Search Results (168,916)

Wet cracks caused by leakage often exhibit visual and structural distortions due to surface contamination, salt crystallization, and corrosion byproducts. These factors significantly degrade the performance of sensor- and vision-based crack detection systems. In moist environments, the initiation and propagation of cracks tend to be highly nonlinear and irregular, making it challenging to distinguish crack regions from the background—especially under visual noise such as reflections, stains, and low contrast. To address these challenges, this study proposes a segmentation framework that integrates a dedicated preprocessing pipeline aimed at suppressing noise and enhancing feature clarity, all without altering the underlying segmentation architecture. The pipeline begins with adaptive thresholding to perform initial binarization under varying lighting conditions. This is followed by morphological operations and connected component analysis to eliminate micro-level noise and restore structural continuity of crack patterns. Subsequently, both local and global contrast are enhanced using histogram stretching and contrast limited adaptive histogram equalization. Finally, a background fusion step is applied to emphasize crack features while preserving the original surface texture. Experimental results demonstrate that the proposed method significantly improves segmentation performance under adverse conditions. Notably, it achieves a precision of 97.5% and exhibits strong robustness against noise introduced by moisture, reflections, and surface irregularities. These findings confirm that targeted preprocessing can substantially enhance the accuracy and reliability of crack detection systems deployed in real-world infrastructure inspection scenarios. Full article

Despite its wide acceptance, one of the most critical limitations of Terahertz wave technology is its high sensitivity to moisture. This limitation can, in turn, be exploited for use in moisture detection applications. This work presents a quantitative, non-invasive characterization of moisture content in standard gypsum drywall using Terahertz Time-Domain Spectroscopy (THz-TDS). With an increase in the moisture content of the drywall sample, experimental results indicated an increase in the dielectric properties such as the refractive index, permittivity, absorption coefficient, extinction coefficient, and dissipation factor. The demonstrated sensitivity to moisture establishes THz-TDS as a powerful tool for structural monitoring, hidden defect detection, and electromagnetic modeling of real-world building environments. Beyond material diagnostics, these findings have broader implications for THz indoor propagation studies, especially for emerging sub-THz and low THz communication technologies in 5G/6G and THz imaging of objects hidden behind the wall. Full article

Background/Objectives: Lidocaine has demonstrated immunomodulatory properties and promising antitumor effects in experimental models, but its impact on long-term outcomes following oncologic surgery remains unclear. This study aimed to compare the impact of intraoperative lidocaine versus remifentanil on long-term cancer outcomes after primary lung cancer surgery. Methods: This is a post hoc analysis of a randomized controlled trial (NCT03905837, EudraCT 2016-004271-52). From 154 patients who underwent elective lung resection via video-assisted thoracoscopic surgery (VATS) between January 2019 and June 2021 and were randomized to receive intraoperative lidocaine (intravenous or paravertebral) or remifentanil, we analyzed data from patients with confirmed primary lung cancer in the surgery specimen. Overall survival (OS) and disease-free survival (DFS) were assessed through May 2025. Survival outcomes were analyzed using Kaplan–Meier curves and log-rank tests. A multivariate Cox proportional hazards model was used to adjust for potential confounders. Results: Among the 97 patients with primary lung cancer finally included in the analysis, those in the lidocaine group exhibited improved OS compared with those who received intravenous remifentanil (log-rank p = 0.022). This association remained significant in the multivariate Cox regression analysis (HR 2.59, 95% CI 1.13–5.96, p = 0.025). No significant differences were observed in DFS overall (log-rank p = 0.283) or in DFS limited to recurrences of cancers present at the time of surgery, either the resected primary tumor or a prior malignancy (log-rank p = 0.080). Conclusions: In this post hoc analysis, lidocaine administration during oncologic lung resection was associated with improved OS in primary lung cancer patients. No differences in DFS were observed between groups; however, a non-significant trend toward improved DFS in lidocaine patients was noted when focusing on recurrences of cancers present at the time of surgery. Further investigation in larger prospective studies is warranted. Full article

A bioresorbable cannulated bone screw was developed using PLA/PCL-based composites reinforced with hydroxyapatite (HA) and titanium dioxide (TiO₂), two additives previously reported to enhance mechanical compliance, biocompatibility, and molding feasibility in biodegradable polymer systems. The design incorporated a crest-trimmed thread and a strategically positioned gate in the thin-wall zone opposite the hexagonal socket to preserve torque-transmitting geometry during micromolding. To investigate shrinkage behavior, a Taguchi orthogonal array was employed to systematically vary micromolding parameters, generating a structured dataset for training a back-propagation neural network (BPNN). Analysis of variance (ANOVA) identified melt temperature as the most influential factor affecting shrinkage quality, defined by a combination of shrinkage rate and dimensional variation. A hybrid AI framework integrating the BPNN with genetic algorithms and particle swarm optimization (GA–PSO) was applied to predict the optimal shrinkage conditions. This is the first use of BPNN–GA–PSO for cannulated bone screw molding, with the shrinkage rate as a targeted output. The AI-predicted solution, interpolated within the Taguchi design space, achieved improved shrinkage quality over all nine experimental groups. Beyond the specific PLA/PCL-based systems studied, the modeling framework—which combines geometry-specific gate design and normalized shrinkage prediction—offers broader applicability to other bioresorbable polymers and hollow implant geometries requiring high-dimensional fidelity. This study integrates composite formulation, geometric design, and data-driven modeling to advance the precision micromolding of biodegradable orthopedic devices. Full article

A mill relining manipulator is key maintenance equipment for liners exchanged and operated by workers inside a grinding mill. To improve the operation efficiency and safety, real-time path planning and end deformation compensation should be performed prior to actual execution. This paper proposes a five-dimensional digital twin framework to realize virtual–real interaction between a physical manipulator and virtual model. First, a real-time digital twin scene is established based on OpenGL. The involved technologies include scene rendering, a camera system, the light design, model importation, joint control, and data transmission. Next, different solving methods are introduced into the service space for relining tasks, including a kinematics model, collision detection, path planning, and end deformation compensation. Finally, a user application is developed to realize real-time condition monitoring and simulation analysis visualization. Through comparison experiments, the superiority of the proposed path planning algorithm is demonstrated. In the case of a long-distance relining task, the planning time and path length of the proposed algorithm are 1.7 s and 15,299 mm, respectively. For motion smoothness, the joint change curve exhibits no abrupt variation. In addition, the experimental results between original and modified end trajectories further verified the effectiveness and feasibility of the proposed end effector compensation method. This study can also be extended to other heavy-duty manipulators to realize intelligent automation. Full article

This research investigates the feasibility of producing eco-friendly self-compacting concrete (SCC) by partially replacing both fine and coarse natural aggregates with waste marble sludge (WMS), a byproduct of the marble industry. The objective is to evaluate whether this substitution enhances or compromises the concrete’s performance while contributing to sustainability. A comprehensive experimental program was conducted to assess fresh and hardened properties of SCC with varying WMS content. Fresh-state tests—including slump flow, T₅₀ time, and V-funnel flow time—were used to evaluate workability, flowability, and viscosity. Hardened properties were measured through compressive, flexural, and Brazilian tensile strengths, along with water absorption after 28 days of curing. The mix with 10% replacement of both sand and coarse aggregate showed the most balanced performance, achieving a slump flow of 690 mm and a V-funnel time of 6 s, alongside enhanced mechanical properties—compressive strength 48.6 MPa, tensile strength 3.9 MPa, and flexural strength 4.5 MPa—and reduced water absorption (4.9%). A complementary cost model quantified direct material cost per cubic meter and a performance-normalized efficiency metric (compressive strength per cost). The cost decreased monotonically from 99.1 $/m³ for the base mix to $90.7 $/m³ at 20% + 20% WMS (−8.4% overall), while the strength-per-cost peaked at the 10% + 10% mix (0.51 MPa/USD; +12% vs. base). Results demonstrate that WMS can simultaneously improve rheology and mechanical performance and reduce material cost, offering a practical pathway for resource conservation and circular economy concrete production. Full article

Unmanned Aerial Vehicles (UAVs) play a crucial role in the continuous monitoring and coordination of disaster relief operations, providing real-time information that aids in decision-making and resource allocation. However, optimizing the deployment of multi-UAV systems in disaster-stricken areas presents a significant challenge. This challenge arises due to conflicting objectives, such as maximizing coverage while minimizing energy consumption, critical to ensuring prolonged operational capability in dynamic and unpredictable environments. To address these challenges, this paper proposes a novel successive deployment method specifically designed for optimizing UAV placements in complex disaster relief scenarios. The overall optimization problem is decomposed into two NP-hard subproblems: the coverage problem and the Energy Consumption (EC) problem. To achieve maximum coverage of the affected area, we employ the Strong Weiszfeld (SW) algorithm to determine optimal UAV placement. Simultaneously, to minimize energy consumption while maintaining optimal coverage performance, we utilize the Steepest Descent with Goldstein (SDG) algorithm. This dual-algorithmic approach is tailored to balance the trade-offs between wide-area coverage and energy efficiency. We validate the effectiveness of the proposed SW + SDG method by comparing its performance against traditional deployment strategies across multiple scenarios. Experimental results demonstrate that our approach significantly reduces energy consumption while maintaining extensive coverage, and outperforms conventional algorithms. This not only ensures a more sustainable and long-lasting operational network but also enhances deployment efficiency and stability. These findings suggest that the SW + SDG algorithm is a robust and versatile solution for optimizing multi-UAV deployments in dynamic, resource-constrained environments, providing a balanced approach to coverage and energy efficiency. Full article

During machining, most of the mechanical energy is converted into heat. A substantial part of this heat is transferred to the cutting tool, causing a rapid rise in tool temperature. Excessive thermal loads accelerate tool wear and lead to displacement of the tool center point, reducing machining accuracy and workpiece quality. This challenge is particularly pronounced when machining titanium alloys. Due to their low thermal conductivity, titanium alloys impose significantly higher thermal loads on the cutting tool compared to conventional carbon steels, making the process more difficult. To reduce temperatures in the cutting zone, cutting fluids are widely employed in titanium machining. They have been shown to significantly extend tool life. Cutting fluids are broadly categorized into cutting oils and water-based cutting fluids. Owing to their distinct thermophysical properties, these fluids exhibit notably different cooling and lubrication performance. However, current research lacks comprehensive cross-comparative studies of different cutting fluid types, which hinders the selection of optimal cutting fluids for process optimization. This study examines the influence of three cutting fluids—emulsion, cutting oil, and synthetic oil-free fluid—on tool wear, temperature, surface quality, and energy consumption during flood-cooled end milling of Ti-6Al-4V. A novel experimental setup incorporating embedded thermocouples enabled real-time temperature measurement near the cutting edge. Tool wear, torque, and surface roughness were recorded over defined feed lengths. Among the tested fluids, emulsion achieved the best balance of cooling and lubrication, resulting in the longest tool life with a feed travel path of 12.21 m. This corresponds to an increase of approximately 200 % compared to cutting oil and oil-free fluid. Cutting oil offered superior lubrication but limited cooling capacity, resulting in localized thermal damage and edge chipping. Water-based cutting fluids reduced tool temperatures by over 300 ${}^{\circ }$C compared to dry cutting but, in some cases, increased notch wear due to higher mechanical stress at the entry point. Power consumption analysis revealed that the cutting fluid supply system accounted for 60–70 % of total energy use, particularly with high-viscosity fluids like cutting oil. Complementary thermal and CFD simulations were used to quantify heat partitioning and convective cooling efficiency. The results showed that water-based fluids achieved heat transfer coefficients up to 175 kW/m²· K, more than ten times higher than those of cutting oil. These findings emphasize the importance of selecting suitable cutting fluids and optimizing their supply to enhance tool performance and energy efficiency in Ti-6Al-4V machining. Full article

This paper presents a novel support vector machine (SVM) classification approach that simultaneously accounts for both overall and extreme misclassification errors via a dual-norm regularization strategy. Traditional SVMs minimize the $L_1$-norm of slack variables to control global misclassification, while least squares SVM (LSSVM) minimizes the sum of squared errors. In contrast, our method preserves the classical $L_1$-norm penalty to maintain overall classification fidelity and incorporates an additional $L_{\infty }$-norm term to penalize the largest slack variable, thereby constraining the worst-case margin violation. This composite objective yields a more robust and generalizable classifier, particularly effective when occasional large deviations disproportionately affect decision boundaries. The resulting optimization problem minimizes a regularized objective combining the model norm, the sum of slack variables, and the maximum slack variable, with two hyperparameters, $C_1$ and $C_2$, balancing global error against extremal robustness. By formulating the problem under convex constraints, the optimization remains tractable and guarantees a globally optimal solution. Experimental evaluations on benchmark datasets demonstrate that the proposed method achieves comparable or superior classification accuracy while reducing the impact of outliers and maintaining a sparse model structure. These results underscore the advantage of jointly enforcing $L_1$ and $L_{\infty }$ penalties, providing an effective mechanism to balance average performance with worst-case error sensitivity in support vector classification. Full article

Robotic milling offers significant advantages for machining large aerospace components due to its low cost and high flexibility. However, compared to computerized numerical control (CNC) machine tools, robot systems exhibit lower stiffness, leading to force-induced deformation during milling process that significantly compromises path accuracy. This study proposed a dynamic robot posture programming method to enhance the stiffness for aluminum alloy milling task. Firstly, a milling force prediction model is established and validated under multiple postures and various milling parameters, confirming its stability and reliability. Secondly, a robot stiffness model is developed by combining system stiffness and milling forces within the milling coordinate system to formulate an optimization index representing stiffness performance in the actual load direction. Finally, considering the constraints of joint limit, singular position and joint motion smoothness and so on, the robot posture in the milling trajectory is dynamically programmed, and the joint angle sequence with the optimal average stiffness from any cutter location (CL) point to the end of the trajectory is obtained. Under the assumption that positioning errors were effectively compensated, the experimental results demonstrated that the proposed method can control both axial and radial machining errors within 0.1 mm at discrete points. For the specific milling trajectory, compared to the single-step optimization algorithm starting from the initial optimal posture, the proposed method reduced the axial error by 12.23% and the radial error by 8.61%. Full article

Chatter vibration in machining operations has been identified as one of the major obstacles to improving surface quality and productivity. Therefore, efficiently and accurately predicting stable cutting regions is becoming increasingly important, especially in high-speed milling processes. In this study, on the basis of a predictor–corrector scheme, the following three error correction methods are developed for milling stability analysis: the Correction Hamming–Milne-based method (CHM), the Correction Adams–Milne-based method (CAM) and the Predictor–Corrector Hamming–Adams–Milne-based method (PCHAM). Firstly, we employ the periodic delay differential equations (DDEs), which are usually adopted to describe mathematical models of milling dynamics, and the time period of the coefficient matrix is divided into two unequal subintervals based on an analysis of the vibration modes. Then, the Hamming method and the fourth-order implicit Adams–Moulton method are separately utilized to predict the state term, and the Milne method is adopted to correct the state term. Based on local truncation error, combining the Hamming and Milne methods creates a CHM that can more precisely approximate the state term. Similarly, combining the fourth-order implicit Adams–Moulton method and the Milne method creates a CAM that can more accurately approximate the state term. More importantly, the CHM and the CAM are employed together to acquire the state transition matrix. Thereafter, the effectiveness and applicability of the three error correction methods are verified by comparing them with three existing methods. The results demonstrate that the three error correction methods achieve higher prediction accuracy without sacrificing computational efficiency. Compared with the 2nd SDM, the calculation times of the CHM, CAM and PCHAM are reduced by around 56%, 56% and 58%, respectively. Finally, verification experiments are carried out using a CNC machine (EMV650) to further validate the reliability of the proposed methods, where ten groups of cutting tests illustrate that the stability lobes predicted by the three error correction methods exhibit better agreement with the experimental results. Full article

Diabetic keratopathy (DK) is a common ocular complication of diabetes mellitus (DM), affecting almost half of all diabetic patients. It is characterized by delayed healing of epithelial wounds, reduced corneal sensitivity, and persistent epithelial defects, which, in turn, significantly impair vision and quality of life. The limited understanding of its pathogenesis and the lack of effective treatments highlight the urgent need for more physiologically relevant experimental models. The three-dimensional (3D) models currently available provide valuable information on the pathophysiology of DK, although none of them yet fully reproduce the diabetic corneal phenotype complex. After a brief overview of corneal anatomy, the present review aims to systematically analyze the current 3D in vitro models developed for the study of DK, in terms of tissue architecture, presence of diabetic stimuli, and ability to replicate pathological traits. Full article

The growing application of 3D concrete printing (3DCP) in construction has raised important questions regarding its long-term durability under freeze–thaw (F–T) exposure, particularly in cold climates. This review paper presents a comprehensive examination of recent research focused on the F–T performance of 3D-printed concrete (3DPC). Key material and process parameters influencing durability, such as print orientation, admixtures, and layer bonding, are critically evaluated. Experimental findings from mechanical, microstructural, and imaging studies are discussed, highlighting anisotropic vulnerabilities and the potential of advanced additives like nanofillers and air-entraining agents. Notably, air-entraining agents (AEA) reduced the compressive strength loss by 1.4–5.3% after exposure to F–T cycles compared to control samples. Additionally, horizontally cored specimens with AEA incorporated into their mixture design showed a 15% higher dynamic modulus after up to 300 F–T cycles. Furthermore, optimized printing parameters, such as reduced nozzle standoff distance and minimized printing time gap, reduced surface scaling by over 50%. The addition of a nanofiller such as nano zinc oxide in 3DPC can result in compressive strength retention rates exceeding 95% even after aggressive F–T cycling. The lack of standard testing protocols and the geometry dependence of degradation are emphasized as key research gaps. This review provides insights into optimizing mix designs and printing strategies to improve the F–T resistance of 3DPC, aiming to support its reliable implementation in cold-region infrastructure. Full article

Laser Powder Bed Fusion (LPBF) has emerged as a leading additive manufacturing (AM) process for producing complex metal components. Despite its advantages, the inherent LPBF process complexity leads to challenges in achieving consistent quality and repeatability. To address these concerns, recent research efforts have focused on sensor fusion techniques for process monitoring, and on developing more elaborate control strategies. Sensor fusion combines information from multiple in situ sensors to provide more comprehensive insights into process characteristics such as melt pool behavior, spatter formation, and layer integrity. By leveraging multimodal data sources, sensor fusion enhances the detection and diagnosis of process anomalies in real-time. Closed-loop control systems may utilize this fused information to adjust key process parameters–such as laser power, focal depth, and scanning speed–to mitigate defect formation during the build process. This review focuses on the current state-of-the-art in sensor fusion monitoring and control strategies for LPBF. In terms of sensor fusion, recent advances extend beyond CNN-based approaches to include graph-based, attention, and transformer architectures. Among these, feature-level integration has shown the best balance between accuracy and computational cost. However, the limited volume of available experimental data, class-imbalance issues and lack of standardization still hinder further progress. In terms of control, a trend away from purely physics-based towards Machine Learning (ML)-assisted and hybrid strategies can be observed. These strategies show promise for more adaptive and effective quality enhancement. The biggest challenge is the broader validation on more complex part geometries and under realistic conditions using commercial LPBF systems. Full article

Detecting physical fatigue can help prevent overexertion. While typically defined at the muscle level, systemic fatigue remains less clear. Heart rate variability (HRV) reflects autonomic adaptability to physical stressors and may provide insight into fatigue-related responses. This study investigated the impact of physical fatigue on HRV and its correlation with endurance performance. Twenty participants (9 F, 11 M; 23.4 ± 5.0 y) walked on the treadmill at 1.25 m/s with progressively increased incline. HRV metrics were derived from baseline standing (STAND), pre-fatigued (PRE) and post-fatigued walking (POST). Time-domain HRV measures (lnTRI and lnTINN) were significantly reduced at POST compared to PRE or STAND (p < 0.05). Non-linear measures (DFA-α1, lnApEn, and lnSampEn) decreased at POST, while lnPoincaré SD2/SD1 increased. Normalized frequency-domain measures showed no condition effects. Baseline non-linear measures (lnApEn, lnSampEn, lnPoincaré SD2/SD1), normalized frequency measures and Total Power were significantly correlated with total fatiguing duration. Significant reductions in HRV and irregularity were observed post-fatigue. Greater baseline variability, irregularity, and high-frequency band power, reflecting parasympathetic activity, were associated with better endurance performance. Time-domain and non-linear measures were more sensitive to fatigue, whereas frequency-domain measures remain useful for identifying associations with endurance. The findings highlight HRV features that could enhance wearable sensing for fatigue and performance. Full article