Search Results (8,822)

Mite assemblages are integral components of forest ecosystems, yet their seasonal dynamics in moss microhabitats remain poorly understood. We investigated moss-dwelling mites in a peri-urban Mediterranean forest in Greece across three sampling periods (March, May, July 2020), analyzing 150 random samples. Diversity was assessed using Hill numbers, rarefaction, and β-diversity partitioning, while Indicator Species Analysis identified taxa linked to specific months. Functional structure was further examined through trophic guilds. Results revealed strong temporal shifts: richness peaked in March, whereas May and July harbored distinct assemblages with unique indicator taxa. Functional analyses indicated seasonal changes in trophic guild representation, reflecting resource-driven dynamics. These findings highlight the importance of moss microhabitats as reservoirs of mite diversity and underscore the need for temporal perspectives in Mediterranean forest biodiversity research. Full article

As artificial intelligence continues to evolve, reinforcement learning (RL) has shown remarkable potential for solving complex sequential decision problems and is now applied in diverse areas, including robotics, autonomous vehicles, and financial analytics. Among the various RL paradigms, multi-agent reinforcement learning (MARL) stands out for its ability to manage cooperative and competitive interactions within multi-entity systems. However, mainstream MARL algorithms still face critical challenges in training stability and policy generalization due to factors such as environmental non-stationarity, policy coupling, and inefficient sample utilization. To mitigate these limitations, this study introduces an enhanced algorithm named MATD3_AHD, developed by extending the MATD3 framework, which integrates TD3 and MADDPG principles. The goal is to improve the learning efficiency and overall policy effectiveness of agents operating in complex environments. The proposed method incorporates three key mechanisms: (1) an Adaptive Exploration Policy (AEP), which dynamically adjusts the perturbation magnitude based on TD error to improve both exploration capability and training stability; (2) a Hierarchical Sampling Policy (HSP), which enhances experience utilization through sample clustering and prioritized replay; and (3) a Dynamic Delayed Update (DDU), which adaptively modulates the actor update frequency based on critic network errors, thereby accelerating convergence and improving policy stability. Experiments conducted on multiple benchmark tasks within the Multi-Agent Particle Environment (MPE) demonstrate the superior performance of MATD3_AHD compared to baseline methods such as MADDPG and MATD3. The proposed MATD3_AHD algorithm outperforms baseline methods—by an average of 5% over MATD3 and 20% over MADDPG—achieving faster convergence, higher rewards, and more stable policy learning, thereby confirming its robustness and generalization capability. Full article

Hand Gesture Recognition (HGR) is a vital technology that enables intuitive human–computer interaction in various domains, including augmented reality, smart environments, and assistive systems. Achieving both high accuracy and real-time performance remains challenging due to the complexity of hand dynamics, individual morphological variations, and computational limitations. This paper presents a lightweight and efficient skeleton-based HGR framework that addresses these challenges through an optimized multi-stream Convolutional Neural Network (CNN) architecture and a trainable ensemble tuner. Dynamic 3D gestures are transformed into structured, noise-minimized 2D spatiotemporal representations via enhanced data-level fusion, supporting robust classification across diverse spatial perspectives. The ensemble tuner strengthens semantic relationships between streams and improves recognition accuracy. Unlike existing solutions that rely on high-end hardware, the proposed framework achieves real-time inference on consumer-grade devices without compromising accuracy. Experimental validation across five benchmark datasets (SHREC2017, DHG1428, FPHA, LMDHG, and CNR) confirms consistent or superior performance with reduced computational overhead. Additional validation on the SBU Kinect Interaction Dataset highlights generalization potential for broader Human Action Recognition (HAR) tasks. This advancement bridges the gap between efficiency and accuracy, supporting scalable deployment in AR/VR, mobile computing, interactive gaming, and resource-constrained environments. Full article

To address global semantic loss, local detail blurring, and spatial–semantic conflict during image restoration under adverse weather conditions, we propose an image restoration network that integrates Mamba with Transformer architectures. We first design a Vision-Mamba–Transformer (VMT) module that combines the long-range dependency modeling of Vision Mamba with the global contextual reasoning of Transformers, facilitating the joint modeling of global structures and local details, thus mitigating information loss and detail blurring during restoration. Second, we introduce an Adaptive Content Guidance (ACG) module that employs dynamic gating and spatial–channel attention to enable effective inter-layer feature fusion, thereby enhancing cross-layer semantic consistency. Finally, we embed the VMT and ACG modules into a U-Net backbone, achieving efficient integration of multi-scale feature modeling and cross-layer fusion, significantly improving reconstruction quality under complex weather conditions. The experimental results show that on Snow100K-S/L, VMT-Net improves PSNR over the baseline by approximately 0.89 dB and 0.36 dB, with SSIM gains of about 0.91% and 0.11%, respectively. On Outdoor-Rain and Raindrop, it performs similarly to the baseline and exhibits superior detail recovery in real-world scenes. Overall, the method demonstrates robustness and strong detail restoration across diverse adverse-weather conditions. Full article

To address uncertainties in how threatened coastal China seas fish communities respond to stressors like overfishing and climate change, this study applied Hierarchical Modelling of Species Communities (HMSC) to disentangle the assembly rules shaping these communities, filling a critical gap in understanding their spatiotemporal dynamics. We analyzed data on 384 fish species (1980–2018) and key environmental factors, with variance partitioning revealing that environmental filtering dominated fish distributions (explaining over 99% of variance), far outweighing random effects (0.60%). Among environmental drivers, sea surface temperature (49.00%) and sea surface salinity (33.25%) were the most influential, while seafloor substrate and water depth played secondary roles; notably, fewer species occupied fine sand habitats, and more preferred silt habitats. Residual species associations—indicative of potential biotic interactions—were most frequent within Gobiidae, likely due to this highly diverse taxon’s specialized resource utilization and wide distribution, highlighting that biotic filtering is concentrated and ecologically relevant within this group. This work demonstrates HMSC’s utility in unraveling coastal fish community assembly, providing a robust basis for predicting community changes and guiding biodiversity conservation efforts that support ocean health and dependent human activities. Full article

Urban ecosystems are structurally and functionally distinct from their natural counterparts, with anthropogenic management potentially altering fundamental ecological processes such as seasonal community dynamics and impairing their sustainability. However, the mechanisms through which management filters plant diversity across seasons remain poorly understood. This study tested the hypothesis that management acts as an abiotic filter, dampening seasonal community variations and increasing biotic homogenization in urban green spaces. In this respect, through an intensive, multi-seasonal case study comparing two Mediterranean urban green spaces under contrasting management regimes, we analysed plant communities across 120 plots over four seasons. Results reveal a contingency cascade under management: while the species composition remains relatively stable (+26% variability, p < 0.001), the demographic success becomes more contingent (+41%, p < 0.001), and the ecological dominance becomes highly stochastic (+90%, p < 0.001). This hierarchy demonstrates that management primarily randomizes which species achieve dominance, in terms of biomass and cover, from a pool of disturbance-tolerant generalists. A 260% increase in alien and cosmopolitan species and persistent niche pre-emption dominance–diversity patterns also indicate biotic homogenization driven by management filters (mowing, trampling, irrigation, and fertilization) that favors species resistant to mechanical stresses and induces a breakdown of deterministic community assembly. These processes create spatially and temporally variable assemblages of functionally similar species, explaining both high structural variability and persistent functional redundancy. Conversely, seasonally structured, niche-based assemblies with clear dominance–diversity progressions are observed in the unmanaged area. Overall, findings demonstrate that an intensive management homogenizes urban plant communities by overriding natural seasonal filters and increasing stochasticity. The study provides a mechanistic basis for sustainable urban green space management, indicating that reduced intervention can help preserve the seasonal dynamics crucial for sustaining biodiversity and ecosystem functioning. Full article

Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to be a leading cause of morbidity and mortality in Mexico, with more than 20,000 new cases annually and a rising proportion of drug-resistant strains. This work addresses the molecular epidemiology of TB in the Mexican context, emphasizing its role in understanding transmission, genetic diversity, and resistance mechanisms. To achieve this, we reviewed molecular typing approaches including spoligotyping, Mycobacterial Interspersed Repetitive Unit–Variable Number Tandem Repeat (MIRU-VNTR) analysis, and whole-genome sequencing (WGS), which have been applied to characterize circulating lineages and identify drug-resistance-associated mutations. The results indicate that the Euro-American lineage (L4) predominates across the country, although significant regional variation exists, with Haarlem, LAM, T, and X sub lineages dominating in different states, and occasional detection of Asian (L2) and Indo-Oceanic (L1) lineages. Key resistance mutations were identified in katG, rpoB, pncA, and gyrA, contributing to the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, particularly in border and marginalized regions. These findings highlight how social factors, such as migration, urban overcrowding, and comorbidities including diabetes and HIV, influence transmission dynamics. We conclude that integrating molecular tools with epidemiological surveillance is crucial for strengthening public health strategies and guiding interventions tailored to Mexico’s heterogeneous TB burden. Full article

Healthcare systems worldwide have faced unprecedented pressure during crises such as the COVID-19 pandemic, exposing limits in managing scarce hospital resources. Many predictive models remain static, unable to adapt to new variants, shifting conditions, or diverse patient populations. This work proposes a dynamic prioritization framework that recalculates severity scores in batch mode when new factors appear and applies them instantly through a streaming pipeline to incoming patients. Unlike approaches focused only on fixed mortality or severity risks, our model integrates dual datasets (survivors and non-survivors) to refine feature selection and weighting, enhancing robustness. Built on a big data infrastructure (Spark/Databricks), it ensures scalability and responsiveness, even with millions of records. Experimental results confirm the effectiveness of this architecture: The artificial neural network (ANN) achieved 98.7% accuracy, with higher precision and recall than traditional models, while random forest and logistic regression also showed strong AUC values. Additional tests, including temporal validation and real-time latency simulation, demonstrated both stability over time and feasibility for deployment in near-real-world conditions. By combining adaptability, robustness, and scalability, the proposed framework offers a methodological contribution to healthcare analytics, supporting fair and effective hospitalization prioritization during pandemics and other public health emergencies. Full article

Ritual prayer (ṣalāh) is one of the most central and enduring practices in Islam, widely recognized for its spiritual significance. However, its educational and formative role in shaping the Muslim’s inner self and moral identity remains insufficiently explored in contemporary scholarship. This paper aims to examine ritual prayer as a core pedagogical tool within Islamic education, focusing on its transformative power in the processes of self-purification (tazkiyah) and identity formation. The study seeks to analyze the ethical and psychological dimensions of ṣalāh, drawing on classical Islamic sources, as well as integrating insights from contemporary critical philosophy—particularly Byung-Chul Han’s Vita Contemplativa—and Islamic virtue ethics, including perspectives such as those advanced by Elizabeth Bucar. Through this framework, the paper explores how prayer shapes inner dispositions like humility, mindfulness, sincerity, patience, and submission, reinforcing both spiritual awareness and communal belonging. Employing a descriptive-analytical methodology, the study engages Qur’anic verses, prophetic traditions, and traditional pedagogical literature to investigate how ṣalāh functions as a lived and repeated experience that cultivates the soul and molds ethical behavior. The discussion highlights how regular performance of prayer integrates belief with action and contributes to the formation of a reflective and morally grounded Muslim identity. This paper contributes to the field of Islamic Practical Theology by demonstrating how ritual prayer operates as a dynamic and holistic model for moral and spiritual development. It provides educators and scholars with a theoretical and applied vision for incorporating ṣalāh-based character education into Islamic curricula. Future research may explore how prayer interacts with modern lifestyles, digital spiritual practices, and intergenerational transmission of religious identity in diverse contexts. Full article

Microencapsulation is a fundamental technology for protecting active compounds from environmental degradation by factors such as light, heat, and oxygen. This process significantly improves their stability, bioavailability, and shelf life by entrapping an active core within a protective matrix. Therefore, a thorough understanding of the physicochemical interactions between these components is essential for developing stable and efficient delivery systems. The composition of the microcapsule and the encapsulation method are key determinants of system stability and the retention of encapsulated materials. Recently, the application of computational tools to predict and optimize microencapsulation processes has emerged as a promising area of research. In this context, molecular dynamics (MD) simulation has become an indispensable computational technique. By solving Newton’s equations of motion, MD simulations enable a detailed study of the dynamic behavior of atoms and molecules in a simulated environment. For example, MD-based analyses have quantitatively demonstrated that optimizing polymer–core interaction energies can enhance encapsulation efficiency by over 20% and improve the thermal stability of active compounds. This approach provides invaluable insights into the molecular interactions between the core material and the matrix, ultimately facilitating the rational design of optimized microstructures for diverse applications, including pharmaceuticals, thereby opening new avenues for innovation in the field. Ultimately, the integration of computational modeling into microencapsulation research not only represents a methodological advancement but also pivotal opportunity to accelerate innovation, optimize processes, and develop more effective and sustainable therapeutic systems. Full article

This paper proposes a novel image tone-mapping framework that incorporates meta-learning, a psychophysical model, Bayesian optimization, and light-field virtual diffraction. First, we formalize the virtual diffraction process as a mathematical operator defined in the frequency domain to reconstruct high-dynamic-range (HDR) images through phase modulation, enabling the precise control of image details and contrast. In parallel, we apply the Stevens power law to simulate the nonlinear luminance perception of the human visual system, thereby adjusting the overall brightness distribution of the HDR image and improving the visual experience. Unlike existing methods that primarily emphasize structural fidelity, the proposed method strikes a balance between perceptual fidelity and visual naturalness. Secondly, an adaptive parameter tuning system based on Bayesian optimization is developed to conduct optimization of the Tone Mapping Quality Index (TMQI), quantifying uncertainty using probabilistic models to approximate the global optimum with fewer evaluations. Furthermore, we propose a task-distribution-oriented meta-learning framework: a meta-feature space based on image statistics is constructed, and task clustering is combined with a gated meta-learner to rapidly predict initial parameters. This approach significantly enhances the robustness of the algorithm in generalizing to diverse HDR content and effectively mitigates the cold-start problem in the early stage of Bayesian optimization, thereby accelerating the convergence of the overall optimization process. Experimental results demonstrate that the proposed method substantially outperforms state-of-the-art tone-mapping algorithms across multiple benchmark datasets, with an average improvement of up to 27% in naturalness. Furthermore, the meta-learning-guided Bayesian optimization achieves two- to five-fold faster convergence. In the trade-off between computational time and performance, the proposed method consistently dominates the Pareto frontier, achieving high-quality results and efficient convergence with a low computational cost. Full article

New York State’s Regional Economic Development Councils (REDCs) were created to support sustainable, equitable, community-driven growth by distributing state funding across diverse regions. While allocations are geographically widespread, our research suggests that structural features of the REDC model may unintentionally reinforce disparities in local capacity and limit long-term impact, particularly in rural and under-resourced communities. This paper asks: To what extent does the REDC model reinforce or reduce disparities in economic development funding? Using qualitative system dynamics, specifically causal loop diagramming, and drawing on public data of RECD funding, interviews with municipal leaders, and public administration theory we examine systemic patterns that shape which municipalities repeatedly secure funding, and which remain excluded, identifying reinforcing and balancing processes that explain such systemic patterns. Key feedback structures include: the Capacity-Investment Loop, where high-capacity communities grow increasingly competitive over time; the Need-Funding Mismatch Loop, where administrative burdens block access for distressed communities; and the Collaboration Loop, which shows how competition can disincentivize shared regional strategies. These loops highlight how program structure—not just intent—shapes outcomes. Our findings suggest that, while the REDC model is intended to promote fairness and efficiency, it risks reproducing the disparities it seeks to address. Adjustments that strengthen regional collaboration, support capacity-building, and align funding with community need may help advance more inclusive and sustainable economic development. Full article

Urban parks provide diverse cultural ecosystem services (CESs), which are crucial for residents’ mental well-being. However, few studies have investigated how urban parks’ CESs and their interactions vary across seasons. In this study, we used the downtown area of Chengdu, China, as a case study, and evaluated urban parks’ CESs based on social media comments and further explored their seasonal dynamics. We then analysed the seasonal trade-offs/synergies of these CESs for service pairs using Pearson correlation and for multiple services using bundle identification. The results show the following: (1) Most CESs except for social interaction had the highest intensities in autumn, and recreational activities and education were the CESs with the highest and lowest intensities among the four seasons, respectively. Education service showed the greatest seasonal variation, while recreational activities and physical and mental recovery were stable among different seasons. (2) Some CES pairs exhibited trade-offs/synergies, but those relationships changed over seasons. Specifically, there were trade-off/synergy relationships between seven CES pairs in spring, three CES pairs in summer and autumn, and four CES pairs in winter. (3) In terms of the trade-offs/synergies among multiple CESs, we identified three types of CES bundles, i.e., physical and mental recovery- and aesthetics-dominated, inspiration- and education-dominated, and social interaction- and recreation-dominated bundles. More than 50% of the urban parks exhibited the physical and mental recovery- and aesthetics-dominated bundle in four seasons, and the seasonal change between this bundle and the social interaction and recreation-dominant bundle was the most obvious among all the bundle changes. This study revealed urban parks’ CES seasonal dynamics and identified the seasonal variations in CES trade-offs/synergies, providing a reference for CES management in urban parks. Full article

Optical fiber technology is becoming essential in modern radiation therapy, enabling precise, real-time, and minimally invasive monitoring. As oncology moves toward patient-specific treatment, there is growing demand for adaptable and biologically compatible sensing tools. Fiber-optic systems meet this need by integrating into clinical workflows with highly localized dosimetric and spectroscopic feedback. Their small size and flexibility allow deployment within catheters, endoscopes, or treatment applicators, making them suitable for both external beam and internal therapies. This paper reviews the fundamental principles and diverse applications of optical fiber sensing technologies in radiation oncology, focusing on dosimetry, spectroscopy, imaging, and adaptive radiotherapy. Implementations such as scintillating and Bragg grating-based dosimeters demonstrate feasibility for in vivo dose monitoring. Spectroscopic techniques, such as Raman and fluorescence spectroscopy, offer real-time insights into tissue biochemistry, aiding in treatment response assessment and tumor characterization. However, despite such advantages of optical fiber sensors, challenges such as signal attenuation, calibration demands, and limited dynamic range remain. This paper further explores clinical application, technical limitations, and future directions, emphasizing multiplexing capabilities, integration and regulatory considerations, and trends in machine learning development. Collectively, these optical fiber sensing technologies show strong potential to improve the safety, accuracy, and adaptability of radiation therapy in personalized cancer care. Full article

The rapid evolution of 5G and emerging 6G networks has increased system complexity, data volume, and security risks, making anomaly detection vital for ensuring reliability and resilience. However, existing machine learning (ML)-based approaches still face challenges related to poor generalization, weak temporal modeling, and degraded accuracy under heterogeneous and imbalanced real-world conditions. To overcome these limitations, a hybrid time series transformer–deep belief network (HTST-DBN) is introduced, integrating the sequential modeling strength of TST with the hierarchical feature representation of DBN, while an improved orchard algorithm (IOA) performs adaptive hyper-parameter optimization. The framework also embodies the concept of symmetry and asymmetry. The IOA introduces controlled symmetry-breaking between exploration and exploitation, while the TST captures symmetric temporal patterns in network traffic whose asymmetric deviations often indicate anomalies. The proposed method is evaluated across four benchmark datasets (ToN-IoT, 5G-NIDD, CICDDoS2019, and Edge-IoTset) that capture diverse network environments, including 5G core traffic, IoT telemetry, mobile edge computing, and DDoS attacks. Experimental evaluation is conducted by benchmarking HTST-DBN against several state-of-the-art models, including TST, bidirectional encoder representations from transformers (BERT), DBN, deep reinforcement learning (DRL), convolutional neural network (CNN), and random forest (RF) classifiers. The proposed HTST-DBN achieves outstanding performance, with the highest accuracy reaching 99.61%, alongside strong recall and area under the curve (AUC) scores. The HTST-DBN framework presents a scalable and reliable solution for anomaly detection in next-generation mobile networks. Its hybrid architecture, reinforced by hyper-parameter optimization, enables effective learning in complex, dynamic, and heterogeneous environments, making it suitable for real-world deployment in future 5G/6G infrastructures. Full article