Articles

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

Breast cancer is the most prevalent cancer in women worldwide, with approximately two million new cases every year. The number of cases increases despite the high survival rate. The aim of this study is, therefore, to understand this cancer by finding out what has been studied in this area using scientific evidence published between 2003 and 2023. A search was therefore carried out for scientific articles and other relevant sources on the subject with free access, and 48 documents were then analyzed. According to the analysis, many studies have been conducted in the area, particularly on quality of life and body image. However, little has been done in terms of environmental sustainability and breast cancer.

The complexity of manufacturing photolithography has increased significantly. The increase in the level of integration has driven smaller feature-sized integrated circuits (ICs). The evolution in stepper technologies has been geometric. This has enabled the printing of printed ICs with a 45 nm feature size. Improvement in lithographic technology is moving towards 32 nm. This feature-size roadmap poses many challenges to semiconductor manufacturing technology. Advanced photomask synthesis, high-NA steppers, and computational lithography are some examples of the solution space. Optical proximity correction (OPC) and model-based optical proximity correction (MBOPC) are subsets of this solution space. OPC has matured significantly and is the de facto solution for manufacturing photomasks up to the 65 nm node. The OPC technique has been further refined as model-based OPC and has been applied to advanced printing technology of 45 nm. The OPC solution for 45 nm technology has limitations of mask rule check (MRC) and manufacturability restrictions. These restrictions are inevitable in OPC and MBOPC solutions because of the limits in lithographic technology. The technology evolution towards 32 nm has equally challenged the non-linear treatment of wafer-level problems in OPC solutions. PBOPC has limitations in reducing the wafer optical proximity error of the granny's issue, edge placement, mask rule check, etc. PBOPC also has limitations in reducing the mask error enhancement factor. With all these challenges, it is still a formidable solution methodology to address the wafer and mask level issues. Such a formidable solution architecture can result in a limited number of PBOPC solutions. This text looks at the performance of advanced PBOPC features on exposure tuning and the effects of higher-order wafer and aerial image effects. This text also discusses the performance of continuous process correction of masks, lenses, and scanners.

Hand hygiene is crucial in preventing healthcare-associated infections. In this study, we aimed to quantify the accuracy of subjective evaluation of hand washing through visual inspection and objective evaluation through images. The participants were 24 consenting nursing students, and the study used black light and fluorescent paint to generate sketches and captured images of the unwashed areas, which were processed. Handwashing training was conducted once a week for four consecutive weeks. We collected data in the first and fourth training sessions. We found that the percentage of the unwashed palmar areas was significantly higher in the images than in the sketches (p<0.05). The percentage of the uncleaned area as recognized visually significantly increased for sketches (p=0.01) and decreased for images (p=0.009) in the fourth session. The difference between the percentages of the image and sketch area notably decreased in the fourth session (p=0.002). When we checked the recognition percentage of the unwashed area by dividing the right-hand palmar side into six areas, the fingertips had the highest percentage, and the ball of the thumb had the lowest percentage. The recognition of the unwashed areas was low when comparing the subjective visual assessment with the objective imagery assessment. In addition, the percentage of the unwashed areas decreased with repeated training, indicating a decrease in the difference between the subjective and objective ratings.

The major automated plants have produced large volumes of high-quality products at low cost by introducing various technologies, including robotics and artificial intelligence. The code of many defects on the surface of products is embedded with economic loss and sometimes functionality loss because products are rarely found with defects. Therefore, most items’ production is done based on prediction and has an invisible fluctuation in production. The detection process for hidden defect images requires a lot of costs and needs to be supported for better progress and quality enhancement. Paint shop defects should be analyzed from color changes to detect defects effectively by preventing the variability of product demand over time. It is not easy to take visible light images without noise because the paint surfaces are glossy. A few parts of illumination and shadows remain in images, even in larger size and high-resolution images. The various painted surfaces are also needed to reflect both color and texture information in computer vision models to classify defects precisely. Several automated detection systems have been applied to paint shop inspections using lasers, infrared, x-ray, electrical, magnetic, and acoustic sensors. The chance of paint shop defects can be low, unnecessarily low, compared to clouds in the sky, but those chances impact defect functionalities. Thus, they are called as “lessons learned.” Lately, artificial intelligence has been introduced to the field of factory automation, and many defect detection feeds have found footsteps in machine learning and deep learning. Recent attempts at deep learning-based defect detection are proposing simple techniques using specific neural network architectures with big data. However, big data is still in its early stages, and significant challenges exist in normalizing and annotating such data. To get cost-efficient and timely solutions tailored to automotive paint shops, it might be a better consideration to combine deep learning solutions with traditional computer vision and more elaborate machine learning methods.

Kaybarkuh (Mount Kaybar) consists of intrusive igneous bodies with two age periods, located in North of Dasht-e-Bayaz left-lateral fault terminal. The spatial and structural analysis of fractures and dike networks may allow for the accurate identification of mineralization zones in the area. This study aims to characterize lineament network in the study area by automatic method using multispectral satellite images from Landsat 8 Operational Land Imager (OLI), visual extraction of lineaments from Landsat-8 and SENTINEL-2 images, and extraction of drainage network as lineament based on digital elevation models (DEMs) and their validation, compared with fault network of the area. The results showed that there is a significant relationship between the trend of studied lines in the region by the three methods mentioned and the overall trend is about N330⁰. This can indicate a tensile regime with a trend perpendicular to the mentioned orientation, which results from the activity of the Dasht-e-Bayaz fault. Finding more evidences requires further studies.

The level of business competition in several online shopping sites for fashion products is currently experiencing quite crucial dynamics. Competitive match will indirectly affect consumer purchasing decisions. This research explores what factors are dominantly influencing the level of product purchase decisions on online shopping sites in terms of product variety, price, brand image and promotion aspects. This research design uses a quantitative research design. Determination of the sample in this study using the convenience sampling method with a total of 110 respondents. Data collection uses a questionnaire distributed online during the period from May to June 2021. This study concludes that product variations and brand image have a positive and significant influence on purchasing decisions. Then for the effect of price and promotion on purchasing decisions obtained a positive but not significant impact. This study confirms the importance of paying attention to aspects of pricing and product promotion strategies on the Reebonz online shopping site.