Search Results (1,341)

To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and extrinsic camera parameters accurately. Based on the pinhole imaging model, disparity maps were obtained via pixel matching to reconstruct high-precision 3D ablation morphology. A mathematical model was established to analyze how key imaging parameters—baseline distance, focal length, and depth of field—affect reconstruction accuracy in micro-imaging environments. Focusing on trace element detection in WC-Co alloy samples, the reconstructed ablation craters enabled the precise calculation of ablation volumes and revealed their correlations with laser parameters (energy, wavelength, pulse duration) and the physical-chemical properties of the samples. Multivariate regression analysis was employed to investigate how ablation morphology and plasma evolution jointly influence LIBS quantification. A nonlinear calibration model was proposed, significantly suppressing matrix effects, achieving R² = 0.987, and reducing RMSE to 0.1. This approach enhances micro-scale LIBS accuracy and provides a methodological reference for high-precision spectral analysis in environmental and materials applications. Full article

Enhancing the wear resistance of structural steels used in demanding industrial applications is critical for extending components’ lifespan and ensuring mechanical reliability. In this study, we investigated the influence of flux-cored arc welding (FCAW) hardfacing on the tensile behavior of S355MC steel. Protective Fe-Cr-C alloy layers were deposited in one and two successive passes using automated FCAW, followed by tensile testing of specimens oriented at varying angles relative to the weld bead direction. The methodology integrated 3D scanning and digital image correlation to accurately capture geometric and deformation parameters. The experimental results revealed a consistent reduction in tensile strength and ductility in all the welded configurations compared to the base material. The application of the second weld layer further intensified this effect, while specimen orientation influenced the degree of mechanical degradation. Microstructural analysis confirmed carbide refinement and good adhesion, but also identified welding-induced defects and residual stresses as factors that contributed to performance loss. The findings highlight a clear trade-off between improved surface wear resistance and compromised structural properties, underscoring the importance of process optimization. Strategic selection of welding parameters and bead orientation is essential to balance functional durability with mechanical integrity in industrial applications. Full article

One of the largest future applications of computer vision is in the healthcare industry. Computer vision tasks are generally implemented in diverse medical imaging scenarios, including detecting or classifying diseases, predicting potential disease progression, analyzing cancer data for advancing future research, and conducting genetic analysis for personalized medicine. However, a critical drawback of using Computer Vision (CV) approaches is their limited reliability and transparency. Clinicians and patients must comprehend the rationale behind predictions or results to ensure trust and ethical deployment in clinical settings. This demonstrates the adoption of the idea of Explainable Computer Vision (X-CV), which enhances vision-relative interpretability. Among various methodologies, attribution-based approaches are widely employed by researchers to explain medical imaging outputs by identifying influential features. This article solely aims to explore how attribution-based X-CV methods work in medical imaging, what they are good for in real-world use, and what their main limitations are. This study evaluates X-CV techniques by conducting a thorough review of relevant reports, peer-reviewed journals, and methodological approaches to obtain an adequate understanding of attribution-based approaches. It explores how these techniques tackle computational complexity issues, improve diagnostic accuracy and aid clinical decision-making processes. This article intends to present a path that generalizes the concept of trustworthiness towards AI-based healthcare solutions. Full article

A scalable synthesis of two-dimensional titanium nitride chloride (TiNCl) via flash Joule heating (FJH) using titanium tetrachloride (TiCl₄) precursor has been developed. This single-step method overcomes traditional synthesis challenges, including high energy consumption, multi-step procedures, and hazardous reagent requirements. The structural and chemical properties of the synthesized TiNCl were characterized through multiple analytical techniques. X-ray diffraction (XRD) patterns confirmed the presence of TiNCl phase, while Raman spectroscopy data showed no detectable oxide impurities. Fourier transform infrared spectroscopy (FTIR) analysis revealed characteristic Ti–N stretching vibrations, further confirming successful titanium nitride synthesis. Transmission electron microscopy (TEM) imaging revealed thin, plate-like nanostructures with high electron transparency. These analyses confirmed the formation of highly crystalline TiNCl flakes with nanoscale dimensions and minimal structural defects. The material exhibits excellent structural integrity and phase purity, demonstrating potential for applications in photocatalysis, electronics, and energy storage. This work establishes FJH as a sustainable and scalable approach for producing MXenes with controlled properties, facilitating their integration into emerging technologies. Unlike conventional methods, FJH enables rapid, energy-efficient synthesis while maintaining material quality, providing a viable route for industrial-scale production of two-dimensional materials. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

Concrete is a cornerstone material in the construction industry owing to its versatile performance; however, its inherent brittleness, low tensile strength, and poor permeability resistance limit its broader application. Graphene, with its exceptional thermal conductivity, stable lattice structure, and high specific surface area, presents a transformative solution to these challenges. Despite its promise, comprehensive studies on the multifunctional properties and underlying mechanisms of graphene-enhanced concrete remain scarce. In this study, we developed a novel concrete composite incorporating cement, coarse sand, crushed stone, water, and graphene, systematically investigating the effects of the graphene dosage and curing duration on its performance. Our results demonstrate that graphene incorporation markedly improves the material’s density, brittleness, thermal conductivity, and permeability resistance. Notably, a comprehensive analysis of scanning electron microscopy (SEM) images and thermogravimetric (TG) data demonstrates that graphene-modified concrete exhibits a denser microstructure and the enhanced formation of hydration products compared to conventional concrete. In addition, the graphene-reinforced concrete exhibited a 44% increase in compressive strength, a 0.7% enhancement in the photothermal absorption capacity, a 0.4% decrease in maximum heat release, a 0.8% increase in heat-storage capacity, and a 200% reduction in the maximum penetration depth. These findings underscore the significant potential of graphene-reinforced concrete for advanced construction applications, offering superior mechanical strength, thermal regulation, and durability. Full article

Interferon-induced transmembrane protein 3 (IFITM3) is a member of the family of interferon-stimulated genes (ISGs) that inhibits a diverse array of enveloped viruses which enter host cells by endocytosis. Porcine reproductive and respiratory syndrome virus (PRRSV) is an enveloped RNA virus causing significant economic losses to the swine industry. Very little is known regarding how IFITM3 restricts PRRSV. In this study, the role of IFITM3 in PRRSV infection was studied in vitro using MARC-145 cells. IFITM3 over-expression reduced PRRSV replication, while the siRNA-induced knockdown of endogenous IFITM3 increased PRRSV RNA copies and virus titers. The colocalization of the virus with IFITM3 was observed at both 3 and 24 h post infection (hpi). Quantitative analysis of confocal microscopic images showed that an average of 73% of IFITM3-expressing cells were stained positive for PRRSV at 3 hpi, while only an average of 27% of IFITM3-expressing cells were stained positive for PRRSV at 24 hpi. These findings suggest that IFITM3 may restrict PRRSV at the post-entry steps. Future studies are needed to better understand the mechanisms by which this restriction factor inhibits PRRSV. Full article

Barite, a mineral composed of barium sulphate, holds global significance due to its wide range of industrial applications. It plays a crucial role as a weighting agent in drilling fluids for the oil and gas industry, in radiation shielding, and as a filler in paints and plastics. Although there are significant deposits of the mineral in commercial quantities in Nigeria, the use of barite of Nigerian origin has been low in the industry due to challenges that require further research and development. This research employed nanoindentation experiments using a model Ti950 Tribo indenter instrument equipped with a diamond Berkovich tip. Using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX), we gained information about the structure and elements in the samples. The load–displacement curves were examined to determine the hardness and reduced elastic modulus of the barite samples. The SEM images showed that barite grains have a typical grainy shape, with clear splitting lines and sizes. XRD and EDX analysis confirmed that the main components are chlorite, albite, barium, and oxygen, along with small impurities like silicon and calcium from quartz and calcite. The average hardness of the IB3 and IB4 samples was 1.88 GPa and 1.18 GPa, respectively, meaning that the IB3 sample will need more energy to crush because its hardness is within the usual barite hardness range of 1.7 GPa to 2.0 GPa. The findings suggest further beneficiation processes to enhance the material’s suitability for drilling and other applications. Full article

Selective laser melting (SLM) offers a novel approach for manufacturing intricate structures, broadening the application of titanium alloy parts in the aerospace industry. After the build period, heat treatments of annealing (AT) and hot isostatic pressing (HIP) are often implemented, but a comparison of their mechanical performances based on the specimen orientation is still lacking. In this study, horizontally and vertically built Ti6Al4V SLM specimens that underwent the aforementioned treatments, together with their microstructural and defect characteristics, were, respectively, investigated using metallography and X-ray imaging. The mechanical properties and failure mechanism, via fracture analysis, were obtained. The critical factors influencing the mechanical properties and the correlation of the fatigue lives and failure origins were also estimated. The results demonstrate that the mechanical performances were determined by the α-phase morphology and defects, which included micropores and fewer large lack-of-fusion defects. Following the coarsening of the α phase, the strength decreased while the plasticity remained stable. With the discrepancy in the defect occurrence, anisotropy and scatter of the mechanical performances were introduced, which was significantly alleviated with HIP treatment. The fatigue failure origins were governed by defects and the α colony, which was composed of parallel α phases. Approximately linear relationships correlating fatigue lives with the X-parameter and maximum stress amplitude were, respectively, established in the AT and HIP states. The results provide an understanding of the technological significance of the evaluation of mechanical properties. Full article

The field of metal additive manufacturing has witnessed significant growth in recent years, with technology offering the ability to produce complex geometries that are challenging to manufacture using the traditional methods. In situ monitoring and control of the manufacturing process are crucial for increasing the production capacity and improving the quality of manufactured parts. This article provides a comparative analysis of computational, indirect, and direct methods for in situ temperature monitoring during additive manufacturing of metal alloy components. Furthermore, it discusses the current status, recent improvements, and perspectives for in situ temperature measurements. The basic principles of thermal imaging, two-color pyrometry, and millimeter-wave radiometry are explored, highlighting their limitations for addressing challenges related to material emissivity and rapid changes in building material composition. Overcoming the challenges related to the inaccessibility of the chamber where the parts are formed, direct temperature measurements would allow for the integration of collected information into big data systems. Within the framework of Industry 4.0, this approach offers a viable alternative to the conventional metal shaping processes, improving the production capacity and part quality. This research aims to contribute to ongoing advancements in metal additive manufacturing and its potential to completely replace traditional metal casting practices in the Industry 4.0 era. Full article

Chemometrics has emerged as a powerful approach for deciphering complex environmental systems, enabling the identification of pollution sources through the integration of faunal community structures with physicochemical parameters and in situ analytical data. Leveraging advanced technologies—including satellite imaging, drone surveillance, sensor networks, and Internet of Things platforms—chemometric methods facilitate real-time and longitudinal monitoring of both pristine and anthropogenically influenced ecosystems. This review provides a critical and comprehensive overview of the foundational principles underpinning chemometric applications in environmental science. Emphasis is placed on identifying pollution sources, their ecological distribution, and potential impacts on human health. Furthermore, the study highlights the role of chemometrics in interpreting multidimensional datasets, thereby enhancing the accuracy and efficiency of modern environmental monitoring systems across diverse geographic and industrial contexts. A comparative analysis of analytical techniques, target analytes, application domains, and the strengths and limitations of selected in situ and remote sensing-based chemometric approaches is also presented. Full article

Moroccan oil shale ash (MOSA) represents an underutilized industrial by-product, particularly in the Rif region, where its high mineral content has often led to its neglect in value-added applications. This study highlights the successful conversion of MOSA into amorphous mesoporous silica (AS-Si) using a sol–gel process assisted by polyethylene glycol (PEG-6000) as a soft template. The resulting AS-Si material was extensively characterized to confirm its potential for environmental remediation. FTIR analysis revealed characteristic vibrational bands corresponding to Si–OH and Si–O–Si bonds, while XRD confirmed its amorphous nature with a broad diffraction peak at 2θ ≈ 22.5°. SEM imaging revealed a highly porous, sponge-like morphology composed of aggregated nanoscale particles, consistent with the nitrogen adsorption–desorption isotherm. The material exhibited a specific surface area of 68 m²/g, a maximum in the pore size distribution at a pore diameter of 2.4 nm, and a cumulative pore volume of 0.11 cm³/g for pores up to 78 nm. DLS analysis indicated an average hydrodynamic diameter of 779 nm with moderate polydispersity (PDI = 0.48), while a zeta potential of –34.10 mV confirmed good colloidal stability. Furthermore, thermogravimetric analysis (TGA) and DSC suggested the thermal stability of our amorphous silica. The adsorption performance of AS-Si was evaluated using methylene blue (MB) and ciprofloxacin (Cipro) as model pollutants. Kinetic data were best fitted by the pseudo-second-order model, while isotherm studies favored the Langmuir model, suggesting monolayer adsorption. AS-Si could be used four times for the removal of MB and Cipro. These results collectively demonstrate that AS-Si is a promising, low-cost, and sustainable adsorbent derived from Moroccan oil shale ash for the effective removal of organic contaminants from aqueous media. Full article

The efficient and reliable maintenance and repair of industrial machinery depend critically on circuit diagrams, which serve as essential references for troubleshooting and must be updated when machinery is modified. However, many circuit diagrams are not available in structured, machine-readable format; instead, they often exist as unstructured PDF files, rendered images, or even photographs. Existing digitization methods often address isolated tasks, such as symbol detection, but fail to provide a comprehensive solution. This paper presents a novel pipeline for extracting the underlying graph structures of circuit diagrams, integrating image preprocessing, pattern matching, and graph extraction. A U-net model is employed for noise removal, followed by gray-box pattern matching for device classification, line detection by morphological operations, and a final graph extraction step to reconstruct circuit connectivity. A detailed error analysis highlights the strengths and limitations of each pipeline component. On a skewed test diagram from a scan with slight rotation, the proposed pipeline achieved a device detection accuracy of 88.46% with no false positives and a line detection accuracy of 94.7%. Full article

Trichomes in cannabis (Cannabis sativa L.) are specialized structures responsible for cannabinoid and terpene biosynthesis, making their density a critical parameter for both research and industrial applications. However, consistent trichome density assessment remains challenging due to anatomical variability and the absence of standardized methodologies. This review critically examines the existing literature on trichome quantification across key floral structures—such as bracts, sugar leaves, calyxes, and the main cola—to identify the most reliable sites and practices for accurate evaluation. Evidence suggests that bracts represent the most consistent sampling unit, given their homogeneous trichome distribution and elevated cannabinoid concentration. Whilst sugar leaves and calyxes are also frequently analyzed, their morphological variability requires cautious interpretation. Furthermore, trichome shape, size, maturity, and vegetal surface expansion/shrinkage during stress must be considered when correlating density with secondary metabolite production. We also highlight the advantages of using more than only one floral structure and integrating microscopic imaging and software-assisted analysis to enhance reproducibility and accuracy. By synthesizing current methodologies and proposing pathways for standardization, this review aims to support more robust trichome assessment protocols, ultimately improving cannabinoid yield optimization, quality control, broader cannabis research frameworks, and an important aesthetic parameter for consumers. Future research efforts should focus on advancing imaging methodologies and optimizing sampling protocols to further improve the precision and reproducibility of trichome density and cannabinoid analyses. Full article

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article