deep reinforcement learning with the seagull optimization algorithm (SOA) for adaptive optimization. The algorithm improves both global and local search capabilities by optimizing key phases of seagull migration, attack, and post-attack refinement. Evaluation against five advanced swarm intelligence algorithms demonstrates that the DRL-SOA outperforms the alternatives in convergence speed and solution diversity, validating its efficacy for solving the MOMCCTAP. Full article

While previous studies have explored the use of digital technologies in cultural heritage site reconstruction, limited attention has been given to systems that simultaneously support cultural restoration and psychological healing. This study investigates how multimodal, deep learning–assisted digital technologies can aid displaced populations by enabling both digital reconstruction and trauma relief within virtual environments. A demonstrative virtual reconstruction workflow was developed using the Great Mosque of Aleppo in Damascus as a case study. High-precision three-dimensional models were generated using Neural Radiance Fields, while Stable Diffusion was applied for texture style transfer and localized structural refinement. To enhance immersion, Vector Quantized Variational Autoencoder–based audio reconstruction was used to embed personalized ambient soundscapes into the virtual space. To evaluate the system’s effectiveness, interviews, tests, and surveys were conducted with 20 refugees aged 18–50 years, using the Impact of Event Scale-Revised and the System Usability Scale as assessment tools. The results showed that the proposed approach improved the quality of digital heritage reconstruction and contributed to psychological well-being, offering a novel framework for integrating cultural memory and emotional support in post-disaster contexts. This research provides theoretical and practical insights for future efforts in combining cultural preservation and psychosocial recovery. Full article

Digital elevation model (DEM) generation using Interferometric Synthetic Aperture Radar (InSAR) in mountainous environments encounters challenges including signal acquisition difficulties, decorrelation, and highly variable topography. To address these challenges, we propose a novel approach termed maximum likelihood curved surface estimation (MLCSE), utilizing multi-baseline InSAR to enhance DEM accuracy in mountainous regions. First, multi-baseline InSAR with Sentinel-1 images is employed to acquire more accurate interferometric phases. Second, two strategies are implemented to improve maximum likelihood elevation estimation, which is particularly susceptible to topographic relief and decorrelation. These strategies include replacing fixed neighborhood size with adaptive neighborhood size selection and estimating parameters of the maximum likelihood local curved surface. Finally, the mean error of the MLCSE DEM results and the proportion of errors less than 10 m are 7.89 m and 70.32%, respectively. The results demonstrate that MLCSE surpasses other InSAR methods, achieving higher elevation estimation accuracy. MLCSE exhibits stable performance across the study areas, reducing elevation errors in hilly, mountainous, and alpine regions. Additionally, hydrological analysis of the elevation results reveals that MLCSE, using the adaptive neighborhood size selection strategy, outperforms other methods in both visual inspection and quantitative comparisons. Moreover, the elevation accuracy achieved by MLCSE meets the standards of the American DTED-2, the Level 2 standard of the 1:50,000 DEM (Mountain), and the Level 1 standard of the 1:50,000 DEM (alpine region) for spatial resolution and height accuracy. Full article

Magnetic hyperthermia is a promising cancer treatment technique that relies on Néel and Brownian relaxation mechanisms to heat superparamagnetic nanoparticles injected into tumor sites. Under low-frequency magnetic fields, nanoparticles generate localized heat, inducing controlled thermal damage to cancer cells. However, radio frequency coils used to generate alternating magnetic fields may suffer from excessive heating, leading to efficiency losses and unintended thermal effects on surrounding healthy tissues. This study proposes novel liquid cooling systems, leveraging the skin effect phenomenon, to improve thermal management and reduce coil size. Finite element method-based simulation studies evaluated coil electrical current and temperature distributions under varying applied frequencies, water flow rates, and cooling microchannel dimensions. A dataset of 300 simulation cases was generated to train a Gaussian Process Regression-based machine learning model. The performance index was also developed and modeled using Gaussian Process Regression, enabling rapid performance prediction without requiring extensive numerical studies. Sensitivity analysis and the ReliefF algorithm were applied for a thorough analysis. Simulation results indicate that the proposed novel liquid cooling system demonstrates higher performance compared to conventional systems that utilize direct liquid cooling, offering a computationally efficient method for pre-manufacturing design optimization of radio frequency coil cooling systems in magnetic hyperthermia applications. Full article

Background/Objectives: Anadenanthera colubrina (popularly known as angico; in this study: Acol) is a bee-pollinated tree with gravity-dispersed seeds that occurs in dry tropical forests (SDTF), one of the most fragmented tropical ecosystems. In this study, we analyzed the resilience of 30 Acol Forest fragments of Ribeirão Preto, São Paulo, Brazil, and the flow of pollinators among these fragments based on the flight ranges of Apis mellifera (6 km) and Trigona spinipes (8 km). Additionally, we investigated genetic diversity, spatial genetic structure (SGS), and phenology across generations of one Acol population (AcolPM), located in the urban fragment M103 in the “Parque Municipal Morro de São Bento” (a municipal park in Ribeirão Preto). Methods: We mapped Acol fragments using geospatial data, with relief and slope analysis derived from digital terrain modeling. We created a flow diagram based on the pollinator’s flight ranges and calculated betweenness centrality. We amplified DNA from AcolPM individuals using 14 SSR molecular markers. Results: Notably, 17 of the 30 fragments occurred on slopes > 12%, terrain unsuitable for agriculture or construction, indicating that the presence of A. colubrina may serve as an indicator of territorial transformations. The AcolPM population (Fragment M103) emerged as a key node among the angicais, connected by the native pollinator T. spinipes, being fundamental for regional gene flow. In this focal population, we observed a slight but significant inbreeding (Fis, Fit, p < 0.01) and an SGS up to ~17 m. Genetic diversity was intermediate (He ≈ 0.62), and PCoA, Fst, and AMOVA values suggest low generational isolation, with most genetic variation within generations. This highlights AcolPM as a promising source for seed collection for reforestation. Phenological observations showed that fructification occurs between September and October, at the beginning of the rainy season. Conclusions: We concluded that Acol resilience is linked to the species’ mixed-mating system and pollinator dynamics-driven connectivity, allowing for the maintenance of genetic diversity in fragmented landscapes, as well as its natural tendency to form dense angicais clusters in non-arable slopes. We reaffirmed A. colubrina as a valuable species for restoration and urban climate resilience, providing cooling shade to humans and wildlife alike while offering refuge and food for local insects and birds in a warming landscape. Full article

Groundwater plays a vital role in sustaining dryland ecosystems, yet our understanding of the spatiotemporal dynamics of groundwater–vegetation interactions in endorheic river basins remains limited. In this study, the covariation between the normalized difference vegetation index (NDVI) and water table depth (WTD) in the Heihe River Basin (HRB), a representative endorheic system, is investigated via multisource data and generalized additive models (GAMs). The results indicate that the NDVI peaks in summer (July), with a corresponding decline in the WTD, indicating a basin-wide negative correlation. Spatial analysis reveals distinct upstream–downstream gradients: upstream regions exhibit strong seasonal synchronization, whereas midstream and downstream areas show weaker correlations because of mixed surface and groundwater influences. Landcover and climate significantly affect these interactions, with arid zones showing the strongest negative correlations (ρ = −0.38), particularly in wetlands, whereas humid regions show nonsignificant relationships. Geomorphological analysis highlights stronger correlations in mountainous areas than in low-relief plains. Positive correlations are the most prevalent in arid regions (54.5%), followed by hyper-arid regions (28.9%), while negative correlations also dominate arid regions (54.6%), followed by semiarid regions (27.6%). Cross-correlation analysis reveals synchronous NDVI–WTD changes at 95% of the grid points, with 5% exhibiting time lags (1–3 months), indicating localized hydrogeological feedback. Notably, 32% of the zones with negative correlations overlap with groundwater-dependent ecosystems (GDEs). GAM analysis reveals that 87.9% of the spatial variability in the NDVI–WTD correlations is attributed to environmental factors, with climate (26.6%) and hydrogeology (19.5%) as the dominant contributors. These findings provide critical insights into groundwater–vegetation interactions in arid ecosystems and offer valuable implications for sustainable water resource management. Full article

Background: Postoperative adhesions are a common complication following abdominal surgery, affecting over 90% of patients and leading to significant morbidity. Current anti-adhesion strategies, such as the use of physical and chemical barriers, have limitations such as short retention time, mechanical fragility, and inefficient drug delivery. This study developed a pectin-based emulsion gel loaded with celecoxib to prevent adhesions and provide localized pain relief. Methods: Formulations (F1–F4) with different pectin concentrations were evaluated for rheological properties, mucoadhesion, degradation rate, and celecoxib release. In vivo efficacy was evaluated in Sprague−Dawley rats via a standardized model of peritoneal abrasion, in which the formulations were compared to a commercially available anti-adhesion barrier. Results: The optimized emulsion gel (F4) exhibited improved mucoadhesion (9009 mPa·s), prolonged retention, and controlled celecoxib release over 14 days, reaching 80% release by day 9. In vivo, formulation F4 significantly reduced adhesions compared to a commercially available product. Pharmacokinetic analysis showed rapid absorption (T_max = 2 h) and sustained celecoxib plasma levels, confirming its effectiveness as a localized drug-delivery system. The celecoxib-loaded pectin-based gel successfully prevented postoperative adhesions and provided sustained pain relief. Conclusions: These findings suggest its potential clinical utility, though further preclinical and clinical evaluations are required. Full article

Rationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care. Objective: Our objective was to develop predictive measures of tumor response to NAC prior to starting for LABC using machine learning and textural computed tomography (CT) features in different level of frequencies. Materials and Methods: A total of 851 textural biomarkers were determined from CT images and their wavelet coefficients for 117 patients with LABC to evaluate the response to NAC. A machine learning pipeline was designed to classify response to NAC treatment for patients with LABC. For training predictive models, three models including all features (wavelet and original image features), only wavelet and only original-image features were considered. We determined features from CT images in different level of frequencies using wavelet transform. Additionally, we conducted a comparison of feature selection methods including mRMR, Relief, Rref QR decomposition, nonnegative matrix factorization and perturbation theory feature selection techniques. Results: Of the 117 patients with LABC evaluated, 82 (70%) had clinical–pathological response to chemotherapy and 35 (30%) had no response to chemotherapy. The best performance for hold-out data splitting was obtained using the KNN classifier using the Top-5 features, which were obtained by mRMR, for all features (accuracy = 77%, specificity = 80%, sensitivity = 56%, and balanced-accuracy = 68%). Likewise, the best performance for leave-one-out data splitting could be obtained by the KNN classifier using the Top-5 features, which was obtained by mRMR, for all features (accuracy = 75%, specificity = 76%, sensitivity = 62%, and balanced-accuracy = 72%). Conclusions: The combination of original textural features and wavelet features results in a greater predictive accuracy of NAC response for LABC patients. This predictive model can be utilized to predict treatment outcomes prior to starting, and clinicians can use it as a recommender system to modify treatment. Full article

Unmanned Aerial Vehicles (UAVs) play a major role in different applications, including surveillance, mapping, and disaster relief, particularly in urban environments. This paper presents a comprehensive framework for UAV localization in outdoor environments using monocular ORB-SLAM3 integrated with optical flow and YOLOv5 for enhanced performance. The proposed system addresses the challenges of accurate localization in dynamic outdoor environments where traditional GPS methods may falter. By leveraging the capabilities of ORB-SLAM3, the UAV can effectively map its environment while simultaneously tracking its position using visual information from a single camera. The integration of optical flow techniques allows for accurate motion estimation between consecutive frames, which is critical for maintaining accurate localization amidst dynamic changes in the environment. YOLOv5 is a highly efficient model utilized for real-time object detection, enabling the system to identify and classify dynamic objects within the UAV’s field of view. This dual approach of using both optical flow and deep learning enhances the robustness of the localization process by filtering out dynamic features that could otherwise cause mapping errors. Experimental results show that the combination of monocular ORB-SLAM3, optical flow, and YOLOv5 significantly improves localization accuracy and reduces trajectory errors compared to traditional methods. In terms of absolute trajectory error and average tracking time, the suggested approach performs better than ORB-SLAM3 and DynaSLAM. For real-time SLAM applications in dynamic situations, our technique is especially well-suited due to its potential to achieve lower latency and greater accuracy. These improvements guarantee more dependable performance in a variety of scenarios in addition to increasing overall efficiency. The framework effectively distinguishes between static and dynamic elements, allowing for more reliable map construction and navigation. The results show that our proposed method (U-SLAM) produces a considerable decrease of up to 43.47% in APE and 26.47% RPE in S000, and its accuracy is higher for sequences with moving objects and more motion inside the image. Full article

Ensuring the accurate prediction, prevention, and control of coal bursts in isolated working faces is crucial for ensuring safe mining operations. Coal bursts are typically caused by the accumulation of stress and energy released in coal seams and the overlying strata. This study focuses on the 76 isolated working faces at Shanxi Wuyang Mine, employing a combination of theoretical analysis, numerical simulation, and field monitoring. Through theoretical analysis, the study examines the influence of the spatial structure of the overlying strata on support stress and develops corresponding estimation functions. Additionally, bearing strength calculation formulas under varying confining pressures are derived. Numerical simulations are used to validate the effectiveness of borehole stress relief, while field monitoring further confirms the accuracy of the proposed model, leading to the development of the “overall–local” coal burst prediction method. The results demonstrate that the proposed method effectively assesses coal burst risks and, based on different coal burst types, recommends borehole stress relief and roof deep-hole blasting as primary mitigation strategies. These methods were successfully applied to the 76 isolated working faces at Wuyang Mine, yielding conclusions of overall stability with localized instability. This study provides new insights into coal burst prediction theory and offers practical guidance for preventive engineering in isolated working faces, demonstrating substantial engineering applicability. Full article

Emergency events pose critical challenges to national and social stability, requiring efficient and timely responses to mitigate their impact. In the initial stages of an emergency, decision-makers face the dual challenge of minimizing transportation costs while adhering to stringent rescue time constraints. To address these issues, this study proposes a two-stage optimization model aimed at ensuring the equitable distribution of disaster relief materials across multiple distribution centers. The model seeks to minimize the overall cost, encompassing vehicle dispatch expenses, fuel consumption, and time window penalty costs, thereby achieving a balance between efficiency and fairness. To solve this complex optimization problem, a hybrid algorithm combining genetic algorithms and particle swarm optimization was designed. This hybrid approach leverages the global exploration capability of genetic algorithms and the fast convergence of particle swarm optimization to achieve superior performance in solving real-world logistics challenges. Case studies were conducted to evaluate the feasibility and effectiveness of both the proposed model and the algorithm. Results indicate that the model accurately reflects the dynamics of emergency logistics operations, while the hybrid algorithm exhibits strong local optimization capabilities and robust performance in handling diverse and complex scenarios. Experimental findings underscore the potential of the proposed approach in optimizing emergency response logistics. The hybrid algorithm consistently achieves significant reductions in total cost while maintaining fairness in material distribution. These results demonstrate the algorithm’s applicability to a wide range of disaster scenarios, offering a reliable and efficient tool for emergency planners. This study not only contributes to the body of knowledge in emergency logistics optimization but also provides practical insights for policymakers and practitioners striving to improve disaster response strategies. Full article