Search Results (157)

Background/Objectives: Falls among older adults are a major public health concern. Although instrumented posturography provides objective balance and fall-risk assessment, its cost and limited portability restrict widespread use. This study aimed to examine the construct and concurrent validity of center of pressure (COP) path length measured using a Wii Balance Board (WBB) in relation to a clinically established posturographic fall-risk construct in community-dwelling older adults and to explore its discriminatory performance across multiple sensory postural conditions. Methods: Sixty adults aged ≥ 65 years participated in this cross-sectional study. COP path length was measured using a WBB under eight postural conditions and compared with the Fall Index derived from a conventional posturography system (Tetrax^Ⓡ). Functional performance was assessed using the Four Square Step Test and the Five Times Sit-to-Stand test. Pearson correlation, receiver operating characteristic (ROC), and exploratory regression analyses were performed. Results: COP path length showed significant positive correlations with the Tetrax^Ⓡ Fall Index across all conditions (r = 0.349–0.561, p < 0.01) and with functional performance tests under most postural conditions (p < 0.05), except for the Normal stability, Open eyes (NO) condition. ROC analysis demonstrated acceptable-to-good discriminatory performance for classifying Tetrax^Ⓡ Fall Index-based risk status (AUC = 0.783–0.865), with the NO condition showing the highest discriminatory capability (AUC = 0.865). Exploratory regression models based on selected postural conditions explained 12.1–40.7% of the variance in the reference Fall Index. Conclusions: COP path length measured using a WBB demonstrated construct validity and acceptable discriminatory capacity in relation to a conventional posturographic fall-risk construct in community-dwelling older adults. These findings support the exploratory feasibility of simplified WBB-based balance assessment approaches for community and clinical screening contexts. Further longitudinal studies incorporating prospective fall outcomes are required to establish predictive validity and broader clinical applicability. Full article

Background/Objectives: Because the numbers of middle-aged and older adults living alone in Korea have substantially increased, which warrants greater attention to their health-related quality of life. Therefore, we aimed to develop a predictive model for the health-related quality of life among community-dwelling middle-aged and older adults living alone. Methods: Using 2022 Korea Health Panel Survey data, 1313 participants with complete EQ-5D component data were analyzed. All candidate predictors were entered into benchmarked models without pre-model feature selection. Preprocessing and 5-fold cross-validated hyperparameter tuning were conducted within the training data. Final performance was evaluated on a held-out test set, and the selected model was interpreted using SHAP. Results: XGBoost had the lowest training cross-validated RMSE and was selected as the final explainable model. On the test set, it showed moderate performance (R² = 0.373, MAE = 0.070, RMSE = 0.096), outperforming the mean baseline model (RMSE = 0.121) but remaining comparable with other top-performing models. Predictions were within absolute errors of 0.05 and 0.10 for 45.6% and 76.4% of participants, respectively. SHAP ranked subjective health, age, walking time, need for care, and monthly household income as the five highest-ranked predictors. Other highly ranked predictors included unmet medical needs, total annual out-of-pocket expenditure, disability, anxiety, and regular exercise. Conclusions: These findings may inform targeted interventions and support strategies, although external validation and longitudinal studies are needed to confirm generalizability and causal relationships. Full article

Rural public spaces are crucial to the daily activities of older adults; however, limited research has examined the effects of their environmental characteristics on older adults’ spatiotemporal behavior and perception from a multisensory perspective. This study hypothesizes that composite sensory environments have significant nonlinear predictive effects on older adults’ behavior types and satisfaction. In this study, 10 sample spaces were selected in Qingdao, China. Multi-source data were collected through a two-week period of unobtrusive observation and subjective questionnaire surveys (N = 241). Multiple logistic regression was used to analyze the main effects of environmental characteristics, and an MLP model with a single hidden layer of 100 units was constructed to predict dwell time and satisfaction. The results show that, in the investigated rural context, older adults’ dominant behavior was social activity (81.12%), which mainly occurred in built spaces such as squares. Multiple logistic regression indicated that, among the various environmental factors, visual aesthetics had a statistically significant effect on behavior types (p = 0.013). The MLP model achieved prediction accuracies of 85.3% for dwell time and 93.1% for satisfaction. The key predictive variables were volume perception (100% importance), the Natural Sound Index (NSI) (92.1%), and visual aesthetics (89.3%). Subgroup heterogeneity analysis further showed that older-old adults and those with poorer health conditions were more sensitive to pavement quality and physical comfort, whereas older adults living alone or with limited household companionship were more strongly influenced by visual aesthetics and natural soundscape quality. The theoretical significance of this study lies in proposing quantitative measures of natural sound and odor indices and revealing that, in the specific northern rural built environment, the coordinated design of visual and auditory environments plays an important role in improving spatial quality. The findings provide empirical support for the age-friendly micro-renewal of rural public spaces in specific regions. However, due to the limitations of single-season data and a relatively small sample size, their generalizability needs to be further verified across regions. Full article

The high-rate recycling of scrap steel introduces persistent residual copper (Cu), which accumulates at prior austenite grain boundaries at the surface, during high-temperature reheating, leading to Cu-induced sensitization and deleterious “hot shortness”. To address this, a predictive analytical framework was derived using Fick’s Second Law and the Sekerka, Jeanfils, and Heckel (SJH) approach to model the dissolution of Cu-rich films as a 1D planar moving boundary problem. The validity of this analytical framework was first established through experimentation on controlled Cu-coated steel wire rods, where theoretical concentration profiles showed strong agreement with empirical depth profiles. When applied to a 0.21 wt.% Cu steel at 1000 °C, the model predicted a critical extinction time (t*) of approximately 8.57 min for the complete dissolution of a 20 nm sensitized film. Experimental trials on sensitized wire rods confirmed this prediction, demonstrating an 89% reduction in the frequency of detectable sensitized zones and a significant decrease in zone width following a 10 min thermal dwell. The approach provides a standardized, scalable, and composition-adaptable methodology, grounded in a 1D planar approximation, for optimizing desensitization heat treatments across a range of Cu contents, offering a practical strategy to increase scrap steel utilization while mitigating hot shortness. Full article

In equestrian circles, horse–rider harmony is understood intuitively, yet clear criteria are lacking. This study examined how different equestrians conceptualized and visually assessed harmony and how this influenced scoring. Qualitative interviews were combined with eye tracking technology. Thirty equestrians assessed five videos of horse–rider combinations performing in dressage, showjumping, eventing, working equitation and Icelandic riding, with eye movements being recorded using a mounted eye tracker. Verbal definitions were analyzed using thematic analysis, revealing three overarching themes: Horse Behavior, Horse–Rider Connection, and Rider Influence. Number of fixations and duration of fixation were reduced using principal component analysis (PCA), yielding five components each. These explained 70.9% and 64.5% of variance, respectively. Linear mixed-effects models showed significant effects for two PCA components: frequent fixation on the horse’s ears and eyes relative to the horse’s shoulder and rider leg predicted lower harmony scores (B = −0.34, SE = 0.13, z = −2.53, p < 0.05), whereas longer dwell time on the rider’s shoulder relative to the rider’s leg predicted higher scores (B = 0.25, SE = 0.12, z = 2.12, p < 0.05). Ears and eyes were also the most frequent first fixation. Harmony appears to be a shared construct at the conceptual level, but is personally enacted at the practical level. Equine facial expressions and rider posture serve as important perceptual indicators. Full article

This paper presents a comprehensive experimental and simulation study on the stick–slip characteristics of pneumatic cylinders operating at low velocities. A pneumatic servo experimental system is constructed to systematically investigate stick–slip motion by measuring piston position, piston velocity, pressures in the two-cylinder chambers, and friction force. Extensive experiments are conducted on three pneumatic cylinders of different types and sizes to examine the influences of airflow rate, air source pressure, external load, and initial piston position on stick–slip behavior. Based on experimental observations, a complete mathematical model of the pneumatic servo system is developed. Unlike conventional approaches that simulate stick–slip motion using friction models driven solely by piston velocity, the proposed system-level model explicitly describes the entire dynamic process from valve control inputs to airflow, pressure evolution in the cylinder chambers, piston motion, and friction force. In addition, a new dynamic friction model is proposed by improving the revised LuGre friction model through the incorporation of a dwell-time-dependent static friction force, which is experimentally observed to play a critical role in governing stick–slip motion. Simulation studies are performed using both the proposed friction model and the revised LuGre friction model. The simulated results are systematically compared with experimental data for all tested cylinders. The results demonstrate that the proposed system model with the new friction formulation significantly improves the prediction of stick–slip characteristics, including the number of stick–slip cycles and the evolution of pressure and friction force, compared with conventional friction-model-based simulations. Full article

The present study intends to evaluate the ratcheting of 304 stainless steel samples at room temperature, subjected to various loading spectra and holding times through the use of the combined Ahmadzadeh–Varvani (A-V) kinematic and Lee–Zavrel (L-Z) isotropic hardening rules. The nonlinear and time-dependent functions $a_{rec}$ and $R_{rec}$ were implemented in the hardening framework to account for the static recovery terms (SRTs) in the kinematic and isotropic hardening descriptions. The static recovery phenomenon promoted ratcheting in steel samples tested under asymmetric loading cycles with holding time peak/valley events. The static recovery phenomenon accounts for the restoration process, elevating the plastic deformation and reducing the number of cycles to material failure. The framework with the SRT enabled the prediction of material ratcheting involving the loading rate and dwell time at room temperature. Full article

Atypical visual attention to aversive or threatening stimuli is a clinically relevant feature of aggressive behavior. However, the developmental dissociation between sustained visual allocation and early orienting remains unclear. This study examined the temporal dynamics of visual attentional biases in a sample of 119 children and adolescents (51 males, 68 females), clinically and behaviorally categorized into aggressive and non-aggressive cohorts. Using a free-viewing paradigm with standardized emotional stimulus pairs selected from the International Affective Picture System (IAPS), eye-tracking analysis focused on first-fixation direction and dwell time. Inferential analyses were conducted using Linear Mixed-Effect Models (LMM) and Generalized Linear Mixed-Effects Models (GLMM). The linear model revealed a significant main effect of behavioral condition: individuals with aggressive traits, regardless of their stage of development, showed greater sustained visual allocation toward negative stimuli. In contrast, the GLMM for first-fixation direction identified a significant age-by-condition interaction, indicating that early orienting differences were more clearly expressed in the aggressive adolescent cohort. These findings suggest that sustained visual preference for negative content may represent a relatively stable correlate of aggressive traits, whereas early orienting differences may vary across developmental stages. Together, these two temporal eye-tracking measures may provide complementary information for future computational approaches to aggression screening. In conclusion, these two temporal oculomotor dimensions may provide a useful feature space for future machine-learning pipelines and may serve as complementary candidate markers for comparing computational predictions against clinically established ground truth in aggression screening research. Full article

Reliable short-term prediction of bus travel time on signalized urban arterials is essential for improving service reliability and may provide a useful forecasting basis for prediction-informed transit signal priority (TSP) and arterial coordination applications. However, bus operations on urban arterials are highly variable due to stop dwell times, signal delays, and interactions with mixed traffic, leading to nonlinear and nonstationary travel time patterns with strong spatiotemporal dependence. This study proposes a hybrid KNN–ConvLSTM framework for short-term arterial bus travel time prediction using real-world field data. A K-nearest neighbors (KNNs) module is first employed to retrieve historical operation sequences that are most similar to the current corridor state, thereby reducing interference from mismatched traffic regimes and improving robustness. Smart-card (IC card) transaction data are incorporated as demand-related features to represent passenger activity and its impact on dwell time and travel time variability. The selected sequences are then organized into a corridor-ordered spatiotemporal representation and further refined by lightweight temporal enhancement operations, including relevance gating, multi-scale aggregation, adaptive feature fusion, and residual enhancement, before being fed into the convolutional long short-term memory (ConvLSTM) predictor. The proposed approach is evaluated using weekday service-hour data extracted from 30 days of real-world bus operation records collected from a typical urban arterial corridor in Changchun, China, and is compared with several benchmark models, including ARIMA, KNN, LSTM, CNN, ConvLSTM, Transformer, and DCRNN. The results indicate that the proposed KNN–ConvLSTM framework achieves an MAE of 40.1 s, an RMSE of 55.8 s, a SMAPE of 10.7%, and an R² of 0.878, outperforming all benchmark models. Specifically, compared with the Transformer baseline, the proposed framework reduces MAE by 1.5%, RMSE by 5.1%, and SMAPE by 7.0%, while increasing R² by 0.014. Compared with the DCRNN baseline, it reduces MAE by 10.7%, RMSE by 1.9%, and SMAPE by 2.7%, while increasing R² by 0.008. These findings demonstrate that similarity-aware retrieval combined with spatiotemporal deep learning can substantially enhance short-term bus travel time prediction on signalized urban arterials. More accurate short-term forecasts may support prediction-informed transit signal priority and arterial coordination by providing more reliable downstream arrival-time estimates. However, the generalizability of the reported results is still constrained by the relatively short 30-day observation period and the single-corridor case setting, and the operational and environmental effects of downstream applications remain to be validated through dedicated closed-loop control evaluation in future work. Full article

The growing need for lightweight, multifunctional, and high-performance structures in the automotive, aerospace, electronics, and medical industries has driven the development of advanced joining technologies for polymers and metal-polymer combinations. Among these, ultrasonic welding (USW) and friction stir spot welding (FSSW) have emerged as promising solid-state techniques capable of producing reliable joints with minimal thermal degradation and enhanced interfacial bonding. This review focuses on recent developments in USW and FSSW of thermoplastics, fiber-reinforced composites, and hybrid metal–polymer systems, with a particular emphasis on process mechanics, microstructural evolution, and joint performance. The mechanisms of heat generation, material flow behavior, and consolidation are discussed in relation to key process parameters, including applied pressure, rotational speed, vibration amplitude, plunge depth, and dwell time. Microstructural transformations such as polymer chain orientation, recrystallization, interfacial diffusion, and defect formation are analyzed to establish process–structure–property relationships. Mechanical performance metrics, including lap shear strength, fatigue resistance, impact behavior, and environmental durability, are critically compared across different materials and welding methods. Furthermore, recent advances in numerical and thermo-mechanical modeling, in situ process monitoring, and data-driven optimization are discussed to highlight pathways toward predictive and scalable manufacturing. Current industrial applications and existing limitations such as challenges in automation, thickness constraints, and hybrid material compatibility are also evaluated. Finally, key research gaps and future directions are identified to improve joint reliability, sustainability, and broader industrial adoption of advanced solid-state welding technologies. Full article

This paper presents a deterministic embedded control architecture for an industrial electroplating line. The validated system includes two autonomous trolleys, 18 station-aligned process positions, shared-track motion, and redundant grouped baths. The proposed controller addresses the limitations of rigid sequential automation by combining asynchronous finite-state trolley execution, runtime allocation of equivalent technological stations, dwell-time-preserving retrieval, distributed thermal supervision, and layered fail-safe protection within a single ATmega2560-based implementation. The core contribution is the integration of virtual process groups and temporal FIFO logic into a compact plant-side embedded controller. This enables adaptive bath selection and process-completion-based retrieval without reliance on a real-time operating system or a computationally heavy supervisory runtime. The architecture also incorporates predictive pre-start validation, runtime software arbitration, hardware-wired interlocks, binary-coded trolley positioning, and a distributed 1-Wire thermal measurement network. Validation was performed in a controller-centered hardware-in-the-loop representation of an 18-station zinc electroplating line. Over a 100-batch horizon, the proposed architecture reduced makespan from 1642 min to 1244 min, corresponding to a 24.2% throughput improvement. Average trolley idle time decreased from 18.4 min/batch to 4.1 min/batch. Grouped-bath utilization increased from 64% to 91%, while tracked bottleneck incidents decreased from 18 to 2. These results show that adaptive, resource-aware, and safety-layered electroplating control can be realized effectively on a compact embedded platform in an industry-representative HIL setting, while preserving dwell-time integrity and controller-level safety invariants. Full article

Objective: The present study aimed to compare muscle thickness and physical performance in different functional tests predicting falls between older adults with low and high fall risk. Methods: Seventy-one community-dwelling older women (74.5 ± 8.5 years old) volunteered for this study. The Berg Balance Scale (BBS) was used to stratify the sample as low and high risk for fall (BBS cutoff = ≥ 50 points). The performance in the Timed Up and Go Test (TUGT), 5-repetition sit-to-stand test (5xSST), 3 m walk test (3mWT), and 3 m backward walk test (3mBWT) was assessed. The elbow flexor and knee extensor muscle thickness were obtained by ultrasound (USD). A linear mixed model analysis was used to determine between-group differences in functional mobility and muscle thickness, and Bayesian analysis was applied to check the probability to replicate the same results (i.e., the magnitude of the evidence). Results: The low-fall-risk group exhibited significantly better performance only in 3mWT (mean difference = 0.84 s [95% CI: 0.40 to 1.29 s]; p = 0.001) and 3mBWT (mean difference = 1.54 s [95% CI: 0.21 to 2.85 s]; p = 0.024). The Bayes Factor (BF) for performance on the 3mWT and 3mBWT shows that the low-fall-risk group has a probability of 98.7% (BF10 = 77.3) and 99.7% (BF10 = 368), respectively, of performing better than the high-fall-risk group. Conclusions: Based on inferential and Bayesian analysis, the performance in 3mWT and that in 3mBWT were classified as very strong to excellent instruments, respectively, for differentiating older women with high fall risk. Full article

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

To address the challenge of autonomous process adaptation in non-standard components with continuously varying groove angles, this study proposes an intelligent decision-making framework based on Response Surface Methodology (RSM) for oscillation welding. Instead of solely identifying a single optimal parameter set, RSM is employed as a knowledge-modeling tool to reveal adaptive relationships between groove geometry and key welding parameters. A Central Composite Design (CCD) is utilized to establish predictive models for weld geometry under varying conditions: wire feed rate (8–12 m/min), travel speed (5–9 mm/s), travel angle (70–110°), oscillation amplitude (2–6 mm), dwell time (0.2–0.6 s), and groove angle (80–100°). The significance and adequacy of the models are validated through analysis of variance (ANOVA), demonstrating high predictive accuracy with all coefficients of determination (R²) exceeding 0.82. Furthermore, defect-aware physical constraints derived from the formation mechanism of bottom humping are incorporated into the optimization process, specifically restricting the travel angle to a push angle of 70–85° to ensure feasible and reliable decision outputs. Based on the established response surfaces, geometry-dependent parameter selection rules are derived to simultaneously optimize root penetration (target 8.5–10.5 mm) and sidewall fusion (>2.5 mm) for groove angles ranging from 80° to 100°. Experimental validation confirms that the proposed decision-making strategy achieves stable bead formation and defect-free fusion, demonstrating high quantitative reliability with root penetration prediction errors below 7% and bead width errors below 13%. This work bridges the gap between geometric perception and process control, providing a practical pathway toward intelligent and adaptive robotic welding of non-standard components. Full article

When is an unknown, confined environment traversable for a specific ground robot using only touch? We answer by (i) giving an environment-anchored definition of traversability, expressed through the max-min value $T^{★}(E;A)={sup}_{\pi \in {\Pi }_{S\to G}}{inf}_{s\in [0,1]}\varphi \left(\pi \left(s\right)\right)$, where the bottleneck margin $\varphi$ aggregates the clearance, curvature ($\rho \ge R_{min}$), slope/step, and friction constraints, and (ii) introducing an on-policy, tactile certificate (TC) that maintains a conservative, monotone lower bound $T_t$ using partial contact histories. The TC fuses pessimistic free-space from contacts and the body envelope, the M3 decaying contact memory as a risk prior, and local bend/FSR proxies; a certificate is issued when $T_t>0$ and the explored corridor graph connects S to G. Relative to Papers 1–2 (tactile traversal; offline software assurance), this work formalizes traversability itself and provides a tactile-only, online certificate computable during runs. In a retrospective analysis of 660 trials across Indoor/Outdoor/Dark lighting environments, (H1) the early TC margin predicts success and traversal time better than contact/dwell heuristics (higher AUC/$R^2$), (H2) the TC predictivity is lighting-invariant, and (H3) speed-gating M3 by a TC margin recovers part of the CB-V speed gap without degrading success. Artifacts include the TC implementation, explored-corridor graphs, and per-trial TC time series added to the Paper-1 log bundle; these materials are available from the corresponding author upon reasonable request. Full article