Search Results (14,527)

This study presents an analytical model to reduce the impact of wave-induced forces on a vertical seawall by introducing a floating elastic plate (EP) located at a specific distance from two bottom-standing porous structures (BSPs). The hydrodynamic interaction with the EP is described using thin plate theory, while the fluid flow through the porous medium is described by the model developed by Sollit and Cross. The resulting boundary value problem is addressed through linear potential theory combined with the eigenfunction expansion method (EEM), and model validation is achieved through consistency checks with recognized results from the literature. A comprehensive parametric analysis is performed to evaluate the influence of key system parameters such as the porosity and frictional coefficient of the BSPs, their height and width, the flexural rigidity of the EP, and the spacing between the EP and BSPs on vital hydrodynamic coefficients, including the wave force on the seawall, free surface elevation, wave reflection coefficient, and energy dissipation coefficient. The results indicate that higher frictional coefficients and higher BSP heights significantly enhance wave energy dissipation and reduce reflection, in accordance with the principle of energy conservation. Oscillatory trends observed with respect to wavenumbers in the reflection and dissipation coefficients highlight resonant interactions between the structures. Moreover, compared with a single BSP, the double BSP arrangement is more effective in minimizing the wave force on the seawall and free surface elevation in the region between the EP and the wall, even when the total volume of porous material remains unchanged. The inter-structural gap is found to play a crucial role in optimizing resonance conditions and supporting the formation of a tranquility zone. Overall, the proposed configuration demonstrates significant potential for coastal protection, offering a practical and effective solution for reducing wave loads on marine infrastructure. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by both motor and non-motor symptoms, including autonomic dysfunction. Structural alterations in the vagus nerve (VN) may contribute to PD pathophysiology, though existing data remain inconsistent. Objective: This study aimed to evaluate morphological changes in the VN using high-resolution ultrasound (USVN) and to investigate associations with autonomic symptoms, heart rate variability (HRV), and clinical characteristics in PD patients. Methods: A cross-sectional study was conducted involving 60 PD patients and 60 age- and sex-matched healthy controls. USVN was performed to assess VN cross-sectional area (CSA), echogenicity, and homogeneity bilaterally. Autonomic symptoms were measured using the Composite Autonomic Symptom Scale 31 (COMPASS-31). HRV parameters—SDNN, RMSSD, and pNN50—were obtained via 24 h Holter monitoring. Additional clinical data included Unified Parkinson’s Disease Rating Scale (UPDRS) scores, transcranial sonography findings, and third ventricle width. Results: PD patients showed significantly reduced VN CSA compared to controls (right: 1.90 ± 0.19 mm² vs. 2.07 ± 0.18 mm²; left: 1.74 ± 0.21 mm² vs. 1.87 ± 0.22 mm²; p < 0.001 and p < 0.02). Altered echogenicity and decreased homogeneity were also observed. Right VN CSA correlated with body weight, third ventricle size, and COMPASS-31 scores. Left VN CSA was associated with body size parameters and negatively correlated with RMSSD (p = 0.025, r = −0.21), indicating reduced vagal tone. Conclusions: USVN detects structural VN changes in PD, correlating with autonomic dysfunction. These findings support its potential as a non-invasive biomarker for early autonomic involvement in PD. Full article

In rural and mountainous regions, power supply reliability remains a persistent challenge due to structural vulnerabilities, data incompleteness, and limited automation. In this study, a data-driven methodology is leveraged, wherein a validated machine learning framework comprising Random Forest (RF), Lasso Regression, and Recursive Feature Elimination (RFE) is applied to analyze outage data. The machine learning models, validated on a held-out test set, demonstrated modest but positive predictive performance, confirming a quantifiable, non-random relationship between grid structure and restoration time. This validation provides a credible foundation for the subsequent feature importance analysis. Through a transparent, consensus-based analysis of these models, the most robust influencing factors were identified. The results reveal that key structural indicators related to network redundancy (e.g., Inter-Bus Loop Rate) and electrical stress (e.g., Peak Daily Load Current, Load Factor) are the most significant predictors of prolonged outages. Furthermore, statistical analyses confirm that increasing structural redundancy and regulating line loads can effectively reduce outage duration. These findings offer practical, data-driven guidance for prioritizing investments in rural grid planning and reinforcement. This study contributes to the broader application of machine learning in energy systems, particularly showcasing a robust methodology for identifying key drivers under data and resource constraints. Full article

Pedestrian accidents involving heavy vehicles such as trucks and buses remain a critical safety issue, primarily due to structural blind spots. While existing systems like radar-based FCW and BSD have been adopted, they are not fully optimized for pedestrian detection, particularly under adverse weather conditions. This study focused on the empirical validation of a 360-degree pedestrian collision avoidance system using multichannel ultrasonic sensors specifically designed for heavy-duty vehicles. Eight sensors were strategically positioned to ensure full spatial coverage, and scenario-based field experiments were conducted under controlled rain (50 mm/h) and fog (visibility <30 m) conditions. Pedestrian detection performance was evaluated across six distance intervals (50–300 cm) using indicators such as mean absolute error (MAE), coefficient of variation (CV), and false-negative rate (FNR). The results demonstrated that the system maintained average accuracy of 97.5% even under adverse weather. Although rain affected near-range detection (FNR up to 17.5% at 100 cm), performance remained robust at mid-to-long ranges. Fog conditions led to lower variance and fewer detection failures. These empirical findings demonstrate the system’s effectiveness and robustness in real-world conditions and emphasize the importance of evaluating both distance accuracy and detection reliability in pedestrian safety applications. Full article

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection. Full article

Free water surface constructed wetlands can be effective systems for contaminant removal, but their performance is sensitive to interactions among flow dynamics, vegetation, and bed topography. This study presents a numerical investigation into how heterogeneous bed topographies influence hydraulic and contaminant transport behavior in a rectangular wetland. Topographies were generated using a correlated pseudo-random pattern generator, and flow and solute transport were simulated with a two-dimensional, depth-averaged model. Residence time distributions and contaminant removal efficiencies were analyzed as functions of the variance and correlation length of the bed elevation. Results indicate that increasing the variability of bed elevation leads to greater dispersion in residence times, reducing hydraulic efficiency. Moreover, as the variability of bed elevation increases, so does the spread in hydraulic performance among wetlands with the same statistical topographic parameters, indicating a growing sensitivity of flow behavior to the specific spatial configurations of bed features. Larger spatial correlation lengths were found to reduce the residence time variance, as shorter correlation lengths promoted complex flow structures with lateral dead zones and internal islands. Contaminant removal efficiency, evaluated under the assumption of uniform vegetation, was influenced by bed topography, with variations becoming more pronounced under conditions of lower vegetation density. The results underscore the significant impact of bed topography on hydraulic behavior and contaminant removal performance, highlighting the importance of careful topographic design to ensure high wetland efficiency. Full article

Mountain glaciers are the most direct and sensitive indicators of climate change. In the context of global warming, monitoring changes in glacier elevation has become a crucial issue in modern cryosphere research. The Global Ecosystem Dynamics Investigation (GEDI) is a full-waveform laser altimeter with a multi-beam that provides unprecedented measurements of the Earth’s surface. Many studies have investigated its applications in assessing the vertical structure of various forests. However, few studies have assessed GEDI’s performance in detecting variations in glacier elevation in land ice in high-mountain Asia. To address this limitation, we selected the Southeastern Tibetan Plateau (SETP), one of the most sensitive areas to climate change, as a test area to assess the feasibility of using GEDI to monitor glacier elevation changes by comparing it with ICESat-2 ATL06 and the reference TanDEM-X DEM products. Moreover, this study further analyzes the influence of environmental factors (e.g., terrain slope and aspect, and altitude distribution) and glacier attributes (e.g., glacier area and debris cover) on changes in glacier elevation. The results show the following: (1) Compared to ICESat-2, in most cases, GEDI overestimated glacier thinning (i.e., elevation reduction) to some extent from 2019 to 2021, with an average overestimation value of about −0.29 m, while the annual average rate of elevation change was relatively close, at −0.70 ± 0.12 m/yr versus −0.62 ± 0.08 m/yr, respectively. (2) In terms of time, GEDI reflected glacier elevation changes at interannual and seasonal scales, and the trend of change was consistent with that found with ICESat-2. The results indicate that glacier accumulation mainly occurred in spring and winter, while the melting rate accelerated in summer and autumn. (3) GEDI effectively monitored and revealed the characteristics and patterns of glacier elevation changes with different terrain features, glacier area grades, etc.; however, as the slope increased, the accuracy of the reported changes in glacier elevation gradually decreased. Nonetheless, GEDI still provided reasonable estimates for changes in mountain glacier elevation. (4) The spatial distribution of GEDI footprints was uneven, directly affecting the accuracy of the monitoring results. Thus, to improve analyses of changes in glacier elevation, terrain factors should be comprehensively considered in further research. Overall, these promising results have the potential to be used as a basic dataset for further investigations of glacier mass and global climate change research. Full article

In modern communication systems, the concatenation of a low-density parity-check (LDPC) code with a cyclic redundancy check (CRC) code is commonly used for error correction. In this paper, we propose a low-complexity two-stage scheme for decoding these codes using their concatenation structures. In the first stage, the traditional belief propagation (BP)-based iterative algorithm with a relative small maximum number of iterations is performed for decoding the LDPC code. If an LDPC codeword is obtained in this stage, the decoding process terminates. Otherwise, the second stage of the decoding process is performed, in which the guessing random additive noise decoding (GRAND) algorithm is applied to the CRC code. A list of information sequences satisfying the CRC check is obtained, each of which is then encoded to an LDPC codeword. The most likely codeword among them is the output of the decoding approach. The simulation results indicate that the proposed two-stage decoding approach can outperform the traditional BP-based iterative algorithm with a large maximum number of iterations. Moreover, the average complexity of the proposed approach is relatively low. Full article

A perforated aquaculture vessel represents an environmentally sustainable approach to fish farming, leveraging seawater circulation to optimize water quality and enhance fish health and growth. The perforations on the side of the fish tank significantly influence its hydrodynamic characteristics. This study investigated the influence of pore parameters on the perforated fishing tank with various pore designs, such as the asymmetric distribution of the opening in depth, windward, and leeward directions. A numerical study was conducted using STAR-CCM+ to analyze the perforated tank under beam wave conditions. This study aimed to analyze the effects of pore location, opening ratio, and asymmetric distribution on the hydrodynamic performance and flow characteristics within aquaculture tanks. The results demonstrated that an asymmetric pore distribution on the windward and leeward sides of the vessel had a notable impact on the roll motion and the flow velocity in the vicinity of the pores. The findings also indicated that the effects of pore distribution were more significant than those of opening ratio, especially regarding asymmetry. The results revealed that higher flow velocities occurred under a smaller opening ratio. Modifying pore structure parameters on the windward and leeward sides can alter the local flow field. Full article

Class imbalance is a persistent limitation in polyp segmentation, commonly resulting in biased predictions and reduced accuracy in identifying clinically relevant structures. This study systematically evaluated 12 loss functions, including standard, weighted, and compound formulas, applied to colon polyp segmentation using the UNet-VGG16 fixed architecture on the Kvasir-SEG dataset. The encoder was frozen to isolate the effect of loss functions under the same training conditions. A fixed random seed was used in all experiments to ensure reproducibility and control variance during training. The results reveal that the combined loss functions, namely WBCE combined with Dice and Tversky combined with Focal, achieved the top Dice scores of 0.8916 and 0.8917, respectively. Tversky plus Focal also provided the highest sensitivity of 0.8885, and WBCE obtained the best average IoU of 0.8120. Tversky loss showed the lowest error rate of 4.99, indicating stable optimization. These results clarify the influence of loss function selection on segmentation performance in scenarios characterized by considerable class imbalance. Full article

In order to explore new wall panel materials and structural systems suitable for prefabricated buildings, this study proposes a polypropylene fiber-reinforced concrete sandwich wall panel (PFRC sandwich wall panel) and a polypropylene fiber-reinforced concrete sandwich wall panel with glass fiber grid (G-PFRC sandwich wall panel). A comparative investigation was conducted using finite element analysis to numerically simulate the mechanical response of these composite wall panels under impact loads. The simulation results were compared with those of an unreinforced concrete sandwich wall panel with glass fiber grid (G-UC sandwich wall panel). Key findings include: (1) Compared with the G-UC sandwich wall panel, the G-PFRC sandwich wall panel exhibited 19.3% lower peak deformation and 23.7% reduced residual deformation; (2) Relative to the standard PFRC sandwich wall panel, the G-PFRC sandwich wall panel demonstrated 16.5% smaller peak deformation and 27.9% less residual deformation under impact loads; (3) Damage analysis revealed that the G-PFRC sandwich wall panel developed fewer cracks with lower damage severity compared to both the PFRC and G-UC sandwich wall panels. Parametric studies further indicated that the G-PFRC sandwich wall panel maintains superior deformation resistance and impact performance across varying impact heights and impact masses. The synergistic combination of polypropylene fiber with a glass fiber grid significantly enhances the impact resistance of composite sandwich panels, providing valuable theoretical insights for engineering applications of these novel wall systems in prefabricated construction. Full article

Nanoparticle-embedded polymeric materials are an important subject in advanced structural applications due to their advantageous combination of low weight and high mechanical performance. Optical coherence tomography (OCT) is a high-resolution imaging technique that enables subsurface defect visualization, which can be used as one of the methods to reveal defects resulting from decomposition pathways or mechanisms of polymers. Nevertheless, the low contrast of polymeric materials, particularly PEEK-based polymers, does not allow for automatic geometry extraction for analytical input. To address the constraint of weak contrast, localized surface plasmon resonance (LSPR) of plasmonic nanoparticle-reinforced polymer materials has been used as an OCT contrast agent to provide the necessary contrast. The backscattering efficiency of light was also theoretically investigated, based on the Lorenz–Mie theory, with a single spherical nanoparticle embedded in a PEEK matrix as a non-absorptive, isotropic and homogeneous medium. In this study, the cases of a single homogeneous $Ti{O}_2$ nanoparticle and a hybrid $Ti{O}_2/Au$ core/shell nanoparticle configuration were considered separately. An examination of the influence of nanoparticle diameter and gold shell thickness on backscattering efficiencies of these nanostructures was performed. The results indicate that $Ti{O}_2/Au$ nanoshells demonstrate superior near-infrared (NIR) light backscattering capabilities at typical OCT operating wavelengths (830–1310 nm). Additionally, the potential of these nanoparticles for application in non-destructive testing-based light backscattering methods was investigated. The findings suggest that $Ti{O}_2/Au$ nanoshells have the ability to effectively backscatter near-infrared light in OCT operating central wavelengths, making them suitable to serve as effective NIR contrast-enhancing agents for OCT within the domain of NDT. Full article

This research has successfully addressed the technical challenge of generating nanobubbles in diesel fuel, which inherently lacks hydrophilic structures and charged ions, enabling the effective production of high-concentration nanobubble diesel fuel. This breakthrough lays a solid foundation for subsequent research into the combustion performance and combustion mechanism of high-concentration nanobubble fuels. Furthermore, it holds promising potential to advance high-concentration nanobubble fuel as a viable new type of energy source. A specialized device was designed to generate nanobubble-embedded diesel, and particle tracking analysis with n-hexadecane dilution was employed to quantify nanobubble concentration. The results demonstrate that the nanobubble concentration in diesel increases with both circulation time and pressure, reaching up to 5 × 10⁸ ± 3.1 × 10⁷ bubbles/mL under a pressure of 2.5 MPa. Stability tests indicate an initial rapid decay (50% reduction within one week), followed by a slower decline, which stabilizes at 4.5 × 10⁷ ± 3.13 × 10⁶ bubbles/mL after two months. Notably, nanobubble concentration has a minimal impact on the density and viscosity of diesel but slightly decreases its surface tension. This study presents a feasible method for preparing high-concentration nanobubble diesel, which lays a foundation for investigating the combustion mode and mechanism of nanobubble diesel fuel. With the goal of enhancing combustion efficiency and reducing pollutant emissions, this work further paves the way for the application of high-concentration nanobubble diesel as a new energy source in fields including automotive, marine, and aerospace industries. Full article

Desert sand (DS) serves as a sustainable alternative to river sand in concrete production, delivering environmental and economic benefits. Furthermore, the durability of concrete structures in cold regions is severely affected by freeze–thaw (F-T) cycles. Therefore, this study employed a central pull-out test to examine the bond performance between desert sand concrete (DSC) and steel bars subjected to F-T cycles, considering the effects of the number of F-T cycles, DS replacement ratios (i.e., the replacement ratio of river sand by DS), and the type of reinforcement. The F-T cycle numbers tested were 0, 25, 50, and 75 cycles. The DS replacement ratios were varied at 0%, 20%, 40%, 60%, 80%, and 100%. The plain and threaded steel bars (PSBs and TSBs) were selected for the experiment. The results indicate a decrease in bond strength for both PSB and TSB specimens with increasing F-T cycle numbers. Regarding the DS replacement ratios, bond strength initially decreased, with an increasing replacement rate, then increased, and eventually reduced again. Notably, significantly improved bonding was observed for steel reinforcement in DSC containing 40% or 60% DS compared to plain concrete. Additionally, the bond strengths of PSB specimens were lower than those of TSB specimens under identical conditions. A calculation formula for the bond–slip characteristic was derived using statistical regression, which considered multiple factors. Eventually, a bond–slip constitutive model was developed for the interface between DSC and reinforced steel, showing a high degree of consistency with the experimental data. Full article

This study evaluates the eco-efficiency and eco-productivity of 141 countries using data-driven analytical frameworks over the period 2018–2023, covering the pre-COVID, COVID, and post-COVID phases. We employ an input-oriented Slack-Based Measure Data Envelopment Analysis (SBM-DEA) under variable returns to scale (VRS), combined with the Malmquist Productivity Index (MPI), to assess both static and dynamic performance. The analysis incorporates three inputs—labor force, gross fixed capital formation, and energy consumption—one desirable output (gross domestic product, GDP), and one undesirable output (CO₂ emissions). Eco-efficiency (the joint performance of energy and carbon efficiency) and eco-productivity (labor and capital efficiency) are evaluated to capture complementary dimensions of sustainable performance. The results reveal significant but temporary gains in eco-efficiency during the peak pandemic years (2020–2021), followed by widespread post-crisis reversals, particularly in labor productivity, energy efficiency, and CO₂ emission efficiency. These reversals were often linked to institutional and structural barriers, such as rigid labor markets and outdated infrastructure, which limited the translation of technological progress into operational efficiency. The MPI decomposition indicates that, while technological change improved in many countries, efficiency change declined, leading to overall stagnation or regression in eco-productivity for most economies. Regression analysis shows that targeted policy stringency in 2022 was positively associated with eco-productivity, whereas broader restrictions in 2020–2021 were less effective. We conclude with differentiated policy recommendations, emphasizing green technology transfer and institutional capacity building for lower-income countries, and the integration of carbon pricing and innovation incentives for high-income economies. Full article