Search Results (9,555)

Background: To evaluate the association of point-of-care ultrasound (PoCUS) performed within one hour of emergency department (ED) arrival with ED length of stay (LOS) and healthcare costs in admitted ED patients with non-traumatic abdominal pain. Methods: This retrospective, inverse probability of treatment weighting (IPTW) cohort study was conducted at a tertiary medical center in Taiwan. This study analyzed data from 2021–2023, focusing on adult patients admitted to an ordinary ward with non-traumatic abdominal pain. Patients discharged from the ED, admitted to the ICU, or receiving PoCUS > 1 h (N = 864) were excluded. The final cohort of 6866 patients comprised those receiving PoCUS within 1 h (N = 1542) and those receiving no PoCUS (N = 5324). Primary and secondary outcomes (ED LOS, costs) were adjusted for age, gender, triage, vital signs, BMI, and comorbidities using generalized linear models with a Gamma distribution. Results: After IPTW adjustment in 6866 admitted abdominal pain patients, PoCUS within one hour was associated with a 14% shorter ED LOS (RM 0.86, 95% CI 0.83–0.89). A notable finding was that PoCUS performed within one hour was associated with 44% higher odds of CT utilization (OR 1.44, 95% CI 1.25–1.65) and 5% lower total healthcare costs (RM 0.95, 95% CI 0.91–0.99). Stratification by CT use revealed distinct patterns underlying these associations: in the non-CT subgroup, PoCUS was associated with 12% lower ED costs (RM 0.88, 95% CI 0.83–0.94), whereas in the CT subgroup, it was associated with 9% lower admission costs (RM 0.91, 95% CI 0.86–0.96). Conclusions: In admitted patients, PoCUS performed within one hour was associated with shorter ED LOS and lower total costs, despite a concurrent association with higher CT utilization. These findings are consistent with a dual, context-dependent role for PoCUS: associated with reduced ED costs in non-CT pathways and lower admission costs in CT pathways. However, as this is an observational study, these results represent associations rather than causal effects and may be influenced by unmeasured confounding. Prospective trials are required to validate these findings. Full article

Research on microplastics (MPs) in agricultural soils has received increasing attention due to their potential ecological risks and adverse effects on the food chain. Recently, geostatistical approaches have been increasingly used to assess the spatial distribution of MPs in soils. Therefore, this study aims to predict the abundance of MPs in the soil of an agricultural micro-watershed using geostatistical methods and to produce a continuous map of the interpolated MPs. Soil samples were collected, and MP abundance was determined using the density separation method. Subsequently, exploratory data analysis was conducted, followed by the construction of the experimental semivariogram, theoretical variogram model fitting, ordinary kriging interpolation, cross-validation and, inverse distance weighting (IDW) interpolation. MPs were detected in all samples, with average abundances of 3826, 2553, and 3407 pieces kg⁻¹ in forest, pasture, and agricultural land use systems, respectively. The experimental semivariogram showed that the spatial distribution of MPs has a weak spatial dependence structure. The Kriging and IDW maps showed a distribution of MPs in the range of 600 to 7400 pieces kg⁻¹, with higher concentrations of MPs for forest and agricultural areas. Additionally, the map reveals a high abundance of MPs, with greater concentrations in depressions and areas near roads and urban centers, allowing for identifying critical points within the micro-watershed. This study offers important insights into the presence of MPs across various land uses, emphasizing the need for proactive measures to prevent and mitigate their accumulation in soil. Full article

Monitoring spindle bearing load is essential for ensuring machining accuracy, reliability, and predictive maintenance in machine tools. This paper presents an approach that combines drive-based cutting force estimation with a finite element method (FEM) spindle model. The drive-based method reconstructs process forces from the motor torque signal of the feed axes by modeling and compensating motion-related torque components, including static friction, acceleration, gravitation, standstill, and periodic disturbances. The inverse mechanical and control transfer behavior is also considered. Input signals include the actual motor torque, axis position, and position setpoint, recorded by the control system’s internal measurement function at the interpolator clock rate. Cutting forces are then calculated in MATLAB/Simulink and used as inputs for the FEM spindle model. Rolling elements are replaced by bushing joints with stiffness derived from datasheets and adjusted through experiments. Force estimation was validated on a DMC 850 V machining center using a standardized test workpiece, with results compared against a dynamometer. The spindle model was validated separately on a MCV 754 Quick machine under static loading. The combined approach produced consistent results and identified the front bearing as the most critically loaded. The method enables practical spindle bearing load estimation without external sensors, lowering system complexity and cost. Full article

Humanity faces converging crises of climate change, biodiversity loss, inequality, and social fragmentation. These challenges are usually treated as technical or policy problems, yet their persistence suggests deeper causes in the paradigms through which human beings understand themselves and act in the world. Systems thinking highlights that paradigms shape perception, motivation, and institutions, but it does not specify which paradigms best support sustainability. This article develops a conceptual framework to examine how paradigms of human nature have shifted historically and how these shifts influence sustainability outcomes. Using a comparative synthesis of wisdom traditions (Greek, Islamic, Christian, Jewish, Hindu, Confucian, and Daoist) together with modern and late-modern frameworks, the study identifies key differences in how human faculties and values are ordered, and how these differences manifest in ecological and social outcomes. A paradigm–perception–intention–action–impact feedback model is introduced to explain how worldviews propagate into institutions and outcomes, and how inversions contribute to ecological overshoot, inequality, and dislocation. The article contributes a synthesized map of paradigms across traditions, a causal schema linking paradigm shifts to sustainability outcomes, practice-oriented design principles, and a research agenda for testing the framework in sustainability transitions. Re-examining paradigms is argued to be a critical leverage point for durable sustainability. Full article

This study assessed the performance of the Clouds and the Earth’s Radiant Energy System (CERES) surface longwave downward radiation (SLDR) products under the atmospheric temperature inversion (ATI) conditions for the first time. Three years of ground-measured SLDRs from 409 globally distributed stations across four flux networks were employed, and the collocated MODIS atmospheric profile product was used to identify the ATI profiles at each flux station. All three SLDR estimate algorithms (Models A, B, and C) show a pronounced accuracy decline under ATI conditions, regardless of region (polar or non-polar) or time of day (daytime or nighttime). Under ATI conditions, the Bias/RMSE increases by approximately 10.0/5.0 W/m² for Models A and B, 5.0/1.0 W/m² for Model C. Sensitivity analysis reveals that the concurrent atmospheric moisture inversion (AMI) compounds this degradation; both the Bias and RMSE increase with the AMI intensity. These results underscore the need to refine CERES SLDR algorithms in the future. Full article

In hyperspectral images, ghost image residuals exceeding a certain threshold not only reduce the recognition accuracy of the imaging detection system but also decrease the target identification rate. Ghost image residuals affect both the recognition accuracy of the detection system and the accuracy of spectral calibration, thereby influencing qualitative and quantitative inversion. Conventional ghost image residual correction methods can significantly affect both the relative and absolute calibration accuracy of hyperspectral images. To minimize the impact on spectral calibration accuracy during ghost image residual correction, we propose a ghost image degradation model and an iterative optimization algorithm. In the proposed approach, a ghost image residual degradation model is constructed based on the point spread function (PSF) of ghost image residuals and their energy distribution characteristics. Using the proportion of ghost image residuals and the accuracy of hyperspectral image calibration as constraints, we iteratively optimized typical regional target ghost image residuals across different spectral channels, achieving automated correction of ghost image residuals in various spectral bands. The experimental results show that the energy proportion of ghost image residuals at different wavelengths decreased from 4.6% to 0.3%, the variations in spectral curves before and after correction were less than 0.8%, and the change in absolute radiometric calibration accuracy was below 0.06%. Full article

This study develops and validates an Extended Analytical Dynamic Model (EADM) of the UR16e 6-Degree-of-Freedom (DoF) industrial robot, incorporating actuator dynamics and a friction model to address the lack of dynamic information provided by the manufacturer. A two-stage validation methodology is proposed using a Multibody Physical Model (MPM) developed in MATLAB^® R2024b/Simscape Multibody^TM as a reference. In the first stage, the Analytical Dynamic Model (ADM) without actuators or friction is evaluated by comparing its inverse dynamics torque with the torque required by the MPM under identical joint references. In the second stage, the EADM and the MPM are tested under a Proportional-Derivative Computed Torque Control (PD-CTC) scheme using Cartesian trajectories, comparing joint torques and positions. The methodology incorporates torque-level validation, a demanding criterion since torque is determined by the dynamic formulation, whereas position may be influenced by closed-loop control. The results show small torque errors in the first stage (e_τ) in the range of 10⁻¹⁷ to 10⁻¹³ Nm) and bounded position and torque errors in the second stage (e_q ≤ 4 × 10⁻⁴ rad; e_τ ≤ 0.4 Nm in q₁ − q₂ and e_τ ≤ 0.5 Nm in q₄ − q₆ ). The methodology provides a systematic validation framework and demonstrates that the EADM accurately matches the MPM’s dynamic behavior.extended analytical dynamic model; multibody physical model; Simscape Multibody; actuators; nonlinear system; PD computed torque control Full article

Adolescence is a critical window for cardiovascular (CV) development, yet the molecular drivers of vascular adaptation to regular exercise in youth remain poorly understood. This cross-sectional study assessed vascular structure and function alongside endothelial, metabolic, and lipoprotein biomarkers in 203 healthy young athletes (aged 10–16). Vascular phenotyping included carotid intima-media thickness (IMT), pulse wave velocity, and carotid deformation indices (strain, strain rate). Circulating nitric oxide (NO), endothelin-1, free triiodothyronine (fT3), leptin, low-density lipoprotein, and high-density lipoprotein were analyzed. Associations were examined using hierarchically adjusted multivariable linear regression, mediation and moderation were tested and sex-stratified/matched analyses were conducted. While training volume was not associated with endothelial markers, leptin was correlated positively with NO and negatively with diastolic strain rate, suggesting dual vascular actions. fT3 was inversely associated with IMT, indicating a potential protective role in vascular remodeling. Lipoprotein profiles showed no independent associations with vascular parameters. Hemodynamic load, particularly systolic blood pressure, emerged as the dominant determinant of arterial stiffness. Sex-specific differences across biomarkers and vascular indices support a multifactorial model: in active youth, vascular phenotype reflects hemodynamics, body composition, and endocrine–metabolic signals more than training; longitudinal mechanistic studies should clarify causal pathways and guide individualized cardiovascular risk profiling. Full article

This work presents a systematic experimental investigation of tapered fiber Bragg gratings (tFBGs) fabricated from standard SMF-28 fiber with waist diameters ranging from 30 to 115 µm. The effects of taper geometry on strain and temperature sensitivities were evaluated using UV inscription through two phase masks to ensure reproducibility. The maximum strain sensitivity achieved was 25.38 ± 0.06 pm/N for the 30 µm waist, corresponding to 20.84 ± 0.05 pm/µε—an enhancement of more than 1600% compared to a standard untapered FBG. In contrast, the thermal sensitivity remained nearly constant at ~12.5 pm/°C for all diameters, confirming that the temperature response is governed by the intrinsic thermo-optic and thermal-expansion properties of silica and is not significantly affected by taper geometry. The measured strain sensitivity exhibited a clear inverse-square dependence on the waist diameter, in excellent agreement with a simple axial-stress model. Consistent Bragg responses obtained using different phase-mask pitches further validated the repeatability of both the tapering and inscription processes. These results demonstrate that tapering standard telecom fiber provides a low-cost, scalable, and robust method to significantly enhance FBG strain sensitivity while preserving thermal stability, enabling compact and high-performance sensors for structural and industrial monitoring applications. Full article

This paper presents a new approach to the dimensional synthesis of a robotic limb mechanism for a wheel-legged robot. The proposed kinematic structure enables independent control of wheel motions relative to the robot platform, allowing each drive to perform a distinct movement. The selection of the mechanism’s common dimensions simplifies platform levelling to a single-drive actuation. The hybrid limb design, which combines features of driving and walking systems, enhances platform stability on uneven terrain and is suitable for rescue, exploration, and inspection robots. The synthesis method defines the desired trajectory of the wheel centre and applies a genetic algorithm to determine mechanism dimensions that reproduce this motion. The stochastic optimisation process yields multiple feasible solutions, enabling the introduction of additional design criteria for optimal configuration selection. Analytical kinematic relations were developed for workspace and trajectory evaluation, solving both direct and inverse kinematic problems. The results confirm the effectiveness of evolutionary optimisation in synthesising complex kinematic mechanisms. The proposed approach can be adapted to other mobile robot structures. Future work will address dynamic modelling, adaptive control for real-time platform levelling, and comparative studies with other synthesis methods. Full article

In imitation learning between humans and robots, the embodiment gap is a key challenge. By focusing on a specific body part and compensating for the rest according to the robot’s size, the embodiment gap can be overcome. In this paper, we analyze dynamic attention to body parts in imitation learning between humans and robots based on a Transformer model. To adapt human imitation movements to a robot, we solved forward and inverse kinematics using the Levenberg–Marquardt method and performed feature extraction using the k-means method to make the data suitable for Transformer input. The imitation learning process is carried out using the Transformer. UMAP is employed to visualize the attention layer within the Transformer. As a result, this system enabled imitation of movements while focusing on multiple body parts between humans and robots with an embodiment gap, revealing the transitions of body parts receiving attention and their relationships in the robot’s acquired imitation movements. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the most common malignancies worldwide, with systemic inflammation increasingly recognised as a determinant of disease progression. This study aimed to establish a taxonomy-based classification of patients with newly diagnosed primary CRC using systemic inflammatory, haematological, and anthropometric markers, and to evaluate its association with tumour stage. Methods: A total of 229 patients (111 women, 118 men) undergoing surgery for primary CRC were included. Blood samples were analysed for haemoglobin, leukocytes, neutrophils, lymphocytes, platelets, C-reactive protein (CRP), and carcinoembryonic antigen (CEA). Anthropometric data were collected. Taxonomic clustering and ordinal logistic regression were used to explore associations with TNM and Astler–Coller classifications. Results: Men had higher neutrophil and leukocyte counts, elevated CEA concentrations (132.8 vs. 81.3 ng/mL), and higher NLR values (4.74 vs. 4.23) compared with women. Logistic regression confirmed that platelet count (OR 1.003; p = 0.004), PLR (OR 1.003; p = 0.003), and CEA (OR 1.03; p < 0.001) were positively associated with advanced TNM stage, while haemoglobin was inversely correlated (OR 0.88; p = 0.045). Among 84 clustering models, two taxonomies were the most clinically informative: Taxonomy I (BMI, neutrophils, platelets) and Taxonomy II (age, lymphocytes, platelets), both significantly associated with T, N, M, overall TNM stage, and Astler–Coller grade. Taxonomy I identified three patient groups. Type 3 represented the poorest phenotype, characterised by low BMI and haemoglobin, high platelets, elevated CEA and PLR, and predominance of TNM IIIC tumours, consistent with a cachectic–inflammatory profile. Type 1 displayed higher BMI, lower inflammation, and earlier-stage disease. Type 2 was characterized by elevated neutrophils and leukocytes. Taxonomy II distinguished four groups, with Type 2 demonstrating the most favourable profile (high haemoglobin and lymphocytes, low NLR and PLR, early TNM stage). Conclusions: Systemic inflammatory markers, haemoglobin, platelets, and CEA strongly predict CRC advancement. The proposed taxonomy provides clinically meaningful stratification of CRC patients and may support personalised risk assessment. This accessible approach may facilitate early identification of high-risk individuals, although validation in prospective studies is required. Full article

Background/Objectives: This study presents a time-dependent mathematical model that describes how progressive amyloid-β (Aβ) accumulation drives the gradual decline of cerebral electrical conductivity during Alzheimer’s disease (AD). Methods: The formulation captures the coupled evolution of molecular burden and electrophysiological function through a pair of interconnected dynamical processes, enabling a mechanistic link between early biochemical alterations and large-scale neural degradation. Results: Simulations reveal a characteristic pattern in which Aβ levels rise steadily toward a pathological plateau, while conductivity follows a delayed but persistent downward trajectory that stabilizes at an impaired state consistent with advanced neurodegeneration. The model reproduces key phenomena reported in experimental and clinical studies, including the slow, irreversible reduction in cortical conductivity and the strong inverse relationship between amyloid burden and electrophysiological integrity. Conclusions: Although intentionally minimal, the framework offers a tractable basis for interpreting disease progression and can be extended to incorporate additional pathological pathways such as tau aggregation, inflammatory responses, or spatial heterogeneity. By providing a compact yet biologically meaningful representation of the interplay between molecular pathology and electrical dysfunction, the model supports the development of computational biomarkers and contributes to a more integrated understanding of AD progression. Full article

Composite structures are generally more susceptible to impact damage than non-composite structures, and early identification of damage is the primary goal of structural health monitoring (SHM). If such damage remains undetected or reaches a critical size, it can lead to sudden collapse and catastrophic failure. Modern SHM methods aim to preserve the integrity of composite structures through continuous inspection, monitoring, and damage assessment, including detection, localization, quantification, classification, and prognosis. These methods use sensor-based technologies to assess vibration, extension, and acoustic and thermal emission. This paper provides a review of various computational methods including physics-based methods (signal processing techniques, modal analysis, and finite element model updating) and optimization methods (inverse problems, particle swarm optimization, topology optimization, genetic algorithms, time series analysis, and hybrid techniques), alongside machine learning methodologies employing neural networks as well as deep learning for damage identification in composite structures. These computational and learning-based techniques are widely applied in the development of algorithms, optimization strategies, and hybrid frameworks for SHM. The review further summarizes the applications, advantages, and limitations of each method according to structure type and damage characteristics. The key emphasis of this review is on integrating computational approaches, as well as machine learning, to enhance the efficiency of damage identification. The conclusion is drawn based on an overview of the literature, focusing on the contributions of different computational methods and machine learning for damage identification in composites. Full article

Microplastic pollution in riverine ecosystems poses critical environmental challenges, yet current modeling approaches inadequately capture the spatial heterogeneity and topological complexity of fluvial systems. This study develops an innovative spatiotemporal graph convolutional network (ST-GCN) framework that integrates hydrological connectivity, flow parameters, and microplastic characteristics for simultaneous migration pathway identification and pollution source tracing. This model constructs multi-scale graph representations encoding system structure and transport dynamics, implements spatial-temporal convolution layers with adaptive attention mechanisms, and employs a backpropagation-based algorithm for inverse source identification. Validation using 18 months of field observations from 45 monitoring nodes across a 127 km river reach demonstrates 87.3% pathway prediction accuracy and 94.3% source localization accuracy (R² = 0.841, p < 0.001), representing substantial improvements over conventional advection–diffusion models. The framework successfully identified three pollution sources during a real contamination incident within 6 h of detection, enabling rapid regulatory intervention. This research advances environmental modeling by demonstrating that graph neural networks effectively capture transport processes in networked hydrological systems, providing practical tools for watershed management and evidence-based pollution control decision-making. Full article