Search Results (114)

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

Background: Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders characterized by a complex, multifactorial etiology with a strong genetic contribution. Our study aimed to evaluate the link between the burden of rare genetic variants within a specific panel of ASD and intellectual disability-associated genes and phenotypic variability in a cohort of children with autism in Slovakia. Methods: Gene burden scores were calculated based on pathogenic, likely pathogenic, and uncertain significance rare DNA variants identified by whole-exome sequencing. We then assessed the effect of three different scoring methods on the variance across 15 psycho-behavioral parameters describing the phenotypic profiles of 117 ASD probands. Results: The burden score showed a significant multivariate effect on the combination of psycho-behavioral parameters. This score was associated with the social affect of ADOS-2, as well as with the socialization domain, and total adaptive behavior scores from the Vineland Adaptive Behavior Scales-3 (VABS). While a score based solely on count of pathogenic and likely pathogenic variants did not show a multivariate effect, incorporating variants of uncertain significance revealed a multivariate effect on two adaptive behavior parameters: daily living skills and total adaptive behavior score (VABS). Conclusions: Our findings partially explain the variability in phenotypic manifestation in our ASD patient cohort, highlighting the importance of considering the cumulative effect of rare genetic variants, including those of uncertain significance, in shaping the diverse clinical presentation of ASD. Full article

In recent years, cross-domain few-shot learning (CDFSL) has demonstrated remarkable performance in hyperspectral image classification (HSIC), partially alleviating the distribution shift problem. However, most domain adaptation methods rely on similarity metrics to establish cross-domain class matching, making it difficult to simultaneously account for intra-class sample size variations and inherent inter-class differences. To address this problem, existing studies have introduced a class weighting mechanism within the prototype network framework, determining class weights by calculating inter-sample similarity through distance metrics. However, this method suffers from a dual limitation: susceptibility to noise interference and insufficient capacity to capture global class variations, which may lead to distorted weight allocation and consequently result in alignment bias. To solve these issues, we propose a novel class-discrepancy dynamic weighting-based cross-domain FSL (CDDW-CFSL) framework. It integrates three key components: (1) the class-weighted domain adaptation (CWDA) method dynamically measures cross-domain distribution shifts using global class mean discrepancies. It employs discrepancy-sensitive weighting to strengthen the alignment of critical categories, enabling accurate domain adaptation while maintaining feature topology; (2) the class mean refinement (CMR) method incorporates class covariance distance to compute distribution discrepancies between support set samples and class prototypes, enabling the precise capture of cross-domain feature internal structures; (3) a novel multi-dimensional feature extractor that captures both local spatial details and continuous spectral characteristics simultaneously, facilitating deep cross-dimensional feature fusion. The results in three publicly available HSIC datasets show the effectiveness of the CDDW-CFSL. Full article

Background: Tobacco use is disproportionately high in rural areas, contributing to elevated cancer mortality, yet it often goes untreated due to limited access to care, high poverty and uninsured rates, and co-occurring substance use disorders (SUDs). This study explored the utility of using rapid qualitative analysis (RQA) to guide the adaptation of a tobacco-free workplace program (TFWP) in Texas healthcare centers serving adults with SUDs in medically underserved areas. Methods: From September–December 2023 and May–July 2024, we conducted 11 pre-implementation, virtual semi-structured group interviews focused on adapting the TFWP to local contexts (N = 69); 7 with providers (n = 34) and managers (n = 12) and 4 with patients (n = 23) in 6 healthcare centers. Two qualified analysts independently summarized transcripts, using RQA templates of key domains drawn from interview guides to summarize and organize data in matrices, enabling systematic comparison. Results: The main themes identified were minimal organizational tobacco cessation support and practices, and attitudinal barriers, as follows: (1) the need for program materials tailored to local populations; (2) limited tobacco cessation practices and partial policies—staff requested guidance on enhancing tobacco screenings and cessation delivery, and integrating new interventions; (3) contradictory views on treating tobacco use that can inhibit implementation (e.g., wanting to quit yet anxious that quitting would cause SUD relapse); and (4) inadequate environmental supports—staff requested treating tobacco-use training, patients group cessation counseling; both requested nicotine replacement therapy. Conclusions: RQA identified key areas requiring capacity development through participants’ willingness to adopt the following adaptations: program content (e.g., trainings and tailored educational materials), delivery methods/systems (e.g., adopting additional tobacco care interventions) and implementation strategies (e.g., integrating tobacco cessation practices into routine care) critical to optimizing TFWP fit and implementation. The study findings can inform timely formative evaluation processes to design and tailor similar intervention efforts by addressing site-specific needs and implementation barriers to enhance program uptake. Full article

Post-earthquake fires (PEFs) represent a complex, cascading hazard in which seismic damage creates ignition conditions that can overwhelm urban infrastructure and severely compromise structural integrity. Despite growing scholarly attention, the literature on PEFs remains fragmented across disciplines, lacking a consolidated understanding of structural vulnerabilities, urban-scale impacts, and response strategies. This study presents a systematic and thematic synthesis of 54 peer-reviewed articles, identified through a PRISMA-guided screening of 151 publications from the Web of Science Core Collection. By combining bibliometric mapping with thematic clustering, the review categorizes research into key methodological domains, including finite element modeling, experimental testing, probabilistic risk analysis, multi-hazard frameworks, urban simulation, and policy approaches. The findings reveal a dominant focus on structural fire resistance, particularly of seismically damaged concrete and steel systems, while highlighting emerging trends in sensor-based fire detection, AI integration, and urban resilience planning. However, critical research gaps persist in multi-hazard modeling, firefighting under partial collapse, behavioral responses, and the integration of spatial, infrastructural, and institutional factors. This study proposes an interdisciplinary research agenda that connects engineering, urban design, and disaster governance to inform adaptive, smart-city-based strategies for mitigating fire risks in seismic zones. This work contributes a comprehensive roadmap for advancing post-earthquake fire resilience in the built environment. Full article

Concrete dams are prone to various hidden dangers after long-term operation and may lead to significant risk if failed to be detected in time. However, the existing hollowing detection techniques are few as well as inefficient when facing the demands of comprehensive coverage and intelligent management for regular inspections. Hence, we proposed an innovative, non-destructive infrared inspection method via constructed dataset and proposed deep learning algorithms. We first modeled the surface temperature field variation of concrete dams as a one-dimensional, non-stationary partial differential equation with Robin boundary. We also designed physics-informed neural networks (PINNs) with multi-subnets to compute the temperature value automatically. Secondly, we obtained the time-domain features in one-dimensional space and used the diffusion techniques to obtain the synthetic infrared images with dam hollowing by converting the one-dimensional temperatures into two-dimensional ones. Finally, we employed adaptive joint learning to obtain the spatio-temporal features. We designed the experiments on the dataset we constructed, and we demonstrated that the method proposed in this paper can handle the low-data (few shots real images) issue. Our method achieved 94.7% of recognition accuracy based on few shots real images, which is 17.9% and 5.8% higher than maximum entropy and classical OTSU methods, respectively. Furthermore, it attained a sub-10% cross-sectional calculation error for hollowing dimensions, outperforming maximum entropy (70.5% error reduction) and OTSU (7.4% error reduction) methods, which shows our method being one novel method for automated intelligent hollowing detection. Full article

Problematic internet use has become an increasing concern among university students, as it may negatively affect academic performance, emotional well-being, and social functioning. Understanding the psychological and emotional factors that influence internet use is crucial to developing effective preventive strategies. This study aimed to examine the relationship between resilience and social–emotional competence and problematic internet use among university students. This study was conducted with the participation of 191 students. The students’ problematic internet use levels were assessed using the Generalized Problematic Internet Use Scale-2, their resilience levels were assessed using the Connor–Davidson Resilience Scale, and their social–emotional competence levels were assessed using the Social–Emotional Competence Scale. Regression analysis was performed using the elastic net regression model and partial least squares (PLC) model. The general resilience level (p = 0.0015) and its sub-dimensions of tenacity (p = 0.0014), tolerance to negative affect (p = 0.0114), and spirituality (p = 0.0278) were found to be significant predictors of problematic internet use. The general social emotional competence level (p = 0.0115) and adaptability (p = 0.0278) were found to significantly predict problematic internet use. The predictive factors for the social interaction domain of problematic internet use were tenacity (p = 0.04), adaptability (p = 0.02), and expressivity (p = 0.03), while for negative results, they were tolerance to negative events (p = 0.05), spirituality (p = 0.04), and adaptability (p = 0.05). The factors affecting emotional regulation were tenacity (p = 0.03), spirituality (p = 0.03), adaptability (p = 0.03), and expressivity (p = 0.03). Only the spirituality (p = 0.05) and expressivity (p = 0.04) levels predicted insufficient self-regulation. The effects of the resilience and social–emotional competence levels on problematic internet use should not be ignored. In the plans and interventions to be developed, it is of great importance to take measures to improve the level of resilience and social–emotional competence skills. Full article

The Quantum Snowflake Algorithm (QSA) is a novel metaheuristic for both continuous and discrete optimization problems, combining collision-based diversity, quantum-inspired tunneling, superposition-based partial solution sharing, and local refinement steps. The QSA embeds candidate solutions in a continuous auxiliary space, where collision operators ensure that agents—snowflakes—reject each other and remain diverse. This approach is inspired by snowflakes which prevent collisions while retaining unique crystalline patterns. Large leaps to escape deep local minima are simultaneously provided by quantum tunneling, which is particularly useful in highly multimodal environments. Tests on challenging functions like Lévy and HyperSphere showed that the QSA can more reliably obtain very low objective values in continuous domains than conventional swarm or evolutionary approaches. A 200-city Traveling Salesman Problem (TSP) confirmed the excellent tour quality of the QSA for discrete optimization. It drastically reduces the route length compared to Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Quantum Particle Swarm Optimization (QPSO), and Cuckoo Search (CS). These results show that quantum tunneling accelerates escape from local traps, superposition and local search increase exploitation, and collision-based repulsion maintains population diversity. Together, these elements provide a well-rounded search method that is easy to adapt to different problem areas. In order to establish the QSA as a versatile solution framework for a range of large-scale optimization challenges, future research could investigate multi-objective extensions, adaptive parameter control, and more domain-specific hybridisations. Full article

Due to the high variability in Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs), hidden stratification, and batch effects, generalizing beyond the training distribution is one of the main challenges in Deep Learning (DL) for Computational Pathology (CPath). But although DL depends on large volumes of diverse and annotated data, it is common to have a significant number of annotated samples from one or multiple source distributions, and another partially annotated or unlabeled dataset representing a target distribution for which we want to generalize, the so-called Domain Adaptation (DA). In this work, we focus on the task of generalizing from a single source distribution to a target domain. As it is still not clear which domain adaptation strategy is best suited for CPath, we evaluate three different DA strategies, namely FixMatch, CycleGAN, and a self-supervised feature extractor, and show that DA is still a challenge in CPath. Full article

Dynamic and changeable business scenarios pose significant challenges to the adaptability and verifiability of process models. Despite its widespread adoption as an ISO-standard modeling language, Business Process Model and Notation (BPMN) faces inherent limitations in formal semantics and verification capabilities, hindering the mathematical validation of process evolution behaviors under scenario changes. To address these challenges, this paper proposes a collaborative modeling framework integrating BPMN with hierarchical colored Petri nets (HCPNs), enabling the efficient iterative evolution and correctness verification of process change through formal mapping and localized evolution mechanism. First, hierarchical mapping rules are established with subnet-based modular decomposition, transforming BPMN elements into an HCPN executable model and effectively resolving semantic ambiguities; second, atomic evolution operations (addition, deletion, and replacement) are defined to achieve partial HCPN updates, eliminating the computational overhead of global remapping. Furthermore, an automated verification pipeline is constructed by analyzing state spaces, validating critical properties such as deadlock freeness and behavioral reachability. Evaluated through an intelligent AI-driven service scenario involving multi-gateway processes, the framework demonstrates behavioral effectiveness. This work provides a pragmatic solution for scenario-driven process evolution in domains requiring agile iteration, such as fintech and smart manufacturing. Full article

In the rapidly evolving field of aerial robotics, the coordinated management of multiple unmanned aerial vehicle (multi-UAV) systems to address complex and dynamic environments is increasingly critical. Multi-UAV systems promise enhanced efficiency and effectiveness in various applications, from disaster response to infrastructure inspection, by leveraging the collective capabilities of UAV fleets. However, the dynamic nature of such environments presents significant challenges in task allocation and real-time adaptability. This paper introduces a novel hybrid algorithm designed to optimize multi-UAV task assignments in dynamic environments. State-of-the-art solutions in this domain have exhibited limitations, particularly in rapidly responding to dynamic changes and effectively scaling to large-scale environments. The proposed solution bridges these gaps by combining clustering to group and assign tasks in an initial offline phase with a dynamic partial reassignment process that locally updates assignments in response to real-time changes, all within a centralized–distributed communication topology. The simulation results validate the superiority of the proposed solution and demonstrate its improvements in efficiency and responsiveness over existing solutions. Additionally, the results highlight the scalability of the solution in handling large-scale problems and demonstrate its ability to efficiently manage a growing number of UAVs and tasks. It also demonstrated robust adaptability and enhanced mission effectiveness across a wide range of dynamic events and different scale scenarios. Full article

The changes in operating conditions of a power transformer can cause a shift in the distribution of partial discharge data, leading to the gradual generation of unlabeled new data, which results in the degradation of the original partial discharge detection model and a decline in its classification performance. To address the aforementioned challenge, a domain-adaptive transformer partial discharge recognition method combining AlexNet-KAN with DANN is proposed. First, the Kolmogorov–Arnold Network (KAN) is introduced to improve the AlexNet model, resulting in the AlexNet-KAN model, which improves the accuracy of transformer partial discharge recognition. Second, the domain adversarial mechanism from domain adaptation theory is applied to the domain of transformer partial discharge recognition, leading to the development of a domain-adaptive transformer partial discharge recognition model that combines AlexNet-KAN with Domain Adversarial Neural Networks (DANNs). Experimental outcomes show that the proposed model effectively adapts transformer partial discharge data from the source domain to the target domain, addressing the issue of distribution shift in transformer partial discharge data with either no labels or very few labels in the new data. Full article

Accumulating evidence suggests that the plasma membrane-localized phosphate transporter 1 (PHT1) family plays a fundamental role in the absorption, translocation, and re-mobilization of phosphorus in plants. Buckwheat (Fagopyrum spp.) exhibits high efficiency in phosphate uptake and wide adaptability to grow in under-fertilized soils. Despite their physiological importance, a systematic analysis of PHT1 genes in buckwheat has not been conducted yet. In this study, we performed a genome-wide identification and expression profile of the PHT1 gene family in Tartary buckwheat (Fagopyrum tataricum Gaertn). A total of eleven putative PHT1 genes (FtPHT1;1 to 1;11) were identified with an uneven distribution on all the F. tataricum chromosomes except for chromosomes 2, 3, and 5. All the FtPHT1s share the conserved domain GGDYPLSATIxSE, a typical signature of PHT1 transporters. A phylogenetic analysis indicated that FtPHT1 proteins could be clustered into four distinct subgroups, well supported by the exon–intron structure, consensus motifs, and the domain architecture. A gene duplication analysis suggested that tandem duplication may largely contribute to the expansion of the FtPHT1 gene family members. In silico predictions of cis-acting elements revealed that low-phosphate-responsive elements, such as W-box, P1BS, and MBS, were enriched in the promoter regions of FtPHT1 genes. Quantitative real-time PCR assays showed differential but partially overlapping expression patterns of some FtPHT1 genes in various organs under limited Pi supply and hormone stimuli, implying that these FtPHT1 transporters may be essential for Pi uptake, translocation, and re-mobilization, possibly through signaling cross-talk between the low phosphate and hormones. These observations provide molecular insights into the FtPHT1 gene family, which paves the way to a functional analysis of FtPHT1 members in the future. Full article

Balancing efficiency and accuracy is often challenging in the numerical solution of three-dimensional (3D) point source acoustic wave equations for layered media. To overcome this, an efficient solution method in the spatial-wavenumber domain is proposed, utilizing the Non-Uniform Fast Fourier Transform (NUFFT) to achieve arbitrary non-uniform sampling. By performing a two-dimensional (2D) Fourier transform on the 3D acoustic wave equation in the horizontal direction, the 3D equation is transformed into a one-dimensional (1D) space-wavenumber-domain ordinary differential equation, effectively simplifying significant 3D problems into one-dimensional problems and significantly reducing the demand for memory. The one-dimensional finite-element method is applied to solve the boundary value problem, resulting in a pentadiagonal system of equations. The Thomas algorithm then efficiently solves the system, yielding the layered wavefield distribution in the space-wavenumber domain. Finally, the wavefield distribution in the spatial domain is reconstructed through a 2D inverse Fourier transform. The correctness of the algorithm was verified by comparing it with the finite-element method. The analysis of the half-space model shows that this method can accurately calculate the wavefield distribution in the air layer considering the air layer while exhibiting high efficiency and computational stability in ultra-large-scale models. The three-layer medium model test further verified the adaptability and accuracy of the algorithm in calculating the distribution of acoustic waves in layered media. Through a sensitivity analysis, it is shown that the denser the mesh node partitioning, the higher the medium velocity, and the lower the point source frequency, the higher the accuracy of the algorithm. An algorithm efficiency analysis shows that this method has extremely low memory usage and high computational efficiency and can quickly solve large-scale models even on personal computers. Compared with traditional FEM, the algorithm has much higher advantages in terms of memory usage and efficiency. This method provides a new approach to the numerical solution of partial differential equations. It lays an essential foundation for background field calculation in the scattering seismic numerical simulation and full-waveform inversion of acoustic waves, with strong theoretical significance and practical application value. Full article

Background/Objectives: Dermatophytosis, a prevalent fungal infection of keratinized tissues, is primarily caused by the filamentous fungus Trichophyton rubrum. Sertraline (SRT), an antidepressant with antifungal activity, has already demonstrated therapeutic potential against this fungus. Elucidating the effects of SRT may provide insights into its mechanism of action and fungal adaptation to this drug. Differential gene expression and alternative splicing (AS) facilitate fungal adaptations to various environmental conditions. This study aimed to provide a comprehensive overview of AS events and their implications in T. rubrum cultivated under sub-inhibitory concentrations of SRT. Method: The transcriptome of T. rubrum challenged with SRT was analyzed to detect AS events. Results: RNA-seq analysis revealed that SRT affected transcriptional and post-transcriptional events in numerous T. rubrum genes, including those encoding transcription factors, kinases, and efflux pumps. Among the AS events, intron retention was predominant. After 12 h of SRT exposure, intron-3 retention levels in the serine/arginine protein kinase mRNA transcripts were significantly increased compared with those in the control. This new isoform would produce a putative protein that partially lost its phosphotransferase domain. Conclusions: These findings highlight the potential mechanisms of action of SRT and suggest how T. rubrum adapts itself to this drug. Full article