Search Results (12,623)

(1) Background: Evoked potentials (EPs) are an elegant, non-invasive, and reliable technique for assessing the functional integrity of neural pathways. They are, however, often limited by the difficulty and time needed to consistently distinguish signal from noise. (2) Methods: We have recently proposed a novel technique based on spectral domain evoked-to-background ratio (EBR) that enables fast data acquisition with online feedback about actual signal quality utilizing state of the art analog-to-digital conversion. Furthermore, we have developed a novel model-based signal-to-noise management concept allowing for suppression of biological and technical interference (swallowing, stimulation artifacts, electropolarization, and powerline potentials) and for online assessment of signal-to-noise ratio (SNR) for EPs. In this work, we experimentally confirmed this concept in ten healthy volunteers by investigating cortical EPs and high-frequency oscillations (HFOs) following median nerve stimulation. (3) Results: Both mathematical model and human data demonstrate that spectral target-band EBR governs the progress in SNR with increasing sweep count. For cortical EPs, SNR exceeded 10 dB beyond 90 averages in all participants. An SNR > 20 dB documented excellent signal quality and reproducibility. For HFOs, the SNR shifted to lower values by 12 dB, displaying pronounced individual variation, however, with smaller variation of HFO-band background activity (1.9 vs. 7.6 dB between the 25% and 75% percentile). Thus, individual HFO responses are more important for actual signal extraction compared to background activity. In subjects displaying a high HFO amplitude, reproducibility was confirmed for less than 1000 sweeps. (4) Conclusions: The present investigations confirm that individual EBR is the major factor defining SNR. Background noise can be reduced to a negligible level. Online assessment of background activity will allow for the most accurate moment-to-moment visualization of raw signal quality. This will facilitate termination of the data acquisition and may be based on quantified signal quality rather than predefined sweep count. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly embedded in menstrual and reproductive health applications, yet little is known about how women in conservative, rapidly digitizing regions evaluate AI-enabled menstrual tracking tools. This study investigates how perceived cultural fit and data privacy concerns are associated with women’s openness to AI-enabled menstrual tracking in Gulf Cooperation Council (GCC) countries. Methods: We conducted an online cross-sectional survey using a convenience sample of adult women residing in GCC countries (n = 273), measuring openness to AI menstrual tools, perceptions of cultural fit (e.g., respect for religious and cultural norms, endorsement by trusted institutions, Arabic language, GCC-specific design), privacy concerns (e.g., worries about who can access menstrual health data), and key sociodemographic characteristics and smartphone use. Results: Higher perceived cultural-contextual fit was strongly and positively associated with openness to AI-enabled menstrual tracking, whereas composite privacy concerns showed no statistically significant association once cultural fit and sociodemographic factors were controlled. Item-level analyses indicated that beliefs about cultural sensitivity and GCC-specific design were the most robust predictors of openness, while concerns about data access, authority endorsement, and language accessibility played a limited role. Homemakers reported higher openness than employed women. Conclusions: Overall, the findings suggest that, in this sample and model, GCC women’s openness to AI-enabled menstrual tracking was more strongly associated with perceived cultural alignment and local design than with the measured privacy-concern construct, highlighting the importance of culturally grounded, trust-building design and communication strategies for AI-based women’s health applications. Full article

Background and Objectives: Rare diseases collectively affect large populations and require coordinated, patient- and caregiver-informed health and social support. This study had an overarching descriptive objective to characterize patient- and caregiver-reported experiences of living with rare diseases in Türkiye and a secondary exploratory objective to assess differences across broad disease categories. Materials and Methods: This cross-sectional, anonymous, voluntary non-probability online survey recruited affected individuals and relatives/caregivers through patient-organization networks. The 44-item questionnaire covered sociodemographic and caregiving context, diagnostic experiences, psychosocial support, healthcare access, and service-improvement priorities. Descriptive analyses, exploratory disease-group comparisons, Cramér’s V, and selected multivariable logistic regression models were used. Results: A total of 1642 responses were analyzed; 76.7% were completed by relatives/caregivers, and 66.4% concerned patients younger than 18 years. Among participants diagnosed after symptom onset (n = 1388), 34.4% reported an incorrect or alternative diagnosis and 64.0% sought at least one additional specialist opinion. Psychosocial support was needed by 74.2%, whereas 34.5% reported receiving professional support. Access to current treatment services was perceived as moderately adequate by 29.3% and adequate by 13.0%. The leading service-improvement priorities were increasing healthcare professionals’ knowledge (82.9%) and establishing specialized rare-disease centers (79.5%). Conclusions: This patient-organization-recruited survey provides a large descriptive account of the experiences and priorities reported by participating patients and caregivers. The findings identify perceived priorities, particularly clinician education and specialized centers, while not constituting nationally representative estimates. Full article

Robotic bin-picking of disordered, randomly stacked workpieces remains challenging because reliable grasping depends on an accurate estimate of object pose, yet many established solutions require high-precision 3D sensing, detailed object models, or large annotated datasets that raise the cost and effort of deployment on a new production line. This work presents a complete binocular vision framework that estimates workpiece pose by image matching and executes vision-guided grasping on a 6-DOF manipulator. A pose-annotated multi-view template library is constructed automatically through robot-driven image acquisition and compressed by a coarse-to-fine clustering scheme, and object pose is estimated by discriminative template matching with rigid refinement. To characterize the geometric reliability of the matched poses, an offline cross-modal analysis relates the 2D templates to a 3D reference model of the object and measures their agreement through region and contour reprojection metrics. Grasp configurations are then generated under orientation and collision constraints and corrected online by closed-loop visual feedback. Experiments on two representative workpieces show template-matching accuracy of 89–90% against classical and learned similarity measures, and grasp success between 81 and 87% across single-object and mixed scenes, outperforming the GraspNet baseline under the tested conditions. The framework offers an accurate and deployment-friendly route to robotic bin-picking. Full article

Background: Population aging has led to an increasing proportion of older adults (aged 65 and older) acting as informal caregivers. These caregivers face risks of burden, stress, and depression. While research frequently documents negative psychological outcomes, resilience represents a crucial protective process. This systematic review synthesizes and maps the empirical evidence regarding the association between the socio-ecological resilience process and the well-being of older informal caregivers. Methods: Following Joanna Briggs Institute (JBI) mixed-methods guidelines, a convergent segregated mixed studies systematic review was conducted. A systematic search of Medline (PubMed), CINAHL, PsycINFO, and Scopus identified primary articles (2014–2025) in English and Spanish. Methodological quality was appraised using JBI critical appraisal checklists and the Mixed Methods Appraisal Tool. PROSPERO registration number: CRD420251142190. Results: Thirty-one studies were included. Elevated caregiver resilience was associated with lower reported symptoms of depression and anxiety, higher self-rated health, and positive psychological adaptation. Framed within a social ecological model, personal resources such as spirituality, hope, and self care, alongside relational assets like dyadic relationship quality and mutual coping, demonstrated a positive association with resilience and a reduction in subjective burden. Within the community context, informal peer networks and Online Health Communities functioned as essential supportive resources. On a structural level, both household wealth and the regional availability of long-term care beds acted as moderators for spousal well-being, whereas exceeding 30 weekly caregiving hours acted as a temporal threshold for positive adaptation. Conclusions: These findings suggest that resilience in older caregivers may be best conceptualized not as a static individual trait, but as a multi-level, dynamic socio-ecological process. Rather than relying on individual coping alone, public policies and clinical practice should prioritize systemic, relational, and structural environmental support, including formal respite services and long-term care infrastructure, to preserve the well-being of older spousal caregivers. Full article

Reliable online condition monitoring is essential for maintaining process stability and guaranteeing machining quality in precision grinding. Against this background, this study proposes an electrostatic induction-based measurement strategy and further performs systematic comparisons with conventional force-based monitoring methods. First, an electrostatic sensor model is established to quantitatively characterize charge transfer behaviors during grinding interactions. A three-axis experimental grinding platform is deployed to correlate electrostatic responses and mechanical force signals with critical grinding variables, including grinding speed, depth of cut, feed rate, and wheel wear severity. To achieve quantitative and objective evaluation, a unit-free Dynamic Sensitivity Change Degree (DSCD) index is introduced. Comparative results based on the DSCD index reveal that electrostatic signals exhibit better performance than traditional force-based signals in tracking grinding wheel speed variations and progressive wear evolution under the tested conditions. Meanwhile, the DSCD fluctuation in electrostatic signals remains within 1% under varying cutting depths, indicating favorable linear stability within the scope of the present experiments. Furthermore, a Generalized Conditional Variational Auto-Encoder (G-CVAE) model is developed to augment insufficient wheel wear datasets. The generated synthetic signals exhibit high fidelity and enable accurate and robust classification of grinding wheel wear states. This study verifies the existence of explicit quantitative correlations between electrostatic induction signals and key grinding parameters as well as wheel wear conditions. The proposed monitoring method can provide high-quality, reliable data support for subsequent grinding condition assessment and intelligent process decision-making. Full article

Rapid reservoir-energy depletion and a low primary recovery factor constrain shale oil development in the Qintong Depression. This study proposes a hybrid CO₂ injection method that combines continuous flooding with cyclic huff-n-puff. Coreflooding experiments with online nuclear magnetic resonance monitoring and compositional numerical simulations were used to identify the recovery mechanisms and optimize the operating parameters for the Luye-1 well block. The core experiment demonstrated the incremental recovery and compositional evolution occurring when cyclic huff-n-puff was applied after continuous CO₂ flooding. Separately, the numerical simulations indicated that the hybrid strategy provided a larger CO₂-contact area and a longer residence time than the individual injection strategies under the modeled conditions. The experiments also showed the sustained recovery of heavier hydrocarbons during successive cycles. A differentiated fracture-network design reduced inter-well gas channeling while maintaining reservoir contact. The selected engineering strategy comprised two asynchronous huff-n-puff cycles followed by hybrid injection, with a flooding-well injection rate of 80 t/d, a cyclic CO₂ injection volume of 7000 t per huff-n-puff well, and a soaking time of 60 days. Under the evaluated model scenario, the joint simulation of this complete parameter combination yielded a recovery-factor increase of 7.76 percentage points and a final recovery factor of 14.19%, with a gas-utilization efficiency of 0.160 t_oil/t_CO2. These scenario-based results suggest the potential of hybrid CO₂ injection for reservoir-energy replenishment and enhanced shale oil recovery. Full article

Kiwifruit softens rapidly after harvest, and its marketable quality depends on firmness, soluble solids, dry matter, acidity, mechanical damage, disease, aroma and other attributes that determine grading, storage and sales. Traditional physicochemical tests are accurate but usually destructive, time-consuming and unsuitable for batch sorting, online monitoring or dynamic quality management. This review summarizes six non-destructive testing approaches: optical, acoustic, electromagnetic, dielectric, mechanical/texture-property and electronic nose technologies. Their principles, system configurations, quality indicators, modeling strategies, applications and limitations are compared, with particular attention to validation design, model transferability, approximate technology maturity and industrial constraints. Current evidence shows that near-infrared spectroscopy and hyperspectral imaging are most widely used for soluble solids, firmness, dry matter, chilling injury and early damage detection; machine vision is effective for appearance grading and surface defects; acoustic and mechanical methods mainly support firmness and texture evaluation; dielectric and electromagnetic techniques help characterize moisture, tissue structure and internal disorders; and electronic noses capture volatile signals related to ripening and spoilage. Future work should prioritize cross-variety, cross-origin and cross-device model transfer, multi-source sensor fusion, interpretable modeling, portable instruments, online deployment and standardized validation. Full article

Background/Objectives: Electroencephalography (EEG), with its high temporal resolution, non-invasiveness, and cost-effectiveness, provides a suitable modality for investigating task-related signal patterns associated with Visual Sustained Attention (VSA) and Visual Internally Directed Cognition (VIDC), but cross-subject generalization remains challenging in reduced-channel settings. This study evaluated PDC-Net under an offline GPU setting. Methods: PDC-Net uses a Temporal Representation Adaptation Block (TRAB) for local temporal transformation and feature-channel recalibration and a Cross-Branch Gated Mamba Interaction (CGMI) module for input-dependent bilateral information exchange and long-range sequence modeling. Results: Under the strict LOSO cross-validation setting, PDC-Net achieved an average decoding accuracy of 76.76%, representing the highest mean accuracy among the 11 evaluated models. The model also maintained a favorable balance between cross-subject decoding performance and computational cost under the evaluated offline GPU setting. Conclusions: These findings support the feasibility of offline, subject-independent VSA/VIDC decoding from dual-channel Fp1/Fp2 EEG under the evaluated controlled conditions and provide a basis for subsequent external, cross-session, online, and hardware-specific evaluation. Full article

Background/Objectives: Dental caries remains one of the most prevalent chronic diseases in childhood, influenced by a combination of hygiene, dietary, and access-related behaviours that are largely shaped by parental involvement. This study aimed to evaluate oral hygiene habits, dietary risk factors, access to dental services, awareness of caries prevention, and the impact of oral health on the child, in relation to parent-reported dental caries in Romanian children and adolescents. Methods: A cross-sectional study was conducted using a structured online questionnaire completed by parents or legal guardians of 508 children and adolescents aged under 18 years. Associations between categorical variables and reported caries were assessed using chi-square tests and Cramér’s V. Ordinal variables were compared using the Mann–Whitney U test. A multivariable logistic regression identified independent predictors of reported caries, with robustness examined through sensitivity analyses. Results: Parent-reported caries was present in 61.2% of children. The strongest associations with caries were the absence of parental supervision during brushing (Cramér’s V = 0.364), the age at which children began brushing unsupervised (V = 0.354), and the reason for the last dental visit (V = 0.426). In the multivariable model, older age, absence of brushing supervision, sweets before bedtime, less frequent dental visits, and rural residence were independent predictors (all p < 0.05; Nagelkerke R² = 0.361). All six assessed dimensions of oral-health impact on the child were significantly associated with caries, with physical discomfort showing the largest effect (V = 0.349). Conclusions: Parent-reported caries risk in this population clustered around a small number of modifiable behaviours rather than any single dominant factor, while reported caries was associated with a substantial burden on children’s daily functioning. These findings point to sustained parental supervision and routine, non-symptomatic dental care as priority targets for future prevention efforts, although intervention studies are needed to confirm their effectiveness. Full article

Accurate state-of-charge (SOC) estimation is essential for range prediction, power allocation, and operational safety in electric vehicles. Low-temperature operation introduces coupled effects of capacity degradation and polarization dynamics, which challenge conventional fixed-capacity models and single-timescale estimation methods. This paper proposes a hierarchical multi-timescale SOC estimation framework that combines data-driven capacity prediction, online parameter identification, and nonlinear state estimation. At the upper layer, a temperature-aware temporal self-attention long short-term memory network (TS-LSTM) estimates the available battery capacity. At the lower layer, forgetting-factor recursive least squares (FFRLS) and a gain-scheduled unscented Kalman filter (UKF) jointly track impedance variations and recursively estimate SOC. The framework is validated on public lithium-ion battery datasets at 4, 24, and 43 °C under pulse and sparse high-current conditions. Under the challenging 4 °C scenario with a 10% initial SOC bias, the method achieves an SOC root mean square error (RMSE) of 1.35%. Additional perturbation tests yield SOC RMSEs of 1.35–2.40% under −5% to +10% initial-SOC offsets, 1 mV voltage noise, a +2 °C temperature bias, and a +20% impedance-prior error. Furthermore, the computational overhead remains substantially lower than the data sampling interval, indicating promising potential for online estimation in battery management systems. Full article

The mission planning problem of using ground-fixed communication base stations to guide Unmanned Aerial Vehicle (UAV) swarms back under communication denial is addressed. The core challenge is to optimally match limited resources with massive UAV demands under constraints such as time windows, base station exclusivity, and relay continuity, which we formulate as an NP-hard combinatorial optimization problem. We first build a static model maximizing comprehensive benefits, incorporating base station heterogeneity and a super-linear congestion penalty for load balancing. We then extend it to a rolling horizon dynamic framework. Through task state partitioning and frozen resource inheritance, this extension decomposes the long-term optimization into sequential finite-horizon subproblems, enabling online decisions as UAV information is gradually revealed. To solve these models, we propose CMSA-MSWOA, which integrates elite opposition-based learning and Lévy flights to navigate the fragmented feasible solution space. Simulation results show 100% guidance coverage across scales from 100 to 500 UAVs in static scenarios, with the benefit advantage over the best benchmark growing from 10.0% to 65.5% as scale increases. In dynamic scenarios, the rolling framework satisfies all constraints and achieves full coverage. While our framework performs robustly in simulations, the current evaluation assumes idealized communication conditions; validation under more complex interference and external testing remains future work. Overall, our model and algorithm offer a useful simulation-based closed-loop framework for resource scheduling in denial environments, providing a foundation for further validation under more realistic field conditions. Full article

Against the backdrop of digital transformation in higher education, campus information is often dispersed across independent platforms, creating fragmented access and retrieval barriers. To address these challenges, this study proposes a multimodal agentic RAG framework for integrating multi-source, heterogeneous university information through a unified natural language interaction portal. Compared with basic RAG architectures, the framework introduces three improvements: (1) vision–language models convert unstructured visual content into indexable textual evidence; (2) BM25 sparse retrieval, dense-vector retrieval, and real-time online retrieval jointly support keyword matching, semantic retrieval, and up-to-date information access; and (3) agent-based dynamic routing adaptively schedules retrieval tools according to query intent and evidence sufficiency, reducing redundant calls. Experiments show that the proposed framework achieves higher overall performance in answer accuracy, factual consistency, evidence coverage, and composite score than general-purpose large language models, traditional single-path RAG, and representative advanced RAG methods including Self-RAG, Adaptive-RAG, and VisRAG. Ablation studies further validate the contributions of the core modules. Overall, the framework provides a low-disruption solution for unified information retrieval and natural language interaction in higher education scenarios. Full article

Objectives: Dysphagia in older adults is associated with malnutrition, aspiration, and reduced quality of life. This study aimed to estimate the prevalence of self-reported swallowing difficulties among community-dwelling older adults in Israel and to identify demographic, medical, and socioeconomic predictors. Method: In this cross-sectional study, 1064 community-dwelling adults aged ≥ 60 years (mean 75.1 ± 7.5 years; 68.1% female) completed an online questionnaire capturing demographics, medical diagnoses, physical activity, income, and place of residence, together with the validated Hebrew and Arabic versions of the Eating Assessment Tool-10 (EAT-10). Dysphagia screening was defined as EAT-10 ≥ 3, and demographic, medical, and socioeconomic predictors were examined using multivariable logistic regression. Results: The prevalence of positive dysphagia screening was 29.1%. In the adjusted model, significant independent predictors were older age (odds ratio [OR] = 1.84)), a dysphagia-related medical diagnosis (OR = 3.10), absence of regular physical activity (OR = 2.19), lower income (OR = 1.36), and lower residential socioeconomic cluster (OR = 1.84). Gender, with a mean age of 75.07 years (SD = 7.52), body mass index (BMI), and residential peripherality were not significant after adjustment; the bivariate peripherality effect appeared to be largely mediated by socioeconomic factors. Conclusions: Nearly one in three community-dwelling Israeli older adults screened positive for dysphagia. Beyond established medical risk factors, physical inactivity and socioeconomic disadvantage were independently associated with positive screening, suggesting potentially modifiable targets for future study. Findings support integrating swallowing screening into routine geriatric care and developing equity-focused screening initiatives for lower-socioeconomic and peripheral communities. Full article

Automated cybergrooming detection can support the review of large volumes of online communication, but public datasets and detection approaches remain predominantly English-centered. This study presents a reproducible workflow for constructing target-language cybergrooming datasets and detection models from English resources, demonstrated for German. To the best of our knowledge, this is the first published study to construct and systematically evaluate German-language cybergrooming datasets and detection models. Four publicly available English cybergrooming datasets were converted into a shared structure and translated into informal German chat language. Translation quality was estimated using an aggregate of COMETKiwi and MetricX scores termed CometricX. Low-quality messages were retranslated and reevaluated. Cybergrooming messages were additionally augmented through German–Catalan backtranslation. The resulting datasets, with and without augmentation, were used to train and evaluate embedding-based linear support vector machines (SVMs) and ModernBERT models. Evaluation comprised translated test data and 156 manually labeled German conversations from five closed law-enforcement cases. The Perverted Justice Zig ChitChat dataset (PJZC) achieved the highest aggregate quality estimate and the most robust detection performance on real-world German conversations. In contrast, performance on machine-translated test data overestimated real-world detection performance. Furthermore, backtranslation yielded only limited and inconsistent improvements. Overall, the proposed workflow provides a practical starting point for developing target-language cybergrooming detectors while highlighting careful source dataset selection and evaluation on authentic target-language data. Full article