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Background and Objective: Vitiligo is a chronic skin damage disease, triggered by differential melanocyte death. Vitiligo (0.5%–1% of the population) is one of the most severe skin conditions. In general, the foundation of the condition of vitiligo remains gradual patchy loss of skin pigmentation, overlying blood, and sometimes mucus. This paper provides a systematic review of the relevant publications and conference papers based on the subject of vitiligo diagnosis and confirmation through computer‐aided machine learning (ML) techniques. Materials and Methods: A search was conducted using a predetermined set of keywords across three databases, namely, Science Direct, PubMed, and IEEE Xplore. The selection process involved the application of eligibility criteria, which led to the inclusion of research published in reputable journals and conference proceedings up until June 2024. These selected papers were then subjected to full‐text screening for additional analysis. Research publications that involved application of ML techniques with targeted population of vitiligo were selected for further systematic review. Results: Ten selected and screened studies are included in this systematic review after applying eligibility criteria along with inclusion and exclusion criteria applied on initial search result which was 244 studies based on vitiligo. Priority is given to those studies only which use ML techniques to perform detection and diagnosis on vitiligo‐targeted population. Data analysis was carried out only from the selected and screened research articles that were published in authentic journals and conference proceedings. Conclusion: The importance of applying ML techniques in the clinical diagnosis of vitiligo can give more accurate results and at the same also eliminate the need of biased human judgement. Based on a comprehensive examination of the research, encompassing the methodologies employed and the metrics utilized to assess outcomes, it was determined that there is a need for further research and investigation regarding the application of ML algorithm for the detection and diagnosis of vitiligo with different datasets and more feature extraction. [ABSTRACT FROM AUTHOR]

In the early 21st century, there was an increase in precipitation during the retreat period of the East Asian summer monsoon (EASM). This study aims to explore the precipitation changes and their possible causes in the context of climate change. The findings indicate that the increased precipitation primarily occurred in the Yellow—Huai River valley during the early autumn. This corresponds to a delayed retreat of the EASM with a northward shift of 0.9°N after 2002. Notably, this anomalous changes in the EASM are associated with the significant warming in two Asian dryland regions. The warming of the central Asian dryland strengthens the midlatitude high‐pressure belt and the anomaly anticyclone over northeast Asia, which restrains the development of wave troughs and westerly cold air activity. Similarly, the warming China‐Mongolia dryland enhances the anomaly anticyclone over northeast Asia through the dry soil moisture feedback and reduced latitudinal temperature gradient. These two Asian drylands thereby hold the northward shift of the westerly jet stream and the northwest extension of the Japan Sea high and the western Pacific subtropical high. These changes result in maintaining the northward EASM circulation and driving the northwest water vapor flux from the northwest Pacific, leading to moisture convergence in northern China. The China‐Mongolia dryland warming also increases the land‐sea thermal contrast, which induces a dipole pattern over East Asia and drives the northward water vapor flux from the South Sea. As a result, the rapidly warming drylands restrict westerly activity and EASM retreat, ultimately leading to increased precipitation. Plain Language Summary: Northern China is experiencing a climate that resembles summer and delayed autumn in September and October, accompanied by increased precipitation, precipitation extremes, and high temperature. We have investigated this phenomenon and found that it is related to delayed retreat of East Asian summer monsoon (EASM) with the northernmost margin of the EASM (EASM‐NM) shifted northward by 0.9° latitude after 2002, which has contributed to a rise in precipitation of 80 mm in the Yellow—Huai River valley during the early autumn. These changes are associated with the rapid warming of the middle‐latitude Asian drylands. The warming of both central Asian dryland and China‐Mongolia dryland strengthens northward westerly jet stream and creates a high‐pressure anomaly from northeast China to Japan Sea. This is achieved through westerly waves and reduced meridional temperature gradient. These changes further maintain the northward circulations of EASM and weaken westerly activity. Consequently, more northwest and southerly water vapor is transported to northern China, resulting in increased precipitation. Key Points: Increased autumn precipitation corresponds to delayed retreat of the East Asian summer monsoon (EASM) with a northward shift of 0.9°NThe warming Asian dryland delays the retreat of EASM that enhances precipitation in early autumn in northern ChinaThe warming Asian drylands drive northward westerly jet and westward Japan Sea high through westerly waves and zonal thermal contrast [ABSTRACT FROM AUTHOR]

Background: Concrete, a fundamental construction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accounting for approximately 1.6 billion metric tons of Portland and modified Portland cement annually. Moreover, addressing extreme conditions with exceptionally nonlinear behavior necessitates a laborious calibration procedure in structural analysis and design methodologies. These methods are also difficult to execute in practice. To reduce time and effort, ML might be a viable option. Material and Methods: A set of keywords are designed to perform the search PubMed search engine with filters to not search the studies below the year 2015. Furthermore, using PRISMA guidelines, studies were selected and after screening, a total of 42 studies were summarized. The PRISMA guidelines provide a structured framework to ensure transparency, accuracy, and completeness in reporting the methods and results of systematic reviews and meta-analyses. The ability to methodically and accurately connect disparate parts of the literature is often lacking in review research. Some of the trickiest parts of original research include knowledge mapping, co-citation, and co-occurrence. Using this data, we were able to determine which locations were most active in researching machine learning applications for concrete, where the most influential authors were in terms of both output and citations and which articles garnered the most citations overall. Conclusion: ML has become a viable prediction method for a wide variety of structural industrial applications, and hence it may serve as a potential successor for routinely used empirical model in the design of concrete structures. The non-ML structural engineering community may use this overview of ML methods, fundamental principles, access codes, ML libraries, and gathered datasets to construct their own ML models for useful uses. Structural engineering practitioners and researchers may benefit from this article's incorporation of concrete ML studies as well as structural engineering datasets. The construction industry stands to benefit from the use of machine learning in terms of cost savings, time savings, and labor intensity. The statistical and graphical representation of contributing authors and participants in this work might facilitate future collaborations and the sharing of novel ideas and approaches among researchers and industry professionals. The limitation of this systematic review is that it is only PubMed based which means it includes studies included in the PubMed database. [ABSTRACT FROM AUTHOR]

Even though it predisposes carriers to hemolysis, glucose-6-phosphate dehydrogenase (G6PD) deficiency is linked with malaria endemicity. This fact supports the malaria prevention theory. The objective of this paper to determine whether and how much there is a protective relationship between malaria and G6PD deficiency. Twelve databases were searched for studies describing any G6PD connection in malaria patients. 38 of the 50 included papers qualified for the review. Results indicated that there was no harmful association between G6PD deficiency and uncomplicated falciparum malaria in Even though it puts carriers at risk for hemolysis, glucose-6-phosphate dehydrogenase (G6PD) deficiency is widespread in areas of Pakistan where malaria is also prevalent. This data supports the malaria protection hypothesis. Pakistan's annual malaria burden is estimated to be 1.5 million cases. The government needs to execute a successful malaria control and eradication program, given the prevailing circumstances. Destroying Plasmodium falciparum gametocytes and eradicating Plasmodium vivax hypnozoite reservoirs are possible with primaquine. However, when using this medication, those who lack the enzyme glucose-6-phosphate (G6PD) experience hemolysis. The distribution of malaria and G6PD deficiency in Pakistan must be mapped to create an effective medication to suppress the disease. No significant reports of G6PD deficiency (G6PDd) in malaria patients have come from Pakistan. This review article seeks to establish the existence and magnitude of a protective connection between malaria and G6PD deficiency. [ABSTRACT FROM AUTHOR]

Long‐term drought monitoring and its assessment are of great importance for meteorological disaster risk management. The recurrent spells of heat waves and droughts have severely affected the environmental conditions worldwide, including Pakistan. The present work sought to investigate the spatiotemporal changes in drought characteristics over Pakistan during Rabi and Kharif cropping seasons. The role of large‐scale circulation and interannual mode of climate variability is further explored to identify the physical mechanisms associated with droughts in the region. Monthly precipitation and temperature data (1983–2019) from 53 meteorological stations were used to study drought characteristics, using the standardized precipitation evapotranspiration index (SPEI). The nonparametric Mann–Kendall, Sen's Slope, and Sequential Mann–Kendall tests were applied on the drought index to determine the statistical significance and magnitude of the historical trend. The state‐of‐the‐art Bayesian Dynamic Linear model was further used to analyse large‐scale climate drivers of droughts, revealed an increase in drought severity, mostly over arid to semiarid regions for both cropping seasons. While temperature played a significant role in defining droughts over dry and hot seasons, rainfall is influential over the western disturbances influenced region. The analysis of atmospheric circulation patterns revealed that large‐scale changes in wind speed, air temperature, relative humidity, and geopotential height anomalies are the likely drivers of droughts in the region. We found that Niño4, sea surface temperature, and multivariate El Niño‐Southern Oscillation (ENSO4.0) Index are the most influential factors for seasonal droughts across Pakistan. Overall, the findings provide a better understanding of drought‐prone areas in the region, and this information is of potential use for mitigating and managing drought risks. [ABSTRACT FROM AUTHOR]