Search Results (45,710)

In recent years, the fusion of camera data with LiDAR measurements has emerged as a powerful approach to enhance spatial understanding. This study introduces a novel, hardware-agnostic methodology that generates colourised point clouds from mechanical LiDAR using multiple camera inputs, providing complete 360-degree coverage. The primary innovation lies in its robustness under low-light conditions, achieved through the integration of a low-light image enhancement module within the fusion pipeline. The system requires initial calibration to determine intrinsic camera parameters, followed by automatic computation of the geometric transformation between the LiDAR and cameras—removing the need for specialised calibration targets and streamlining the setup. The data processing framework uses colour correction to ensure uniformity across camera feeds before fusion. The algorithm was tested using a Velodyne Puck Hi-Res LiDAR and a four-camera configuration. The optimised software achieved real-time performance and reliable colourisation even under very low illumination, successfully recovering scene details that would otherwise remain undetectable. Full article

Accurate monitoring of settlement in high-speed railway embankments is critical for operational safety and long-term serviceability. This study investigates the applicability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) for quantifying millimeter-scale deformations and emphasizes how surrounding environmental factors influence measurement accuracy. Using 29 TerraSAR-X images acquired between 2016 and 2018, PS-InSAR-derived settlements were compared with precise leveling survey data across twelve representative embankment sections of the Honam High-Speed Railway in South Korea. Temporal and spatial discrepancies between the two datasets were harmonized through preprocessing, allowing robust accuracy assessment using mean absolute error (MAE) and standard deviation (SD). Results demonstrate that PS-InSAR reliably captures settlement trends, with MAE ranging from 1.7 to 4.2 mm across different scenes. However, significant variability in accuracy was observed depending on local land-cover composition. Correlation analysis revealed that vegetation-dominated areas, such as agricultural and forest land, reduce persistent scatterer density and increase measurement variability, whereas high-reflectivity surfaces, including transportation facilities and buildings, enhance measurement stability and precision. These findings confirm that environmental conditions are decisive factors in determining the performance of PS-InSAR. The study highlights the necessity of integrating site-specific land-cover information when designing and interpreting satellite-based monitoring strategies for railway infrastructure management. Full article

This study evaluates the cosmetic degradation of polyethylene (PE) and polypropylene (PP) automotive components under four exposure scenarios—no exposure, outdoor exposure with and without glass shielding, and accelerated UV chamber weathering (ASTM G154)—through the evolution of visible (VIS) reflectance. Thirty-two samples (16 PE, 16 PP) were monitored over five time points; surface reflectance was recorded at 21 wavelengths and summarized into seven VIS bands, and hardness (Shore D) was measured pre/post-exposure. Repeated-measures univariate and multivariate analyses consistently revealed significant effects of Condition, Time, and their interaction on reflectance, with initial-reflectance adjustment improving inference stability across bands. PE exhibited more gradual and coherent reflectance decay, whereas PP showed greater band-to-band variability—most notably under UV chamber exposure. Additionally, hardness decreased in most exposed groups, aligning with optical changes. To place spectral trajectories in a kinetic context, a family of exponential models with small-sample information criterion selection was fitted, yielding $\eta \left(t\right)$—a dimensionless degradation efficiency summarizing spectral change. The contribution of this work is a multi-statistical framework—combining VIS-band-aware summaries with covariate-adjusted univariate/multivariate testing—that supports comparisons across materials and exposure conditions, underscoring the practical value of UV chamber protocols as surrogates for outdoor weathering. In sum, the study demonstrates the effectiveness of multivariate and covariate-adjusted models in quantifying optical degradation of polyolefins, offering pragmatic guidance for assessing mid- to long-term performance in automotive applications. Full article

COVID-19, caused by SARS-CoV-2, has created unprecedented global health challenges, necessitating rapid and reliable diagnostic strategies. The spike (S) protein, particularly its S1 subunit, plays a critical role in viral entry, making it a prime biomarker for early detection. In this study, we present a disposable, low-cost, and portable electrochemical biosensor employing specifically optimized aptamers (Optimers) for SARS-CoV-2 S1 recognition. The sensing approach is based on aptamer–protein complex formation in solution, followed by immobilization onto pencil graphite electrodes (PGEs). The key parameters, including aptamer concentration, interaction time, redox probe concentration, and immobilization time, were systematically optimized by performing electrochemical measurement in redox probe solution containing ferri/ferrocyanide using differential pulse voltammetry (DPV) technique.Under optimized conditions, the biosensor achieved an ultralow detection limit of 18.80 ag/mL with a wide linear range (10⁻¹–10⁴ fg/mL) in buffer. Importantly, the sensor exhibited excellent selectivity against hemagglutinin antigen and MERS-CoV-S1 protein, while maintaining high performance in artificial saliva with a detection limit of 14.42 ag/mL. Furthermore, its integration with a smartphone-connected portable potentiostat underscores strong potential for point-of-care use. To our knowledge, this is the first voltammetric biosensor utilizing optimized aptamers (Optimers) specific to SARS-CoV-2 S1 on disposable PGEs, providing a robust and field-deployable platform for early COVID-19 diagnostics. Full article

Lateral elbow pain is a common condition often misattributed solely to tendinopathy, while subtle instability may represent a significant underlying cause. Traditional classifications of elbow instability primarily address traumatic or grossly unstable patterns, leaving minor forms underrecognized. Recent evidence has emphasized the role of the Radial-Lateral Collateral Ligament (R-LCL) in maintaining joint stability, and its elongation has been linked to Symptomatic Minor Instability of the Lateral Elbow (SMILE). This model describes a horizontal type of radiocapitellar instability, where ligamentous incompetence leads to compensatory overload of the extensor carpi radialis brevis, ultimately producing chronic pain. Advances in diagnostic tools—including dynamic ultrasound (HELP-US test), CT arthrography with the SMILE Index, and arthroscopic signs such as the Loose Collar Sign—have improved recognition of this condition. However, surgical controversies remain, particularly regarding the potential destabilizing role of lateral release in patients with unrecognized R-LCL pathology. Arthroscopic stabilization techniques, such as R-LCL plication or imbrication, have shown promising outcomes, offering pain relief and functional recovery with minimally invasive approaches. This review integrates anatomical, biomechanical, and clinical evidence into a structured diagnostic and therapeutic algorithm, aiming to reduce diagnostic uncertainty and guide tailored interventions. Recognition of microinstability, and, in particular, the SMILE model, is crucial to optimize management of patients with chronic lateral elbow pain refractory to conservative measures. Full article

This paper proposes an event-driven dynamic output feedback dissipative fuzzy (EDDOFDF) control strategy for direct current (DC) microgrids with nonlinear constant power loads (CPLs) subject to hybrid attacks and noises. Firstly, using the measurement output of the microgrid’s fuzzy model and information of hybrid attacks, a Zeno-free resilient event-triggered communication mechanism (RETM) is designed, which can save limited resources such as network bandwidth and actively exclude attack-induced packet dropouts. Secondly, by designing an EDDOFDF security controller, a closed-loop switched fuzzy system model is established, which presents a unified platform to study the impacts of hybrid attacks, RETM, noises, microgrid plant, and controllers. Thirdly, by introducing a piecewise Lyapunov functional, exponential stability conditions in mean square with guaranteed dissipative performance are obtained. Further, sufficient conditions for designing both the EDDOFDF controller and state-feedback switched fuzzy controller are derived. Examples illustrate the effectiveness of the proposed method. Full article

Endometriosis is a chronic condition characterized by the presence of endometrial-like tissue outside the uterine cavity. It affects ~10% of reproductive-aged individuals and is associated with dysmenorrhea and infertility. Although imaging modalities have improved diagnosis, laparoscopy is required in many cases, contributing to 4–11 years of diagnostic delay. Non-invasive biomarkers could improve diagnosis and clinical decision-making, yet no candidate has achieved sufficient accuracy for routine use. Galectins, a family of β-galactoside-binding lectins involved in angiogenesis, immune regulation, and fibrosis, have emerged as promising biomarkers. In this study, we measured serum Galectin-1 (Gal-1) concentrations in 80 women with endometriosis and 15 controls using ELISA at four time points. Preoperative Gal-1 levels were significantly higher in endometriosis patients, particularly in Stage III–IV disease. ROC analysis yielded a modest diagnostic performance (AUC 0.692; p = 0.011) with high sensitivity (91.3%) and excellent negative predictive value (96.8%) but low specificity (46.7%) at a study-derived threshold (>14.06 ng/mL). Longitudinally, Gal-1 levels decreased immediately after surgery and rose above baseline by one year, while no significant correlations with preoperative pain severity were observed. These findings suggest that serum Gal-1 alone is insufficient as a diagnostic test but may be useful for multi-marker strategies to improve early diagnosis. Full article

We evaluate tactile-first robotic traversal on the Department of Homeland Security (DHS) figure-8 mobility test using a two-way repeated-measures design across various algorithms (three tactile policies—M1 reactive, M2 terrain-weighted, M3 memory-augmented; a monocular camera baseline, CB-V; a tactile histogram baseline, T-VFH; and an optional tactile-informed replanner, T-D* Lite) and lighting conditions (Indoor, Outdoor, and Dark). The platform is the custom-built Eleven robot—a quadruped integrating a joint-mounted tactile tentacle with a tip force-sensitive resistor (FSR; Walfront 9snmyvxw25, China; 0–10 kg range, ≈0.1 N resolution @ 83 Hz) and a woven Galvorn carbon-nanotube (CNT) yarn for proprioceptive bend sensing. Control and sensing are fully wireless via an ESP32-S3, Arduino Nano 33 BLE, Raspberry Pi 400, and a mini VESC controller. Across 660 trials, the tactile stack maintained ∼21 ms (p50) policy latency and mid-80% success across all lighting conditions, including total darkness. The memory-augmented tactile policy (M3) exhibited consistent robustness relative to the camera baseline (CB-V), trailing by only ≈3–4% in Indoor and ≈13–16% in Outdoor and Dark conditions. Pre-specified, two one-sided tests (TOSTs) confirmed no speed equivalence in any M3↔CB-V comparison. Unlike vision-based approaches, tactile-first traversal is invariant to illumination and texture—an essential capability for navigation in darkness, smoke, or texture-poor, confined environments. Overall, these results show that a tactile-first, memory-augmented control stack achieves lighting-independent traversal on DHS benchmarks while maintaining competitive latency and success, trading modest speed for robustness and sensing independence. Full article

Prior work has demonstrated that higher surprisal, a measure quantifying the unexpectedness of a trigger exposure, predicts headache onset over 12 to 24 h. However, these analyses relied on retrospective expectations of trigger exposure formed after extended data collection. To operationalize surprisal prospectively, Bayesian methods could update expectations dynamically over time. The objective of this study was to extend the application of surprisal theory for predicting migraine attack risk by developing methods to estimate trigger variable likelihood in real time, under conditions of limited personal observation. In a prospective daily diary study of individuals with migraine (N = 104), data were collected over 28 days, including stress, sleep, and exercise exposures. Bayesian models were applied to estimate daily expectations for each variable under uninformative and empirical priors derived from the sample. Stress was modeled using a hurdle-Gamma distribution, sleep using discrete outcomes from a Normal distribution, and exercise using a Bernoulli distribution. Surprisal was calculated based on the predictive distribution at each time point and compared to static empirical surprisal values obtained after full data collection. Dynamic Bayesian surprisal values systematically differed from retrospective empirical estimates, particularly early in the observation period. Divergence was larger and more variable under uninformative priors but attenuated over time. Empirically informed priors produced more stable, lower-bias surprisal trajectories. Substantial individual variability was observed across exposure types, especially for exercise behavior. Prospective surprisal modeling is feasible but highly sensitive to prior specification, especially in sparse data contexts (e.g., a binary exposure). Incorporating empirical or individually informed priors may improve early model calibration, though individual learning remains essential. These methods offer a foundation for real-time headache forecasting and dynamic modeling of brain–environment interactions. Full article

Background: The intracranial space has limited capacity; thus, volume changes in any component can raise intracranial pressure and cause mass effect. This mechanism underlies many neurological disorders. Artificial Intelligence, increasingly applied in medicine and diagnostic imaging, may support the evaluation of such conditions. This systematic review investigates AI-based models for cerebrospinal fluid segmentation and analysis on computed tomography. Methods: In December 2024, a systematic review was conducted across MEDLINE (PubMed), Scopus, Web of Science, Embase, and Cochrane Library. From 559 identified studies, 14 were included after independent review by two evaluators. Extracted data covered study characteristics, AI model design, dataset composition, and performance metrics for CSF segmentation. Quality assessment followed PRISMA 2020 and used JBI, AMSTAR 2, and CASP checklists. Results: The 14 studies demonstrated applications of AI in CSF segmentation and volumetric assessment, primarily for hydrocephalus diagnosis, mass effect evaluation, and stroke outcome prediction. Convolutional Neural Networks and Random Forests were the most frequent approaches. Reported segmentation accuracy was high, with Dice Similarity Coefficient values ranging from 0.75 to 0.95 and strong volumetric correlations (r up to 0.99) between AI-based and manual measurements. Conclusions: AI-assisted CSF segmentation from CT images shows promising accuracy and efficiency, with potential to enhance neurological diagnostics. Remaining challenges include dataset variability, inconsistent algorithm performance, and limited clinical validation. Future research should prioritize standardization of methods, larger and more diverse training datasets, and integration of AI tools into clinical workflows. Full article

Grinding semiconductor wafers with high hardness, such as SiC, remains a significant challenge due to the need to maximize material removal rates while minimizing subsurface damage. In the back-grinding process, two key parameters—the elastic modulus (Eb) of the grinding wheel bond and the fracture toughness (K_IC) of the wafer—play a critical role in governing the behavior of diamond and the extent of wafer damage. This study systematically investigated the effect of Eb and K_IC on diamond protrusion height (h_p), surface roughness (Ra), grinding forces, and the morphology of generated debris. The study encompassed four wafer types—Si, GaP, sapphire, and ground SiC—using five Back-Grinding Wheels (BGWs), with Eb ranging from 95.24 to 131.38 GPa. A log–linear empirical relationship linking ℎₚ to Eb and K_IC was derived and experimentally verified, demonstrating high predictive accuracy across all wafer–wheel combinations. Surface roughness (Ra) was measured in the range of 0.486 − 1.118𝜇m, debris size ranged from 1.41 to 14.74𝜇m, and the material removal rate, expressed as a thickness rate, varied from 555 to 1546𝜇m/h (equivalent to 75−209 mm³/min using an effective processed area of 81.07 cm²). For SiC, increasing the bond modulus from 95.24 to 131.38 GPa raised the average h_p from 9.0 to 1.2 um; the removal rate peaked at 122.07 GPa, where subsurface damage (SSD) was minimized, defining a practical grindability window. These findings offer practical guidance for selecting grinding wheel bond compositions and configuring process parameters. In particular, applying a higher Eb is recommended for harder wafers to ensure sufficient diamond protrusion, while an appropriate dressing must be employed to prevent adverse effects from excessive stiffness. By balancing removal rate, surface quality, and subsurface damage constraints, the results support industrial process development. Furthermore, the protrusion model proposed in this study serves as a valuable framework for optimizing bond design and grinding conditions for both current and next-generation semiconductor wafers. Full article

This study presents a self-supervised learning framework for detecting frequency disturbances in power systems using high-resolution time series data. Employing data from the UK National Grid, we apply the Temporal Contrastive Self-Supervised Learning (TC-TSS) approach to learn task-agnostic embeddings from unlabelled 60-s rolling window segments of frequency measurements. The learned representations are then used to train four traditional classifiers, Logistic Regression (LR), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF), for binary classification of frequency stability events. The proposed method is evaluated using over 15 million data points spanning six months of system operation data. Results show that classifiers trained on TC-TSS embeddings performed better than those using raw input features, particularly in detecting rare disturbance events. ROC-AUC scores for MLP and SVM models reach as high as 0.98, indicating excellent separability in the latent space. Visualisations using UMAP and t-SNE further demonstrate the clustering quality of TC-TSS features. This study highlights the effectiveness of contrastive representation learning in the energy domain, particularly under conditions of limited labelled data, and proves its suitability for integration into real-time smart grid applications. Full article

Objective: Allergic rhinitis (AR) continues to adversely affect the life quality of a substantial patient population, highlighting the necessity for enhanced treatment modalities. Our research utilized licorice extract (LE) in nasal irrigation for managing this condition, with its therapeutic efficacy gauged against traditional saline nasal irrigation (SNI) through clinical trials. Additionally, the study incorporated traditional Chinese medicine (TCM) principles, measuring not just subjective symptom relief but also the objective shifts in lung meridian electrical conductance (MEC), to provide a comprehensive evaluation of the treatment’s effectiveness. Methods: Based on our previous laboratory and animal studies, we developed an LE solution and applied it through nasal irrigation to treat AR. In a one-month controlled trial, 60 patients with AR received either licorice nasal irrigation (LNI) or SNI daily. We assessed treatment efficacy by subjective questionnaire scores (Total Nasal Symptom Score [TNSS] and 22-item Sino-Nasal Outcome Test [SNOT-22]) and objective lung MEC analysis. Result: In the trial, 30 participants were randomly allocated to each group, and 28 individuals in the LNI group and 24 in the SNI group finished the study without any side effects. The LNI group had better improvements in sneezing, nasal itchiness, and rhinorrhea, along with a greater overall TNSS reduction. On the SNOT-22, the LNI group scored better across most nasal and extra-nasal symptoms, sleep, and physiological and psychosocial well-being. Participants were sorted into low, normal, and high lung MEC subgroups. After treatment, those in the LNI group normalized their lung MEC levels in both the low and high subgroups, which was not observed in the SNI group. Conclusions: LNI markedly improves symptoms in patients with AR, enhancing their quality of life. This treatment method, integrating Western and TCM practices, also normalizes abnormal lung MEC values following therapy. It offers a method of objectively validating the effectiveness of treatments based on TCM theories. Full article

The effective design and operation of hydraulic structures, particularly open channel spillways, are crucial for water resource management and flood risk reduction in dams. A clear understanding of flow properties, such as velocity fluctuations and discharge, across various depths is essential for optimizing performance. In this study, experimental analysis and numerical simulation using FLOW-3D were combined to investigate the hydraulic parameters of a scaled model of the Romaine IV spillway located in Quebec, Canada. Measurements focused on flow properties, including velocity fluctuations at various discharge rates in specific flow depths, at selected points along the spillway. The numerical model was assessed by reproducing experimental geometry, initial water levels, and boundary conditions, and through sensitivity analyses to ensure accurate flow representation. Comparisons of flow rates of 180, 240, and 340 L/s showed that while simulations with the renormalized group (RNG) turbulence model reliably predicted average velocities, they underestimated maximum values and overestimated minimum values, especially at higher discharges. The results highlight the difficulty of accurately capturing velocity extremes in turbulent flows and the need for further model refinement. This was evident from the 60% discrepancy in minimum velocities observed at the channel center. Despite these discrepancies, the study advances our understanding of spillway performance and identifies avenues to improve the accuracy of numerical modeling in hydraulic engineering. Full article

Patients on hemodialysis (HD) often experience changes in body composition due to metabolic disorders. Phase angle (PhA) is a marker of tissue integrity and may reflect overall functional condition. This study evaluated body composition and its relationship with PhA in 53 HD patients (27 women, 26 men) over 40 years old, compared with 106 age- and sex-matched healthy controls. Body composition was assessed using bioelectrical impedance analysis (BIA), measuring skeletal muscle mass (SMM), fat tissue, total bone mass (BM), and PhA. HD patients had significantly lower fat mass and PhA than controls (p < 0.001). The prevalence of low SMM and BM was higher in patients, though not statistically significant. Sex differences were generally not significant, except for a higher prevalence of low BM in female controls (p < 0.001). After adjusting for age and sex, PhA was positively associated with SMM% (p = 0.021) and BM (p = 0.035) in HD patients only. These results indicate that PhA–body composition relationships differ between HD patients and healthy individuals, highlighting PhA as a potential marker of body composition disturbances in HD. Full article