Search Results (22,444)

Rolling bearings constitute critical rotating components within rolling mill equipment. Production efficiency and the operational safety of the whole mechanical system are directly governed by their operational health state. To address the dual challenges of the over-reliance of conventional diagnostic methods on expert experience and the scarcity of fault samples in industrial scenarios, we propose a virtual–physical data fusion-optimized intelligent fault diagnosis framework. Initially, a dynamics-based digital twin model for rolling bearings is developed by leveraging their geometric symmetry. It is capable of generating comprehensive fault datasets through parametric adjustments of bearing dimensions and operational environments in virtual space. Subsequently, a symmetry-informed architecture is constructed, which integrates multi-scale convolutional neural networks with attention mechanisms and bidirectional gated recurrent units (MCNN-AT-BiGRU). This architecture enables spatiotemporal feature extraction and enhances critical fault characteristics. The experimental results demonstrate 99.5% fault identification accuracy under single operating conditions. It maintains stable performance under low SNR conditions. Furthermore, the framework exhibits superior generalization capability and transferability across the different bearing types. Full article

Background: Diarrheagenic Escherichia coli (DEC) remains relevant to public health and agri-food chains. The context in Brazil, as a major food producer and exporter, reinforces the need for genomic surveillance. Objective: We aimed to characterize Brazilian diffusely adhering (DAEC), enteropathogenic (EPEC), and Shiga toxin-producing E. coli (STEC) sequences in silico across O-serogroups, in addition to sequence-type (ST), virulence, resistome, and phylogenomic relationships. Methodology: We retrieved 973 genomes assigned to Brazil from NCBI Pathogen Detection Database and performed virtual-PCR screening for key DEC-genes. We then typed O-serogroups (ABRicate/EcOH), Multi-Locus Sequencing Type (MLST), virulome (Ecoli_VF), resistome (ResFinder), and characterized stx genes. Results: DEC represented 18.7% of genomes, driven primarily by EPEC. In EPEC, the eae β-1 subtype was most common; we detected, for the first time in Brazilian sequences, ξ-eae subtype and ST583/ST301. Seventy-eight percent of DAEC isolates were multidrug-resistant (MDR), and two ST were newly reported in the country (ST2141/ST500). In STEC, O157 formed a largely susceptible clade with uniform eae γ-1, whereas 57% of non-O157 were MDR. New STs (ST32/ST1804) were observed, and three genomes were closely related to international isolates. Conclusions: Despite the low DEC representation in the dataset, new STs and eae subtypes were detected in Brazil. Also, MDR in DAEC and non-O157 STEC reinforces the need for antimicrobial-resistance genomic surveillance. Full article

Crossfire attacks disrupt network services by targeting critical links of server groups, causing traffic congestion and server failures that prevent legitimate users from accessing services. To counter this threat, this study proposes a novel topology spoofing defense mechanism based on a sequence-based Graph Neural Network–Moving Target Defense (ENRNN-MTD). During the reconnaissance phase, the method employs a GNN to generate multiple random and diverse virtual topologies, which are mapped to various external hosts. This obscures the real internal network structure and complicates the attacker’s ability to accurately identify it. In the attack phase, an IP random-hopping mechanism using a chaotic sequence is introduced to conceal node information and increase the cost of launching attacks, thereby enhancing the protection of critical services. Experimental results demonstrate that, compared to existing defense mechanisms, the proposed approach exhibits significant advantages in terms of deception topology randomness, defensive effectiveness, and system load management. Full article

The growing penetration of renewable energy has reduced system inertia and damping, threatening grid stability. This paper proposes a novel control strategy that seamlessly integrates virtual synchronous generator (VSG) emulation with low-voltage ride-through (LVRT) capability for direct-drive permanent magnet synchronous generators (PMSGs). The unified control framework enables simultaneous inertia support during frequency disturbances and compliant reactive current injection during voltage sags—eliminating mode switching. Furthermore, the proposed strategy has been validated through both a single-machine model and actual wind farm topology. Results demonstrate that the strategy successfully achieves VSG control functionality while simultaneously meeting LVRT requirements. Full article

Background/Objectives: This systematic review with meta-analysis synthesizes current evidence on the effectiveness of non-immersive virtual reality (VR) interventions in enhancing gross motor function, balance, and functional independence in children with cerebral palsy (CP). Methods: A systematic search was performed across six databases (PubMed, Web of Science, Scopus, MEDLINE, CINAHL Complete, and Psychology and Behavioral Sciences Collection) to identify randomized controlled trials (RCTs) published up to July 2025. Primary outcomes included gross motor function (GMFM-D/E), balance (Pediatric Balance Scale, PBS), and functional independence (WeeFIM). Risk of bias was assessed using the RoB 2 tool, and the certainty of evidence was evaluated with GRADE. Results: From 1233 retrieved records, 13 RCTs involving 624 participants fulfilled the inclusion criteria. Pooled analyses demonstrated significant improvements with non-immersive VR in gross motor function (GMFM-D: ES = 2.04, p = 0.02; GMFM-E: ES = 2.02, p < 0.001), balance (PBS: ES = 1.34, p = 0.02), and functional independence (WeeFIM: ES = 0.99, p < 0.001). Conclusions: Non-immersive VR interventions were associated with meaningful gains in gross motor function, balance, and independence in children with CP. Significant differences were consistently observed in GMFM-D, GMFM-E, PBS, and WeeFIM outcomes when compared with control groups. Full article

Background/Objectives: In computer graphics, virtual worlds are constructed and visualized through algorithmic processes. These environments are typically populated with objects defined by mathematical models, traditionally based on Euclidean geometry. However, there is increasing interest in exploring non-Euclidean geometries, which require adaptations of the modeling techniques used in Euclidean spaces. Methods: This paper focuses on defining parametric curves and surfaces within elliptic and hyperbolic geometries. We explore free-form splines interpreted as hierarchical motions along geodesics. Translation, rotation, and ruling are managed through supplementary curves to generate surfaces. We also discuss how to compute normal vectors, which are essential for animation and lighting. The rendering approach we adopt aligns with physical principles, assuming that light follows geodesic paths. Results: We extend the Kochanek–Bartels spline to both elliptic and hyperbolic geometries using a sequence of geodesic-based interpolations. Simple recursive formulas are introduced for derivative calculations. With well-defined translation and rotation in these curved spaces, we demonstrate the creation of ruled, extruded, and rotational surfaces. These results are showcased through a virtual reality application designed to navigate and visualize non-Euclidean spaces. 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

ERK5, a member of the MAP kinase family, has been implicated in several cancer types due to its role in regulating cell proliferation, survival, and migration. In this study, structure-based virtual screening was employed, followed by cell assays, and molecular dynamics simulations to identify novel ERK5 inhibitors. A commercially available library of 1.6 million compounds was subjected to a three-stage docking process (HTVS, SP, and XP), using the docking module in Schrodinger Maestro, yielding 40 candidates with superior docking scores compared to the co-crystallized ligand. These compounds were then tested for antiproliferative activity using an MTT assay in A549 and H292 lung cancer cell lines. Among the hits, STK038175, STK300222, and GR04 showed significant activity with IC₅₀ values of ranging from 10 to 25 µM. Western blot analysis revealed that STK300222 at 50 µM reduced the phosphorylation of ERK5 downstream targets similarly to a known inhibitor, while wound healing assays confirmed a dose-dependent decrease in cell migration. Molecular dynamics simulations of 200 ns further demonstrated that all three compounds form stable complexes with ERK5 that are comparable to the co-crystallized ligand in 5BYZ. The MD simulations also revealed strong electrostatic and solvation interactions observed for STK300222 and GR04 particularly. Furthermore, by calculating the MM-GB/SA scores from the MD trajectories, the binding affinities of the three hits, along with the co-crystallized ligand in 5BYZ, were re-scored. Although the co-crystallized ligand had the highest MM-GB/SA score at −38.96 Kcal mol⁻¹, STK300222 had a comparable score of −35.45 Kcal mol⁻¹. These results highlight STK300222 and GR04 as promising candidates for further optimization and in vivo validation as ERK5 inhibitors. Full article

Background: Hemorrhagic transformation (HT) is a major complication of endovascular thrombectomy (EVT) for acute ischemic stroke (AIS). Objectives: To investigate the factors as sociated with HT in patients with successful recanalization and examine the impact of collateral status (CS) on ischemic progression and outcomes. Methods: We retrospectively analyzed patients with AIS with successful recanalization (modified treatment in cerebral infarction (mTICI) 2B-3) who underwent dual-energy CT (DECT) within 24 h and MRI within 10 days post-EVT. Patients with posterior circulation stroke, missing multiphase CT angiography (CTA) collateral scores, or missing 3-month modified ranking scale scores were excluded from the study. Results: Among the 86 patients, those with HT had a significantly lower proportion of 3-month excellent outcomes and worse imaging scores, including non-contrast CT (NCCT)-Alberta Stroke Program Early CT Score (ASPECTS), virtual non-contrast (VNC)-ASPECTS, and diffusion-weighted imaging (DWI)-ASPECTS. Patients with HT with poor CS had a significantly lower proportion of 3-month excellent outcomes, poorer post-EVT National Institutes of Health Stroke Scale (NIHSS) score, worse imaging scores, including VNC-ASPECTS, and DWI-ASPECTS. In the predictive factor analysis, post-EVT NIHSS and VNC-ASPECTS scores were significantly associated with 3-month excellent functional outcomes (modified Rankin Scale (mRS) 0-1). Conclusions: In patients with successfully recanalized AIS, HT with poor CS was associated with poorer functional outcomes and worse imaging scores, and a 24 h combined measure (post-EVT NIHSS and DECT VNC-ASPECT) show promise for early risk stratification; prospective external validation is warranted before routine use. Full article

Computer-assisted surgery (CAS) and virtual surgical planning (VSP) have transformed jaw reconstruction, allowing immediate insertion of dental implants during surgery for better rehabilitation of occlusal function. However, traditional planning for optimal location and angulation of dental implants and fibula relies on experience and can be time-consuming. This study aimed to propose a function-driven workflow and develop an automatic computer program for optimal positioning of simultaneous dental implants and fibula segments. A customized computer program was developed using MATLAB. Computed tomography (CT) of the lower limbs of ninety-one Southern Chinese individuals was retrieved and cross-sections of three-dimensional (3D) fibula models were comprehensively investigated for implant installation. Our research proves that the accuracy of the program in identifying the anatomical orientation of the fibula was 92%. The ideal location, angulation and length of implant could be automatically generated based on any selected implant diameter, with a surgical feasibility of 94%. To the best of our knowledge, this is the first study to develop and validate a customized automatic computer program for osseointegrated implant design in fibula flap surgery. This program can be incorporated into the current workflow of CAS to further the development of reliable and efficient surgical planning for function-driven jaw reconstruction. Full article

Background: Orthognathic surgery aims to align the jaws with the facial skeleton and correct dental occlusion. This paper introduces the concept of planning the maxillomandibular complex (MMC) as a whole, utilizing a t-forming set of landmarks: the maxillary central incisor, the chin, and the occlusal plane. Methods: The background, hypothesis, and rationale of the new T concept are explained. A case of a 28-year-old male with skeletal class III malocclusion and an open bite was used to illustrate the application of the T concept in step-by-step surgical planning. The planning encompasses four phases: Phase One involves correcting frontal deformity and various asymmetries, Phase Two involves correcting chin anterior–posterior deformity, Phase Three involves correcting anterior–posterior and vertical MMC position, and Phase Four involves correcting MMC rotation. Results: The T concept provided a structured approach to plan MMC as a whole and integrate all structures into harmony. Conclusions: The T concept provides a logical approach to MMC positioning in orthognathic surgery, addressing functional and aesthetic concerns. It acts as a checkpoint to verify MMC position, helping surgeons achieve better results and avoid compensatory procedures. Full article

The exponential growth of human interactions on social media via the internet has revolutionized global communication, but it has also emerged as a critical factor in mental health linked to suicidal ideation and mental disorders. This systematic review and meta-analysis aimed to synthesize evidence on the most prevalent disorders in adolescents and young adults associated with social media use based on previous research, highlighting risk factors and key findings. Publications from 2020 to 2024 in highly relevant databases were reviewed following the PRISMA declaration guidelines. The meta-analysis (conducted in R software) of the included documents (24 studies, 68 effects) verified a significant and positive association between exposure to risk factors in social networks and various disorders in adolescents and young adults (aggregate correlation r = 0.2173, 95% CI [0.1826, 0.2520], p ≤ 0.0001), although with high heterogeneity (I² = 99.66%). Prevention strategies were indicated by revealing data from contexts in which 40% of adolescents who died by suicide had developed online identities focused on suicidal thoughts. Full article

Since it is difficult to obtain the positions of dynamic loads on structures, this paper suggests a new method to identify the locations of dynamic loads step-by-step based on the correlation coefficients of dynamic responses. First, a recognition model for dynamic load position based on a finite-element scheme is established, with the finite-element domain divided into several regions. Second, virtual loads are applied at the central points of these regions, and acceleration responses are calculated at the sensor measurement points. Third, the maximum correlation coefficient between the calculational and measured accelerations is obtained, and the dynamic load is located in the region with the virtual load corresponding to the maximum correlation coefficient. Finally, this region is continuously subdivided with the refined mesh until the dynamic load is pinpointed in a sufficiently small area. Different virtual load construction methods are proposed according to different types of loads. The frequency response function, unresolvable for the actual problem due to the unknown location of the real dynamic load, can be transformed into a solvable form, involving only known points. This transformation simplifies the analytical process, making it more efficient and applicable to analysis of the dynamic behavior of the system. The identification of the dynamic load position in the entire structure is then transformed into a sub-region approach, focusing on the area where the dynamic load acts. Simulations for case studies are conducted to demonstrate that the proposed method can effectively identify positions of single and multiple dynamic loads. The correctness of the theory and simulation model is verified with experiments. Compared to recent methods that use machine learning and neural networks to identify positions of dynamic loads, the approach proposed in this paper avoids the heavy computational cost and time required for data training. Full article

Background: Artificial intelligence (AI), the overarching field that includes machine learning (ML) and its subfield deep learning (DL), is rapidly transforming clinical research by enabling the analysis of high-dimensional data and automating the output of diagnostic and prognostic tests. As clinical trials become increasingly complex and costly, ML-based approaches (especially DL for image and signal data) offer promising solutions, although they require new approaches in clinical education. Objective: Explore current and emerging AI applications in oncology and cardiology, highlight real-world use cases, and discuss the challenges and future directions for responsible AI adoption. Methods: This narrative review summarizes various aspects of AI technology in clinical research, exploring its promise, use cases, and its limitations. The review was based on a literature search in PubMed covering publications from 2019 to 2025. Search terms included “artificial intelligence”, “machine learning”, “deep learning”, “oncology”, “cardiology”, “digital twin”. and “AI-ECG”. Preference was given to studies presenting validated or clinically applicable AI tools, while non-English articles, conference abstracts, and gray literature were excluded. Results: AI demonstrates significant potential in improving diagnostic accuracy, facilitating biomarker discovery, and detecting disease at an early stage. In clinical trials, AI improves patient stratification, site selection, and virtual simulations via digital twins. However, there are still challenges in harmonizing data, validating models, cross-disciplinary training, ensuring fairness, explainability, as well as the robustness of gold standards to which AI models are built. Conclusions: The integration of AI in clinical research can enhance efficiency, reduce costs, and facilitate clinical research as well as lead the way towards personalized medicine. Realizing this potential requires robust validation frameworks, transparent model interpretability, and collaborative efforts among clinicians, data scientists, and regulators. Interoperable data systems and cross-disciplinary education will be critical to enabling the integration of scalable, ethical, and trustworthy AI into healthcare. Full article

Background: Neoadjuvant chemotherapy (NAC) is a standard preoperative intervention for early-stage breast cancer (BC). Dynamic contrast-enhanced magnetic resonance imaging (CE-MRI) has emerged as a critical tool for evaluating treatment response and pathological complete response (pCR) following NAC. Computational modeling offers a robust framework to simulate tumor growth dynamics and therapy response, leveraging patient-specific data to enhance predictive accuracy. Despite this potential, integrating imaging data with computational models for personalized treatment prediction remains underexplored. This case study presents a proof-of-concept prognostic tool that bridges oncology, radiology, and computational modeling by simulating BC behavior and predicting individualized NAC outcomes. Methods: CE-MRI scans, clinical assessments, and blood samples from three retrospective NAC patients were analyzed. Tumor growth was modeled using a system of partial differential equations (PDEs) within a reaction–diffusion mass transfer framework, incorporating patient-specific CE-MRI data. Tumor volumes measured pre- and post-treatment were compared with model predictions. A 20% error margin was applied to assess computational accuracy. Results: All cases were classified as true positive (TP), demonstrating the model’s capacity to predict tumor volume changes within the defined threshold, achieving 100% precision and sensitivity. Absolute differences between predicted and observed tumor volumes ranged from 0.07 to 0.33 cm³. Virtual biomarkers were employed to quantify novel metrics: the biological conversion coefficient ranged from 4 × 10⁻⁷ to 6 × 10⁻⁶ s^-1, while the pharmacodynamic efficiency coefficient ranged from 1 × 10⁻⁷ to 4 × 10⁻⁴ s^-1, reflecting intrinsic tumor biology and treatment effects, respectively. Conclusions: This approach demonstrates the feasibility of integrating CE-MRI and computational modeling to generate patient-specific treatment predictions. Preliminary model training on retrospective cohorts with matched BC subtypes and therapy regimens enabled accurate prediction of NAC outcomes. Future work will focus on model refinement, cohort expansion, and enhanced statistical validation to support broader clinical translation. Full article