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Background: Sericulture is a labor-oriented agro-based industry that significantly elevates India's rural economy. During weaving operations, workers adopt awkward postures and work long hours, which is one of the most important factors in their poor working efficiency and prevalence of musculoskeletal disorders. Objectives: This study aimed to find the correlation between grip strength, the Michigan hand outcome questionnaire (MHQ), and work-related postural disorders among power loom silk weavers. Design: Correlation study. Methods: This study was conducted in a community setting. 120 power loom silk weavers were screened for work-related musculoskeletal disorders. A face-to-face interview was conducted to assess Grip strength, hand function, and work-related musculoskeletal disorders using the Hand dynamometer, MHQ, and RULA scales, respectively. Results: By Spearman's correlation, the correlation between Bilateral Hand grip strength and MHQ was statistically significant p<0.001 and shows a strong positive correlation (r = 0.787) for right hand and (r = 0.741), a strong negative correlation (r = -0.594) for right hand and (r = -0.538) for left hand was found between bilateral hand grip strength and RULA and the correlation between MHQ and RULA scores were statistically significant (p<0.001) shows a strong negative correlation (r = -0.583).The intercept is 8.298. An increase in MHQ (-0.022) and right-hand grip strength (-0.030) will decrease RULA scores. Conclusion: The study concluded that subjects with reduced hand grip strength and hand function were at higher risks of developing work-related musculoskeletal disorders, and hand grip strength and MHQ are equally good predictors of these disorders. [ABSTRACT FROM AUTHOR]

Shear slowness data are crucial data in rock physics analysis and seismic reservoir characterization. In petrophysical formation evaluation, the use of sonic data is limited, and hence, sonic data, especially shear sonic, are not considered as critical. In many deep-water wells to save the cost of operations, shear sonic data are not recorded. In these scenarios for rock physics analysis, it becomes necessary to predict shear sonic data from other available datasets. Conventional techniques for shear slowness predictions rely on empirical relations and rock physics modeling. However, these approaches require extensive information as input and additionally carry assumptions and multiple prerequisites. Presently with the advancement of computing power Machine learning (ML) emerges as a robust and optimized technique for predicting precise DTS in quick time and with fewer input datasets. In this study, wells located in the deep-waters of the East Coast of India and penetrated siliciclastic reservoirs of both compacted sand and soft high porosity sands were used as input to train the ML algorithm. Random Forest machine learning algorithm is best used for both classification and regression tasks, and this algorithm is used here for the data prediction. As a comparison, the convolutional LSTM method is also used for data prediction. To comply with the geological variability in the prediction and to enhance the prediction accuracy, rock physics understandings were used as a guide in feature engineering. The RF prediction shows a good match of ~ 93%, and the LSTM model prediction shows ~ 94% correlation at validation well. Both the model predicted data show good agreement with the rock physics modeling interpretations at the target well. [ABSTRACT FROM AUTHOR]

Background: The role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide, contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance. Purpose: The purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features. Materials and Methods: Pretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital, were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC), were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation. Results: The average prediction accuracy was found to be 0.65 (95% CI: 0.60– 0.70), 0.72 (95% CI: 0.63–0.81), and 0.77 (95% CI: 0.72–0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62–0.76), 0.79 (95% CI: 0.72–0.86), 0.71 (95% CI: 0.62–0.80), and 0.72 (95% CI: 0.66–0.78) for LR, RF, SVC and GBC models developed on three datasets respectively. Conclusion: Our study shows the promising predictive performance of a robust radiomic signature to predict 5-year overall survival in cervical cancer patients. [ABSTRACT FROM AUTHOR]

Objectives: Chronic kidney disease (CKD) significantly increases the risk of infectious diseases (IDs), leading to heightened morbidity and mortality. However, there remains a lack of detailed, region-specific studies. This study investigates the clinical spectrum, etiologies, outcomes, and baseline predictors of mortality of ID emergencies in CKD patients in North India. Methods: This retrospective study was conducted at the Postgraduate Institute of Medical Education and Research, Chandigarh, from January 2021 to December 2022. It included patients aged ≥13 years with CKD and IDs admitted to the Acute Care and Emergency Medicine Unit. Results: We enrolled 248 patients (mean age 50 years, 58.1% males). About 60% had CKD stage 5, and 46% were on maintenance hemodialysis. Diabetic kidney disease was the predominant etiology (38.7%). The principal IDs were pneumonia (27.4%), urinary tract infection (UTI) (21.4%), sepsis of unknown primary focus (15.7%), tuberculosis (8.1%), and multisite infections (7.7%). Patients commonly have atypical clinical presentation, e.g., absence of fever and nonspecific symptoms such as shortness of breath and altered mental status. An emergence of multidrug-resistant organisms, e.g., Enterococcus faecium for UTI and Stenotrophomonas maltophilia for catheter-related bloodstream infections, was noted. In-hospital mortality rate was 33.5%, higher with multisite infections (58%) and pneumonia (47%). A low baseline Glasgow coma scale (GCS) was an independent predictor of mortality [odds ratio (OR) 0.786, 95% confidence interval (CI) 0.693-0.891, p-value <0.001]. Conclusion: Effective management and early intervention are needed to improve outcomes in CKD patients with ID emergencies, given the high mortality and atypical clinical presentations. [ABSTRACT FROM AUTHOR]

With the emerging era of E‐commerce and online shopping, people are also in a habit to receive default product recommendations on the web pages that they access. Google is already providing such suggestions. Till now recommendations were made only based on previous sentiments or feedback or ratings, but this research has improved the product recommendation method by including one more parameter for the same. This article represents two parameters for making predictions of product allocation to a new customer. These parameters are ratings given by the existing users for that particular product and the region to which the new customer belongs. Following these parameters, a prediction model and an algorithm, Improved_Collab_Similarity, have been implemented. The dataset has been developed where India as a country along with all its States has been considered for products which are popular for their creation based on regional and ancient skills of the people belonging to that area. Results for the mentioned prediction model have been discussed in this article where generally precision increases with the increase in a number of products but at some points, it does not increase when a smaller number of that product was purchased by the customers. [ABSTRACT FROM AUTHOR]

In this comprehensive study, we have delved into the intricate realm of lightning forecasting, a captivating yet challenging pursuit due to the elusive nature of storm electrification. Our focus rested on evaluating the skill of lightning forecasts of NCMRWF Regional Ensemble Prediction System (NEPS-R), based on the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Based on the lightning hotspots in the observed seasonal mean lightning flash count, study regions were identified over East-Northeast India (ENEI) and Peninsular India (PI) during pre-monsoon season and Central and East-Northeast India (CENEI) for monsoon season during the year 2022. Probabilistic and deterministic skill of NEPS-R in lightning prediction using rank histogram, Relative Operating Characteristic (ROC), Brier Skill Score (BSS), Continuous Ranked Probability Skill Score (CRPSS), reliability diagrams, Probability of Detection (POD), False Alarm ratio (FAR), Equitable Threat Score (ETS), and Fractions Skill Score (FSS) were investigated in the present study. Rank histograms brought out the negative bias, which increases with increasing lead time over all the study regions. While ROC and BSS indicate that forecast is skillful on day-1 over ENEI for all the thresholds (> 1, > 5 and > 10) and over PI on day-2 and day-3, CRPSS has a contrasting trend with most skillful forecast over CENEI region. Reliability diagrams indicate under-forecasting for lower probabilities and over-forecasting for higher probabilities for all the thresholds for all the study areas. The overconfidence in the forecast could be at least partially attributed to the sampling error caused by a small ensemble size of NEPS-R, which also displayed similar trend of skill as BSS. Higher POD values in the CENEI region have surpassed those in ENEI and PI. Lower FAR and higher ETS are found over PI region compared to ENEI and CENEI. Furthermore, FSS (threshold > 1) indicates that CENEI region attains better skill (> 0.5) at a neighborhood size of 36 km in day-1 forecast compared to ENEI (68 km) and PI (84 km). The skill scores do emphasize the model's ability in predicting the dominant large scale features of monsoon. This is reflected in the better skill of CENEI region during monsoon as compared to ENEI and PI regions during the pre-monsoon season, where the model is falling short in predicting the prominent local scale features. Also, the model's inadequacy in proper representation of microphysics and kinematics does affect the lightning prediction capability of the model which could be addressed with improved model initial conditions and parameterization schemes and finer horizontal resolution. [ABSTRACT FROM AUTHOR]

This experimental study aimed to investigate the potential control of microstructural-mineralogical characteristics on thermal properties and its relation with the deformational behaviour in mild to high temperature (25°–800°C) regime of coal-bearing sandstone from an underground coal mine fire region. Barakar sandstone from Jharia Coalfield, India, one of the world's largest underground coal mine fire regions, was chosen. The thermal parameters and their relations to the mineralogical, microstructural, and damage characteristics of Barakar sandstone have been discussed. Thermal conductivity (λ), thermal diffusivity (κ), and specific heat capacity (Cp) of heat-treated samples were determined using the transient plane source method. Additionally, detailed petrography, scanning electron microscopy, and electron probe micro-analyser were used to understand the thermal effect on mineralogy and microstructures. The deformational behaviour with elevated temperature was investigated through the universal testing machine. The obtained results suggest three characteristic temperature regimes: mild temperature regime (25°–300°C), transitional temperature regime (300º–500°C) and high temperature regime (500º–800°C). Moreover, prediction models for thermal properties have also been developed using multi-gene genetic programming (MGGP), considering physical properties as input parameters. In view of experimental constraints to determine the thermos-elastic properties of a rock due to tedious experimental processes, the proposed prediction models shall be very useful to indirectly estimate the thermal properties of sandstone rocks using easily determined physical parameters. The outcome of the present study may be useful to understand the subsidence induced by underground coalmine fire, which is a distressing issue in the Jharia region due to persistent coalmine fire. [ABSTRACT FROM AUTHOR]

• In this work, we suggested a computerized decision-making process for deciding on the best birthing style. • A deep neural network model (DNN) was developed by using train set with h2o package. The model was selected on the basis of AUC (Area Under the Curve) & KS (Kolmogorov-Smirnov) score. The AUC, KS score for train set were 0.99, 0.98 respectively. • The prediction error rates for caeseraen & vaginal classes in train data are 0.02 & 0.00 respectively. • The results support the use of these algorithms in the creation of a clinical decision system to help gynecologists choose the most appropriate delivery method. One of the factors that worry obstetricians the most is the method of delivery. In recent years, the rate of caesarean sections has steadily climbed and now exceeds the threshold advised by medical organizations. Obstetricians typically lack the tools they need to assess whether vaginal delivery or a caesarean delivery is more appropriate. In this work, we suggested a computerized decision-making process for deciding on the best birthing style. The data was collected from 101 pregnant subjects who were admitted to hospital in eastern India for delivery from January 2021 to September 2021.The data set had 101 instances & 11 variables. The response was a binary variable with "caesarean" & "vaginal" as the outputs. A deep neural network model (DNN) was developed by using train set with h2o package. The model was selected on the basis of AUC (Area under the Curve) & KS (Kolmogorov-Smirnov) score. The AUC, KS score for train set were 0.99, 0.98 respectively. The prediction error rates for caeseraen & vaginal classes in train data are 0.02 & 0.00 respectively. The results support the use of these algorithms in the creation of a clinical decision system to help gynaecologists choose the most appropriate delivery method. [ABSTRACT FROM AUTHOR]

Field experiments were carried out to study the population dynamics of citrus psylla, Diaphorina citri Kuwayama on Kinnow Mandarin for twelve years (2007-08 to 2019-20) in relation to different weather parameters and to develop population prediction model. Adult psyllids were observed throughout the year but the population was very low during November-January. Two population peaks were observed during March and September. Highest mean population (80.8 nymphs/25 twigs) was observed during second fortnight of September which varied in different years. Psyllid population showed significant and positive correlation with maximum and minimum temperature, vapour pressure, wind speed, sunshine hours, rainfall and negative correlation with relative humidity and number of rainy days. A weather-based model was developed to predict psylla population seven days in advance. The validation of the model carried out using various indices viz, root mean square error, coefficient of determination, Nash-Sutcliffe efficiency and mean bias error suggested that, the model predicted the population of citrus psylla quite satisfactorily. Thus, the developed model can be used satisfactorily for weather-based prediction of citrus psylla in Punjab, seven days in advance. [ABSTRACT FROM AUTHOR]

An accurate and reliable prediction of future energy patterns is of utmost significance for the smooth operation of several related activities such as capacity or generation unit planning, transmission network optimization, better resources availability, and many more. With the availability of historical load datasets through smart grid systems, artificial intelligence and machine learning-based techniques have been extensively developed for achieving the desired objectives. However, effectively capturing strong randomness and non-linear fluctuations in the load time-series remains a critical issue that demands concrete solutions. Considering this, the current research proposes a hybrid approach amalgamating data smoothing and decomposition strategy with deep neural models for improving forecasting results. Moreover, an attention mechanism is integrated to capture relevant portions of the time series, thus achieving the desired ability to capture long-term dependencies among load demand observations. This integration enhances the prediction and generalization capabilities of the proposed model. To validate the performance benefits achieved by the proposed approach, a comparative evaluation is conducted with state-of-the-art neural-based load series prediction models. The performance assessment is carried out on a novel real-world dataset of five southern states of India, and the superiority of the proposed in capturing load time-series variations is well observed and demonstrated in terms of several performance indicators. [ABSTRACT FROM AUTHOR]

Solid process fine waste or tailings of a uranium mill is a potential source of release of radiologically significant gaseous radon (222Rn). A number of variables such as radium (226Ra) content, porosity, moisture content, and tailings density can affect the extent of emanation from the tailings. Further, if a cover material is used for remediation purposes, additional challenges due to changes in the matrix characteristics in predicting the radon flux can be anticipated. The uranium mill tailings impoundment systems at Jaduguda have been in use for the long-term storage of fine process waste (tailings). A pilot-scale remediation exercise of one of the tailings ponds has been undertaken with 30 cm soil as a cover material. For the prediction of the radon flux, a numerical model has been developed to account for the radon exhalation process at the remediated site. The model can effectively be used to accommodate both the continuous and discrete variable inputs. Depth profiling and physicochemical characterization for the remediated site have been done for the required input variables of the proposed numerical model. The predicted flux worked out is well below the reference level of 0.74 Bq m−2 s−1 IAEA (2004). [ABSTRACT FROM AUTHOR]

Background: There is a scarcity of literature about Internet addiction (IA) in common mental disorders (CMDs) such as depression, anxiety, obsessive-compulsive disorder, and substance use disorders. Early identification of IA in CMDs can help in better management of both disorders. Aims: To assess the extent and severity of IA in persons with CMDs. Methodology: This was a cross-sectional, single-group, exploratory study conducted at the Department of Psychiatry of tertiary care teaching hospital in North India. Patients aged 18 years and above and diagnosed with CMDs (Neurotic and stress-related disorders, Depressive Disorder, and substance use disorder) as per the ICD-11 criteria were included in the study after taking written informed consent. The severity of CMDs was assessed on Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HAM-D), Yale-Brown Obsessive-compulsive Scale, Severity of Alcohol Dependence Questionnaire and Severity of Opioid Dependence Questionnaire. The level of IA was assessed using Young's IA Test. Results: A total of 150 patients diagnosed with CMDs were included. Males were slightly more than females (56% v/s 44%), most were younger than 40 years of age (55.3%), married (66.7%) and from urban (65.3%) background. Out of 150, 84 (56%) had depressive disorder, 45 (30%) had anxiety disorder, 15 (10%) had OCD and only 6 (4%) had substance use disorders. 69 (46%) participants had IA (36 mild and 33 moderate) and none had severe IA. A significant association was found between IA and CMDs (P < 0.05). Age, marital status, and type of residence were independent predictors or risk factors for IA. Conclusion: The index study concludes that there is a strong association between CMDs and IA. About half of persons with CMDs have IA. [ABSTRACT FROM AUTHOR]

Traffic noise prediction is the fastestgrowing development that reflects the rising concern of noise as environmenta l pollution. Prediction of noise exposure levels can help policy makers and government authorities to make early decisions and plan effective measures to mitigate noise pollution and protect human health. This study examines the application of M5P model tree and Artificial Neural Network (ANN) for prediction of traffic noise on Highways of Delhi. In total 865 data sets collected from 36 sampling stations were used for development of model. Effects of 13 independent variables were considered for prediction. Model selection criteria like determination coefficient (R²), root mean square error (RMSE), Mean absolute error (MSE) are used to judge the suitability of developed models. The work shows that both the models can predict traffic noise accurately, with R2 values of 0.922(M5P), 0.942(ANN) and RMSE of 2.17(M5P),1.95(ANN). The results indicate that machine learning approach provides better performance in complex areas, with heterogenous traffic patterns. M5p Model tree gives linear equations which are easy to comprehend and provides better insight, indicating that M5P model trees can be effectively used as an alternative to ANN for predicting traffic noise. [ABSTRACT FROM AUTHOR]

Background and introduction: Statisticians rank oral and lip cancer sixth in global mortality at 10.2%. Mouth opening and swallowing are challenging. Hence, most oral cancer patients only report later stages. They worry about surviving cancer and receiving therapy. Oral cancer severely affects QOL. QOL is affected by risk factors, disease site, and treatment. Using oral cancer patient questionnaires, we use light gradient Boost Tree classifiers to predict life quality. Methods: DIAS records were used for 111 oral cancer patients. The European Organisation for Research and Treatment of Cancer's QLQ-C30 and QLQ-HN43 were used to document the findings. Anyone could enroll, regardless of gender or age. The IHEC/SDC/PhD/OPATH-1954/19/TH-001 Institutional Ethical Clearance Committee approved this work. After informed consent, patients received the EORTC QLQ-C30 and QLQ-HN43 questionnaires. Surveys were in Tamil and English. Overall, QOL ratings covered several domains. We obtained patient demographics, case history, and therapy information from our DIAS (Dental Information Archival Software). Enrolled patients were monitored for at least a year. After one year, the EORTC questionnaire was retaken, and scores were recorded. This prospective analytical exploratory study at Saveetha Dental College, Chennai, India, examined QOL at diagnosis and at least 12 months after primary therapy in patients with histopathologically diagnosed oral malignancies. We measured oral cancer patients' quality of life using data preprocessing, feature selection, and model construction. A confusion matrix was created using light gradient boosting to measure accuracy. Results: Light gradient boosting predicted cancer patients' quality of life with 96% accuracy and 0.20 log loss. Conclusion: Oral surgeons and oncologists can improve planning and therapy with this prediction model. [ABSTRACT FROM AUTHOR]

The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction ( R v a l 2 ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model's performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions. [ABSTRACT FROM AUTHOR]

Background: Tobacco use in any form is a major public health concern. It accounts for nearly 1.35 million deaths every year. Many chronic illnesses occur due to the consumption of tobacco, either smoke or smokeless form. The study aims to explore how smoking and smokeless tobacco consumption are distributed across a range of demographic and socioeconomic markers among Khasi indigenous people in Northeast India. Materials and Methods: It is a case-control study conducted in adults over 31 years of age. The study was carried out between June 2020 and August 2021. An Independent t-test was performed to determine the significant difference in age between smoking/smokeless tobacco users and non-tobacco users. Univariate and Multivariate analyses were conducted to determine the significant predictors affecting the use of smoking/smokeless tobacco in the respondents. Results: A total of 505 respondents were included in the present study. There was a high statistically significant difference in the income level of tobacco and non-tobacco users (P value- 0.002). In Univariate analysis, it was found that the odds of consuming both forms of tobacco were significantly lower in the respondents aged between 51--60 (OR = 0.61, P value = 0.0453). The participants who studied till graduation and above had lower odds of both using forms of tobacco as compared to respondents who were illiterate/primary educated only (OR = 0.85, P value = 0.046). Conclusion: In conclusion, smoking and smokeless tobacco consumption are still widely prevalent in this region and strongly associated with age, gender, level of education, and place of residence. There is a need for further detailed analysis to identify risk factors that are strongly associated with the use of smoke and smokeless tobacco so that the community will be conscious about the hazardous effects of tobacco use in any form. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases (CVDs) account for over three-quarters of all deaths taking place in developing nations. Objective: The present study aims to stratify noncommunicable disease (NCD) patients using the Globorisk chart for predicting their 10-year risk of a major (fatal or nonfatal) CVD event and to estimate the level of agreement between this country-specific chart and the existing World Health Organization (WHO)/International Society of Hypertension (ISH) risk strata. Methods: A record-based cross-sectional analytical study was conducted in 2018 among adults attending the NCD clinic of one rural and one urban primary health center in Puducherry. Laboratory and office risk calculators of the Globorisk chart were used to calculate the risk. Results: The median age (interquartile range (IQR)) of the 760 study participants was 58 (50--65) years. When calculated using the Globorisk prediction chart, 22.1% (n = 168) of the participants had a <10% risk for any CVD event in the next 10 years, whereas the same risk was found in 71.1% (n = 540) by using the WHO/ISH risk chart. There was no agreement found between the two risk charts (k = 0.0174; P-value = 0.26). Conclusion: The Globorisk chart was found to identify more patients as belonging to the higher risk category as compared to WHO/ISH charts. [ABSTRACT FROM AUTHOR]

Forecasting of load is essential for operating power systems. India recently witnessed one of the worst power crisis with the highest ever power demand of 207 GW on April 29, 2022. The demand in the month of May and June 2022 was estimated to reach 215 GW. The peak demand this year 2023, according to the electricity ministry, is predicted to be around 230 GW from April to June. The inability to meet certain fundamental issues as power can take a toll on any country's economy. Proper prediction helps in proper decision making and planning. The main objective of this paper is to predict day ahead electrical load demand for Assam. Statistical and Machine Learning Algorithms has been studied. The study has been carried out using real-time data for the years 2016, 2017 and 2018. The paper presents a detailed analysis of the different hyper parameters of the deep learning models and their effect is seen on the learning efficiency. A novel stacked forecasting model is proposed using neural networks as base learners and CatBoost as the meta-learner. The performance of the proposed model has been evaluated and compared with individual models in terms of training time and accuracy using different error metrics namely MAE, MSE, RMSE, MAPE and R2 score. A comparison of the proposed prediction model with the prediction models available in literature has been presented. The conclusion states that both the statistical and machine learning algorithms used in this study act as useful tools for daily load forecasting with considerable accuracy; yet machine learning algorithm outperforms the statistical methods. The entire work has been done in Google Colaboratory using Python as the programming language. [ABSTRACT FROM AUTHOR]

Introduction: The present research highlights the promising factors that suggest the possible occurrence of the fourth COVID wave in India. India is among the major victim of this pandemic and has already witnessed the three dangerous COVID waves, and the lives of Indian people are put to a major setback. When it appears as the destruction caused by the third wave seems to getting over and people are trying to return to their normal lives, meanwhile with the mutation of the virus, the new COVID variants (COVID XBB 1.16 or Arcturus, XBB.1.5, and Delta Omicron) are again shaking our universe. Methods: Research studies and developed predictive models are suggesting the possible occurrence of the fourth COVID wave in the coming months. Considering the predictions and model developed, a critical insight is provided in the present study, which predicts the prospect of the fourth COVID wave in India. Results: Research proposed that the new COVID variants (COVID XBB 1.16, XBB.1.5, and Delta Omicron) are again leading to an increase in the number of active infected cases. Several factors, as mentioned in the research, are responsible for this spread of infection. Conclusions: Important factors are highlighted, which portray the probability of the fourth coronavirus wave in India. Important factors are presented in individuals, and an attempt is made to relate them with the occurrence or nonoccurrence of the fourth wave. [ABSTRACT FROM AUTHOR]

Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combination of wavelet with deep learning, machine learning, and stochastic model have been proposed. The denoised series are fitted with various benchmark models, including long short-term memory (LSTM), support vector regression (SVR), artificial neural network (ANN), and autoregressive integrated moving average (ARIMA) models. The effectiveness of a wavelet-based denoising approach was investigated on monthly wholesale price data for three major spices (turmeric, coriander, and cumin) for various markets in India. The predictive performance of these models is assessed using root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The wavelet LSTM model with Haar filter at level 6 emerged as a robust choice for accurate price predictions across all spices. It was found that the wavelet LSTM model had a significant gain in accuracy than the LSTM model by more than 30% across all accuracy metrics. The results clearly highlighted the efficacy of a wavelet-based denoising approach in enhancing the accuracy of price forecasting. [ABSTRACT FROM AUTHOR]

In current studies, India is a farming country, and the accomplishment or disappointment of the crop mainly depends on the country's rainfall design. Generally, India's farming production is primarily based on the nature of the precipitation of the rainy season rainfall. The rainy season is the primary source of water in India. Regular rainfall forecasting is the primary source for crop development. Several analyses have defined the direct effect of rainwater on harvests. The main motive of this research work is proper and early rainfall prediction, which is helpful to people who live in northeast regions inclined to natural disasters like floods, etc. It helps agriculture with decision-making in their crop and water management (WM) using extensive dataset analysis that generates maximum terms of production for farmers and profits. This proposed work introduced an improved rainfall forecasting framework, a hybrid feature selection gradient-based RNN (HFSGRNN) model with an RNN algorithm. The research uses the HFSGRNN model steps, such as initial data preprocessing steps, which are used for forecasting rainfall, handling missing value outliers, and typecasting the rainfall dataset collected from the government site. After that, an HFSGRNN method is implemented to select the valuable using stochastic gradient descent (SGD) and optimal solutions calculated by particle swarm optimization (PSO) from the preprocessed data. The hybrid optimized feature sets are fed to the rainfall forecasting of the RNN classifier. Lastly, the valuable feature sets are forecasted using decision-making, and the simulation outcome shows that the research approach performed better in rainfall forecasting. The simulation results define that the HFSGRNN model delivered the minimum value of Root means square error (RMSE= 0.10) and maximum value of accuracy rate (acc = 98.1%) compared with existing methods, such as logistic regression (LR), Long Short Term -Convolutional Neural Network (LSTM-CNN), etc. The outcomes of the research analysis will help the farmers accept efficient modeling methods for forecasting long-term seasonal rainfall. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is one of the pollutants that can cause potential damage to the ecosystem. NO2 emitted from vehicles forms the primary precursor for ground-level ozone. In this study, an analysis of the daily average of NO2 concentration with meteorology measured for two years 2021 and 2022 is being carried out. It is evident from the analysis that NO2 concentration followed an apparent diurnal pattern with a maximum value in the morning hours and a minimum during the afternoon hours. Summer months recorded the highest, and North East Monsoon (NEM) recorded the lowest values of NO2. A statistically significant positive correlation was found between NO2 and Temperature. An autoregressive model was formulated to forecast the daily average values of NO2 concentration. Unit root test was performed to check the stationarity of the data points, which is important in determining trends and seasonal changes. From the model procedure, the order that best fits the data was identified as AR (4), in which the process has the current value based on the previous three values. The Akaike Information Criterion (AIC) and Schwartz Criterion (SC), which are estimators of prediction error for AR (4), are low. The Jarque confirmed the normal distribution-Bera test, which again approves the satisfactoriness of the model.. [ABSTRACT FROM AUTHOR]

Introduction: Jabalpur Prognostic Scoring System (JPSS), a simple scoring system, was developed to prognosticate the outcome for perforated peptic ulcer (PPU) in 2003 when there was no dedicated Surgical Intensive Care Unit (SICU) in the authors' institution. It is based on six easily available parameters, namely patient's age, perforation-operation interval, mean systolic blood pressure, heart rate, serum creatinine and the presence of comorbid illness. Its accuracy has since been validated by many authors and has been found to be comparable with other prognostic scoring systems. Availability of SICU in the last one decade has prompted us to re-assess the utility and validity of the JPSS in the current age. Methods: Prospectively collected JPSS data of 214 consecutive patients (prospective group, PG), who underwent Graham's patch omentopexy closure of a PPU, was compared with the JPSS cohort of 2003 (original group, OG). Sensitivity and specificity were calculated, and accuracy was measured using receiver operating characteristic curve analysis. Results: The PG had 214 patients, while the OG had 140 patients. PG had significantly more women patients, and a shorter hospital stay, otherwise, both groups were comparable. Postoperative mortality and morbidity, both overall and in different JPSS classes were statistically similar. Overall accuracy of JPSS in PG was 77% with a cutoff of 11 as compared to the OG where these values were 92% and 9 respectively. Conclusion: JPSS continues to be effective and accurate in predicting the outcome of patients with PPU in the current age. [ABSTRACT FROM AUTHOR]

Background: Hypertensive disorders of pregnancy (HDP) affect approximately 10% of pregnancies and remain a significant contributor to maternal mortality. Current predictive tools lack both cost-effectiveness and diagnostic accuracy. This study aims to develop a cost-effective prediction model for preeclampsia outcomes utilizing the HDP Gestosis score, Mean Arterial Pressure (MAP), and serum homocysteine levels. Methods: Conducted at the Department of Obstetrics and Gynaecology, Government Medical College and Hospital, Tiruppur, a tertiary care facility in South India, this retrospective randomized control pilot study involved 60 patients. Patients were categorized retrospectively into low, medium, and high-risk groups based on HDP Gestosis score. Mean arterial pressure at 20-24 weeks gestation and serum homocysteine levels were recorded. Results: Analysis of data from 60 patients revealed varying diagnostic accuracies: HDP Gestosis score alone (71.67%), mean arterial pressure alone (55%), and serum homocysteine levels alone (55%). Serum homocysteine levels demonstrated the highest positive predictive value (96.67%) and were significantly elevated in the preeclampsia group compared to the low-risk group (p<0.0001). The combined GESTO-MAP-H model showed moderate positive correlation (r=0.6) and a cutoff value of 100 exhibited 93.3% sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy in predicting preeclampsia-related complications. Conclusion: While prevention remains a future goal, early prediction and identification of preeclampsia, coupled with timely delivery, are crucial in reducing maternal morbidity and mortality. GESTO-MAP-H emerges as a promising, economical, user-friendly predictive tool, aiding in standardized decision-making to mitigate preeclampsia-related complications. [ABSTRACT FROM AUTHOR]

Purpose: Epidemiological studies have shown an association between coronary artery disease (CAD) and osteoporosis. We studied the prevalence of CAD among postmenopausal women with osteoporosis. Factors that were significantly associated with CAD were also assessed. Methods: This was a cross-sectional study conducted over a period of 2 years. Consecutive postmenopausal women aged ≥50 years were recruited. The details of an underlying CAD were obtained. Bone biochemical parameters, bone mineral density and body composition were assessed. Results: A total of 370 postmenopausal women with mean (standard deviation [SD]) ages of 61.6 (6.2) and 60.1 (6.0) years and a body mass index of 25.3 (14.1) kg/m² were recruited. Among them, 110 of 370 patients (29.7%) had an underlying CAD and 222 of 370 (60%) had osteoporosis at either the femoral neck or lumbar spine (LS). The odds of CAD among those with osteoporosis were 3.5 (95% confidence interval [CI]: 2.1-5.9). An LS T-score of ≤-2.2 had a sensitivity of 80% and a specificity of 45% in predicting CAD (area under the curve, AUC: 0.736; 95% CI: 0.677-0.795; p<0.001). A femoral neck T-score of ≤-1.9 had a sensitivity of 80% and a specificity of 60% in predicting CAD (AUC: 0.748; 95% CI: 0.696-0.800; p<0.001). On a logistic regression analysis after adjusting for various clinical parameters, femoral neck osteoporosis had the highest odds of CAD. Conclusion: The prevalence of CAD was higher among postmenopausal women with osteoporosis. Femoral neck osteoporosis conferred the highest odds of CAD after adjustment for other clinical factors. [ABSTRACT FROM AUTHOR]

The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mean Squared Error, Mean Absolute Error and R-squared and identified that after the inclusion of gradient boosting regression, the accuracy increased to 92.77%. The MAE value decreased from 26.20 Mg/ha to 21.58 Mg/ha. The results indicate that machine learning models can improve the prediction of crop yield. [ABSTRACT FROM AUTHOR]

To address the issues that environmental elements have a significant impact on total nitrogen (TN) in effluent throughout the wastewater treatment process, and existing analysis methods and instruments are difficult to measure in real-time, a soft sensor model based on the fractional-order difference algorithm (FDA) and the stochastic configuration network (SCN) is proposed for the soft measurement of effluent TN in wastewater treatment plants. First of all, the significance of the pertinent parameters impacting effluent TN is assessed by using the grey correlation analysis approach, and the predictors with high evaluation are screened out as input variables of the soft sensor model. Secondly, a method of data sampling based on FDA is employed to generate multiple training subsets to train each SCN sub-model in parallel, and fuse the output results of multiple sub-models according to combination rules as the output of the model. The proposed approach can increase the model's generalizability and maintain data information while guaranteeing data stability. Finally, the soft sensor model is verified by the actual data of a wastewater treatment process and the data collected from the Ganges River, India. Compared with prediction models including SCN, ELM, FDA-ELM, CNN-LSTM, Elman, BP, LSTM, etc, the results indicate that after sampling the data by FDA, smaller model prediction errors and higher prediction accuracy can be obtained, which can achieve high accuracy prediction of effluent TN in wastewater treatment plants. [ABSTRACT FROM AUTHOR]

Domestic violence, especially intimate partner violence (IPV), is an important issue worldwide, especially in India. Those that experience it may not always be able to come forward or have access to the required social support to act against it. We use National Family Health Survey data (n = 66,013 women) to create machine learning models which can predict IPV instances with a recall of 78%. We use the top 15 best predicting questions that avoid sensitive issues to create a field tool that frontline health workers can use to identify women with a high risk of IPV and provide the support they need. [ABSTRACT FROM AUTHOR]

Background: Gaming is a new phenomenon and there are increasing concerns over its negative impact. After the official recognition by diagnostic and statistical manual-5 and International Classification of Disorder-11, much of the research is directed toward understanding the different constructs of the gambling disorder. Peer influence is an established factor in substance use but less researched in gaming. Aims of the Study: The aim of this study is to explore and understand the influence of peer pressure on Internet gaming. Materials and Methods: Individuals between 15 and 35 years were asked to participate after explaining the study objectives and assessed on the Peer Pressure Scale and Internet Gaming Disorder (IGD) Scale. Responses were analyzed using the R language with R studio as the integrated development environment. Generalized additive model was used to identify the best-fit model. The Akaike information criterion was used to select the best model. Results: Battle Ground Mobile India is the most played game. Peer pressure alone accounts for 46.2% of the variability in Internet gaming and along with factors such as student; real friends; strategy games; online mode of playing, night time playing; and hours spent playing, increasing it to 56%. Peer pressure is positively correlated with IGD (P < 0.001, R = 0.65). Conclusions: Peer pressure is directly associated and plays a role in developing Internet gaming along with factors such as being a student; playing with real friends; strategy game genre; online mode of playing games; and nightly playing. Substance use such as tobacco may influence peer pressure and Internet gaming, acting as a co-factor for both. [ABSTRACT FROM AUTHOR]

Purpose: In the context of a developing country, Indian buildings need further research to channelize energy needs optimally to reduce energy wastage, thereby reducing carbon emissions. Also, reduction in smart devices' costs with sequential advancements in Information and Communication Technology have resulted in an environment where model predictive control (MPC) strategies can be easily implemented. This study aims to propose certain preemptive measures to minimize the energy costs, while ensuring the thermal comfort for occupants, resulting in better greener solutions for building structures. Design/methodology/approach: A simulation-based multi-input multi-output MPC strategy has been proposed. A dual objective function involving optimized energy consumption with acceptable thermal comfort has been achieved through simultaneous control of indoor temperature, humidity and illumination using various control variables. A regression-based lighting model and seasonal auto-regressive moving average with exogenous inputs (SARMAX) based temperature and humidity models have been chosen as predictor models along with four different control levels incorporated. Findings: The mathematical approach in this study maintains an optimum tradeoff between energy cost savings and satisfactory occupants' comfort levels. The proposed control mechanism establishes the relationships of output variables with respect to control and disturbance variables. The SARMAX and regression-based predictor models are found to be the best fit models in terms of accuracy, stability and superior performance. By adopting the proposed methodology, significant energy savings can be accomplished during certain hours of the day. Research limitations/implications: This study has been done on a specific corporate entity and future analysis can be done on other corporate or residential buildings and in other geographical settings within India. Inclusion of sensitivity analysis and non-linear predictor models is another area of future scope. Originality/value: This study presents a dynamic MPC strategy, using five disturbance variables which further improves the overall performance and accuracy. In contrast to previous studies on MPC, SARMAX model has been used in this study, which is a novel contribution to the theoretical literature. Four levels of control zones: pre-cooling, strict, mild and loose zones have been used in the calculations to keep the Predictive Mean Vote index within acceptable threshold limits. [ABSTRACT FROM AUTHOR]

Background: The rising burden of thyroid cancer (TC) is a public health problem in Asia. Predicting the future burden of TC in Asian countries is essential for disease prevention. Methods: Data were obtained from the Global Burden of Disease 2019 for five Asian countries. We applied Bayesian age-period-cohort models to predict morbidity and mortality to 2035 and calculated age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR). Furthermore, the estimated annual percentage change was calculated to evaluate the variation of ASIR and ASMR. Results: By 2035, predictions suggest that cases of TC will reach 75.56 x 10³ in China, 70.22 x 10³ in India, 15.78 x 103 in the Republic of Korea, 9.01 x 10³ in Japan and 5.55 x 10³ in Thailand, respectively. Except Japan, a significant upward trend of ASIR of TC will be observed in five Asian countries. The deaths from TC will increase in five countries and India will become the highest reaching 14.07 x 10³. The ASMR will rise to 0.83/100 000 in India and 1.06/100 000 in the Republic of Korea, while it will drop to 0.35/100 000 in China, 0.43/100 000 in Japan and 0.50/100 000 in Thailand. In further predictions projected by sex, the growth rate of ASIR is reported higher in males than in females among most countries. ASMR of male will exceed that of females in China and Thailand by 2035. Conclusion: The disease burden caused by TC will further increase in five Asian countries, especially for men. It is necessary to develop more rational and timely disease prevention and manage strategies facing this disease trend. [ABSTRACT FROM AUTHOR]

Globally, there are significant worries about the rise in air pollution (AP) from substances that are harmful to human health, different living forms, and unfavorable environmental imbalances. To overcome the problem, AI-based prediction model is the need of the hour. Therefore, an attempt was made to develop a novel AP prediction system based on Xavier Reptile Switan-h-based Long-Short Term Memory (XRSTH-LSTM), which undergoes fine-tuning at various steps such as pre-processing, attribute extraction, and air-quality index prediction, in order to reduce computational cost and also to increase accuracy as well as precision. The dataset used to train the proposed methodology is Air Quality Data in India (2015–2020), taken from publically available sources Kaggle. The dataset includes information on the AQI and air quality at different stations in numerous Indian cities at hourly and daily intervals. The accuracy has been calculated using MSE, MAPE, RMSE, precision, recall, and F-measure. The robustness of the proposed model is tested using parameters such as negative predicted value and Mathew correlation coefficient. The proposed model is found to efficiently process air quality with an improved accuracy of 98.52% and precision of 99.79%, which is 0.74% higher than the existing state-of-the-art model. The testing findings showed that the proposed approach worked better than the current models and offered a higher rate of accuracy in predicting air pollution. [ABSTRACT FROM AUTHOR]

Background: Recognition of clinically significant bleeding (CSB) is vital for effective management of dengue patients. The primary objective was to identify the predictors of CSB among dengue patients and to formulate a simple scoring system. The secondary objective was to compare the grades of bleeding and severity of thrombocytopenia. Materials and methods: We conducted a retrospective study of adults aged above 18 years with dengue, admitted to the intensive care unit (ICU) of a tertiary care hospital in South India from 2015 to 2021. Demographic, clinical, and laboratory variables on admission were collected. The association of clinically significant bleeding with the above parameters was assessed by univariate and multivariate analysis. Results: A total of 9,817 dengue cases were hospitalized during the study period. A total of 120 patients with thrombocytopenia (<100000 cells/mm3) were admitted to the ICU and of them 38 (31.6%) had CSB. On univariate analysis fever, sequential organ function assessment (SOFA) score, elevated activated partial thromboplastin time (aPTT), and altered sensorium were significantly associated with CSB. The multivariate model identified SOFA score [adjusted odds ratio (aOR): 1.52; 95% confidence interval (CI): 1.11-2.08], temperature >38.3°C (aOR: 2.71; 95% CI: 1.1-6.47) and elevated aPTT > 40 seconds (aOR: 4.66; 95% CI: 1.42-15.3) as independent risk factors. A clinical predictive score was developed incorporating these three parameters. The performance of the score identified by the receiver operating characteristic (ROC) curve [area under the curve (AUC): 0.81; 95% CI: 0.73-0.91] demonstrated a sensitivity of 81% and specificity of 77%. Conclusion: This study revealed that temperature above 38.3°C, elevated aPTT, and an increase in SOFA score were identified as independent risk factors for CSB. A clinical predictive score derived from these variables can identify patients likely to develop CSB. [ABSTRACT FROM AUTHOR]

The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping. [ABSTRACT FROM AUTHOR]

The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India. [ABSTRACT FROM AUTHOR]

In the months of March-June, India experiences high daytime temperatures (Tmax), which sometimes lead to heatwave-like conditions over India. In this study, 10 different machine learning models are evaluated for their ability to predict the daily Tmax anomalies 10 days ahead in the months of March-June. Several model experiments were carried out to identify an optimal model to predict daily Tmax anomalies over India. The results indicate that the AdaBoost regressor with Multi-layer Perceptron as the base estimator is an optimal model to predict the Tmax anomalies over India in the months of March-June. The optimal model predictions are benchmarked against 10-day persistence predictions and the predictions from the Climate Forecast System (CFS) reforecast. The results indicate that the machine learning model skill is higher than persistence and comparable to CFS reforecast 10-day predictions in April and May. In March and June, the machine learning models have low skill scores and perform no better than persistence. These results indicate that the machine learning models are promising tools to predict the surface air maximum temperature anomalies over India in April and May and can complement predictions from more sophisticated numerical models. [ABSTRACT FROM AUTHOR]

Road accidents are one of the biggest concerns to the road safety of developing nations. In India, around 150,000 fatal accidents occur annually. Road accident prediction models help in accessing the factors responsible for and those that contribute more to accidents. Most of the prediction models focus on the parameters like road characteristics, traffic characteristics, driver characteristics, and road geometrics. In this study, we considered socio-economic and land-use parameters as input data for accident prediction modeling. The socio-economic and land-use variables data of 20 Indian metro cities were collected. The data were collected for a period of 5 years ranging from 2016 to 2020. A multiple linear regression model was developed between the total number of accidents that happened in the 20 metro cities and the socio-economic and land-use variables. ANN model was also developed to check its applicability to this study and the results obtained are satisfactory. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to analyze the cross-presentation of allopeptides derived from donor major histocompatibility (MHC) molecules by recipient class I MHC molecules in the context of transplantation rejection. Materials and Methods: The MHC molecules were selected based on frequency distribution in the Kerala (India) population using the "allelefrequency. net" database. The most frequent alleles were chosen as donor and recipient alleles. Different immunoinformatics servers such as Netcytotoxic T lymphocyte, NetMHCpan, and Immune Epitope Database MHC I were used for predicting potential T-cell epitopes, and MHC class I epitopes, respectively, from the chosen donor MHC molecule. Three peptides with strong binding affinities in all three predictions were selected for further analysis. The binding of these peptides to the second MHC class I molecule, the recipient one, and transporter associated with antigen processing protein was confirmed by molecular docking using the "AutoDockVina" tool. The binding interactions were visualized using the "Discovery Studio Visualizer" platform. Results: The ability of the selected class I MHC molecule, human leukocyte antigens (HLAs)-A* 11:01, considered the donor allele, to act as an exogenous antigen and to get processed into peptides and presented on the second class I MHC molecule, HLA-A*02:01, considered the recipient allele was confirmed. This immunoinformatics approach could help us to identify potential epitopes from an HLA class I molecule and to measure its ability to get cross-presented on a second HLA class I molecule to trigger the process of cell-mediated rejection. Conclusion: This immunoinformatics approach could help us to identify potential epitopes from an HLA class I molecule and to measure its ability to get cross-presented on a second HLA class I molecule to trigger the process of cell-mediated rejection. Our study provides an easy in silico method for predicting the chances of cell mediated rejection in organ transplantations involving an unrelated donor. We hope that this computational analysis will pave the way for simplifying the process involved in organ rejection prediction. [ABSTRACT FROM AUTHOR]

Introduction: The global incidence of chronic otitis media (COM) is approximately 4.76 per thousand, with 31 million cases causing various complications. Most of these patients require one of the various forms of mastoid surgeries. High-resolution computed tomography (HRCT) has greatly improved the ability to assess both bony architecture and soft-tissue status. Aim: The aim is to assess the correlation of HRCT temporal bone with intraoperative findings in tympanomastoid surgery and to evaluate the sensitivity, specificity, positive predictive value, and negative predictive value of HRCT temporal bone. Materials and Methods: This prospective study was conducted in the Department of Otorhinolaryngology, SDM College of Medical Sciences and Hospital, Dharwad from November 2012-April 2014. Fifty patients of COM were taken up for study who underwent preoperative HRCT temporal bone followed by appropriate tympanomastoid surgery. Results: HRCT temporal bone showed increased sensitivity and specificity in detecting ossicular chain erosion and soft tissue abnormality within the middle ear and mastoid air cells. Facial nerve canal erosion was correctly detected in 62.50%, and overall agreement between surgical findings and the scan was high (90%). It also showed a very high specificity of 95.24%. For semicircular canal erosion and dural plate erosion, sensitivity was 50% each, specificity was 100% and had a very good correlation with surgical findings. Two cases of erosion of the posterior bony wall of the external auditory canal were correctly detected by the HRCT scan. Conclusion: HRCT temporal bone was found to have a high overall correlation with surgical findings and can be a useful method for early diagnosis of complications of COM and preoperative assessment. [ABSTRACT FROM AUTHOR]

In India, cotton yield estimates are done using crop cutting experiments (CCEs) conducted within the framework of the general crop estimation surveys (GCES) methodology. In recent times, for obtaining reliable estimates at levels lower than the district, the number of CCEs has increased in comparison to the existing set-up of GCES. This puts an additional financial burden on Government agencies. There is a possibility of reducing the number of CCEs under the GCES methodology and predicting the remaining CCE points using an appropriate spatial prediction model. In this article, the predictive performance of different spatial models has been compared. Furthermore, district-level estimate of average productivity of cotton has been determined using the geographically weighted regression (GWR) technique and the results compared with those obtained using the traditional GCES methodology. The proposed spatial estimator of the average yield of cotton obtained using the GWR approach is more efficient and the results are comparable with the estimates obtained using the GCES methodology. The developed methodology can be utilized to reduce the number of CCEs and capture the spatial non-stationarity present in the cotton crop yield. [ABSTRACT FROM AUTHOR]

Background: The Oxford and Swedish indexes were developed to predict in-hospital colectomy in acute severe ulcerative colitis (ASUC), but not long-term prediction, and all these indexes were based on Western data. Our study aimed to analyze the predictors of colectomy within 3 years of ASUC in an Indian cohort and derive a simple predictive score. Methods: A prospective observational study was conducted in a tertiary health care center in South India over a period of 5 years. All patients admitted with ASUC were followed up for a period of 24 months after the index admission, to look for progression to colectomy. Results: A total of 81 (47 male) patients were included in the derivation cohort. Fifteen (18.5%) patients required colectomy during a follow-up period of 24 months. On regression analysis, C-reactive protein (CRP) and serum albumin were independent predictors of 24-month colectomy. The CRAB (CRP + AlBumin) score was obtained by multiplying coefficient of beta to albumin and CRP (CRAB score = CRP x 0.2 – Albumin x 0.26). The CRAB score demonstrated an AUROC of 0.923 and a score of >0.4 with a sensitivity of 82% and specificity of 92% for the prediction of 2-year colectomy following ASUC. The score was validated in a validation cohort of 31 patients, and at >0.4, the score had a sensitivity of 83% and a specificity of 96% in predicting colectomy. Conclusion: CRAB score is a simple prognostic score that can predict 2-year colectomy in ASUC patients with high sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Blasting is a cost-efficient and effective technique that utilizes explosive chemical energy to generate the necessary pressure for rock fragmentation in surface mines. However, a significant portion of this energy is dissipated in undesirable outcomes such as flyrock, ground vibration, back-break, etc. Among these, flyrock poses the gravest threat to structures, humans, and equipment. Consequently, the precise estimation of flyrock has garnered substantial attention as a prominent research domain. This research introduces an innovative approach for demarcating the hazardous zone for bench blasting through simulation of flyrock trajectories with probable launch conditions. To accomplish this, production blasts at five distinct surface mines in India were monitored using a high-speed video camera and data related to blast design and flyrock launch circumstances including the launch velocity (vf) were gathered by conducting motion analysis. The dataset was then used to develop ten Bayesian optimized machine learning regression models for predicting vf. Among all the models, the Extremely Randomized Trees Regression model (ERTR-BO) demonstrated the best predictive accuracy. Moreover, Shapely Additive Explanation (SHAP) analysis of the ERTR-BO model unveiled bulk density as the most influential input feature in predicting vf, followed by other features. To apply the model in a real-world setting, a user interface was developed to aid in flyrock trajectory simulation during bench blast designing. [ABSTRACT FROM AUTHOR]

This study is based on temperature prediction in the capital of India (New Delhi). We have adopted different ML models such as (MPR and DNN) which are designed and implemented for temperature prediction. The MPR models are varied on the degree of the polynomial, whereas the DNN models differ in the number of input parameters. DNNM‐1 takes date, time, and temperature as input, and DNNM‐2 receives date, time, temperature, pressure, humidity, and dew point as input parameters, whereas DNNM‐3, is a complex model that takes date, time, temperature, pressure, humidity, dew point, and 32 weather conditions as input. To evaluate the accuracy of the predictions, a comparison of the predicted temperature and the actual recorded temperature is done, and the performance and accuracy of the models are examined. The MPR models work well in case of fewer input features, but as the number of input features grows, the DNN model outperforms the MPR models. The DNN model (DNNM‐3) outperformed the other models with better accuracy as compared to past evidence. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the influence of antenatal factors such as anxiety, depression, perceived stress, marital satisfaction, maternal antenatal attachment, and social support on postnatal maternal attachment and competence in women who received assisted reproductive treatment. A prospective longitudinal cohort design was adopted with two groups—50 women who received assisted reproductive treatment and 50 women who had natural conception. Both the groups were assessed using self-report measures over three time points: T1, 7th month of pregnancy; T2, 2 weeks postpartum; and T3, 3 months postpartum. A final sample of 44 women who had assisted conception and 47 women who had natural conception completed assessments across all three time points. Descriptive, bivariate analyses, and stepwise multiple linear regression analyses were carried out. In the assisted conception group, maternal antenatal attachment, depression, and marital satisfaction significantly predicted postnatal maternal-infant attachment. Perceived social support, depression, and duration of marriage significantly predicted postnatal maternal competence. In the naturally conceived group, maternal antenatal attachment and social support significantly predicted postnatal maternal-infant attachment; perceived stress significantly predicted postnatal maternal competence. Antenatal depressive symptoms and relational factors significantly influenced postnatal maternal attachment and competence highlighting the need for screening and targeted psychological interventions during pregnancy. [ABSTRACT FROM AUTHOR]

This paper highlights the application of non-destructive tests (NDT) to evaluate the pavement for maintenance in a road network. The falling weight deflectometer (FWD) and the network survey vehicle (NSV) test data which is rational in terms of technical, financial, and environmental aspects have been used. The data using NDT devices were collected from a network of roads from Odisha state, India, and analyzed to assess the structural adequacy of the pavements. Further, using the FWD deflection data, the resilient moduli (MR) of the pavements were back-calculated using a cuckoo search (BACKCSA) model developed as a part of this research work. The back-calculated layer moduli (MR) was used as an input for developing the pavement condition index (PCI) prediction model. In addition, the other pavement distresses viz., international roughness index (IRI), rut depth, cracking, and raveling was also collected using the NSV. The data collected for three cycles with a minimum difference of one year between each cycle was used for the development of perdition models. The NSV data was used for developing the multilinear regression (MLR) prediction models for building a comprehensive pavement management system. This paper highlights the novel approach in arriving at the MR, and incorporating the same in MLR models for predicting the maintenance strategy for a road network. [ABSTRACT FROM AUTHOR]

Two-lane rural roads constitute a significant part of the roadway system in India. Geometric characteristics such as facility type, lane width, shoulder width, and horizontal and vertical alignments, are essential parameters that influence vehicle behavior and traffic flow characteristics on two-lane rural roads. Among all geometric characteristics, the magnitude of the grade most substantially impacts the operational characteristics of traffic flow on two-lane rural roads. The present study investigates the effect of grade on the operating speed and capacity of two-lane rural roads under mixed traffic conditions. Traffic video data for eight road sections with grades varying from 1% to 8% were collected under dry weather conditions. The investigation revealed a significant effect of grade on the operating speeds of different vehicle types. The operating speed decreased with an increase in the grade magnitude. The road capacity was derived by calibrating various single regime models. The Northwestern model was deemed appropriate to derive the capacity values, based on theoretical and statistical investigation. The results showed that the capacity decreases by 6.4% with every 1%increase in grade. Further, the effect of grade on passenger car units (PCU) of different vehicle types was investigated. For varying volume-to-capacity (V/C) ratios, it was observed that the PCU of heavy vehicles increases as the magnitude of the grade increases. The present study develops operating speed and capacity prediction models as an essential practical outcome. The developed models can facilitate planners and traffic engineers to estimate operating speed and capacity based on the magnitude of the grade. [ABSTRACT FROM AUTHOR]

The present study analyses the performance of recent seasonal prediction coupled models in simulate the seasonal extremes (June through September) over the India using February and May initializations (hear after FEBIC & MAYIC) for a period of 1981–2018. All the models show significant seasonal dry bias over the northern and western coast of Indian regions (associated with northerly wind bias at lower troposphere), whereas strong wet bias over the southeastern India which is associated with cyclonic circulation (at 850 hPa) over the central Bay of Bengal. CFSv2 hindcasts has more dry bias over India for FEBIC due to strong northerly wind bias over the Northern India and strong cyclonic circulation over the North West Pacific, whereas, that dry bias is reduced significantly over the Northern Indian region in all the models except GEM-NEMO in MAYIC. CFSv2 reproduces the orographic rainfall over the west coast and northeastern parts of India in both leads. The models are not able to reproduced the extreme years that are associated with El Niño, Indian Ocean Dipole and Atlantic Niño boundary forcing (BF), while the observed normal years which co-occur with these BF are represented as excess years in the models. Categorical skill scores suggest that, all the models can capture the normal years only as similar to observations whereas deficient and excess years have more false alarms mainly due to the misrepresentation of ascending and descending branches of the Walker circulation over the tropics during the extreme years. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to study the prevalence of augmented renal clearance (ARC) and validate the utility of ARC and ARCTIC scores. We also aimed to assess the correlation and agreement between estimated GFR (eGFR-EPI) and 8-hour measured creatinine clearance (8 hr-mCLcr). Study design and methodology: This was a prospective, observational study done in the mixed medical-surgical intensive care unit (ICU) and 90 patients were recruited. 8 hr-mCLcr, ARC, and ARCTIC scores and eGFR-EPI were calculated for all patients. ARC was said to be present if 8 hr-mCLcr was ≥ 130 mL/min. Results: Four patients were excluded from the analysis. The prevalence of ARC was 31.4%. The sensitivity, specificity, and positive and negative predictive values of ARC and ARCTIC scores were found to be 55.6, 84.7, 62.5, 80.6, and 85.2, 67.8, 54.8, and 90.9 respectively. AUROC for ARC and ARCTIC scores were 0.802 and 0.765 respectively. A strong positive correlation and poor agreement were observed between eGFR-EPI and 8 hr-mCLcr. Conclusion: The prevalence of ARC was significant and the ARCTIC score showed good potential as a screening tool to predict ARC. Lowering the cut-off of ARC score to ≥5 improved its utility in predicting ARC. Despite its poor agreement with 8 hr-mCLcr, eGFR-EPI with a cut-off ≥114 mL/min showed utility in predicting ARC. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has affected thousands of people around the world. In this study, we used artificial neural network (ANN) models to forecast the COVID‐19 outbreak for policymakers based on 1st January to 31st October 2021 of positive cases in India. In the confirmed cases of COVID‐19 in India, it's critical to use an estimating model with a high degree of accuracy to get a clear understanding of the situation. Two explicit mathematical prediction models were used in this work to anticipate the COVID‐19 epidemic in India. A Boltzmann Function‐based model and Beesham's prediction model are among these methods and also estimated using the advanced ANN‐BP models. The COVID‐19 information was partitioned into two sections: training and testing. The former was utilized for training the ANN‐BP models, and the latter was used to test them. The information examination uncovers critical day‐by‐day affirmed case changes, yet additionally unmistakable scopes of absolute affirmed cases revealed across the time span considered. The ANN‐BP model that takes into consideration the preceding 14‐days outperforms the others based on the archived results. In forecasting the COVID‐19 pandemic, this comparison provides the maximum incubation period, in India. Mean square error, and mean absolute percent error have been treated as the forecast model performs more accurately and gets good results. In view of the findings, the ANN‐BP model that considers the past 14‐days for the forecast is proposed to predict everyday affirmed cases, especially in India that have encountered the main pinnacle of the COVID‐19 outbreak. This work has not just demonstrated the relevance of the ANN‐BP techniques for the expectation of the COVID‐19 outbreak yet additionally showed that considering the incubation time of COVID‐19 in forecast models might produce more accurate assessments. [ABSTRACT FROM AUTHOR]