Today, the presentation of a three-dimensional model of real-world features is very important and widely used and has attracted the attention of researchers in various fields, including surveying and spatial information systems, and those interested in the three-dimensional reconstruction of buildings. The building is the key part of the information in a three-dimensional city model, so extracting and modeling buildings from remote sensing data is an important step in building a digital model of a city. LiDAR technology due to its ability to map in all three modes of one-dimensional, two-dimensional, and three-dimensional is a suitable solution to provide hyperspectral and comprehensive images of the building in an urban environment. In this review article, a comprehensive review of the methods used in identifying buildings from the past to the present and appropriate solutions for the future is discussed.

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.

Background: The sacroiliac joint (SIJ) plays a crucial role in transmitting axial loads and maintaining pelvic stability. Sacroiliac joint arthropathy (SIJA) accounts for 10%–30% of low back pain cases but remains underrecognized due to overlapping pain referral patterns and nonspecific imaging findings. Diagnosis relies primarily on characteristic pain distribution and provocative maneuvers, with image-guided intra-articular block serving as the diagnostic gold standard. This study aimed to characterize the clinical profile of block-confirmed SIJA, emphasizing referral pain distribution, triggering position, and provocative test responses. Methods: A cross-sectional study was conducted on 98 patients with diagnostic block–confirmed SIJA at Siloam Hospital Lippo Village, Indonesia. Demographic data, referral pain sites, sitting duration, and results of FABER, compression, and distraction tests were analyzed descriptively. Results: The mean age was 52.07 ± 14.17 years, with 72.4% females. Referral pain most frequently involved the lower back (28.6%) and thigh (28.6%), with occasional extension to the groin (8.2%) or calf (4.1%). Over half of patients (55.1%) reported sitting more than six hours daily. Pain was predominantly triggered during sit-to-stand transitions (85.7%) and while sitting (74.5%). SIJ tenderness (98.0%) and FABER positivity (75.5%) were most consistent. Conclusion: The dominant referral pain in SIJA involves the lower back and posterior thigh. Sit-to-stand transition is the most frequent triggering position, while FABER testing demonstrates the highest diagnostic yield among provocative maneuvers. These consistent patterns may serve as practical clinical indicators to improve diagnostic accuracy in suspected SIJ-related pain.

This article is a comparative analysis of the short stories «Longing» by Anton Chekhov and «Prayer for Happiness» by Lu Xun, exploring the themes of social exclusion, grief and the impossibility of communication in society at the end of the XIX – beginning of the XX century. The article examines how both authors portray the struggle of the main characters to connect with other people in the face of personal tragedy and the indifference of society. The author analyzes the narrative techniques, symbolic images and character development used by Chekhov and Lu Xun to convey universal human experiences of loss, loneliness and the search for meaning in an indifferent world.

Background: Epigenetic aging, measured through various DNA methylation-based clocks, may have implications for predicting disease risk. However, the sensitivity of different epigenetic clocks that have emerged as biomarkers for biological aging and in predicting physical and mental health outcomes remains uncertain. This study examines the age and sex-adjusted associations between multiple epigenetic age acceleration measures and three key health indicators, including self-rated health, depressive symptoms, and body mass index (BMI), in a nationally representative sample of U.S. middle-aged and older adults. Methods: We analyzed data from 4,018 adults in the 2016 wave of the Health and Retirement Study (HRS), which included several epigenetic age acceleration measures: HORVATH, HANNUM, LEVINE, HORVATHSKIN, LIN, WEIDNER, VIDALBRALO, YANG, ZHANG, BOCKLANDT, GARAGNANI, and GRIMAGE. Linear regression models were used to assess the associations between epigenetic age acceleration and self-rated health (poor health), depressive symptoms, and BMI, adjusting for age and sex. Results: We found significant positive associations between epigenetic age acceleration and worse self-rated health, higher depressive symptoms, and increased BMI. However, these associations varied across different epigenetic clocks, with some measures potentially having more consistent utility for specific health outcomes than others. Conclusion: Epigenetic age acceleration is linked to poorer self-rated health, greater depressive symptoms, and higher BMI, but choosing which epigenetic clock(s) to use is also important. These findings underscore the need to consider multiple epigenetic aging markers when assessing health risks and highlight the potential for particular clocks to serve as more sensitive indicators of physical and mental health outcomes.

Background: Early life socioeconomic conditions and race/ethnicity are critical determinants of long-term health and behavioral outcomes. Epigenetic changes, particularly those measured by the GrimAge biomarker, may mediate the impact of these early adversities on later life outcomes. This study investigates the relationships between race/ethnicity, poverty at birth, epigenetic aging at age 15, and subsequent self-rated health, school discipline, depression, and school dropout at age 22. We explored sex differences in these paths. Methods: Data were drawn from the Fragile Families and Child Wellbeing Study (FFCWS), which included 733 youth with comprehensive follow-up data up to age 22. Structural Equation Modeling (SEM) was employed to assess the pathways from race/ethnicity and poverty at birth to epigenetic aging (GrimAge) at age 15, and subsequently to self-rated health and school discipline at age 22. The model controlled for potential confounders including sex, family structure, and parental education. Results: Race/ethnicity and poverty at birth were significantly associated with higher GrimAge scores at age 15 (p < 0.05). Higher GrimAge scores were predictive of poorer self-rated health (β = -0.08, p < 0.05) and increased instances of school discipline (β = 0.13, p < 0.01) at age 22. The indirect effects of race/ethnicity and poverty at birth on self-rated health and school discipline through GrimAge were also significant (p < 0.05), suggesting that epigenetic aging partially mediates these relationships. Sex differences were also observed. Poverty at birth predicted faster epigenetic aging at age 15 for males not females. We also observed that faster epigenetic aging at age 15 was predictive of school discipline of male not female participants at age 22. In contrast, faster epigenetic aging at age 15 was predictive of self-rated health (SRH) of female not male participants at age 22. Conclusions: This study provides evidence that with some sex differences, race/ethnicity and poverty at birth contribute to accelerated epigenetic aging (GrimAge) by age 15, which in turn predicts poorer self-rated health and increased school discipline issues by age 22. These findings emphasize the importance of early interventions targeting social determinants to mitigate long-term health and behavioral disparities. Addressing these early life conditions is crucial for improving health equity and outcomes in young adulthood.

Epigenetic studies, which can reflect biological aging, have shown that measuring DNA methylation (DNAm) levels provides new insights into the biological effects of social environment and socioeconomic position (SEP). This study explores how race, family structure, and SEP (income to poverty ratio) at birth influence youth epigenetic aging at age 15. Data were obtained from the Future of Families and Child Wellbeing Study (FFCWS) cohort, with GrimAge used as a measure of DNAm levels and epigenetic aging. Our analysis included 854 racially and ethnically diverse participants followed from birth to age 15. Structural equation modeling (SEM) examined the relationships among race, SEP at birth, and epigenetic aging at age 15, controlling for sex, ethnicity, and family structure at birth. Findings indicate that race was associated with lower SEP at birth and faster epigenetic aging. Specifically, income to poverty ratio at birth partially mediated the effects of race on accelerated aging by age 15. The effect of income to poverty ratio at birth on DNAm was observed in male but not female youth at age 15. Thus, SEP partially mediated the effect of race on epigenetic aging in male but not female youth. These results suggest that income to poverty ratio at birth partially mediates the effects of race on biological aging into adolescence. These findings highlight the long-term biological impact of early-life poverty in explaining racial disparities in epigenetic aging and underscore the importance of addressing economic inequalities to mitigate these disparities. Policymakers should focus on poverty prevention in Black communities to prevent accelerated biological aging and associated health risks later in life. Interventions aimed at eliminating poverty and addressing racial inequities could have significant long-term benefits for public health. Future research should explore additional factors contributing to epigenetic aging and investigate potential interventions to slow down the aging process. Further studies are needed to understand the mechanisms underlying these associations and to identify effective strategies for mitigating the impact of SEP and racial disparities on biological aging.

This article seeks to relate the narrative strategies used by video channels on YouTube with the advertising of toys and electronic games intended for the Alpha generation. Based on the principle that we live in a society composed of different age groups, the main theoretical references that make it possible to understand generation as a local and global phenomenon will be exposed. After such concepts, the rise of the image and its importance for the new generations will be approached, to support the selection of the audiovisual platform YouTube and its growing fascination by the public. Finally, concepts from semiotics will be applied to verify the narrative and discursive power of video channels for contemporary children, also known as the Alpha generation.

The purpose of the study was to examine the Motives of Tourists; Socio-Economic and Challenges of tourism in Kwahu in the Eastern Region of Ghana The study adopted a descriptive survey research design. The population of the study comprised six (6) communities (Mpraeso, Atibie, Obomeng, Obo Oworobong, and Nketepa in Kwahu South District Assembly (KSDA) in the Eastern Region of Ghana. Purposive and convenient sampling techniques were employed to select two hundred (200) respondents for the study. The main instrument used for data collection was questionnaires. The study employed the statistical package for social sciences (SPSS) to code and process the collected data. Descriptive and relational statistical techniques involving frequencies, percentages, summations, diagrams, and tables were employed in analysing the data. The Chi-square test analysis was used to explore the relationships and differences in perceptions. The study indicated that every tourist, whether local (Ghanaian) or foreign, had at least one of the following motives in mind for participating in the festival; To socialize; For relaxation; For education to participate and witness the paragliding festivals; To take photographs of festival scenes; Other motives like to sell items, especially souvenirs. The study also revealed that the KEF has had some positive socio-economic impact or implications on the area. These among others include: job creation, income generation for locals of the area, infrastructural development, and projection of the image of the area as the festival has become one of the biggest gatherings of revellers in the country, drawing people from all walks of life, nationally and internationally as a result of the introduction of paragliding since 2005, socialization enhancement, medium for cultural exchange and education, and finally serves as a medium for portraying the cultural identity of the people of Kwahu. The study also indicated that the major challenges encountered by tourists during the event were listed in order of degree of intensity: High cost of living, poor road network in the area, intermitted electricity and water supply, poor sanitary conditions in the area, poor health facilities, and unwelcoming attitude of some local residents of the festival area. It is recommended that, residents must be educated about the potential benefits of tourism as an industry helping to achieve sustainable community development. It is also recommended that Ghana tourism authority and Kwahu District Assembly should collaborate to improve on social amenities in the municipality to attract more foreign and local tourists during the festivity.

Street vending is a vital part of global urban life and not a local phenomenon. It can be found in various countries and forms; stationary and mobile. In Egypt, street vendors’ activities are considered illegal, an image of backwardness, blocking investors and tourism. This study aims at monitoring and investigates the female street vendors' role in placemaking in Heliopolis, Cairo. Challenging the authoritarian illegality aspect, literature review, observational walks, and spontaneous interviews are adopted in obtaining data and evaluating the female street vendors’ role in constructing a sense of place and identity. Female street vendors' expression, displaying arrangement, socio-cultural identities and chancy events create livable public places, territorial identities and a sense of place.

An advertisement is the promotion of a product, brand, or service to customers in order to pique their attention and increase sales. Advertisement comes in many forms, like video, picture, and song. The main purpose of advertising is to make the product or brand known to the public and bought by people. In advertising, the producer or company will use the slogan as the product identity itself. Slogan can give bridge the advertisement about the image of product. In slogan there are short words, often memorable to send a message of the advertisement to the people. This study aimed to analyze the type of figurative language used in advertisement slogan. The design of this research is descriptive qualitative method. In this research, the researchers focused on English slogan of Indonesia advertising. The data were collected from internet, newspaper and television. Furthermore, the collected data were analyzed by Kennedy’s (1983) theory. The researchers found there were 15 English slogan of advertisement. Based on the data analysis, the result of the research showed that the most types figurative language used in advertisement slogan was Metaphor (33,33%) or 5 slogans, personification (26,66%) or 4 slogans, hyperbole (26,66% ) or 4 slogans and symbol (13,33%) 2 slogans. The researchers did not found type of figurative language Simile, Litotes, Synecdoche, Allusion, Paradox, Irony, Ellipsis and Metanymy in advertisement slogans. As we can see, the dominant type of figurative language used in advertisement slogan was Metaphor with total amount 5 slogans (33,33%) from the data.

This study seeks to introduce an interactive design concept by merging Adinkra and Bogolanfini symbols into textile designs and convert them into utilitarian products. The qualitative research design method and the human-centred design approach were employed to identify, understand and assess how effective Adinkra and Bogolanfini ideographs can be adapted for textile designs and other product applications. The target samples for this study comprises variety of Adinkra symbols and Bogolanfini patterns. The significance of sampling in this study was to select suitable kinds of Adinkra and Bogolanfini patterns. Lines, shapes and texture were utilised to determine which particular symbols were included and which were not suitable. The CorelDraw vector software was used to convert and develop images of the Adinkra symbols and Bògòlanfini patterns and then manipulated into the final textile design. The design outcomes are indicative of the fact that varieties of Adinkra symbols and Bogolanfini patterns are prospective image resources for textile designs. The study recommends that textile design students and practitioners at various institutions and dispositions should be encouraged to explore the breadth of ideographs available across the West African sub-region for design ideas.

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.

Recent technologies have made life smarter. vehicles are vital components in daily life that are getting smarter for a safer environment. Advanced Driving Assistance Systems (ADAS) are widely used in today's vehicles. It has been a revolutionary approach to make roads safer by assisting the driver in difficult situations like collusion, or assistance in respecting road rules. ADAS is composed of a huge number of sensors and processing units to provide a complete overview of the surrounding objects to the driver. In this paper, we introduce a road signs classifier for an ADAS to recognize and understand traffic signs. This classifier is based on a deep learning technique, and, in particular, it uses Convolutional Neural Networks (CNN). The proposed approach is composed of two stages. The first stage is a data preprocessing technique to filter and enhance the quality of the input images to reduce the processing time and improve the recognition accuracy. The second stage is a convolutional CNN model with a skip connection that allows passing semantic features to the top of the network in order to allow for better recognition of traffic signs. Experiments have proved the performance of the CNN model for traffic sign classification with a correct recognition rate of 99.75% on the German traffic sign recognition benchmark GTSRB dataset.

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.

We here report a case of an acute cephalic vein thrombophlebitis in a 34-year-old female patient with no known thromboembolic risk factors and no medical history or ongoing treatment. We present the images of her diagnosis, which was made in the presence of a painful and indurated hyperchromic cord of the arm and confirmed by Doppler ultrasound. In the absence of deep extension of the thrombus, his management was limited to a symptomatic treatment without anticoagulation and the symptomatology was amended without complication or recurrence.

Cell division that is not controlled leads to cancer, an incurable condition. An early diagnosis has the potential to lower death rates from breast cancer, the most frequent disease in women worldwide. Imaging studies of the breast may help doctors find the disease and diagnose it. This study explores an effectiveness of DL and ML models in a classification of mammography images for breast cancer detection, utilizing the publicly available CBIS-DDSM dataset, which comprises 5,000 images evenly divided between benign and malignant cases. To improve diagnostic accuracy, models such as Gaussian Naïve Bayes (GNB), CNNs, KNN, and MobileNetV2 were assessed employing performance measures including F1-score, recall, accuracy, and precision. The methodology involved data preprocessing techniques, including transfer learning and feature extraction, followed by data splitting for robust model training and evaluation. Findings indicate that MobileNetV2 achieved a highest accuracy99.4%, significantly outperforming GNB (87.2%), CNN (96.7%), and KNN (91.2%). The outstanding capacity of MobileNetV2 to identify between benign and malignant instances was shown by the investigation, which also made use of confusion matrices and ROC curves to evaluate model performance.

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.

Generative AI refers to deep learning technology that can automatically produce original text, images, audio, video, and other outputs. With its emerging capabilities, Generative AI can radically change the dynamics of key operational processes in most industries. In this document, we illustrate how it is possible to integrate Generative AI technologies into the Financial Reporting System (FRS) of a corporation. The integration will allow the FRS to deliver on demand concise and lucid insights to its associated users on what is happening in the corporation and different aspects of the corporation performance assessment, such as its liquidity, solvency, profitability, organizational structure, and share buy back decision. The integration will also facilitate the delivery of what-if analyses associated with different strategic and tactical decisions taken by the corporation management, such as capital budgeting and profit distribution decisions. The unique added value of several attributes of these insightful analytics is automating the responses to ongoing requests of the FRS users on the corporation. Generative AI capabilities are rapidly expanding. The integration can be applied not only for the corporate FRS but any FRS at the national or global levels delivered by a central bank or an accounting standards setter. Any of these FRS can be made into a unique “hub” for the integrated Generative AI technologies. An equally innovative possible generalized integration could put any corporate process at the center and its supporting FRS tasks and deliverables in its periphery.

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.