Search Results (1,049)

Introduction: Microplastic (MP) contamination can endanger marine ecosystems and indirectly affect the well-being of humans through the ingestion of marine species. While most research investigates the digestive system, such as the gills and gastrointestinal tract of fish, it still fails to address a major oversight in understanding MP deposition in edible tissues, which is the primary route of human exposure. The differences in contamination levels among pelagic, demersal, and benthic fish in Malaysian waters remain poorly understood. This preliminary study uses Laser Direct Infrared Imaging (LDIR), a new, high-resolution, automated technique, to examine synthetic MP contamination in the edible portion of fish. Materials and Methods: The MPs were extracted from the edible tissue of three fish species representing pelagic (Fish A), benthic (Fish B), and demersal (Fish C) using KOH and sieved onto a gold mesh filter before analysis using LDIR. Results and Discussion: LDIR identified 162 MP particles, revealing clear differences by polymer type and habitat. Pelagic species mostly contained polyethylene (PE) and rubber (n = 8). Demersal species had mostly polyethylene terephthalate (PET) with small amounts of PE and rubber (n = 57). Benthic species showed the highest load, dominated by PET and polypropylene (PP) (n = 97). The morphological assessment of the MPs indicated that the polymers in pelagic fish were smaller, with an area of 2047.82 µm² and a circularity range of 0.14–0.74, indicating consistent shape. Conversely, MPs are irregular and larger in benthic fish, with areas up to 38,837.50 µm² and circularities ranging from 0.02 to 0.81. This pattern reflects specific accumulation related to habitat and potential environmental degradation processes. Conclusions: This preliminary study demonstrates the effectiveness of LDIR for detecting MPs in edible fish tissues. The findings provide a fundamental dataset on MP contamination in edible tissue and emphasize its distribution across ecological zones. Nevertheless, broader research is required to substantiate these data and assess the implications of MP contamination for the environmental stability of human and marine well-being. Full article

Sleep apnea is a prevalent sleep-related breathing disorder characterized by recurrent cessations or reductions in airflow during sleep. It significantly impacts the quality of life, yet current diagnostic methods like polysomnography (PSG) are expensive and uncomfortable, limiting accessibility and ease of use. We developed a novel non-contact biosensing system using secondary laser speckle pattern analysis and dedicated image processing algorithms for apnea-like breathing cessations. The proposed method was tested on $14$ healthy subjects with diverse body characteristics, aged 22–50 years (mean $33.1 \pm 9.3$ years) and body mass index (BMI) ranging from $19.6$ to $28.7$ kg/m² (mean $24.6 \pm 3.0$ kg/m²) at different ‘simulated’ sleeping positions (back-lying, stomach-lying and side-lying), using voluntary breath-holding protocols to simulate apnea-like cessations lasting 10–20 s (short duration) and 20–30 s (long duration). To evaluate the performance of the system without selection bias, two complementary five-fold cross-validation procedures were applied: a participant-level and a class-level stratification. Using class-wise stratification, the system achieved an overall accuracy of $87.0 \pm 3.0\%$ (95% CI: [85.3%, 88.7%]), long-cessation sensitivity of $91 \pm 12.4\%$$(95\% \mathrm{C}\mathrm{I}:[83.8\%,98.2\%\left]\right)$ and a short-cessation sensitivity of $88.0 \pm 11\%$$\left(95\% \mathrm{C}\mathrm{I}:[81.6\%,94.4\%]\right)$. The two-class classification strategy confirm the robustness of the approach, supporting the potential of secondary laser speckle pattern analysis as a low-cost, non-contact alternative for home-based sleep apnea screening. Full article

Product defect inspection is critical in industrial applications; however, it remains increasingly challenging in mass production environments, particularly for glossy or curved surface products. Conventional inspection of such surfaces typically relies on manual visual examination using gauges and operator judgment, which is time consuming and prone to inconsistency. This study proposes a robust defect detection framework for curved and reflective surfaces using a KEYENCE displacement laser sensor. The system integrates the Dijkstra algorithm, the Nearest Neighbor Algorithm, and a Genetic Algorithm to optimize the laser scanning path for structured image data generation. To validate the proposed framework, datasets were generated from both healthy and defective samples and used to train multiple deep learning models. A comparative analysis was conducted using YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures. Experimental results demonstrate that YOLOv11 achieved the best overall performance, attaining an mAP50 score of 0.844 while also exhibiting lower computational complexity and faster inference. Full article

The interaction between crossflows from sprinkler nozzles and airflow is crucial for engineering applications, particularly affecting the efficiency of sprayed areas. This study investigates the deformation of a continuously injected conical water spray subjected to horizontal airflow, using a planar laser imaging method as a visualisation technique. Experiments were conducted in a wind tunnel at a constant water pressure of 0.2 MPa and four airflow rates (0.1, 0.2, 0.4, and 0.6 m³·s⁻¹) to systematically vary the air-to-water momentum ratio. A grayscale-based analysis method was developed using a per-pixel Look-Up Table (LUT), enabling indirect assessment of droplet concentrations and spray structure. This approach allowed for a detailed examination of changes in the spray cone shape under flowing air. By assessing the water spray across three vertical planes intersecting the spray cone, it became possible to calculate lateral area and cone volume at different air-to-water mass flow ratios. The spray formation region exposed to airflow exhibited larger cone volumes than those with minimal airflow. The changes in apparent spray angles for the tested nozzles were determined to characterize the cone shape. The apparent spray angle varies systematically with the air-to-water mass flow ratio, confirming the dominant role of aerodynamic forces. These findings improve the understanding of spray behavior under crossflow and provide a basis for validating numerical models of air–water interactions. Full article

The dynamic behavior within the melt pool governs the final quality of components fabricated by laser powder bed fusion (LPBF). To address key technical challenges—rapid keyhole evolution, low absorption contrast from metal vapor, and difficulties in quantifying internal flow fields—this study introduces move contrast X-ray imaging (MCXI), a technique leveraging time-series frequency characteristics. Combined with a multi-scale Horn–Schunck global optical flow method, MCXI enables full-field quantitative extraction of the melt-pool velocity field. Experimental validation across feature points shows a relative deviation of less than 2% compared to independent manual feature-point tracking, confirming consistency with the best available experimental ground truth. Analysis reveals the keyhole tail evolution cycle comprises three distinct dynamic stages: expansion, stratification, and contraction, with its area increasing from 1329 μm² to 6508 μm² before stabilizing. For the first time, pore pinch-off events were quantitatively measured, revealing front and rear wall collision velocities of 7.98 m/s and 8.04 m/s, respectively, consistent with available high-fidelity simulations. Furthermore, analysis of the overall melt-pool momentum field demonstrates a near-equal distribution of positive and negative momentum, providing an internal self-consistency check confirming the absence of systematic directional bias in the extracted velocity field. This study enables quantitative analysis of LPBF melt-pool dynamics, providing a novel tool for process optimization and defect control. Full article

Silicon photomultipliers (SiPMs) are vital for calorimetric applications in high-energy physics and medical imaging due to their high gain, compactness, and insensitivity to magnetic fields. However, their finite pixel count induces non-linear response behaviour at high photon fluxes, affecting energy resolution and systematic accuracy. This work presents a comprehensive methodology to characterise SiPM response functions and derive correction curves using a single-step laser-based measurement approach. Three SiPMs with varying pixel sizes (15, 25 and 50 µm) are studied under controlled temperature conditions, with response functions extracted across different overvoltages and integration windows. The correction method, independent of precise light source calibration, effectively linearises the response up to saturation levels exceeding 100% of the pixel count, achieving deviations of the order of 3% across a broad operational parameter space, and outperforming the traditional calibration model. The analysis demonstrates minimal dependence of the correction on temperature, overvoltage, and pixel size, indicating universal applicability. These findings enhance SiPM performance in high-energy calorimetry and offer a practical framework for improving detector linearity and dynamic range extensions in large-scale applications. Full article

This study presents a comparative evaluation of multiple-approach optical spectroscopic sensor—Laser-Induced Breakdown Spectroscopy (LIBS), Mid-Infrared Spectroscopic sensing (MIRS), and Hyperspectral Imaging (HSI)-based sensors operating in the Visible–Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) ranges—for the quantitative detection of calcium carbonate (CaCO₃) in pelletized CaCO₃-CaO mixtures. The objective was to assess and compare the sensing performance of these optical sensor platforms for carbonate quantification. Each spectroscopic sensor dataset was processed using chemometric calibration methods, including Partial Least Squares Regression (PLSR), to ensure robust and reproducible quantitative predictions. Although the samples consisted of binary CaCO₃-CaO mixtures, the sensing task focused exclusively on CaCO₃ content. Results indicate that LIBS, MIRS, and HSI-SWIR-based sensing approaches achieved comparable quantitative performance, with LIBS providing the highest prediction accuracy. In contrast, the HSI-VNIR sensor configuration demonstrated lower predictive capability relative to the other optical sensing modalities. These findings highlight the potential and limitations of different optical sensor technologies for carbonate detection in heterogeneous mineral systems. Full article

Hair loss, particularly androgenetic alopecia (AGA) and alopecia areata (AA), is a prevalent condition with widespread psychosocial impact. Recently, low-level laser therapy (LLLT) has emerged as a promising non-invasive therapeutic alternative due to its bioregulatory effects and favorable safety profile compared to conventional pharmacological treatments. In this study, we employed fluorescence lifetime imaging microscopy (FLIM) to investigate the effects of red-light irradiation on hair follicle dynamics and the cutaneous microenvironment in a C57BL/6 mouse model. A hair regeneration model was established to evaluate the efficacy of 650 nm red-light irradiation (bandwidth ± 20 nm). Then, the skin tissue was stained with hematoxylin and eosin (H&E) and followed by FLIM analysis to provide a multidimensional assessment of tissue morphology and metabolic status. Results showed that red-light irradiation significantly increased hair follicle numbers and enhanced adenosine triphosphate (ATP) levels in the skin tissue. FLIM analysis further revealed prolonged fluorescence lifetime values across different epidermal and dermal layers in the irradiated group, indicating significant alterations in the skin metabolic microenvironment. Furthermore, phasor plot analysis enabled precise differentiation between hair follicles and their surrounding skin structures, highlighting FLIM’s high sensitivity and accuracy in evaluating hair growth. In conclusion, this study has provided novel imaging-based insights into the mechanisms of LLLT-induced hair regeneration, highlighting the potential of FLIM as a powerful tool for characterizing the cutaneous microenvironment and quantitatively evaluating phototherapeutic efficacy in future translational applications. Full article

Ti alloys are widely used in aerospace and biomedical fields due to their high mechanical properties under severe loading. Interest in additively manufactured Ti6Al4V has increased, but further research is needed to fully characterize their properties. This work compares the effects of surface properties, internal defects, microstructure, hardness, and Hot Isostatic Pressing (HIP) or Vacuum Heat Treatment (VHT) on the fatigue behavior of Ti6Al4V produced by Selective Laser Melting (SLM) and Electron Beam Melting (EBM). Printing parameters and post-processing were optimized to achieve high density and minimal porosity, providing a solid basis for realistic fatigue comparisons. Samples were characterized in terms of microstructure (optical microscopy and SEM), mechanical properties (hardness mapping), surface texture (confocal microscopy), and internal defects (image-based analysis). Uniaxial fatigue limits were determined by a Dixon-Mood staircase method, and failed specimens were analyzed for fracture surfaces and defect areas. Applied load on flaws was evaluated to identify root causes of fatigue failure. Results showed that fatigue of as-printed samples is governed by surface roughness, while machined specimens are controlled by internal defect size. Machining increased the fatigue limit roughly threefold, and HIP further improved it by 10–20% by reducing internal porosity. In conclusion, with properly optimized melting parameters, both EBM and SLM produce similar mechanical performance at comparable roughness, supporting their use for structural components. Full article

This work presents an experimental aerodynamic study of a Mars rover model, aimed at characterizing its flow behavior under Martian environmental conditions. Due to the extremely low Reynolds numbers associated with Mars’ thin atmosphere, the experiments were conducted using a scaled model of the rover manufactured via additive techniques. The study first focuses on understanding how the geometry of the rover influences the overall flow field, identifying key aerodynamic features such as separation zones, vortical structures, and flow reattachment regions driven by the complexity of the vehicle. A comprehensive investigation of the flow around the model was performed using both a hydrodynamic towing tank with dye injection for qualitative visualization, and particle image velocimetry (PIV) for quantitative flow field analysis in wind tunnel tests. After the general flow characterization, a more detailed local analysis was conducted using laser Doppler anemometry (LDA). This phase of the study targeted precise velocity measurements at specific locations corresponding to the MEDA (Mars Environmental Dynamics Analyzer) wind sensors onboard the rover. Quantitative results indicate that the central body induces a local flow acceleration of 20% to 40% relative to the free stream while severe turbulence was recorded in specific angular sectors, with velocity fluctuations reaching up to 120% for Sensor 1 and 90% for Sensor 2. Full article

Background: Early detection and monitoring of enamel changes during caries lesion formation are essential for preventive management. This study aimed to evaluate time-dependent enamel demineralization using micro-computed tomography (micro-CT) and to compare its diagnostic performance with laser fluorescence and digital colorimetric image analysis. Methods: Twelve sound human permanent teeth were subjected to a gel-based lactic acid demineralization for 14 days. Assessments were performed at baseline and after 3, 7, and 14 days. Enamel mineral density (MD) and demineralization depth (DD) were measured using micro-CT. Laser fluorescence was evaluated using DIAGNOdent, while colorimetric changes were analyzed through standardized digital imaging using the CIE Lab* system, including ΔE and Whiteness Index (WI). Statistical analysis included repeated measures ANOVA and Pearson correlation (p < 0.05). Results: A significant time-dependent progression of enamel demineralization was observed. Demineralization depth increased from 0.0828 mm (3 days) to 0.234 mm (14 days) (p < 0.001), while mineral density decreased significantly over time (p < 0.001). DIAGNOdent values showed significant increases after 7 and 14 days (p = 0.002). Colorimetric analysis revealed early detectable changes, with ΔE exceeding clinically perceptible thresholds as early as day 3. WI values increased progressively, indicating enhanced enamel opacity. A weak but significant negative correlation between MD and DD was found (p = 0.04). Conclusions: Enamel demineralization progresses in a time-dependent manner and can be effectively monitored using micro-CT, laser fluorescence, and colorimetric analysis. Digital colorimetric analysis may serve as a valuable adjunctive tool in caries diagnostics. Full article

Surgical precision is critical in oncology, where complete tumor resection while preserving healthy tissue directly influences patient outcomes. Traditional intraoperative diagnostic tools, such as frozen-section analysis, are limited by time constraints, tissue sampling, and interpretative variability. Intraoperative mass spectrometry (MS) has recently emerged as a transformative approach, enabling rapid, label-free molecular profiling of surgical specimens in real time. Several technologies—including Rapid Evaporative Ionization Mass Spectrometry (REIMS, “iKnife”), Desorption Electrospray Ionization (DESI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS) Imaging, Picosecond InfraRed Laser mass spectrometry (PIRL-MS), and novel devices such as the MasSpec Pen—offer unique strategies for intraoperative tumor characterization. Applications have been demonstrated across multiple cancer types, including brain, breast, gastrointestinal, and urogenital malignancies, where MS can improve margin assessment, tumor classification, and surgical guidance. Beyond its clinical promise, intraoperative MS faces technical and translational challenges, including high infrastructure costs, a lack of standardization, and the need for robust multicenter validation. Integration with artificial intelligence, imaging modalities, and multi-omics approaches may further enhance its clinical utility. This review summarizes current technologies, clinical applications, limitations, and future perspectives of intraoperative MS in oncology, highlighting its potential to reshape surgical oncology practice. Full article

Background: Magnetic resonance spectroscopic imaging (MRSI) offers valuable metabolic information for assessing brain tumor progression and therapeutic response, but its performance in rodent models is often hindered by the low signal-to-noise ratio (SNR) and spatially heterogeneous spectral quality, particularly in peripheral voxels. These issues reduce the number of usable spectra available for quantitative and classifier-based analyses. To address this, we implemented a multi-voxel MRSI-semiLASER sequence—widely recommended in clinical practice—on a 7T Bruker Biospec system running ParaVision 5.1 to improve spectral homogeneity in mouse brain tumor studies. Results: Compared with the vendor CSI-PRESS sequence, MRSI-semiLASER produced more uniform spectra across the grid and achieved up to a 1.2-fold SNR increase in murine glioma, enabling a 20% reduction in slice thickness without compromising spectral quality. Importantly, the sequence produced a substantial gain in the proportion of spectra amenable to analysis, particularly in outer grid voxels that frequently fail with CSI-PRESS. The implemented MRSI-semiLASER sequence and instructions are openly available to the community. Conclusions: MRSI-semiLASER improves spectral homogeneity, increases the proportion of usable spectra, and supports higher spatial detail. These technical improvements may enhance data yield per subject and may facilitate future applications such as more robust pattern recognition workflows and greater data efficiency in longitudinal studies, although such aspects were not evaluated here. Full article

Calcium silicate-based sealers are widely used in contemporary endodontics, but their strong interaction with dentinal substrates may complicate their removal during nonsurgical retreatment and potentially hinder canal disinfection. This ex vivo study evaluated the effectiveness of different irrigation activation techniques in removing a calcium silicate-based sealer from oval-shaped root canals. Sixty extracted single-rooted teeth were instrumented and obturated using the single-cone technique with NeoSealer Flo, followed by retreatment using a reciprocating system. Specimens were randomly assigned to four final irrigation protocols: conventional needle irrigation (CNI) with NaOCl/EDTA, ultrasonic activation (US), diode laser activation (LI), and Er:YAG laser activation using the SWEEPS mode (SW) (n = 15). Residual filling material was quantified before and after final irrigation using stereomicroscopic imaging and ImageJ (version 1.54) analysis. Dentinal surface morphology and residual sealer were further evaluated using SEM–EDS. Statistical analysis included one-way ANOVA and chi-square tests (p < 0.05). All protocols significantly reduced residual filling material compared with mechanical retreatment alone (US 15.08%, CNI 7.89%, LI 8.01%, SW 7.20%) (p < 0.01). US resulted in significantly greater sealer removal compared with CNI, LI, and SW, with mean differences ranging from 7.08% to 7.88% (p < 0.05). These findings indicate that irrigation activation enhances the removal of NeoSealer Flo calcium silicate-based sealer, with ultrasonic activation demonstrating greater effectiveness among the evaluated techniques, under the conditions of this experimental setup. Full article

Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity of laser point cloud poses a significant challenge to feature extraction and matching in odometry estimation. In this paper, we investigate odometry estimation from two aspects, i.e., algorithm optimization, and system design/implementation. In algorithm optimization, we present an image feature-assisted odometry estimation scheme that leverages the richness of image information captured by a companion camera to enhance the accuracy of laser point cloud matching. This also serves as a screening mechanism to reduce the matching size and lower the computing complexity for a higher estimation rate. In addition, various schemes, such as adaptive threshold in image feature point selection, principal component analysis (PCA)-based plane fitting for laser point interpolation, and Gauss–Newton optimization for calculating the transform matrix, are also employed to improve the accuracy of odometry estimation. The performance of improved odometry estimation is verified using an existing FLOAM (Fast Lidar Odometry and Mapping) framework. The KITTI dataset for autonomous vehicles with ground truth was used as the test bench. Simulation results indicate that the translation error and rotation error can be reduced by 16.6% and 1.3%, respectively. Computing complexity, measured as the software execution time, also reduced by 63%. In system implementation, a hardware/software (HW/SW) co-design strategy was adopted, where complexity profiling was first conducted to determine the task partitioning and time-consuming tasks are offloaded to a hardware accelerator. This facilitates real-time execution on a resource-constrained embedded platform consisting of a microprocessor module (Raspberry Pi) and an attached FPGA board (Pynq Z2). Efficient hardware designs for customized DSP functions (adaptive threshold and PCA) were developed in an FPGA capable of completing one data frame in 20ms. The final system implementation met the target throughput of 10 estimations per second, and can be scaled up further. Full article