Search Results (19,158)

This paper proposes and implements a method to efficiently parallelize constraint solving in rigid body simulation using GPUs. Rigid body simulation is widely used in robot development, computer games, movies, and other fields, and there is a growing need for faster computation. As current computers are reaching their limits in terms of scale-up, such as clock frequency improvements, performance improvements are being sought through scale-out, which increases parallelism. However, rigid body simulation is difficult to parallelize efficiently due to its characteristics. This is because, unlike fluid or molecular physics simulations, where each particle or lattice can be independently extracted and processed, rigid bodies can interact with a large number of distant objects depending on the instance. This characteristic causes significant load imbalance, making it difficult to evenly distribute computational resources using simple methods such as spatial partitioning. Therefore, this paper proposes and implements a computational method that enables high-speed computation of large-scale scenes by hierarchically clustering rigid bodies based on their number and associating the hierarchy with the hardware structure of GPUs. In addition, to effectively utilize parallel computing resources, we considered a more relaxed parallelization condition for the conventional Gauss–Seidel block parallelization method and demonstrated that convergence is guaranteed. We investigated how speed and convergence performance change depending on how much computational cost is allocated to each hierarchy and discussed the desirable parameter settings. By conducting experiments comparing our method with several widely used software packages, we demonstrated that our approach enables calculations at speeds previously unattainable with existing techniques, while leveraging GPU computational resources to handle multiple rigid bodies simultaneously without significantly compromising accuracy. Full article

Local leaf size diversity (LLSD) is an essential functional indicator of plant biodiversity; however, massive challenges are encountered when quantifying it and decoding its global ecological patterns. To address this limitation, the present study defined a quantitative indicator of LLSD, termed coefficient of variation index (CVI), for the leaf sizes, regardless of plant species, collected in each sampling site. Then, we innovatively derived a set of global CVI values from a published dataset, which was obtained through a meta-analysis of global leaf area samples and their related climate factors. Our macroecological analyses indicate that the CVI values vary across continents and fluctuate with latitude. The global CVI values are predominantly influenced by the mean temperature of the coldest month during the growing season in the negative correlation mode. When two leading climate drivers are considered, the global CVI values are primarily influenced by the mean temperature during growing season and the mean annual sum precipitation. Overall, all of these contributions are pioneering in their implications for characterizing the global distribution and ecological patterns of LLSD and advancing the cutting-edge research domain of leaf functional biodiversity to a new quantitative stage. Full article

Cross-scene classification of hyperspectral images poses significant challenges due to the lack of a priori knowledge and the differences in data distribution across scenes. While traditional studies have had limited use of a priori knowledge from other modalities, recent advancements in pre-trained large-scale language-vision models have shown strong performance on various downstream tasks, highlighting the potential of cross-modal assisted learning. In this paper, we propose a Semantic-aware Collaborative Parallel Network (SCPNet) to mitigate the impact of data distribution differences by incorporating linguistic modalities to assist in learning cross-domain invariant representations of hyperspectral images. SCPNet uses a parallel architecture consisting of a spatial–spectral feature extraction module and a multiscale feature extraction module, designed to capture rich image information during the feature extraction phase. The extracted features are then mapped into an optimized semantic space, where improved supervised contrastive learning clusters image features from the same category together while separating those from different categories. Semantic space bridges the gap between visual and linguistic modalities, enabling the model to mine cross-domain invariant representations from the linguistic modality. Experimental results demonstrate that SCPNet significantly outperforms existing methods on three publicly available datasets, confirming its effectiveness for cross-scene hyperspectral image classification tasks. Full article

Modern healthcare and engineering both rely on robust reliability models, where handling censored data effectively translates into longer-lasting devices, improved therapies, and safer environments for society. To address this, we develop a novel inferential framework for the ZLindley (ZL) distribution under the improved adaptive progressive Type-II censoring strategy. The proposed approach unifies the flexibility of the ZL model—capable of representing monotonically increasing hazards—with the efficiency of an adaptive censoring strategy that guarantees experiment termination within pre-specified limits. Both classical and Bayesian methodologies are investigated: Maximum likelihood and log-transformed likelihood estimators are derived alongside their asymptotic confidence intervals, while Bayesian estimation is conducted via gamma priors and Markov chain Monte Carlo methods, yielding Bayes point estimates, credible intervals, and highest posterior density regions. Extensive Monte Carlo simulations are employed to evaluate estimator performance in terms of bias, efficiency, coverage probability, and interval length across diverse censoring designs. Results demonstrate the superiority of Bayesian inference, particularly under informative priors, and highlight the robustness of HPD intervals over traditional asymptotic approaches. To emphasize practical utility, the methodology is applied to real-world reliability datasets from clinical trials on leukemia patients and hydrological measurements from River Styx floods, demonstrating the model’s ability to capture heterogeneity, over-dispersion, and increasing risk profiles. The empirical investigations reveal that the ZLindley distribution consistently provides a better fit than well-known competitors—including Lindley, Weibull, and Gamma models—when applied to real-world case studies from clinical leukemia trials and hydrological systems, highlighting its unmatched flexibility, robustness, and predictive utility for practical reliability modeling. Full article

This study provides a comprehensive assessment of the distribution, bioaccumulation, and health risks associated with heavy metals in the soil–coffee system of Pu’er City, a major coffee-producing region in China. An analysis of the soil and corresponding plant samples (including fruit, stem, and leaf) from representative plantations revealed that, although the heavy metal concentrations in soils generally exceeded the local background levels, they remained below national risk screening thresholds. Hg was identified as the primary pollutant of concern, showing moderate to significant enrichment (EF: 2–20) and posing a moderate to considerable ecological risk (E_i: 40–160). In coffee plants, most heavy metals accumulated predominantly in the stems, whereas Pb and As were more concentrated in the leaves and fruits, respectively. Among the studied metals, only Cu exhibited a notable bioconversion tendency, with a biota soil accumulation factor (BSAF) close to 1, while other metals showed limited transfer (BSAF < 1). A generally negative correlation was observed between the soil metal content and BSAF, suggesting that elevated total concentrations do not necessarily enhance bioavailability. The health risk assessment indicated that coffee consumption poses no significant non-carcinogenic risk (HI < 1). However, the carcinogenic risks for Cr and As, albeit within acceptable limits (LCR between 10⁻⁶ and 10⁻⁴), still warrant attention. These findings underscore the importance of implementing targeted source control for Hg and Cr in soils and further investigating the transfer mechanisms of As to support the sustainable and safe production of coffee in this region. Full article

Pharmacist interventions (PIs) are central to optimising pharmacotherapy, preventing drug-related problems, and improving patient outcomes. In Portugal, the absence of a validated tool to consistently document and classify PIs limits data comparability and service development. Given these gaps, this study aimed to describe hospital pharmacists’ attitudes towards PI documentation and classification, following confirmatory factor analysis (CFA) of a survey instrument, and to provide a comprehensive overview of current practices and behaviours in hospital settings across Portugal. An online questionnaire, previously validated, was distributed online to all hospital pharmacists registered with the Portuguese Pharmaceutical Society (October–December 2024). Sociodemographic data and the cognitive and behavioural domains of pharmacists’ attitudinal model were analysed descriptively, and CFA tested the three-factor structure (Process, Outcome, Satisfaction) of the attitudinal affective domain. Of 1848 pharmacists, 260 responded (14%). Respondents reported performing a mean of 49 PIs/month (SD = 196), although many never recorded (28.8%), classified (56.2%), or analysed (52.3%) interventions. Only 2.7% declared to use a validated classification framework. The CFA supported the structural coherence of the Process factor but revealed some overlapping between Process and Outcome and instability in the Satisfaction factor. The nationwide scope and application of CFA provided partial support for the hypothesised model and highlighted areas for refinement, including revision of Satisfaction items and reconsideration of Process and Outcome as overlapping constructs. Findings highlight strong professional commitment to PIs but persistent barriers, including less clear procedures and satisfaction, underscoring the need for a unified, standardised national system to support consistent recording, classification, and evaluation. Full article

Spatially explicit tools are essential for assessing biodiversity and guiding land use decisions at broad scales. This study presents a national-level approach for evaluating habitat quality as a proxy indicator for biodiversity, using Latvia as a case study. The approach integrates land use data, landscape structure, and habitat characteristics to generate habitat quality indices for agricultural and forest land. It addresses a common limitation in biodiversity planning, namely, the lack of consistent species-level data, by providing a comparative and conceptually robust way to assess how different land use types support biodiversity potential. The methodology was applied to assess current habitat quality and to simulate changes under two policy-relevant land use scenarios: the expansion of protected areas and a shift to organic farming. Results showed that expanding protected areas increased the national habitat quality index by 8.47%, while conversion to organic farming produced a smaller but still positive effect of 0.40%. Expansion of protected areas, therefore, led to a greater improvement in habitat quality compared to converting farmland to organic systems. However, both strategies offer complementary benefits for biodiversity at the landscape scale. Although national-level changes appear moderate, their spatial distribution enhances connectivity, particularly near existing protected areas, and may facilitate species movement. This approach enables national-level modelling of biodiversity outcomes under different policy measures. While it does not replace detailed species assessments, it provides a practical and scalable method for identifying conservation priorities, particularly in regions with limited biodiversity monitoring capacity. Full article

Distributed denial of service (DDoS) attacks pose a serious risk to the operational stability of a network for companies, often leading to service disruptions and financial damage and a loss of trust and credibility. The increasing sophistication and scale of these threats highlight the pressing need for advanced mitigation strategies. Despite the numerous existing studies on DDoS detection, many rely on large, redundant feature sets and lack validation for real-time applicability, leading to high computational complexity and limited generalization across diverse network conditions. This study addresses this gap by proposing a feature-optimized and computationally efficient ML framework for DDoS detection and mitigation using benchmark dataset. The proposed approach serves as a foundational step toward developing a low complexity model suitable for future real-time and hardware-based implementation. The dataset was systematically preprocessed to identify critical parameters, such as packet length Min, Total Backward Packets, Avg Fwd Segment Size, and others. Several ML algorithms, involving Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Cat-Boost, are applied to develop models for detecting and mitigating abnormal network traffic. The developed ML model demonstrates high performance, achieving 99.78% accuracy with Decision Tree and 99.85% with Random Forest, representing improvements of 1.53% and 0.74% compared to previous work, respectively. In addition, the Decision Tree algorithm achieved 99.85% accuracy for mitigation. with an inference time as low as 0.004 s, proving its suitability for identifying DDoS attacks in real time. Overall, this research presents an effective approach for DDoS detection, emphasizing the integration of ML models into existing security systems to enhance real-time threat mitigation. Full article

Predicting the electric field distribution inside microfluidic devices featuring an embedded array of electrical insulating pillars is critical for applications that require the electrokinetic manipulation of particles (e.g., bacteria, exosomes, microalgae, etc.). Regularly, these predictions are obtained from finite element method (FEM)-based software. This approach is costly, time-consuming, and cannot effortlessly reveal the dependency between the electric field distribution and the microchannel design. An alternative approach consists of analytically solving Laplace’s equation subject to specific boundary conditions. This path, although precise, is limited by the availability of suitable coordinate systems and can only solve for the simplest case of a single pair of pillars and not for a rectangular array of pillars. Herein, we propose and test the hypothesis that the electric field across a longitudinal path within the microchannel can be estimated from an electric circuit model of the microfluidic device. We demonstrate that this approach allows estimating the electric field for whatever pillar shape and array size. Estimations of the electric field extracted from a commercial FEM-based software were used to validate the model. Moreover, the circuit model effortlessly illustrates the relationships between the electric field and the geometrical parameters that define the microchannel design. Full article

Uneven distribution of passenger flows across metro entrances/exits is prevalent. Previous studies primarily examined built-environment factors influencing established exit-level flow disparities from an objective perspective. This study, however, incorporates passengers’ subjective preferences to provide a more comprehensive understanding of the environment–behavior mechanisms shaping entrance/exit choice. A visual stated preference method was employed to construct choice scenarios with 12 environmental attributes grouped into two complementary dimensions of path accessibility and environmental quality. Multinomial logit models were then applied to estimate passengers’ entrance/exit choice preferences, and the results informed a two-dimensional exit-level evaluation framework, demonstrated through a case study of Xujiahui Station in Shanghai. Compared with empirical studies, this study employs a discrete choice experiment, which circumvents the modeling challenges posed by the limited number of entrances/exits at individual stations and systematically integrates a range of station-internal and urban environmental attributes into a unified utility-based framework to evaluate their contributions. The results reveal the relative importance of various environmental attributes, together with their varying levels, in shaping passengers’ entrance/exit choices and indicate that path accessibility exerts a stronger influence on decision-making than environmental quality. The proposed exit-level evaluation framework also serves as a practical tool for assessing resource allocation status at individual station areas, providing a foundation for policy formulation to support more human-centered, equitable, and fine-grained station-area governance. Full article

Distributed measurement networks, from semiconductor testing arrays to environmental sensor grids, medical diagnostic systems, and agricultural monitoring stations, face a fundamental challenge: undetected site-to-site variations that silently corrupt data integrity. These variations create systematic biases between supposedly identical measurement units, which undermine scientific reproducibility and yield. The current site-to-site variation detection methods require extensive sampling or make rigid distributional assumptions, making them impractical for many applications. We introduce a novel framework that transforms measurement data into density-based feature vectors using Kernel Density Estimation, followed by anomaly detection with Isolation Forest. To automate the final classification, we then apply a novel probabilistic thresholding method using Gaussian Mixture Models, which removes the need for user-defined anomaly proportions. This approach identifies problematic measurement sites without predefined anomaly proportions or distributional constraints. Unlike traditional methods, our method works efficiently with limited samples and adapts to diverse measurement contexts. We demonstrate its effectiveness using semiconductor multisite testing as a case study, where our approach consistently outperforms state-of-the-art methods in detection accuracy and sample efficiency when validated against industrial testing environments. Full article

Background/Objectives: Satellite DNAs (satDNAs) are tandemly repeated sequences that play essential roles in chromosome structure, genome organization, and evolution. Despite their importance, the satellitome (the complete collection of satDNAs) of most avian lineages remains unexplored. We sought to describe the repeatome of three trogonid species, Trogon surrucura, T. melanurus, and Apaloderma vittatum with a focus on the satellitome to evaluate the general features of this lineage. Methods: Herein, we provide the first comparative characterization of the repeatome, with a particular focus on the comparative characterization of satDNAs in three trogonid species: T. surrucura, T. melanurus, and A. vittatum. Using a combination of bioinformatic pipelines and cytogenetic approaches. Results: We identified 16 satDNA families in T. surrucura, 15 in T. melanurus, and only 3 in A. vittatum. Sequence comparisons revealed that five families are shared between the two Trogon species, consistent with the library hypothesis, whereas no satDNAs were shared with A. vittatum. While both Trogon species exhibited a predominance of GC-rich repeats, A. vittatum represents the first bird described with a satellitome dominated by AT-rich satDNAs. In situ mapping in T. surrucura revealed chromosome-specific satDNAs restricted to pairs 1 and 2 and a Z-specific repeat that was strongly accumulated on its long arms, an atypical feature among birds. Conversely, the W chromosome showed a surprisingly low number of satDNAs, limited to centromeric signals. Conclusions: Our results reveal highly divergent satellitome landscapes among trogonids, characterized by lineage-specific differences in repeat composition, abundance, and chromosomal distribution. These findings support the view that satDNAs are dynamic genomic elements, whose amplification, loss, and chromosomal redistribution can influence genome architecture and play a role in avian speciation. Full article

With the increasing access capacity of new energy, the impact of extreme weather on source–load is intensifying, threatening the balance of supply and demand in the power system. Aiming at the systemic risks caused by the uncertainty and volatility of the spatiotemporal distribution of source–load under extreme weather conditions, this paper proposes a new method for power system operation risk assessment considering the spatiotemporal distribution of source–load under extreme weather. Firstly, the influence of various meteorological factors on the output and load of new energy under extreme weather is studied, and the meteorological sensitivity model of source–load is established. Secondly, aiming at the problem of limited historical data of extreme weather scenarios, this paper proposes a method for generating annual operation scenarios of power systems considering extreme weather: using Gaussian process regression to reconstruct extreme weather scenarios, and fusing them into typical meteorological year series through quantile incremental mapping method, forming meteorological scenarios with both typical characteristics and extreme events, and combining the source-load model to obtain the system operation scenario. Thirdly, a new power system risk assessment model considering the impact of extreme weather is established, and the risk indicators such as load shedding, line overlimit, and wind and solar curtailment on a long-term scale are evaluated by using the daily operation simulation in the annual operation scenario of the system. Finally, the IEEE 24-node System is used to analyze the numerical examples, which show that the proposed method provides a quantitative risk assessment framework for the power system to cope with extreme weather, which is helpful to improve the resilience and reliability of the system. Full article

Cryptosporidiosis is a zoonotic disease of medical and veterinary importance caused by Cryptosporidium spp. This study conducted a systematic review to assess the occurrence and distribution of Cryptosporidium spp. in humans in Brazil, with emphasis on C. parvum. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol and using five databases, 3689 articles were screened, and 48 met the inclusion criteria. Most studies were concentrated in the Southeast Region, particularly São Paulo, while major gaps were identified in the North and Midwest Regions. The mean prevalence was 8.9% using direct methods and 52.2% using indirect methods, with the highest positivity reported in the Northeast Region. Microscopy was the most frequently employed diagnostic tool, although it showed limited ability to differentiate species. When combined with molecular approaches, C. parvum and C. hominis were identified as the predominant species. Infection was most common among children and immunocompromised individuals, especially those with HIV and kidney diseases. Overall, the findings highlight substantial research gaps regarding cryptosporidiosis in Brazil and its disproportionate impact on vulnerable populations. Expanding regional studies, integrating molecular methods for species characterization, and implementing targeted public health strategies are essential to improve epidemiological knowledge and guide prevention and control measures. Full article

Satellite-derived bathymetry (SDB) using Ice, Cloud, and Land Elevation satellite-2 (ICESat-2) LiDAR data and remote sensing images faces challenges in the difficulty of uniform coverage of the inversion area by the bathymetric control points due to the linear sampling pattern of ICESat-2. This study proposes a novel virtual control point optimization framework integrating inverse distance weighting (IDW) and spectral confidence analysis (SCA). The methodology first generates baseline bathymetry through semi-empirical band ratio modeling (control group), then extracts virtual control points via SCA. An optimization scheme based on spectral confidence levels is applied to the control group, where high-confidence pixels utilized a residual correction-based strategy, while low-confidence pixels employed IDW interpolation based on a virtual control point. Finally, the preceding optimization scheme uses weighting-based fusion with the control group to generate the final bathymetry map, which is also called the optimized group. Accuracy assessments over the three research areas revealed a significant increase in accuracy from the control group to the optimized group. When compared with in situ data, the determination coefficient (R²), RMSE, MRE, and MAE in the optimized group are better than 0.83, 1.48 m, 12.36%, and 1.22 m, respectively, and all these indicators are better than those in the control group. The key innovation lies in overcoming ICESat-2’s spatial sampling limitation through spectral confidence stratification, which uses SCA to generate virtual control points and IDW to adjust low-confidence pixel values. It is also suggested that when applying ICESat-2 satellite data in active–passive-fused SDB, the distribution of training data in the research zone should be adequately considered. Full article