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[Purpose] How to prevent fake news without spoiling the freedom of speech is a growing concern among governments across the world. Some countries see legislation as being the best approach to counter fake news. In the legislation proposals, accountability is mostly placed on technology companies, but also individuals seem to have responsibility in the legislation of some countries. Some other governments see non-legislative means to counter fake news. But it's a fact that countering fake news without compromising free speech is a high priority across governments in the world and a challenging task too. This paper investigates the India scenario and tries to list out other than legislation what other measures are required. [Methodology] This paper takes a survey of mitigation efforts in select countries. This survey is used to testify against similar efforts in India, if any and adopts comparative approach to understand where Indian efforts stand at. [Findings] From using fact-checking tools available online, finding the source, locating how many people viewed a particular story to check grammar and spelling, and developing a critical mindset; plenty of things become a critical means in fighting down fake news. Legislation alone is insufficient. Media literacy, public scrutiny, good citizenship, and education along with sensitive civil society require playing its significant part in India to fight fake news. In India, the policy is vague. It gives the government enormous power to surveillance in the name of fake news. [ABSTRACT FROM AUTHOR]

Background: India is a large and populous country where reliable data on headache disorders are relatively scarce. This study in northern India (Delhi and National Capital Territory Region [NCR], including surrounding districts in the States of Haryana, Uttar Pradesh and Rajasthan) continues the series of population-based studies within the Global Campaign against Headache and follows an earlier study, using the same protocol and questionnaire, in the southern State of Karnataka. Methods: This cross-sectional study used the Global Campaign's established methodology. Biologically unrelated Indian nationals aged 18–65 years were included through multistage random sampling in both urban and rural areas of NCR. Interviews at unannounced household visits followed the structured Headache-Attributed Restriction, Disability, Social Handicap and Impaired Participation (HARDSHIP) questionnaire in its original English version or in the validated Hindi version. Demographic enquiry was followed by a neutral headache screening question and diagnostic questions based on the International Classification of Headache Disorders edition 3 (ICHD-3), which focused on each respondent's most bothersome headache. Questions about headache yesterday (HY) enabled estimation of 1-day prevalence. A diagnostic algorithm first identified participants reporting headache on ≥ 15 days/month (H15+), diagnosing probable medication-overuse headache (pMOH) in those also reporting acute medication use on ≥ 15 days/month, and "other H15+" in those not. To all others, the algorithm applied ICHD-3 criteria in the order definite migraine, definite tension-type headache (TTH), probable migraine, probable TTH. Definite and probable diagnoses were combined. Results: Adjusted for age, gender and habitation, 1-year prevalences were 26.3% for migraine, 34.1% for TTH, 3.0% for pMOH and 4.5% for other H15+. Female preponderance was seen in all headache types except TTH: migraine 35.7% vs. 15.1% (aOR = 3.3; p < 0.001); pMOH 4.3% vs. 0.7% (aOR = 5.1; p < 0.001); other H15 + 5.9% vs. 2.3% (aOR = 2.5; p = 0.08). One-day prevalence of (any) headache was 12.0%, based on reported HY. One-day prevalence predicted from 1-year prevalence and mean recalled headache frequency over 3 months was slightly lower (10.5%). Conclusions: The prevalences of migraine and TTH in Delhi and NCR substantially exceed global means. They closely match those in the Karnataka study: migraine 25.2%, TTH 35.1%. We argue that these estimates can reasonably be extrapolated to all India. [ABSTRACT FROM AUTHOR]

The road is the most commonly used means of transportation and serves as a country's arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance. [ABSTRACT FROM AUTHOR]

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

Chronic wounds are associated with significant clinical, economic and quality‐of‐life burden. Despite the variety of wound imaging systems available in the market for wound assessment and surveillance, few are clinically validated among patients of Asian ethnicity. We aimed to clinically validate the accuracy of a smartphone wound application (Tissue Analytics [TA], Net Health Systems Inc, Florida, USA), versus conventional wound measurements (visual approximation and paper rulers), in patients of Asian ethnicity with venous leg ulcers (VLU). A prospective cohort study of patients presenting with VLU to a specialist wound nurse clinic over a 5‐week duration was conducted. Each patient received seven wound measurements: one by a trained wound nurse clinician, and three separate wound measurements using TA on each of the iOS and Android operating systems. Inter‐rater and intra‐rater reliability between clinical and TA‐based measurements were analysed using intra‐class correlation statistics, with values of <0.5, 0.5 to 0.75, 0.75 to 0.9, and >0.9 indicating poor, moderate, good and excellent reliability, respectively. 82 patients (51% males), with a mean age at 65.8 years, completed the 5‐week study duration. 25 (30%) had underlying diabetes mellitus. Chinese, Malay and Indian ethnicity comprised 68%, 12% and 11%, respectively. The VLU healed in 26 (32%) of patients within the study period. In total, 358 wound episodes with 2334 wound images were analysed. Inter‐rater reliability for length, width and area between wound nurse measurements and TA application measurements was good (range 0.799‐0.919, P < 0.001). Separate measurements of intra‐rater reliability for length, width and area within the iOS or Android systems were excellent (range 0.967‐0.985 and range 0.977‐0.984 respectively, P < 0.001). Inter‐rater reliability between TA used on the iOS and Android systems was also excellent (0.987‐0.989, P < 0.001). Tissue Analytics, a smartphone wound application, is a useful adjunct for wound assessment and surveillance in VLU patients of Asian ethnicity. [ABSTRACT FROM AUTHOR]

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance. [ABSTRACT FROM AUTHOR]

Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) Systematic and robust techniques for selecting suitable forecasting models and (2) Techniques to find appropriate combination weights. To address these challenges a novel framework for optimization-based ensemble technique, 'OptiSembleForecasting' has been proposed in this study. The three components of the proposed framework are (a) Principal Component Analysis-based error index, (b) Model Confidence Set algorithm and (c) Optimization techniques. A total of thirteen forecasting models consisting of five deep learning, five machine learning and three stochastic models and twenty optimization techniques have been implemented in the proposed framework. To examine the effectiveness of the proposed technique, wholesale price of three commodities (TOP: Tomato, Onion, and Potato) each with two major markets in India has been considered. The empirical evaluation of the predictive accuracy of different models with that of the proposed techniques has been carried out by means of root mean square error and mean absolute percentage error. The findings of this study demonstrated the superiority of the proposed algorithm. Moreover, a R-package, namely 'OptiSembleForecasting', has been developed to make the implementation of this technique simple and user-friendly. [ABSTRACT FROM AUTHOR]

Big data analytics involve a systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involves grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It applies feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. The system also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. The overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 82%, and it can be increased as a future work by including deep learning with time-series grape leaf images. [ABSTRACT FROM AUTHOR]

Communication establishment is crucial for rescue operations in disaster affected areas. A standard tool for communication is the use of cell phones. However, they can be useless in situations where the cellular network's base stations are damaged in a disaster. A contemporary approach to re-establishing a communication network is by hosting base stations in drones. However, low battery life and difficulty in calculating the number of drones needed in different terrains are limitations of the above approach. This paper introduces a novel terrain-aware algorithm that calculates the minimum number of drones needed to cover an area with no voids in the network coverage. Our method ensures that the drones are deployed at optimal heights to maximize the average leftover energy in the network. We apply the algorithm for an actual location in Pettimudi, India and find the optimal number and positions of the drones to cover the area effectively without voids. In addition, we provide a simulation of the the communication establishment using above drones, and our experiments yield an average network efficiency of 98%, showing the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components. [ABSTRACT FROM AUTHOR]

Monitoring the Earth's surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth's surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth's surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring. [ABSTRACT FROM AUTHOR]

Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets. [ABSTRACT FROM AUTHOR]

The present paper reports a study, where growing hierarchical self-organising map (GHSOM) has been applied to achieve a visual cluster analysis to the Indian rainfall dataset consisting of 142 years of Indian rainfall data so that the yearly rainfall can be segregated into small groups to visualise the pattern of clustering behaviour of yearly rainfall due to changes in monthly rainfall for each year. Also, through support vector machine (SVM), it has been observed that generation of clusters impacts positively on the prediction of the Indian summer monsoon rainfall. Results have been presented through statistical and graphical analyses. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to ascertain the awareness and use of cloud computing technologies among the library professionals in the Kerala University Library system, India. A survey was conducted using questionnaire among the 102 library professionals employed in the central and departmental libraries of the University of Kerala. The study revealed that 42.16% of the library professionals did not have much idea about cloud computing technology. Analysis showed that Facebook and Google Apps like Gmail, Google Doc etc are the cloud computing technologies used by majority of the respondents. It is also worth to note that the library professionals in Kerala University Library are using cloud computing technologies without being aware of doing so. Library professionals' awareness of cloud service models is relatively very low. Web OPAC and Journal Discovery Service are the areas known to the respondents in applying cloud computing technologies in libraries. Out of 102 respondents, 14.71% of the library professionals in the University of Kerala have average skill in using these technologies. The findings of the study throw lights into the need of providing training for the library professionals in handling technology enriched library services to the users. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969-2005). [ABSTRACT FROM AUTHOR]

Aim: The aim of the study was o define the preoperative diagnostic clinical and radiological features in girls with congenital pouch colon (CPC). Materials and Methods: Records of 47 girls with CPC, managed from 1996 to 2018, were reviewed. There were two age groups: Group A (newborn to 12 months; n = 26) and Group B (>12 months to 20 years; n = 21). The important clinical and radiologic features to help in a preoperative diagnosis were noted. Results: The most common subtype was Type II (57.4%), followed by Type I (23.4%) and Type III (12.8%). The features common to both the groups were abdominal distension (A = 53.8%; B = 9.52%), severe perineal excoriation (A = 19.2%; B = 23/8%), and urinary incontinence (A = 30.7%; B = 85.7%). In addition, in Group B, fecaloma on abdominal palpation was noted in 28.6% of patients. The characteristic appearance of the perineum including external genitalia and findings on plain abdominal X-ray (AXR) were 100% accurate and hence diagnostic. These unique features helped us formulate an algorithm for preoperative diagnosis of this uncommon form of anorectal malformation in girls seen in North India. Conclusions: The characteristic features on clinical examination should alert one to the presence of CPC in the outpatient clinic. The AXR was diagnostic in 100% of cases and is mandatory. If any doubt persists, examination of the genitalia under anesthesia with more retraction of the labial folds and endoscopy can be performed for confirmation. These measures should enable a clinician to make an accurate preoperative diagnosis in every girl with CPC. [ABSTRACT FROM AUTHOR]

In this paper, we examine patterns of self-reported diagnosis of noncommunicable diseases (NCDs) and prevalences of algorithm/measured test-based, undiagnosed, and untreated NCDs in China, Ghana, India, Mexico, Russia, and South Africa. Nationally representative samples of older adults aged ≥50 years were analyzed from wave 1 of the World Health Organization's Study on Global Ageing and Adult Health (2007-2010; n = 34,149). Analyses focused on 6 conditions: angina, arthritis, asthma, chronic lung disease, depression, and hypertension. Outcomes for these NCDs were: 1) self-reported disease, 2) algorithm/measured test-based disease, 3) undiagnosed disease, and 4) untreated disease. Algorithm/measured test-based prevalence of NCDs was much higher than self-reported prevalence in all 6 countries, indicating underestimation of NCD prevalence in low- and middle-income countries. Undiagnosed prevalence of NCDs was highest for hypertension, ranging from 19.7% (95% confidence interval (CI): 18.1, 21.3) in India to 49.6% (95% CI: 46.2, 53.0) in South Africa. The proportion untreated among all diseases was highest for depression, ranging from 69.5% (95% CI: 57.1, 81.9) in South Africa to 93.2% (95% CI: 90.1, 95.7) in India. Higher levels of education and wealth significantly reduced the odds of an undiagnosed condition and untreated morbidity. A high prevalence of undiagnosed NCDs and an even higher proportion of untreated NCDs highlights the inadequacies in diagnosis and management of NCDs in local health-care systems. [ABSTRACT FROM AUTHOR]

The objective of this article is to develop a new positioning algorithm to estimate the position accurately in low latitude region and assess the algorithm's performance in terms of accuracy and precision. The Kalman Filter (KF) has been widely recognized as one of the most powerful state estimation techniques in estimating system state variables and suppressing measurement noise. The Kalman Filter is desirable because the uncertainty in the Minimum Mean Square Error (MMSE) estimation can be minimized. In this article, we implemented a new algorithm called as Cross-Correntropy Kalman Filter (CCKF) to enhance the position accuracy and performance of the GPS receiver. Primarily the improvement depends on the Cross-Correntropy criterion which is a measure of local similarity, and a novel Fixed-Point algorithm for updating subsequent estimates. In this work, we present a thorough derivation of the method suggested, how to accurately measure the estimate of the GPS receiver position. Furthermore, in tabular forms, we provide a comparison of receiver position error (X, Y, Z coordinates) and performance metrics (2-D & 3-D) together with graphical representations for both algorithms (KF & CCKF). [ABSTRACT FROM AUTHOR]

Background: With the Wuhan pandemic spread to India, more than a lakh of population were affected with COVID-19 with varying severities. Physiotherapists participated as frontline workers to contribute to management of patients in COVID-19 in reducing morbidity of these patients and aiding them to road to recovery. With infrastructure and patient characteristics different from West and lack of adequate evidence to existing practices, there was a need to formulate a national consensus. Materials and methods: Recommendations were formulated with a systematic literature search and feedback of physiotherapist experiences. Expert consensus was obtained using a modified Delphi method. Results: The intraclass coefficient of agreement between the experts was 0.994, significant at p < 0.001. Conclusion: This document offers physiotherapy evidence-based consensus and recommendation to planning physiotherapy workforce, assessment, chest physiotherapy, early mobilization, preparation for discharge planning, and safety for patients and therapist in acute-care COVID 19 setup of India. The recommendations have been integrated in the algorithm and are intended to use by all physiotherapists and other stakeholders in management of patients with COVID-19 in acute care settings. [ABSTRACT FROM AUTHOR]

This paper proposes an expert system approach to short-term expansion planning (STEP). The rules which drive STEP can be classified into MW, MVAR, and ampacity management rules. MW and ampacity management rules are for alleviating transmission line congestion. Reactive power management is required for voltage control at load busses, conformity to the capacity curve of the generators, and containing the MW losses within acceptable limits. Embedding reactive power management in STEP is a challenging task since ac load flow may not converge in absence of proper reactive power planning and load modeling. Therefore, we also propose enhancements to the fast decoupled load flow algorithm for on-the-fly reactive power management. The enhanced algorithm not only can detect divergent load flow scenarios but also self-correct it by restarting the whole process with greater degree of freedom in reactive power controls. The proposed approach leads to development of an automated tool for STEP which has the capability to work, even with incomplete information. A simple method for evaluating location and requirement of shunt reactor is also proposed. By analysis and comparative evaluation, we show that the proposed system can arrive at a solution which is close to optimal. Results on the Western Regional Grid of India with an approximate load of 28000 MW and 1200 nodes are presented to demonstrate effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Recent growth in seaborne trade has increased the tugboat operation cycle to maneuver incoming marine vessels at port. To meet the tugboat load demand for maneuvering, multiple power sources are being employed on-board. This necessitates a coordinated control strategy for the effective operation of these multiple power sources. To design an effective coordinated control strategy, this article initially estimates multiple power sources capacities (doubly fed induction machine (DFIM) and battery storage system) for a diesel-electric tugboat system based on a real-time tugboat operation cycle obtained from Visakhapatnam port, India. With respect to these multiple power sources, a coordinated control strategy for a better fuel efficiency is proposed based on a state machine control algorithm. The proposed control strategy operates a variable speed diesel-electric generator (diesel engine coupled DFIM) at a minimum specific fuel consumption value and battery based on its state of charge. To show the efficacy of the proposed variable speed diesel-electric system, fuel consumption is computed, which resulted in 26.42% of saving in comparison with a traditionally used diesel-mechanical tugboat. To evaluate the real-time system (2 MW DFIM + 150 kWh battery), MATLAB/Simulink tool is used and an experimental demonstration is performed on a laboratory prototype. [ABSTRACT FROM AUTHOR]

This study addressed the problem of scheduling walk-in patients in real time. Outpatient clinics encounter uncertainty in patient demand. In addition, the disparate departments are locally (department-centric) organized, leading to prolonged waiting times for patients. The proposed integral patient scheduling model incorporates the status and information of all departments in the outpatient clinic along with all possible pathways to direct patients, on their arrival, to the optimal path. The developed hybrid ant agent algorithm identifies the optimal path to reduce the patient waiting time and cycle time (time from registration to exit). An outpatient clinic in Aravind Eye Hospital, Madurai, has a huge volume of walk-in patients and was selected for this study. The simulation study was performed for diverse scenarios followed by implementation study. The results indicate that integral patient scheduling reduced waiting time significantly. The path optimization in real time makes scheduling effective and efficient as it captures the changes in the outpatient clinic instantly. [ABSTRACT FROM AUTHOR]

We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-implemented the main method in the original study since the source code is not available. The original study used non-public fundus images from EyePACS and three hospitals in India for training. We used a different EyePACS data set from Kaggle. The original study used the benchmark data set Messidor-2 to evaluate the algorithm's performance. We used another distribution of the Messidor-2 data set, since the original data set is no longer available. In the original study, ophthalmologists re-graded all images for diabetic retinopathy, macular edema, and image gradability. We have one diabetic retinopathy grade per image for our data sets, and we assessed image gradability ourselves. We were not able to reproduce the original study's results with publicly available data. Our algorithm's area under the receiver operating characteristic curve (AUC) of 0.951 (95% CI, 0.947-0.956) on the Kaggle EyePACS test set and 0.853 (95% CI, 0.835-0.871) on Messidor-2 did not come close to the reported AUC of 0.99 on both test sets in the original study. This may be caused by the use of a single grade per image, or different data. This study shows the challenges of reproducing deep learning method results, and the need for more replication and reproduction studies to validate deep learning methods, especially for medical image analysis. Our source code and instructions are available at: . [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5–15% and 5–17% compared with the GP model, 12–21% and 10–22% compared with the M5T model, and 7–15% and 5–18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models. [ABSTRACT FROM AUTHOR]

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001–2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 km × 30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r = 0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product. [ABSTRACT FROM AUTHOR]

This paper describes experience with application of operations research techniques in planning new roads and social service centers for the Dharampur area in India. The work involved designing a road network using a minimal spanning tree algorithm; selecting the number and location of the service centers to be constructed over a five year period using a set covering algorithm and developing a schedule for the construction of the service centers using dynamic programming. Substantial economic benefits resulted from using the mathematical models. The experience shows that operations research models can be useful in providing an objective rationale for administrative decisions, thus playing an important role in the resolution of political conflict. [ABSTRACT FROM AUTHOR]

Background: Limb‑girdle muscular dystrophy (LGMD) is the most common adult‑onset class of muscular dystrophies in India, but a majority of suspected LGMDs in India remain unclassified to the genetic subtype level. The next‑generation sequencing (NGS)‑based approaches have allowed molecular characterization and subtype diagnosis in a majority of these patients in India. Materials and Methods: (I) To select probable dysferlinopathy (LGMD2B) cases from other LGMD subtypes using two screening methods (i) to determine the status of dysferlin protein expression in blood (peripheral blood mononuclear cell) by monocyte assay (ii) using a predictive algorithm called automated LGMD diagnostic assistant (ALDA) to obtain possible LGMD subtypes based on clinical symptoms. (II) Identification of gene pathogenic variants by NGS for 34 genes associated with LGMD or LGMD like muscular dystrophies, in cases showing: absence of dysferlin protein by the monocyte assay and/or a typical dysferlinopathy phenotype, with medium to high predictive scores using the ALDA tool. Results: Out of the 125 patients screened by NGS, 96 were confirmed with two dysferlin variants, of which 84 were homozygous. Single dysferlin pathogenic variants were seen in 4 patients, whereas 25 showed no variants in the dysferlin gene. Conclusion: In this study, 98.2% of patients with absence of the dysferlin protein showed one or more variants in the dysferlin gene and hence has a high predictive significance in diagnosing dysferlinopathies. However, collection of blood samples from all over India for protein analysis is expensive. Our analysis shows that the use of the “ALDA tool” could be a cost‑effective alternative method. Identification of dysferlin pathogenic variants by NGS is the ultimate method for diagnosing dysferlinopathies though follow‑up with the monocyte assay can be useful to understand the phenotype in relation to the dysferlin protein expression and also be a useful biomarker for future clinical trials. [ABSTRACT FROM AUTHOR]

Road traffic accidents are a major social concern as well as a crucial issue for the public in recent days due to the risk factors involved. Analysing and identifying the major risk factors of road accident is still a challenging task. In this paper, a fuzzy Context-free Grammar (FCFG)-based association rule mining (ARM) technique is proposed to categorize a heterogeneous road accident dataset into two categories based on the critical factors such as total number of accidents (TA), persons killed (PK) and persons injured (PI). The role of the fuzzy grammar in this paper is to govern the entire algorithm using the prescribed grammar rules to proceed further. The considered road accident dataset does not have class labels; hence there is a need to assign class labels for the available data instance. The accident data with assigned class labels are given as input to K-nearest neighbour (KNN) machine learning algorithm in order to train the classifier for testing purpose. Further, the collected test data from the user are utilized by the KNN classifier for carrying out the performance analysis of the proposed algorithm. The case study is conducted on the National Highway roads, India, to examine the proposed approach. The experimentations are executed for road accident records using MATLAB software and the analysis is made using the following performance measures: accuracy, recall or sensitivity, precision or specificity and F1 score. A comparative study is accomplished with existing algorithms in order to show that the proposed algorithm works with improved accuracy of more than 83%. The results suggested that the road users are responsible for the acceptance or rejection of safe or un-safe roads, respectively. [ABSTRACT FROM AUTHOR]

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The level 2 aerosol backscatter and extinction profiles from the NASA Micropulse Lidar Network (MPLNET) at Kanpur, India, have been studied from May 2009 to September 2010. Monthly averaged extinction profiles from MPLNET shows high extinction values near the surface during October-March. Higher extinction values at altitudes of 2-4 km are observed from April to June, a period marked by frequent dust episodes. Version 3 level 2 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol profile products have been compared with corresponding data from MPLNET over Kanpur for the above-mentioned period. Out of the available backscatter profiles, the 16 profiles used in this study have time differences less than 3 h and distances less than 130 km. Among these profiles, four cases show good comparison above 400 m with R² greater than 0.7. Comparison with AERONET data shows that the aerosol type is properly identified by the CALIOP algorithm. Cloud contamination is a possible source of error in the remaining cases of poor comparison. Another source of error is the improper backscatter-to-extinction ratio, which further affects the accuracy of extinction coefficient retrieval. [ABSTRACT FROM AUTHOR]

We develop two Feldkamp-type reconstruction algorithms with no backprojection weight for circular and helical trajectory with planar detector geometry. Advances in solid-state electronic detector technologies lend importance to CT systems with the equispaced linear array, the planar (flat panel) detectors, and the corresponding algorithms. We derive two exact Hilbert filtered backprojection (FBP) reconstruction algorithms with no backprojection weight for 2D fan-beam equispace linear array detector geometry (complement of the equi-angular curved array detector). Based on these algorithms, the Feldkamp-type algorithms with no backprojection weight for 3D reconstruction are developed using the standard heuristic extension of the divergent beam FBP algorithm. The simulation results show that the axial intensity drop in the reconstructed image using the FDK algorithms with no backprojection weight with circular trajectory is similar to that obtained by using Hu's and T-FDK, algorithms. Further, we present efficient algorithms to reduce the axial intensity drop encountered in the standard FDK reconstructions in circular cone-beam CT. The proposed algorithms consist of mainly two steps: reconstruction of the object using FDK algorithm with no backprojection weight and estimation of the missing term. The efficient algorithms are compared with the FDK algorithm,Hu's algorithm, T-FDK, and Zhu et al.'s algorithm in terms of axial intensity drop and noise. Simulation shows that the efficient algorithms give similar performance in axial intensity drop as that of Zhu et al.'s algorithm while one of the efficient algorithms outperforms Zhu et al.'s algorithm in terms of computational complexity. [ABSTRACT FROM AUTHOR]

Non-local hydrogen bonding interactions between main chain amide hydrogen atoms and polar side chain acceptors that bracket consecutive βα or αβ elements of secondary structure in αTS from E. coli, a TIM barrel protein, have previously been found to contribute 4-6 kcal mol-1 to the stability of the native conformation. Experimental analysis of similar βα-hairpin clamps in a homologous pair of TIM barrel proteins of low sequence identity, IGPS from S. solfataricus and E. coli, reveals that this dramatic enhancement of stability is not unique to αTS. A survey of 71 TIM barrel proteins demonstrates a 4-fold symmetry for the placement of βα-hairpin clamps, bracing the fundamental βαβ building block and defining its register in the (βα)8 motif. The preferred sequences and locations of βα-hairpin clamps will enhance structure prediction algorithms and provide a strategy for engineering stability in TIM barrel proteins. [ABSTRACT FROM AUTHOR]

This article outlines a method of estimating probabilities of gross transfers from one age to another due to misreporting of age. An essential ingredient in the computation is the information on the true age structure of the population, which may be estimated by using generalized stable population relationships. The method consists essentially of iteratively adjusting rows and columns of an initial guess matrix so that the application of the resultant transition matrix to the true age distribution produces the recorded age distribution. The initial guess matrix can be either empirically based or theoretically derived. The method is illustrated by using data on India, 1971-1981. The application reveals that in India, although the tendency to exaggerate age is strong at adult ages, the bias does not increase appreciably at older ages, as is commonly believed. [ABSTRACT FROM AUTHOR]

Penetration level of solar photovoltaic (PV) energy in the utility network is steadily increasing. This changes the fault level and causes protection problems. Furthermore, multi-tapped structure of distribution network deployed to integrate solar PV energy to the grid and supplying loads at the same time also raised the protection challenges. Hence, this manuscript is aimed at introducing an algorithm to identify and classify the faults incident on the network of utilities where penetration level of the solar PV energy is high. This fault recognition algorithm is implemented in four steps: (1) calculation of Stockwell transform-based fault index (STFI) (2) calculation of Wigner distribution function-based fault index (WDFI) (3) calculation of combined fault index (CFI) by multiplying STFI and WDFI (4) calculation of index for ground fault (IGF) used to recognize the involvement of ground in a fault event. The STFI has the merits that its performance is least affected by the noise associated with the current signals and it is effective in identification of the waveform distortions. The WDFI employs energy density of the current signals for estimation of the faults and takes care of the current magnitude. Hence, CFI has the merit that it considers the current magnitude as well as waveform distortion for recognition of the faults. The classification of faults is achieved using the number of faulty phases. An index for ground fault (IGF) based on currents of zero sequence is proposed to classify the two phase faults with and without the ground engagement. Investigated faults include phase to ground, two phases fault without involving ground, two phases fault involving ground and three phase fault. Fault recognition algorithm is tested for fault recognition with the presence of noise, various angles of fault incidence, different impedances involved during faulty event, hybrid lines consisting of overhead line (OHL) and underground cable (UGC) sections, and location of faults on all nodes of the test grid. Fault recognition algorithm is also tested to discriminate the transients due to switching operations of feeders, loads and capacitor banks from the faulty transients. Performance of the fault recognition algorithm is compared with the algorithms based on discrete wavelet transform (DWT), Stockwell transform (ST) and hybrid combination of alienation coefficient and Wigner distribution function (WDF). Effectiveness of the fault recognition algorithm is established using a detailed study on the IEEE-13 nodes test feeder modified to incorporate solar PV plant of capacity 1 MW in MATLAB/Simulink. Algorithm is also validated on practical utility grid of Rajasthan State of India. [ABSTRACT FROM AUTHOR]