Search

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]

Over the past few decades, South Asia (SA) has experienced an upsurge in the frequency of severe monsoonal compound drought and heatwave (CDHW) occurrences. Climate models that identify land‐atmosphere coupling as a major contributing factor for this exacerbation and anticipate an increase in the intensity and frequency of CDHW occurrences in future also represent this. For the first time, this study investigated the future evolution of monsoonal CDHW events based on new generations of the CMIP6 and population products by applying a multivariate framework. Specifically, this study explored the impacts of natural climate variability and future land‐atmosphere coupling on the monsoonal CDHW event risks and their bivariate return periods for two future time‐periods and emission scenarios across SA and its subregions. The odds of CDHW occurrences were then examined using a logistic regression model and its association with the natural and anthropogenic drivers was determined. The results indicate that the monsoonal CDHWs occurrence is anticipated to increase substantially during the late twenty‐first century (2056–2090). The 50‐year CDHW events might increase by two‐fold across most of SA by the mid‐21st century under the high emission scenario. We find that the co‐occurring dry and warm conditions rapidly strengthens with soil moisture and temperature coupling and are further exacerbated by land‐atmospheric feedback loops. Our findings show that persistent dry spells contribute significantly to heatwave events, emphasizing regional exposure to changing climates. Plain Language Summary: CDHW (compound drought and heatwave) events may have caused more severe effects on agriculture, water resources, and human society than their occurrences. Investigating future changes in CDHW is critical for anticipating and reducing the adverse effects of climate change and variability, especially in a densely populated region like South Asia (SA), where most of the population still depends on agricultural productivity. Here, we propose a novel compound and multivariate risk assessment framework to capture weekly droughts and heatwaves at a daily time scale using advanced scientific methods. With this objective, the outcomes of the current study can directly map the vulnerable areas in SA and expose the relationship between two events (drought and heatwave). The current research also explores the risk and population exposure due to CDHW events across SA under current and future warming climates by considering the joint distribution of CDHW severity and duration. The study findings will help inform stakeholders about vulnerable areas where the CDHW events are more likely to become more frequent and severe due to climate change. Key Points: CDHW occurrences are projected to increase significantly during the period mid and late 21st century under the business‐as‐usual scenarioThe population exposure from 50‐year CDHW event might increase two‐fold across 70% of the SA landmasses already by the mid‐21st centuryAnthropogenic global warming show significant positive influence on the most vulnerable climatic regions [ABSTRACT FROM AUTHOR]

Every year, millions of people are at risk due to droughts in South Asia (SA). The likely impacts of droughts are projected to increase with global warming. This study uses the new ensemble mean of 23 global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6), population and gross domestic product (GDP) projections to quantify future changes in increasing drought risks and associated socioeconomic exposure across SA and its subregions under 1.5°C and 2°C of warming. We used two shared socioeconomic pathways (SSPs), SSP2‐4.5 and SSP5‐8.5. The most likely realization copula functions are used to model the joint distribution of drought severity and duration. Simultaneously, changes in the bivariate return period are calculated under a warming climate. The frequency of 50‐year historical droughts (under a bivariate framework) might double across 80% of the SA land area under 1.5°C of warming. Conversely, 12% of SA landmasses may suffer extreme droughts under 2°C of warming. The severe drought episode frequency is expected to increase under 1.5°C (40%–75%) and 2°C (60%–90%) of warming relative to the recent climate. The largest exposure increase is projected in R2 and R4, then R1. Additionally, 75% (65%) of the SA population (GDP) could suffer from increased drought risks under the 1.5°C warmer climate, whereas the additional 0.5°C warming will lead to an unbearable regional situation. Limiting global warming to 1.5°C compared with 2°C can significantly reduce the drought risk influence in SA. These findings can help disaster‐risk managers to adopt climate‐smart management strategies. Plain Language Summary: The world has warmed rapidly since 1970, encouraging efforts to reduce climate change and to stabilize global temperatures to between 1.5°C and 2°C warming targets above preindustrial levels. With these aims, the present study explores the spatiotemporal changes in future drought events and their associated socioeconomic exposure over South Asia (SA) and its subregions under a warmer climate. We find that amplified drought risks are projected to increase over southern and southwestern SA under 1.5°C of warming for the SSP2‐4.5 and SSP5‐8.5 scenarios. As the world continues to warm, some land locations, such as the interior of South India and Pakistan, may experience a temporary emergence of a climate change signal that weakens if the climate stabilizes and the Paris Agreement goals are met. In addition, we also found that the frequency of 50‐year historical droughts might double across 80% of the SA land area under the 1.5°C warming target. In contrast, limiting global warming to 1.5°C instead of 2°C can increase the projected population exposures to increased drought risks by almost half. We believe that the study findings will help disaster‐risk managers adopt climate‐smart policies. Key Points: The frequency of 50‐year historical droughts might double across 80% of South Asia (SA) land area under 1.5°C warmingSouthwestern SA is projected to have the largest increase in exposure compared to the other regionsLimiting global warming to 1.5°C instead of 2°C can increase the projected population exposures to increased drought risks by almost half [ABSTRACT FROM AUTHOR]

Background. Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population. Objectives. The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria. Materials and Methods. A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022. Result. This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%. Conclusion. In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records. [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]

The risk of heatwave events and their persistence has intensified in recent past and is expected to increase faster in future. However, the anticipated changes in socioeconomic exposure to heatwaves are still unexplored. Here, we investigate the projected heat stress and associated socioeconomic exposure across South Asia (SA) and its subregions using the newly released ensemble mean of 23 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), population, and Gross Domestic Product (GDP) projections. We used two Shared Socioeconomic Pathways (SSPs), namely SSP2‐4.5 and SSP5‐8.5, and three‐time periods, that is, near‐term, midterm, and long‐term relative to the base period (1985–2005). We found that SA region has the potential for widespread changes to Wet bulb globe temperature (WBGT) of 6.5°C, which can exceed the theoretical limits of human tolerance by the mid of 21st century. The SA population's exposure significantly increases during midterm and long‐term periods by ∼750×106 $750\times {10}^{6}$ person‐hours under the SSP5‐8.5 scenario. The GDP exposure is the greatest for the same period's up to 200×109 $200\times {10}^{9}$ dollar‐hours under the SSP2‐4.5. Moreover, the foothills Himalayans and northern parts of Pakistan are presently unaffected by WBGT during midterm and long‐term periods under both scenarios. Among subregions (hereafter R1, R2, R3, and R4), the frequency of subdaily WBGT is projected to increase in the region R2 and R4 by ∼70% and ∼90% under the SSP2‐4.5 and SSP5‐8.5 scenarios relative to the base period. The highest upsurge in exposure is anticipated for R2, including southern Pakistan and southwestern India, followed by R1 and R3. Notably, the climate effect is more dominant than the population, whereas changes in GDP effect contribute to the total change in GDP exposure. Plain Language Summary: SA is one of the hotspot regions to the climatic extremes where the earliest exposure to heat waves is expected in future warmer climates. We show that the SA population is highly exposed to subdaily WBGT for midterm and long‐term periods. In contrast, the robust change in GDP exposure appeared for the same periods under SSP2‐4.5 and SSP5‐8.5. In contrast, relatively less frequent WBGT exhibited over foothills Himalayans and northern parts of Pakistan. Still, the spatial magnitude of WBGT is more likely to be intensified by the end of the 21st century. Regarding regional aggregate changes, R2 and R4 are anticipated to upsurge in subdaily WBGT relative to the base period under SSP2‐4.5 and SSP5‐8.5 scenarios. We also found that southern Pakistan and south‐northern India are projected to increase exposure to heat stress, followed by R1 and R3. Overall, the projected changes in exposure are mainly due to the interaction effect accounted for ∼650 (800) × 106 person‐hours under SSP2‐4.5/SSP2 and SSP5‐8.5/SSP5 scenarios. It can be inferred that the climate influence is more dominant than the population, particularly for southwestern Pakistan and most parts of India. Key Points: The region of South Asia experiences widespread changes to Wet bulb globe temperature of 6.5°CThe spatial magnitude of Wet bulb globe temperature is likely to be intensified by the end of the 21st centuryProjected changes in socioeconomic exposure are mainly due to the interaction effect under Shared Socioeconomic Pathways [ABSTRACT FROM AUTHOR]

Drought is one of the most frequent natural disasters occurring in Pakistan and has a great influence on livelihood, agriculture, and economy. The availability of long‐term high‐quality reanalysis products over Pakistan has been of great concern in recent decades. Here, we conduct drought assessment in Pakistan based on the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) at 3, 6, and 12 months timescales during 1983–2018. We use long‐term in situ observations to evaluate the accuracy of reanalysis products, including Climatic Research Unit (CRU TS), National Centers for Environmental Prediction version II (NCEP‐2), European Centre for Medium‐Range Weather Forecasts Version‐5 (ERA‐5), and Modern‐Era Retrospective analysis for Research and Applications version II (MERRA‐2). The main results are summarized as follows: (a) drought indices and drought areas assessed from reanalysis products are relatively more representative of historical droughts that had occurred in southern Pakistan and overestimation is evident for drought severity in western than eastern Pakistan; (b) statistically significant increasing trends (1984–1998 and 2000–2010) in monthly drought areas and occurrence are evident by CRU TS and MERRA‐2 in dominant arid and semiarid regions; (c) climate variables and drought features of southern Pakistan are best represented by CRU TS and MERRA‐2, while that of southwestern and western parts are best represented by ERA‐5; (d) the Nash–Sutcliffe efficiency (NSE) results range from −2 to 1, where the NSE of SPEI values (−1.0) show relatively weaker than SPI values (0.5) in most parts of the regions, specifically in the southern Pakistan; (e) a strong positive linear relationship on a monthly scale is evident in CRU TS, MERRA‐2, and ERA‐5 exhibiting relatively high correlation coefficient (0.84), except for NCEP‐2. Furthermore, the SPEI results are found to be better than SPI; thus, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change. [ABSTRACT FROM AUTHOR]

Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to do with algorithms for the detection of speech noise. The idea is to detect the disease pathology from the voice. First, we apply the feature extraction on the SVD dataset. After the feature extraction, the system input goes into the 27 neuronal layer neural networks that are convolutional and recurrent neural network. We divided the dataset into training and testing, and after 10 k-fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. A 10-fold cross-validation is used to evaluate the performance of the classifier. On a Linux workstation with one NVidia Titan X GPU, program code was written in Python using the TensorFlow package. [ABSTRACT FROM AUTHOR]