Search Results (505)

Unmanned helicopters play a critical role in various fields including defense, disaster response, and infrastructure inspection. Military platforms such as the MQ-8C Fire Scout represent high-value assets exceeding $40 million per unit including development costs, particularly when compared to expendable multicopter drones costing approximately $500–2000 per unit. Unexpected failures of these high-value assets can lead to substantial economic losses and mission failures, making the implementation of Health and Usage Monitoring Systems (HUMS) essential. However, the scarcity of failure data in unmanned helicopters presents significant challenges for HUMS development, while the economic feasibility of investing resources comparable to manned helicopter programs remains questionable. This study presents a novel cross-scale transfer learning framework for vibration-based anomaly detection in unmanned helicopters. The framework successfully transfers knowledge from a source domain (Airbus large manned helicopter) using publicly available data to a target domain (Stanford small RC helicopter), achieving excellent anomaly detection performance without labeled target domain data. The approach consists of three key processes. First, we developed a multi-task learning transformer model achieving an F-β score of 0.963 (β = 0.3) using only Airbus vibration data. Second, we applied CORAL (Correlation Alignment) domain adaptation techniques to reduce the distribution discrepancy between source and target domains by 79.7%. Third, we developed a Control Effort Score (CES) based on control input data as a proxy labeling metric for 20 flight maneuvers in the target domain, achieving a Spearman correlation coefficient ρ of 0.903 between the CES and the Anomaly Index measured by the transfer-learned model. This represents a 95.5% improvement compared to the non-transfer learning baseline of 0.462. Full article

Background: This propensity-score-matched retrospective study compared radiographic accuracy and short-term functional outcomes between MAKO robotic-arm-assisted and conventional dual-incision minimally invasive total hip arthroplasty (THA). It was hypothesized that robotic assistance would provide superior radiographic accuracy, primarily smaller absolute deviations from the planned acetabular inclination and anteversion and a higher proportion of cups within the Lewinnek safe zone, without improving early functional outcomes. Methods: Consecutive patients who underwent dual-incision total hip arthroplasty were retrospectively analyzed at two affiliated institutions between March 2023 and March 2025. The study included 52 robotic-arm-assisted cases. The dual-incision technique used an anterolateral incision for acetabular preparation and cup implantation and a posterolateral incision for femoral preparation and stem implantation. Propensity score matching (1:1) generated 52 balanced pairs for age, sex, body mass index (BMI), preoperative Harris Hip Score (HHS), ASA class, and diagnosis. Operative time, blood loss, radiographic accuracy (acetabular anteversion, inclination, leg-length discrepancy [LLD], femoral and combined offsets, and stem subsidence), and functional outcomes (HHS, Oxford Hip Score [OHS], Forgotten Joint Score-12 [FJS-12]) were compared. Results: The robotic group achieved smaller deviations from the planned anteversion (1.15° vs. 3.0°, p < 0.001) and inclination (1.33° vs. 4.5°, p < 0.001), with a higher proportion of cups within the Lewinnek safe zone (98.1% vs. 82.7%, p = 0.016). Significant improvements were also seen in femoral stem subsidence (p = 0.006) and offset restoration, although the reduction in leg-length discrepancy did not reach statistical significance. Operative time was longer (77.8 vs. 65.0 min, p = 0.001), while blood loss and 6-month functional scores were comparable (HHS, p = 0.144; OHS, p = 0.328). Multivariable regression confirmed that greater deviations in acetabular orientation, higher LLD, and increased subsidence were independent predictors of poorer functional outcomes. Conclusions: MAKO robotic-arm assistance was associated with improved radiographic accuracy and biomechanical restoration in dual-incision THA, but no direct short-term functional advantage was observed. Greater radiographic precision was independently associated with better patient-reported outcomes, suggesting that technical precision is a key factor in optimizing early postoperative outcomes, highlighting the importance of technical accuracy in total hip arthroplasty. Full article

Ancient walnut trees (Juglans regia L.), revered as “cultural heritage in motion,” have coexisted harmoniously with dense clusters of Tibetan traditional villages for centuries. However, accelerating climate change and expanding human activities along the middle reaches of the Yarlung Tsangpo River have increasingly threatened their survival. To quantitatively evaluate the health of these ancient trees and identify the underlying driving mechanisms, this study developed a remote sensing-enhanced Structural Equation Model (SEM) that integrated satellite-derived ecological indices, land-use intensity, and field-measured morphological and physiological indicators. A total of 135 ancient walnut trees from villages such as Gamai in Jiacha County, Tibet, were examined. Key findings: (1) The SEM demonstrated an excellent model–data fit (Minimum Discrepancy Divided by Degrees of Freedom (CMIN/DF) = 1.372, Root Mean Square Error of Approximation (RMSEA) = 0.053, Tucker–Lewis Index (TLI) = 0.956, and Comparative Fit Index (CFI) = 0.962), confirming its robustness. (2) Among the latent variables, overall condition exerted the strongest influence (weight = 0.360), whereas foliage condition contributed least (0.289). (3) Approximately 35.56% of trees were healthy or sub-healthy, while 61.48% showed varying levels of decline. (4) Tree health was jointly shaped by intrinsic and extrinsic factors, with intrinsic drivers exhibiting stronger explanatory power. Externally, human disturbance negatively affected health, whereas ecological quality was positively associated. These results highlight the effectiveness of integrating remote sensing and SEM for ancient tree assessment and underscore the urgent need for long-term monitoring and adaptive conservation strategies to enhance ecological resilience. Full article

Ring-type wearable sensors are increasingly used for continuous monitoring of heart rate (HR) and heart rate variability (HRV); however, evidence regarding their measurement validity and potential lateral differences remains limited. This study aimed to evaluate (1) the validity of HR and HRV obtained from ring-type photoplethysmography (PPG) sensors under multiple activity conditions (Experiment 1), and (2) the presence of lateral differences in autonomic indices when worn on the left versus right hand (Experiment 2). In Experiment 1, HR and HRV indices from the ring sensor were compared with those from a Holter electrocardiogram (ECG) and a wrist device during rest, low activity, and moderate activity. Mixed-model analysis revealed significant differences in very low frequency (VLF) power between the left- and right-hand rings (p = 0.001). Additionally, significant interactions between device side and measurement condition were observed for HR and low-frequency (LF) components, indicating that lateral differences were condition-dependent. In Experiment 2, participants wore two ring sensors simultaneously to assess left–right discrepancies under rest and exercise conditions. The SDPP index showed a significant difference (p = 0.017), with mean values differing between Rest and Exercise, demonstrating condition-related variability but limited systematic lateral bias. Overall, ring-type wearable sensors demonstrated high validity for HR and acceptable performance for selected HRV metrics during rest and low-activity states. While some condition-dependent lateral differences emerged for specific HRV parameters, the practical impact on overall measurement performance remained modest. These findings support the utility of ring-type wearable devices for autonomic monitoring while highlighting the importance of considering activity level and side–condition interactions. Full article

The global response to the COVID-19 pandemic was characterized by a patchwork of government policies in countries around the world, many of which limited civil liberties in unprecedented ways. Here, our objective was to analyze the scope and spatio-temporal patterns of workplace vaccination mandates. Using daily policy data from the Oxford COVID-19 Government Response Tracker for 2021–2022, we developed a simple mandate intensity index representing the number of affected employment sectors and the duration of each mandate by country. These metrics suggest a largely inconsistent pandemic response. We found that less than one-third of the 185 countries included in the dataset implemented such “no jab, no job” policies. Even among groups of culturally and politically aligned countries, such as the core Anglosphere, policies varied greatly: between one (United Kingdom) and 10 (Australia) out of 12 employment sectors had vaccination mandates. The most frequently and longest mandated sectors included government officials and healthcare workers, two broad groups with different risk profiles. We discuss these discrepancies from a critical perspective, considering the limited evidence for the mandates’ effectiveness along with their potential to cause harmful outcomes, and recommend careful cost–benefit analyses in the future. Full article

(1) Background: Pain assessment still relies primarily on subjective self-report. To address these limitations, we developed Piq, an EEG-based index derived from beta-band brain activity (Piq_β) aimed at providing objective pain identification and quantification. (2) Methods: The study combined cross-sectional and longitudinal designs. Resting-state brain activity was recorded for five minutes, and EEG signals were preprocessed using a dedicated algorithm. Piq_β performance was assessed by identifying an optimal cutoff to discriminate pain from no pain, evaluating its association with VNRS, and estimating agreement using a modified concordance criterion (exact match or ±1 category). A graded scale was also established to classify pain into distinct categories, according to intensity. (3) Results: An optimal cutoff of 10% for Piq_β yielded 97.8% sensitivity and 88.2% specificity. Piq_β correlated with self-reported scores (ρ = 0.60, p < 0.0001) with acceptable agreement (mean bias −1.02), accounting for clinically acceptable discrepancies. Five levels of pain were proposed, and Piq_β demonstrated the ability to track intra-individual fluctuations over time, accounting for clinically acceptable discrepancies. (4) Conclusions: These findings provide strong evidence to support the Piq_β index as a valuable complement to subjective pain ratings. Full article

Different approaches have been developed to assess the phenological dynamics of ecosystems. However, diverse data sources and extraction methods for assessing ecosystem phenology can result in discrepant and inaccurate results, especially across different types of vegetation under various climate classifications. Based on the phenology of dominant plant species (Phe_plant) obtained from ground monitoring in an alpine shrub meadow at Haibei Station (HBS) on the Qinghai–Tibetan Plateau and in a broad-leaved Korean pine forest at Changbai Mountain (CBF) in Northeastern China, we extracted vegetation phenology from the Normalized Difference Vegetation Index (Phe_NDVI) and photosynthetic phenology from gross primary productivity (Phe_GPP) using five common methods. These methods included Gaussian fitting, single logistic function fitting, double logistic function fitting, and smoothing techniques combined with fixed threshold and derivative-based determination approaches. There was no consistent interannual trend in either plant phenology or environmental factors at the two sites. Among the three types of plant phenology, a similar interannual pattern in the start of the growing season (SOS) was observed, whereas the interannual patterns for the end of the growing season (EOS) and the growing season length (GSL) were asynchronous. Compared to Phe_plant, both Phe_NDVI and Phe_GPP exhibited an earlier SOS, a delayed EOS, and consequently an extended GSL. The SOS derived from both Phe_NDVI and Phe_GPP was advanced by increasing spring temperatures at both sites, while the relationship between EOS and air temperature was relatively weak. The discrepancy between Phe_NDVI and Phe_GPP was more pronounced at CBF than at HBS, likely due to the complex vegetation composition and structure of the mixed forest. The different extraction methods produced more consistent and less variable estimates of SOS compared to EOS and GSL at both sites. Among the five methods, the dynamic threshold approach showed a relatively small difference between Phe_NDVI and Phe_GPP, suggesting that it could provide a more consistent estimate of plant phenology across the two sites. This study clearly reveals the inherent discrepancies associated with using different types of phenological data and the influence of extraction methods on phenology across different plant functional types. More attention should be given to improving the accuracy of EOS and understanding the influence of vegetation composition on phenological variation in future studies. Full article

Background: Sedentary behavior in adolescents is a major pediatric health concern. Prolonged inactivity can negatively affect physical and mental health, potentially leading to body image distortion, especially among adolescents. This study aims to explore the relationship between sedentary behavior and body image distortion in adolescents, considering sedentary purpose and sex differences. Methods: This study analyzed data from the 2021–2023 Korea Youth Risk Behavior Survey (KYRBS), comprising 150,025 middle and high school students. Sedentary time was self-reported as the average daily sedentary duration over 7 days. Body image distortion was defined as a discrepancy between the body mass index and perceived body image of participants. Data analysis included descriptive statistics, chi-square tests, and logistic regression, stratified by sex and sedentary purpose. Increased sedentary behavior was significantly associated with body image distortion. Results: Among female adolescents, educational sedentary time had a stronger effect on body image distortion (odds ratio: 1.06, 95% confidence interval: 1.02–1.09). In contrast, male adolescents showed no significant association. Conclusions: This study highlights the significant association between sedentary behavior and body image distortion in adolescents. Future research should further explore the long-term effects of sedentary behavior on the physical and mental health of adolescents. Full article

Chaotic dynamical systems are ubiquitous in nature and modern technology, with applications ranging from secure communications and cryptography to the design of chaos-based sensors and modeling biological phenomena such as arrhythmias and neuronal behavior. Given their complexity, precise analysis of these systems is crucial for both theoretical understanding and practical implementation. The characterization of chaotic dynamical systems typically relies on conventional measures such as Lyapunov exponents and fractal dimensions. While these metrics are fundamental for describing dynamical behavior, they are often computationally expensive and may fail to capture subtle changes in the overall geometry of the attractor, limiting comparisons between systems with topologically similar structures and similar values in common chaos metrics such as the Lyapunov exponent. To address this limitation, this work proposes a geometric framework that treats chaotic attractors as spatial objects, using topological tools—specifically the $\alpha$-sphere—to quantify their shape and spatial extent. The proposed method was validated using Chua’s system (including two reported variations), the Rössler system (standard and piecewise-linear), and a fractional-order multi-scroll system. A parametric characterization of the Rössler system was also performed by varying parameter b. Experimental results show that this geometric approach successfully distinguishes between attractors where classical metrics reveal no perceptible differences, in addition to being computationally simpler. Notably, we observed geometric variations of up to 80% among attractors with similar dynamics and introduced a specific index to quantify these global discrepancies. Although this geometric analysis serves as a complement rather than a substitute for chaos detection, it provides a reliable and interpretable metric for differentiating systems and selecting attractors based on their spatial properties. Full article

The Frontal Area Index (FAI) is a commonly used, cost-effective preliminary screening tool for identifying the Least Cost Path (LCP) of urban ventilated corridors and mitigating the Urban Heat Island (UHI) effect, particularly in situations where data and budget availability are limited. Although its theoretical basis and simulation studies have been extensively examined, empirical validation through field measurements remains limited. This study assesses the FAI method’s applicability in two representative U.S. Midwest cities—St. Louis and Chicago—and proposes key modifications based on field-measurement validation. FAI simulations were conducted to identify optimal ventilation corridors, and the results were subsequently compared with in situ field measurements. Our findings indicated a strong correlation between FAI predictions and field data in St. Louis. In contrast, significant discrepancies were observed in Chicago, where simulated ventilation performance did not align with measured conditions, revealing the standard method’s limitations in complex urban topographies. To address these shortcomings, this study proposes four modifications to enhance the model’s accuracy for U.S. Midwest cities: (1) adjusting the model for varying urban morphologies, (2) limiting the calculation scope, (3) implementing a distinct approach for riverine areas, and (4) adopting a plot-based division for areas with large-scale buildings. This research verifies and refines the FAI method, creating a more reliable tool for diverse urban contexts. The optimized approach provides robust support for wind environment analysis, ventilation corridor planning, and UHI mitigation strategies. Full article

This paper introduces a similarity index aimed at modeling psychological well-being through a set-theoretic formalization of self–ideal alignment. Inspired by Tversky’s feature-based model of similarity, the proposed index quantifies the degree of overlap and divergence between the current self-perception and the ideal self, each represented as a vector of signed attributes. The formulation extends traditional approaches in Personal Construct Psychology by incorporating directional and magnitude-based comparisons across constructs, and its mathematical properties can be expressed within a fuzzy similarity space that ensures boundedness and internal coherence. Unlike standard correlational methods commonly used in psychological assessment, this model provides an alternative framework that allows for asymmetric weighting of discrepancies and non-linear representations of similarity. Developed within the WimpGrid formalism—a graph-theoretical extension of constructivist assessment—the index offers potential applications in clinical modeling, idiographic measurement, and the mathematical analysis of dynamic self-concept systems. We discuss its relevance as a generalizable tool for quantitative psychology, and its potential for integration into computational models of personality and self-organization. Full article

Rear-end collisions involving maintenance vehicles remain a critical source of severe injuries and fatalities in highway work zones. Existing studies on Rear Impact Guards (RIGs) and Truck-Mounted Attenuators (TMAs) have primarily relied on vehicle-based acceleration metrics or low-speed tests, leaving uncertainty regarding their performance under high-energy impact conditions. This study investigates occupant injury risk and vehicle crash behavior through full-scale frontal impact tests conducted at 80 km/h using a 2002 Renault SM520 passenger car against (1) a truck equipped with a RIG and (2) the same truck equipped with a TMA. Hybrid III 50th percentile ATDs, high-speed imaging, and multi-axis accelerometers were employed to measure occupant kinematics and injury responses. Occupant Risk Indices (THIV (Theoretical Head Impact Velocity), ASI (Acceleration Severity Index), PHD (Post-impact Head Deceleration), and ORA (Occupant Ridedown Acceleration)) and the ATD-based HIC36 were evaluated to assess crash severity. The RIG test exhibited severe underride, resulting in an HIC36 value of 1810, far exceeding the FMVSS 208 limit. In contrast, the TMA significantly reduced occupant injury risk, lowering HIC36 by 83.5%, and maintained controlled vehicle deceleration without compartment intrusion. Comparisons between FSM-based indices and ATD-measured injury responses revealed discrepancies in impact timing and occupant motion, highlighting limitations of current evaluation methodologies. The findings demonstrate the necessity of high-speed testing and ATD-based injury assessment for accurately characterizing RIG/TMA performance and provide evidence supporting improvements to roadside safety hardware standards and work-zone protection strategies. Full article

Reflectance normalization is critical for minimizing temporal discrepancies and facilitating reliable multi-temporal satellite analysis. However, this process is challenged by the risks of under-normalization and over-normalization, which stem from the inherent complexities of varying atmospheric conditions, data acquisition, and environmental dynamics. Under-normalization occurs when multi-temporal variations are insufficiently corrected, resulting in temporal reflectance inconsistencies. Over-normalization arises when overly aggressive adjustments suppress meaningful variability, such as seasonal and phenological patterns, thereby compromising data integrity. Effectively addressing these challenges is essential for preserving the spatial and temporal fidelity of satellite imagery, which is crucial for applications such as environmental monitoring and long-term change analysis. This study introduces a novel graph-based relaxation for reflectance normalization aimed at addressing issues of under- and over-normalization through a two-stage structural normalization strategy: intra-normalization and inter-normalization. A graph structure represents adjacency and similarity among image instances, enabling an iterative relaxation process to adjust reflectance values. In the proposed framework, the intra-normalization stage aligns images within the same reflectance group to preserve temporally local reflectance patterns, while the inter-normalization stage harmonizes reflectance across different groups, ensuring smooth temporal transitions and maintaining essential temporal variability. Experimental results with the metrics root mean squared error (RMSE) and Structural Similarity Index Measure (SSIM) demonstrate the effectiveness of the proposed method. Specifically, the proposed method achieves around 37% improvement measured by RMSE in the transition of two adjacent image groups compared with related normalization methods. Graph-based relaxation preserves seasonal dynamics, ensures smooth transitions, and improves vegetation indices, making it suitable for both short-term and long-term environmental change analysis. Full article

Hyperspectral data from CRISM have proven invaluable for analyzing the mineralogical composition of the Martian surface. However, processing such datasets remains challenging due to their high dimensionality and systematic noise, such as striping artifacts caused by the pushbroom imaging technique. Building on previous research, this study introduces a framework that forms the basis for an automated pipeline that combines preprocessing, dimensionality reduction using UMAP, k-means clustering, and an adaptive stripe correction filter to generate mineral maps of the Martian surface. Additionally, the pipeline integrates a noise variance estimation step based on PCA to assess the feasibility and expected efficacy of stripe removal before applying the filter. We validate the methodology across multiple CRISM datasets, including regions such as Jezero Crater, Nili Fossae, and Mawrth Vallis. Comparative analyses using metrics such as the CH index, DB index, and SC demonstrate improved clustering performance and robust mineralogical mapping, which indicates a step toward more reliable and automated clustering of CRISM data. Furthermore, the pipeline leverages spectral libraries for automated mineral classification, yielding results comparable to expert-defined maps while addressing discrepancies caused by residual noise or clustering limitations. This study represents a step toward fully automated, scalable geospatial analysis of CRISM Martian surface data, offering a robust framework for processing large hyperspectral datasets and supporting future planetary exploration missions. In the future, we intend to deploy an automated analysis pipeline as a freely accessible web service. Full article

Prefabricated construction has emerged as a key strategy to enhance productivity and quality in infrastructure projects. Yet, construction scheduling for prefabricated infrastructure projects often suffers from persistent discrepancies between planned and actual performance due to static assumptions of task durations and fragmented management methods. To address this challenge, this study proposes a closed-loop framework that integrates probabilistic estimation, prescriptive planning, and performance feedback for prefabricated girder bridge construction. Standard task time (ST) is dynamically modeled using Bayesian regression, which incorporates prior knowledge and updates continuously with new field data. The updated ST distributions are embedded into a time–cost trade-off (TCTO) optimization algorithm to generate resource-constrained schedules. Execution data are captured through an object-based digital logging system, and performance is evaluated using the Schedule Performance Index (SPI). The accumulated results are then used to update the Bayesian model, creating a self-correcting cycle of plan → execution → performance → updating. Using eleven prefabricated girder projects, we standardized task definitions and quantified the plan and actual gaps that motivate the framework. Six projects formed the training set for Bayesian regression to estimate ST with priors; four projects were scheduled with TCTO using the posterior ST, and execution outcomes were compared with the generated plans to validate accuracy, while the collected evidence was used to update the Bayesian model; one final project received the full closed-loop application for comparative assessment of plan versus outcome, with SPI used in the closed-loop evaluation. The deployments improved alignment between plan and actual, narrowed uncertainty in ST over time, and supported credible schedules, real time progress visibility, and resource efficient planning in repetitive prefabrication. From a managerial perspective, the implemented system operationalizes feedback between planning and execution with configurable update cadences such as daily logs, repetitive unit cycles, and project close out. This study provides a validated and extensible template for closed-loop schedule management in prefabricated settings and clarifies the novelty of unifying Bayesian estimation, TCTO optimization, and digital performance feedback in one practical workflow. Full article