Search Results (253)

Anticipation allows individuals to prepare actions by predicting upcoming events, yet its influence on motor learning and its practical relevance for rehabilitation remain unclear. This study investigates how anticipation mechanisms shape motor learning and skill acquisition in a virtual leader–follower task and explores their potential for adaptive training. Forty-nine healthy adults performed a joystick-controlled tracking task in virtual reality, following a dynamic leader that was always visible (Control), became invisible at regular intervals (Deterministic Anticipation), or disappeared randomly (Stochastic Anticipation) to elicit anticipatory behavior. Kinematic and kinetic metrics and time-series analysis were used to evaluate synchrony, smoothness, and coordination. Performance improved from baseline to retention, with no distinct differences in final performance between the groups. However, slope-based analyses found that anticipation-based training accelerated learning, especially in the novice subgroup (baseline score < 35), with marked improvements in metrics such as score pause duration, temporal lag, and spatial error. Although participants reached similar final performance levels across protocols, the rate and pattern of learning differed across training protocols. Anticipation accelerates early-stage improvements, with the strongest effects observed in novice participants. The paradigm provides a high-resolution framework for adaptive motor training and assessment. Full article

The need for reliable software for data acquisition, processing and communication with laboratory instruments, as well as for extending laboratory findings to real-scale structures, is imperative. In this context, ReplicaXLite is presented: an open-source software framework designed to facilitate and organize structural experimental testing on seismic tables. The software enables the creation of digital twin models and real-time sensor data recording. Furthermore, it allows for the processing, storage and visualization of results within a graphical interface. It features two primary modes of operation: (a) via terminal with specific Application Programming Interfaces (APIs) and (b) via a Graphical User Interface (GUI), adapting to the user’s expertise level. The software lies on top of open-source libraries like OpenSeesPy and opstool. It supports many material types, such as concrete, steel, fibers and composites, among others. Models produced by ReplicaXLite demonstrate strong agreement with experimental data across varying structural configurations. For both acceleration and displacement, the framework yielded satisfactory accuracy at the top slab with mean envelope correlations ranging from 0.91 to 0.97 and mean Pearson correlations generally between 0.83 and 0.95 for varying seismic intensities (0.1 g to 1.4 g). The numerical framework successfully captured global stiffness degradation, with Normalized Root Mean Square Errors (NRMSE) well-constrained between 2.3% and 7.9% across both acceleration and displacement response metrics. The architecture allows for the one-click execution of custom user codes, providing full access to the source code and the ability to perform live toolkit modifications via the “app.” terminal variable. Finally, it provides mid-simulation modification of the mass and elements of the model. Full article

Flood hazard mitigation programs increasingly rely on property buyouts and home elevation, yet participation remains sensitive to program design details that affect household constraints. This study estimates homeowner preferences for buyout and elevation program attributes using a stated-preference discrete-choice experiment administered to N = 1560 homeowners, in which each respondent completed up to 4 choice tasks with 3 alternatives (Buyout, Elevation, and Neither). Choices are modeled in a random-utility framework with a multinomial logit as the primary specification and a mixed logit as a robustness specification. Observed choices favor Buyout (51.2%) over Elevation (29.6%) and the status quo (19.2%). In the estimated utility model, higher buyout offers increase acceptance, longer payment delays significantly reduce acceptance, and longer time to vacate increases acceptance; the acquisition option feature also raises buyout utility. These timing effects imply economically meaningful offer-equivalent tradeoffs: at representative baselines, an additional month of payment delay requires approximately a 6.45 percentage-point increase in offer (as a share of home value) to maintain acceptance, while households would trade about 8.02 percentage points of offer to obtain one additional month to vacate. Heterogeneity results indicate lower baseline participation among low-income respondents and attenuated marginal benefits of longer vacate time among respondents reporting damage. Respondent-level cross-validation shows stable predictive performance and similar accuracy across MNL and mixed logit models. The results highlight that accelerating payments and offering flexible time to vacate can increase program uptake, and that complementary supports may be needed to reduce participation barriers for economically vulnerable households. Full article

Heritage Building Information Modeling (HBIM) is accelerating the transition from reactive restoration to preventive conservation in architectural heritage management. Nevertheless, research at the heritage-cluster scale remains limited, particularly in terms of multi-source data integration, dynamic value–risk coupling, and lifecycle-oriented decision support. This study proposes an intelligent HBIM-based framework designed to support integrated data processing, automated value–risk assessment, and preventive intervention planning for masonry heritage clusters. The framework is validated through its application to the Suopo Ancient Watchtower Complex in Danba, Sichuan, consisting of 84 polygonal stepped-in stone towers. By integrating 3D laser scanning, unmanned aerial vehicle (UAV) oblique photogrammetry, and historical archival data, a closed-loop workflow is established, spanning data acquisition, parametric semantic modeling, and intervention prioritization. A dedicated parametric component library and hierarchical semantic database tailored to irregular polygonal masonry significantly enhance modeling consistency, semantic coherence, and cross-building reusability. Leveraging the Revit Application Programming Interface (API) and Dynamo, the framework embeds a value–risk model (P = V × R), enabling automated component-level evaluation, real-time visualization of conservation priorities, and one-click generation of intervention lists. Results demonstrate improved modeling accuracy, efficiency, and decision reliability compared with conventional manual workflows. The framework offers a scalable and replicable pathway for sustainable conservation of masonry heritage clusters in high-seismic regions and provides a foundation for future integration with IoT-enabled digital twin systems. Full article

Early survival of marine fish larvae depends on the timely development of feeding and swimming functions. This study examined ontogenetic changes in relative growth patterns of feeding- and locomotion-related body parts in the mottled spinefoot rabbitfish, S. fuscescens. Larvae and early juveniles were reared under controlled conditions, and morphometric measurements were analyzed using log–log segmented regression. Body length increased gradually during the early larval stage and accelerated after approximately 10 days post-hatching. Three developmental phases were identified, with breakpoints at approximately 5 mm, 7–9 mm, and 17–19 mm body length. In the early larval phase (NL < ~5 mm), eye diameter, upper jaw length, snout length, and caudal peduncle depth showed strong positive allometry, indicating rapid acquisition of feeding and swimming functions. This was followed by a mid-larval phase characterized by near-isometric growth and stabilized body proportions. During the late larval to early juvenile phase, body depth and caudal peduncle depth again exhibited positive allometry, reflecting reorganization toward juvenile morphology. These results reveal a stage-specific growth strategy in S. fuscescens and provide a morphological basis for improving larval rearing and feeding practices. Full article

Background/Objectives: Magnetic resonance arthrography is the reference standard for evaluating glenoid labral lesions. Deep learning (DL) reconstruction algorithms may accelerate 3D acquisitions while maintaining image quality. This study assesses the diagnostic accuracy of DL-based isotropic 3D MR imaging for detecting glenoid labral lesions. Methods: This prospective study included 128 consecutive patients (79 men, 49 women; mean age 38.4 years) undergoing shoulder MR arthrography between June 2023 and April 2025. DL-based 3D sequences (acquisition time: 3:26) were compared with conventional multiplanar TSE and PD-FS sequences (acquisition time: 24–28 min). Two independent radiologists assessed glenoid labral lesions, bone marrow edema, and rotator cuff abnormalities using a four-point Likert scale. Sensitivity, specificity, and interobserver agreement were calculated. Results: DL-based 3D sequences demonstrated 94.7–95.1% sensitivity and 100% specificity for glenoid labral lesions, with excellent interobserver agreement (κ = 0.812). The area under the ROC curve was 0.894. Combined 3D protocols (T1 + PD-FS) showed superior accuracy (97.8%) compared to single sequences (90.5%, p = 0.012). For bone marrow edema, sensitivity was 82.9% with 100% specificity. Rotator cuff evaluation achieved 75% sensitivity with 100% specificity. Conclusions: DL-based isotropic 3D sequences provide high diagnostic accuracy for glenoid labral pathology while reducing scan time by 75%. Combined T1 and PD-FS protocols optimize performance. These findings support selective implementation of DL-accelerated 3D protocols in shoulder MR arthrography, particularly for labral assessment, while acknowledging that conventional protocols may remain preferable in specific clinical scenarios. Full article

With the increasing complexity of power grid operations, operator training requires timely feedback and objective assessment. Traditional approaches based on lectures and scripted simulations provide limited personalization and weak explainability. This paper presents AI Instructors, an intelligent simulation training system for power-grid control and dispatching. The system is organized into learning, training, assessment, and analysis modules, and is built around two core technical components: (i) parameterized item generation from rule/knowledge bases using a phrase-enhanced transformer (PET), and (ii) solver-grounded, topology-aware grading with hierarchical feedback for both numeric and free-text responses. A voice interaction module is integrated to simulate telephone-based dispatch orders. We validate the system through a pilot deployment with licensed dispatch operators and scenario experiments on benchmark cases. Compared with a conventional scripted DTS workflow, AI Instructors achieves higher stepwise procedure accuracy (68%→90%), a lower topology-violation rate (32%→11%), and shorter response time (120 s→72 s), while increasing the proportion of parameterized questions and accelerating skill acquisition. These results suggest that combining adaptive sequencing with topology-safe, explainable evaluation can improve training effectiveness and operational safety. Full article

Introduction: Rapid and reliable neurovascular imaging is critical for time-sensitive diagnosis in acute cerebrovascular disorders, yet conventional magnetic resonance imaging (MRI) workflows remain constrained by acquisition speed, motion sensitivity, and limited integration of physiological context. We introduce the Intelligent Neurovascular Imaging Engine (INIE), a sensor-informed, topology-aware framework that jointly optimizes accelerated data acquisition, physics-grounded reconstruction, and cross-scale physiological consistency. Methods: INIE combines adaptive sampling, structured low-rank (Hankel) priors, and topology-preserving objectives with multimodal physiological sensors and scanner telemetry, enabling phase-consistent gating and confidence-weighted reconstruction under realistic operating conditions. The framework was evaluated using synthetic phantoms, a translational porcine stroke recanalization model with repeated measures, and retrospective human datasets. Across $N_{\mathrm{runs}}=120$ acquisition–reconstruction runs derived from $N_{\mathrm{animals}}=18$ pigs with animal-level train/validation/test separation, performance was assessed using image quality, topological fidelity, and cross-modal consistency metrics. Multiple-comparison control was performed using Bonferroni/Holm–Bonferroni procedures. Results: INIE achieved acquisition acceleration exceeding 70% while maintaining high reconstruction fidelity (PSNR $\approx 35$–36 dB, SSIM $\approx 0.90$–0.92). Topology-aware analysis showed an approximately twofold reduction in Betti number deviation relative to baseline accelerated methods. Cross-modal validation in a PET subset demonstrated strong agreement between MRI-derived perfusion parameters and metabolic markers (Pearson $r\approx 0.9$). INIE improved large-vessel occlusion detection accuracy to approximately 93% and reduced automated time-to-decision to under three minutes. Conclusions: These results indicate that sensor-informed, topology-aware, closed-loop imaging improves the reliability and physiological consistency of accelerated neurovascular MRI and supports faster, more robust decision-making in acute cerebrovascular imaging workflows. Full article

ALS is a multistep disease, in which (epi)genetic, environmental, and age-related processes, including senescence, converge over decades to reduce resilience resulting in self-sustaining symptomatic disease. The multistep model visualizes five to six impactful events in sporadic ALS, but fewer in those carrying high-penetrance mutations, such as SOD1, FUS, or C9orf72 expansions. The timing, duration, and cumulative effects of specific steps are presumed to have individual variability but, the steps themselves are inferred since they have not been observed and remain agnostic as to biological identity. Nevertheless, the model gives an opportunity to integrate genetics, aging, environmental exposures, and systems-level vulnerability into a single framework. Acting as step modifiers, environmental exposures including trauma lower the threshold for step acquisition, accelerate the accumulation of steps, influence the anatomical site of disease onset, and unmask preclinical disease. Because ALS emerges from the gradual collapse of multiple layers of biological robustness, tackling a single pathway will be insufficient and the multistep model forces a reconsideration of therapeutic timing and strategies. Protection against early-life insults, anti-aging, and anti-senescent therapies may curtail step accumulation preventing ALS from exceeding threshold and disease manifestation. Full article

Recent developments in unmanned aerial vehicle (UAV) activity highlight the need for advanced electromagnetic spectrum monitoring systems that can detect drones operating near sensitive or restricted areas. Such systems can identify emissions from drones even under frequency-hopping conditions, providing an early warning system and enabling a timely response to protect critical infrastructure and ensure secure operations. In this context, the present work proposes the development of a high-performance multichannel broadband monitoring system with real-time analysis capabilities, designed on an SDR architecture based on USRP with three acquisition channels: two broadband (160 MHz and 80 MHz) and one narrowband (1 MHz) channel, for simultaneous, of extended spectrum segments, aligned with current requirements for analyzing emissions from drones in the 2.4 GHz and 5.8 GHz ISM bands. The processing system was configured to support cumulative bandwidths of over 200 MHz through a high-performance hardware platform (powerful CPU, fast storage, GPU acceleration) and fiber optic interconnection, ensuring stable and lossless transfer of large volumes of data. The proposed spectrum monitoring system proved to be extremely sensitive, flexible, and extensible, achieving a reception sensitivity of −130 dBm, thus exceeding the values commonly reported in the literature. Additionally, the parallel multichannel architecture facilitates real-time detection of signals from different frequency ranges and provides a foundation for advanced signal classification. Its reconfigurable design enables rapid adaptation to various signal types beyond unmanned aerial systems. Full article

Magnetic resonance imaging (MRI) super-resolution (SR) enables high-resolution reconstruction from low-resolution acquisitions, reducing scan time and easing hardware demands. However, most deep learning-based SR models are large and computationally heavy, limiting deployment in clinical workstations, real-time pipelines, and resource-restricted platforms such as low-field and portable MRI. We introduce CHARMS, a lightweight convolutional–Transformer hybrid with attention regularization optimized for MRI SR. CHARMS employs a Reverse Residual Attention Fusion backbone for hierarchical local feature extraction, Pixel–Channel and Enhanced Spatial Attention for fine-grained feature calibration, and a Multi-Depthwise Dilated Transformer Attention block for efficient long-range dependency modeling. Novel attention regularization suppresses redundant activations, stabilizes training, and enhances generalization across contrasts and field strengths. Across IXI, Human Connectome Project Young Adult, and paired 3T/7T datasets, CHARMS (~1.9M parameters; ~30 GFLOPs for 256 × 256) surpasses leading lightweight and hybrid baselines (EDSR, PAN, W2AMSN-S, and FMEN) by 0.1–0.6 dB PSNR and up to 1% SSIM at ×2/×4 upscaling, while reducing inference time ~40%. Cross-field fine-tuning yields 7T-like reconstructions from 3T inputs with ~6 dB PSNR and 0.12 SSIM gains over native 3T. With near-real-time performance (~11 ms/slice, ~1.6–1.9 s per 3D volume on RTX 4090), CHARMS offers a compelling fidelity–efficiency balance for clinical workflows, accelerated protocols, and portable MRI. Full article

Background/Objectives: Recurrent oral ulcers (ROUs) are a common condition that significantly impacts patients’ quality of life. This pilot study was conducted to evaluate the feasibility and preliminary results of using non-thermal plasma (NTP) compared to low-level laser therapy (LLLT) and placebo to treat these ulcers. Methods: A prospective, controlled, randomised, parallel-group pilot study was conducted using a convenience sample of 50 patients with ROUs. Patients were randomly assigned (2:2:1) to one of three groups: NTP (n = 20), LLLT (n = 20), and placebo (n = 10). Feasibility and preliminary data acquisition were the primary goals. Exploratory outcomes included ulcer size reduction and safety profile. This was a single-blinded trial, where participants and outcome assessors were masked to group assignment. Ulcer size, pain perception, and time to complete healing were measured. For statistical analysis, ANOVA was used, with a p-value ≤ 0.05. Results: The groups were comparable at baseline. Exploratory results suggest that NTP demonstrated a promising trend in accelerating healing, with a mean healing time difference of 5.5 days compared to LLLT (2.5 ± 1.9 days vs. 8.0 ± 4.3 days) and 7.1 days compared to placebo (2.5 ± 1.9 days vs. 9.6 ± 5.3 days) (p < 0.001). Regarding pain, NTP provided significant and sustained relief. Patients in the NTP group were asymptomatic on day 2, unlike the LLLT and placebo groups, where pain persisted significantly (NTP VAS score at 1 h: 1.1 ± 2.1 vs. LLLT/Placebo VAS score at 1 h: 3.4 ± 2.4 and 7.3 ± 1.9, respectively) (p < 0.001). NTP was well tolerated, and no adverse events were reported. Conclusions: This pilot study suggests that NTP is a potentially safe and effective therapy for recurrent oral ulcers. Preliminary results indicate that it may accelerate healing and offer superior pain relief, warranting a large-scale clinical trial to confirm these findings. Full article

Climate challenges, along with a projected global population increase of 2 billion by 2080, are intensifying pressures on agricultural systems, leading to biodiversity loss, land use constrains, soil fertility declining, and changes in water cycles, while crop yields struggle to meet the rising food demand. These challenges, coupled with evolving legislation and rapid technology advancements, require innovative sustainable agricultural solutions. By reshaping farmers’ daily operations, real-time data acquisition and predictive models can support informed decision-making. In this context, smart farming (SM) applied to plant breeding can improve efficiency by reducing inputs and increasing outputs through the adoption of digital and data-driven technologies. Examples include the investment on common ontologies and metadata standards for phenotypes and environments, standardization of HTP protocols, integration of prediction outputs into breeding databases, and selection workflows, as well in building multi-partner field networks that collect diverse envirotypes. This review outlines how AI and machine learning (ML) can be integrated in modern plant breeding methodologies, including genomic selection (GS) and genetic algorithms (GAs), to accelerate the development of climate-resilient and sustainably performing crop varieties. While many reviews address smart farming or smart breeding independently, herein, these domains are bridged to provide an understandable strategic landscape by enhancing breeding efficiency. Full article

Drug discovery is a complex and multi-stage process that requires advanced analytical technologies capable of accelerating preclinical evaluation and improving the precision of therapeutic design. The combination of fluorescence and magnetic resonance imaging (MRI) within multimodal imaging plays an increasingly important role in modern pharmacokinetics, integrating the high molecular sensitivity of fluorescence with the non-invasive anatomical visualization offered by MRI. Fluorescence enables real-time monitoring of cellular processes, including drug–target interactions and molecular dynamics, whereas MRI provides detailed structural information on tissues without exposure to ionizing radiation. Hybrid probes—such as superparamagnetic iron oxide nanoparticles (SPIONs) functionalized with near-infrared (NIR) fluorophores or gadolinium-based complexes linked to optical dyes—enable simultaneous acquisition of molecular and anatomical data in a single examination. These multimodal systems are being explored in oncology, neurology, and cardiology, where they support improved visualization of tumor biology, amyloid pathology, and inflammatory processes in vascular disease. Although multimodal imaging shows great promise for enhancing pharmacokinetic and pharmacodynamic studies, several challenges remain, including the potential toxicity of heavy-metal-based contrast agents, limited tissue penetration of fluorescence signals, probe stability in vivo, and the complexity and cost of synthesis. Advances in nanotechnology, particularly biodegradable carriers and manganese-based MRI contrasts, together with the integration of artificial intelligence algorithms, are helping to address these limitations. In the future, fluorescence–MRI hybrid imaging may become an important tool in personalized medicine, supporting more precise therapy planning and reducing the likelihood of clinical failure. Full article

Silicon nanowire (SiNW) photodetectors exhibit high sensitivity for natural light detection but suffer from significant performance degradation due to thermal interference. To overcome this limitation, this paper presents a high-performance, CMOS-compatible SiNW array natural light photodetector with monolithic integration of an on-chip temperature sensor and an embedded intelligent compensation system. The device, fabricated via microfabrication techniques, features a dual-array architecture that enables simultaneous acquisition of optical and thermal signals, thereby simplifying peripheral circuitry. To achieve high-precision decoupling of the optical and thermal signals, we propose a hybrid temperature compensation algorithm that combines Particle Swarm Optimization (PSO) with a Back Propagation (BP) neural network. The PSO algorithm optimizes the initial weights and thresholds of the BP network, effectively preventing the network from getting trapped in local minima and accelerating the training process. Experimental results demonstrate that the proposed PSO-BP model achieves superior compensation accuracy and a significantly faster convergence rate compared to the traditional BP network. Furthermore, the optimized model was successfully implemented on an STM32 microcontroller. This embedded implementation validates the feasibility of real-time, high-accuracy temperature compensation, significantly enhancing the stability and reliability of the photodetector across a wide temperature range. This work provides a viable strategy for developing highly stable and integrated optical sensing systems. Full article