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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]

Automated handwritten digit identification has become essential for many daily operations such as verifying the authenticity of a cheque or deciphering a postal code. Researchers have been putting a lot of effort into figuring out how to automatically recognize and sort handwritten digits because of the advent of robotic technology in recent decades. Due to the complexity of the Arabic language and a lack of publicly available Arabic handwritten digit datasets, previous research has primarily concentrated on automating the recognition of English and European digits on availability of relevant datasets. Arabic handwritten recognition also plays a significant role in electronic-learning (e-learning) systems. It has not taken into account the recognition of handwritten Arabic digits. Arabic handwritten digits vary in size, form, slant, and image noise, which can cause changes in numeral topology. These factors make it difficult to classify and recognize Arabic handwritten digits. In order to overcome these challenges, we implemented a biologically plausible technique to classify Arabic handwritten digits. The objective of the research is to address the unique challenges of style, size, shape, slant variations, and image noise of Arabic handwritten digits in classification and recognition. To categorize Arabic handwritten digits, we employed convolutional spiking neural network (CSNN) and spike neural network (SNN) models. The reason for using SNN and SCNN is that the second-generation neural networks accumulate accolades on various computer vision tasks, i.e., pattern recognition, segmentation, and classification. However, there are certain challenges, i.e., energy inefficiency and computational cost, presented by these networks. The spiking neural network models also address these issues. These models are low-power, high-performance neural network models. We trained the spiking neural network model on Arabic digit datasets using a rate-based, non-spiking algorithm, i.e., backpropagation and soft-LIF, then integrated them with a spiking neural network. We have also used an STDP (spike time-dependent plasticity)-based spiking neural network for Arabic digit recognition. We attenuate the spiking variability in CSNN by adding noise during training, compensating the training errors, and bringing robustness against the spiking variability. We experimented with soft-LIF, a non-spiking version of the LIF neuron, and a variant of STDP rules for Arabic digit classification. Experimental results illustrate that CSNN achieves high performance at a significantly low energy and computational cost. We attained 98.98% and 91.16% recognition rates for the Arabic digit dataBase (ADBase) dataset using the convolutional spiking model and STDP spiking model, respectively. [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]

In recent decades, water scarcity is a significant constraint for socioeconomic development and threatens livelihood in an agriculture-based developing country like Pakistan. The water crisis in the country is projected to exacerbate in the coming years, especially in the southern parts. This dire situation calls for an investigation of major droughts, associated water scarcity, and changes in teleconnection patterns over Southern Pakistan. Moderate to low Southeastern monsoon (SEM) precipitation triggered the extreme drought episode (2017--2020) over Southern Pakistan and intensified the water scarcity. This study explored the severity of the respective drought event, underlying mechanisms, and changes in water scarcity over Southern Pakistan. To investigate the future changes (1980--2050) in water scarcity, coupling models (global hydrological models (GHMs)-global climate models (GCMs)) have been used to achieve the interannual performance of water availability and total water consumption. Besides, future scenarios used in this study are the combinations of SSPs and RCPs, including middle-of-the-road (SSP1-RCP4.5) and fossil-fueled development (SSP2-RCP8.5). The findings indicated a precipitation deficit of 45% during the 4-year (2017--2020), depicting the worst events in the past 50 years. South Pakistan observed the worst SEM droughts over the last 50 years, as 2000--2005 was the worst drought (precipitation deficit of 75%), followed by 2017--2020 with a 49% of precipitation deficit. Water scarcity was exacerbated by the extreme dry spells that developed over most of southern Pakistan between 2017 and 2020 as a result of moderate-to- exceptionally low SEM precipitation. Furthermore, this drought episode was accompanied by the cool phase in the Pacific and equatorial Indian Oceans. The future changes in water scarcity over the southern regions of Pakistan present a sharp increase under the SSP2-RCP8.5 scenario and are anticipated to be intensified in already stressed regions. This research is essential for environmentalists, and water resources managers, and provided crucial information to identify the hot spot areas in the target region so that water scarcity problems could be reduced in the future. [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]

Respiratory diseases such as Asthma, COVID-19, etc., require preventive and precautionary measures. Due to the lack of medical treatment for the masses, researchers are currently focusing on clinical decision support systems (CDSS). CDSS for respiratory diseases utilizes Machine Learning (ML) techniques to classify the symptoms into a possible diseases. This approach not only grasps the attention of researchers worldwide but also assists medical doctors in the early diagnosis of the disease. In this review paper, PRISMA guidelines are used to conduct a detailed overview of the early detection of respiratory diseases using ML techniques are identified. Among various ML techniques, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (D-Tree), Logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), and AdaBoost are discussed. Then respiratory diseases are identified whose CDSS are available with the ML techniques and possible future direction for its improvement. Furthermore, the tools and ML techniques are compared with each other to enhance the researcher's clarity for future use. The paper concluded with the future direction of the ML in the successful implementation of the CDSS in the field of respiratory disease. [ABSTRACT FROM AUTHOR]

This paper's primary goal is to develop an agriculture drone for spraying pesticides. We discuss an architecture based on unmanned aerial vehicles (UAVs) in this study. Pesticides must be used in agriculture if the quality of largescale output is to be maintained. It is crucial to increase agriculture's production and efficiency by employing cuttingedge technology to replace employees with intelligent equipment like robots. The research suggests a novel method to replace people in a number of agricultural tasks, including the identification of insect presence, the application of pesticides and fertilizers, etc. The created method entails building a prototype that makes use of basic, affordable equipment including a microprocessor, different motors, and terminal equipment to assist farmers in a variety of operations related to crop fields. Design and build an autonomous drone-based surveillance system capable of identifying injured crops and spraying pesticides in specified regions as needed. To combine an image processing and Artificial Intelligence based real time algorithm to determine crop health and evaluate the need for pesticides, as well as a weather monitoring system that can assist anticipate weather conditions. [ABSTRACT FROM AUTHOR]

Surface electromyography (EMG) has emerged as a promising clisnical decision support system, enabling the extraction of muscles' electrical activity through non-invasive devices placed on the body. This study focuses on the application of machine learning (ML) techniques to preprocess and analyze EMG signals for the detection of muscle abnormalities. Notably, state-of-the-art ML algorithms, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Random Forests (RF), and Naive Bayes (NB), have been harnessed by researchers in the biomedical sciences to achieve accurate surface EMG signal detection. Within this paper, we present a meticulously conducted systematic review, employing the PRISMA method to select relevant research papers. Various databases were thoroughly searched, and multiple pertinent studies were identified for detailed examination, weighing their respective merits and drawbacks. Our survey comprehensively elucidates the latest ML techniques used in surface EMG detection, offering valuable insights for researchers in this domain. Additionally, we outline potential future directions that can guide further advancements in this critical area of research. [ABSTRACT FROM AUTHOR]

Purpose: This systematic review details the diseases that influence banana production and their detection. A common method for identifying plant diseases in plants is image processing. Segmentation is one method for using image processing to establish medical diagnosis. The main objective of this study is to identify, categorize, and evaluate several image processing techniques used to control pests in a banana crop. Methodology: An electronic search was conducted using relevant keywords on openly available databases including IEEE Xplore, PubMed, Science Direct, and Google Scholar. 104 items were discovered by the search engine. After removing the duplicates, there were 56 research papers remained, but 22 of them were discarded after title and abstract checks since they did not address insect detection in banana fields. Results: 22 papers that come under the headings of image classification, AI/ML, deep learning, and mobile applications provide usable and reliable detection techniques in this systematic review. [ABSTRACT FROM AUTHOR]

The present study uses observation-based gridded air temperature and precipitation datasets to investigate the spatiotemporal changes in meteorological drought from 1981 to 2020 in South Asia (SA). The drought characteristics i.e., duration, area, frequency, intensity, and severity are calculated using Run theory and the Standardized Precipitation Evapotranspiration Index (SPEI) on annual and seasonal timescales. SA is divided into four homogeneous subregions using the k-means clustering algorithm, while the trends were estimated using Sen's Slope estimator and modified Mann-Kendall (MMK) test. A state-of-the-art Bayesian Dynamic Linear (BDL) model was employed to assess the sub-regional drought variability and their possible links with large-scale atmospheric drivers such as the Indian Ocean Dipole (IOD), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO). It is found that the SA winter season experienced a significant drying long-term trend, especially southwestern and northeastern parts of SA. Drought durations show dipolar patterns with longer drought duration in the southwestern compared to the northern parts of SA. Notably, arid and semi-arid regions have shown significant drying trends in terms of their area, frequency, and severity, implying that meteorological droughts are relatively more severe in those regions. Similarly, the relationship varies over time between drought variability and climate drivers. In particular, the IOD has an impact on drought episodes over southwest SA relative to the northern parts and is mostly affected by the Sea surface temperature (SST) variability. The study results contribute to a better understanding of the meteorological drought characteristics over SA, which is important for disaster risk managers and policymakers to mitigate drought impacts. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered instabilities in various aspects of daily life. This includes economic, social, financial, and health crisis. In addition, the COVID-19 pandemic with the evolution of different virus strains such as delta and omicron has led to frequent global lockdowns. These lockdowns have caused disruption of trade activities that in turn have led to the shortage of medical supplies, especially personal protective equipment's (PPE's). Health-care workers (HCW's) have been at the forefront of the fight against this pandemic and are responsible for saving millions of lives worldwide. However, the PPE's available to HCW's in the form of face shields and face masks only provide face and eye protection without encapsulating the ability to continuously monitor vital COVID-19 parameters including body temperature, heart rate, and SpO2. Hence, in this study, we propose the design and utilization of a PPE in the form of smart face shield. The device has been integrated with the MAX30102 sensor for measuring the heart rate and oxygen saturation (SpO2) and the DS18B20 body temperature measuring sensor. The readings of these sensors are analyzed by a NodeMCU ESP8266 and measurements are displayed on a laptop screen. Also, the Wi-Fi module of NodeMCU ESP8266 enables compatibility with the ThingSpeak mobile application and permits HCW's and patients recovering from COVID-19 to keep a track of their physiological parameters. Overall, this PPE has been observed to provide reliable readings and the results indicate that the designed prototype can be used for monitoring COVID-19 essential parameters. [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]

South Asia is home to one of the fastest-growing populations in Asia, and human activities are leaving indelible marks on the land surface. Yet the likelihood of successive observed droughts in South Asia (SA) and its four subregions (R-1: semi-arid, R-2: arid, R-3: subtropical wet, and R-4: tropical wet and dry) remains poorly understood. Using the state-of-the-art self-calibrated Palmer Drought Severity Index (scPDSI), we examined the impact of different natural ocean variability modes on the evolution, severity, and magnitude of observed droughts across the four subregions that have distinct precipitation seasonality and cover key breadbaskets and highly vulnerable populations. The study revealed that dryness had significantly increased in R-1, R-2, and R-4 during 1981–2020. Temporal analysis revealed an increase in drought intensity for R-1 and R-4 since the 2000s, while a mixed behavior was observed in R-2 and R-3. Moreover, most of the sub-regions witnessed a substantial upsurge in annual precipitation, but a significant decrease in vapor pressure deficit (VPD) during 1981–2020. The increase in precipitation and the decline in VPD partially contributed to a significant rise in Normalized Difference Vegetation Index (NDVI) and a decrease in dryness. In contrast, a strong positive correlation was found between drought index and precipitation, and NDVI across R-1, R-2, and R-4, whereas temperature and VPD exhibited a negative correlation over these regions. No obvious link was detected with El-Niño Southern Oscillation (ENSO) events, or Indian Ocean Dipole (IOD) and drought evolution, as explored for certain regions of SA. The findings showed the possibility that the precipitation changes over these regions had an insignificant relationship with ENSO, IOD, and drought onset. Thus, the study results highlight the need for considering interactions within the longer climate system in describing observed drought risks rather than aiming at drivers from an individual perspective. [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]

Understanding the prevailing changes in temperature and relative humidity (RH) is of crucial importance for climate risk reduction and management. Despite their importance, trends and temperature variability associated with other climate variables over the Southeast Asian nation of Myanmar are not fully understood. This study investigates the annual and seasonal variations in air temperature and RH, as well as dew point temperature and their relationships, based on 47 meteorological stations located around Myanmar from 2001 to 2019. The results indicate that an increasing trend in air temperature was observed in the central, western, deltaic and southern regions of Myanmar. In contrast, air temperatures trended downward in the eastern (southern Shan state), northern (Hkakabo Razi Mountain) and western (Chin state) parts of the country. RH exhibited a significant increase in the northern region and a decrease in the central dry zone. A lower RH always accompanied high temperatures. Dew points increased in the deltaic and southern parts of Myanmar, as opposed to in the eastern (south Shan state) and western (Chin state) parts of the country. Moreover, in comparison to the daily RH variability, the observed daily temperature variability had a relatively stronger influence on Myanmar’s climate, whereas dew points typically remained stationary for a day. The associated linkage between the RH and the dew point temperature was significantly linear, with a correlation coefficient (R2) of 0.65. The annual (seasonal) correlation of air temperature and dew point was highly correlated in the winter, where R2 was measured at 0.71 (0.75). During the rainy season, however, the annual (seasonal) R2 was measured at only 0.30 (0.04). However, the air temperature and RH showed a weak positive correlation of 0.20 (0.26) in summer (winter) and a weak positive correlation in the rainy season (0.01). This study’s findings are important for enhancing seasonal forecasts of extreme heat and can aid policy-makers in formulating better climate change adaptation plans. [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]

This document is a corrigendum for an article titled "Recent and projected changes in water scarcity and unprecedented drought events over Southern Pakistan." The correction addresses an error in the funding statement of the article, providing the correct funding information. The authors express their gratitude for the financial support and state that the correction does not affect the scientific conclusions of the article. The document also includes a note from the publisher, stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. The authors and their affiliations are listed at the end of the document. [Extracted from the article]

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]

Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and prioritize the messages in two different ways. One is the message nature way, while the other one is deadline and size way of messages prioritization. SDN controller categorized in safety and non-safety messages and forward to the destination. After sending messages to their destination, we check their acknowledgment; if the destination receives the messages, then no action is taken; otherwise, we use a fault tolerance mechanism. We send the messages again. The proposed model is implemented in CloudSIm and iFogSim, and compared with the latest models. The results show that our proposed model decreased response time by 50% of the safety and non-safety messages by using fog nodes for the SDN controller. Furthermore, we reduced the execution time of the safety and non-safety messages by up to 4%. Similarly, compared with the latest model, we reduced the task failure ratio by 20%, 15%, 23.3%, and 22.5%. [ABSTRACT FROM AUTHOR]

In this study, we investigated the interdecadal variability in monsoon rainfall in the Myanmar region. The gauge-based gridded rainfall dataset of the Global Precipitation Climatology Centre (GPCC) and Climatic Research Unit version TS4.0 (CRU TS4.0) were used (1950–2019) to investigate the interdecadal variability in summer monsoon rainfall using empirical orthogonal function (EOF), singular value decomposition (SVD), and correlation approaches. The results reveal relatively negative rainfall anomalies during the 1980s, 1990s, and 2000s, whereas strong positive rainfall anomalies were identified for the 1970s and 2010s. The dominant spatial variability mode showed a dipole pattern with a total variance of 47%. The power spectra of the principal component (PC) from EOF revealed a significant peak during decadal timescales (20–30 years). The Myanmar summer monsoon rainfall positively correlated with Atlantic multidecadal oscillation (AMO) and negatively correlated with Pacific decadal oscillation (PDO). The results reveal that extreme monsoon rainfall (flood) events occurred during the negative phase of the PDO and below-average rainfall (drought) occurred during the positive phase of the PDO. The cold phase (warm phase) of AMO was generally associated with negative (positive) decadal monsoon rainfall. The first SVD mode indicated the Myanmar rainfall pattern associated with the cold and warm phase of the PDO and AMO, suggesting that enhanced rainfall for about 53% of the square covariance fraction was related to heavy rain over the study region except for the central and eastern parts. The second SVD mode demonstrated warm sea surface temperature (SST) in the eastern equatorial Pacific (El Niño pattern) and cold SST in the North Atlantic Ocean, implying a rainfall deficit of about 33% of the square covariance fraction, which could be associated with dry El Niño conditions (drought). The third SVD revealed that cold SSTs in the central and eastern equatorial Pacific (La Niña pattern) caused enhance rainfall with a 6.7% square covariance fraction related to flood conditions. Thus, the extra-subtropical phenomena may affect the average summer monsoon trends over Myanmar by enhancing the cross-equatorial moisture trajectories into the North Atlantic Ocean. [ABSTRACT FROM AUTHOR]

Myanmar is located in a tropical region where temperature rises very fast and hence is highly vulnerable to climate change. The high variability of the air temperature poses potential risks to the local community. Thus, the current study uses 42 synoptic meteorological stations to assess the spatiotemporal changes in air temperature over Myanmar during 1971–2013. The nonparametric sequential Mann-Kendall (SqMK), linear regression, empirical orthogonal function (EOF), Principal Component Analysis (PCA), and composite analysis were used to assess the long-term trends in maximum (Tmax) and minimum (Tmin) temperature series and their possible mechanism over the study region. The results indicate that the trend of Tmax has significantly increased at the rates of 90% in summer season, while the Tmin revealed a substantial positive trend in winter season time series with the magnitude of 30%, respectively. Moreover, during a rapid change of climate (1995–2013) we observed an air temperature increase of 0.7 °C. The spatial distributions of EOF revealed relatively warmer temperatures over the whole region except the south in the summer; however, a similar pattern can be seen for the rainy season and winter, implying warming in the central part and cooling in the northern and southern parts. Furthermore, the Indian Ocean Dipole (IOD) influence on air temperature over Myanmar is more prevalent than that of the El Niño Southern Oscillation (ENSO). The result implies that the positive phase of the IOD and negative phase of the Southern Oscillation Index (SOI; El Niño) events led to the higher temperature, resulting in intense climatic extremes (i.e., droughts and heatwaves) over the target region. Therefore, this study's findings can help policymakers and decision-makers improve economic growth, agricultural production, ecology, water resource management, and preserving the natural habitat in the target region. [ABSTRACT FROM AUTHOR]