Search

[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]

Introduction: Data mining in healthcare is a nascent arena of research in healthcare. Heterogeneity of Diabetes Mellitus in terms of clinical presentation calls for newer methods of research to study potential risk factors. Aim: The paper aims to use clustering techniques to identify the relationship between the four variables, namely the pre-prandial and postprandial sugar level, age and sex. Methods: The data was taken from a diagnostic laboratory in Wagholi, Pune. We conducted K-mean algorithm, EM algorithm, model-based clustering and t-mixture model. Results: It is evidenced that the data was best fitted to the t-mixture model. Our 50% samples were people with diabetes, 17% had prediabetes. Trivial correlation existed between age and sugar level. Males and females were equally at risk of having diabetes. Data presented concludes that age and sex have no effect on the risk of having diabetes. Data mining can be used to deduce meaningful clusters to drive plan-based interventions in the population. Conclusion: Methods of data mining can be used to deduce meaningful clusters in a heterogeneous dataset thus providing policymakers and healthcare researchers with novel information that will potentially contribute in formulating evidence-based policies. [ABSTRACT FROM AUTHOR]

In schools, children are taught algorithm for extracting the square and cube roots of a number. However, they are not informed that Aryabhata (b. 476 CE) was the first Indian mathematician to present a systematic algorithm for the extraction of the square and cube root of a number. This algorithm has been succinctly presented by Aryabhata in two successive verses in the Ganitapada of his Aryabhatiyam composed in 499 CE. A careful study of the algorithm given by Aryabhata shows that it can be easily extended to find higher order roots. It also clearly reveals that the decimal place value system should have been in vogue in India for quite a long time before him, because this algorithm is heavily based on a thorough understanding of the decimal place value system. In our paper, we will discuss some of these aspects as well as outline how his algorithm can be extended to find the higher order roots. [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]

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]

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]

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]

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [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]

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]

Deficits in social verbal communication in individuals with Social Communication Disorder (SCD) is of concern and SCD in the human community is prevalent in large population throughout the globe. Deficits in verbal social communication are prevalent in a large population. This paper aimed to propose internet connected multi-system architecture which is capable to support verbal communication in a social environment for individuals with social communication deficits. Implementation methodology was included with corpus collection for specific communication, deep learning based machine training for intelligent communication, and implementation of the trained algorithm on internet connected electronic multiple social communication devices. The implemented system is smart enough to initiate and maintain two types of communication; the first type includes communication between multiple individuals on the remote location and the second type includes communication with the individual present in the physical listening range. The system was investigated in terms of its algorithmic parameters and found 97% to 100% in terms of training and testing accuracy with negligible mean squared error. Vocal-Friend analysed results based on audio-bot simulative conditions provide more than 91% accuracy, interaction rate and fallback rate. On the basis of the satisfaction analysis, above average results were noticed. In terms of technical implementations and satisfaction analysis, results found acceptable with above average score. Proposed framework is easy to use by caregivers with even having little knowledge. Support individual with deficit to learn social verbal communication skill to survive in society. Aiding parents, caregivers and professionals to understand the communication needs of individuals with communication deficits. Since technology is also grooming in the domain of rehabilitation, so this system could be used in various future applications such as social robots, social virtual assistants etc. [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]

The wireless sensor network is gaining significant attention because of its ubiquitous deployability nature. In general, the role of the wireless sensor network is remarkable in smart city applications for data sensing, collecting, and transmitting. Advanced metering infrastructure is an automatic system for the reading of electricity consumption by individual users. The reading of data from meters normally communicated over a wireless medium. Apart from collecting and sensing, routing is one of the major attributes in the AMI network. Successful communication is possible when the link and node are in the proper state. In this paper, we propose a fault-tolerant routing mechanism named Grid Topological Routing scheme to provide an efficient route in case of encountering a faulty path. The simulation shows better performance against the Routing Protocol for Low-Power and Lossy Networks and Ad-Hoc On-Demand Vector algorithm in terms of power consumption and packet delivery ratio. This model is explicitly designed for the smart grid in India. [ABSTRACT FROM AUTHOR]

Renewable energy presently occupies a prominent position in India's overall energy generation scheme. In the midst of numerous alternative energy resources, solar energy is widely used as it persists in large volumes with varying specification ratings. It is suited for both stand-alone and grid-coupled application. The Maximum Power Tracking (MPPT) scheme has a profound effect on the operating efficiency of a Photovoltaic (PV) power plant. This paper proposes an inventive hybridized Human Psychology Optimization-Perturb and Observation (HPO-PO) MPPT approach for obtaining the optimal duty cycle of the boost converter to harvest global maxima from a grid-connected Total Cost Tied (TCT) configured PV array of 4080 W. The suggested method provides enriched performance both in steady-state, as well as in rapid and randomly changing weather conditions. Comparison studies of various MPPT procedures, including Perturbation and Observation (PO), Artificial Bee Colony (ABC), and Human Psychology Optimization (HPO) in MATLAB environment, illustrate the usefulness of the evoked system in meeting its goals. The suggested MPPT procedure has offered enhanced activities in terms of voltage quality, maximum power tracking capability, and converter efficiency compared to other methods. The recommended hybridized MPPT approach is experimentally validated on a hardware set-up using a 16-bit dsPIC30F2010 Digital Signal Controller in enhancing the behavior of the grid-connected PV system. [ABSTRACT FROM AUTHOR]

Few studies on single-frame super-resolution (SFSR) exist for real-world hyperspectral images due to fewer training samples and paucity of freely available high-resolution ground truth for validation. Consequently, a user relies on the simulated results from test datasets. This gap is filled by super-resolving real-time Hyperion and PRISMA data of Ahmedabad in India and introducing a structure of comparing and validating super-resolved outputs. Proposing open-source panchromatic and multispectral data as visual examination references, the first-ever SFSR results on PRISMA are presented. Findings suggest reconstruction-based SFSR algorithm outperforms at scale factors (SFs) 2 and 4 for Hyperion and SFs 2, 3 and 4 for PRISMA. The hybrid SFSR algorithm stands out at SF 3 for Hyperion. External dictionaries utilized by learning-based SFSR algorithms experience degradation with increasing spatial resolution. Although image quality declines slightly at higher SFs, out of the chosen algorithms, reconstruction and hybrid-based algorithms outperform learning-based algorithms in producing enhanced spatial and spectral fidelity outputs in the present case. [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]

False information is becoming more frequent in distributing disinformation by distorting people's awareness and decision-making by altering their views or knowledge. The propagation of disinformation has been aided by the proliferation of social media and online forums. Allowing it to readily blend in with true information. Parody news and rumors are the most common types of misleading and unverified information, and they should be caught as soon as possible to avoid their disastrous consequences. As a result, in recent years, there has been a surge in interest in effective detection approaches. For this study, a customized dataset was built that included both real and parody tweets from Pakistan and India. This study proposes a two-step strategy for detecting parody tweets. In the first stage of the approach the unstructured data is converted into structured data set. In the second step, multiple supervised artificial intelligence algorithms were employed. An experimental assessment of the different classification methods inside a customized dataset was undertaken in this study, and these classification models were compared using evaluation metrics. Our results showed accuracy of 92%. [ABSTRACT FROM AUTHOR]

The aquaculture expansion has made significant contributions to global food security, socio-economic development and, if implemented sustainably, can help preserve stable coastal environments. This study explicitly details the rapid expansion of largescale aquaculture growth across the Kolleru and Upputeru regions of South India. We developed a novel classification method for automated extraction of aquaculture ponds in the Kolleru zone using the Canny Edge-Otsu algorithm to segment and extract the ponds applied to SAR-VV images in Google Earth Engine. This approach enables the area estimation of dense aquaculture ponds are essential for monitoring and management purposes. The results indicated that this method could effectively map the aquaculture ponds and the overall accuracy achieved in 2020 for the Kolleru and Upputeru areas by 90.6% and 95.7%, respectively. The aquaculture maps of this study can help government organizations, resource managers, stakeholders, and decision-makers understand the dynamics and plan sustainable measures in this area. [ABSTRACT FROM AUTHOR]

Conventional load flow algorithms are very commonly used in the system planning. In the conventional load flow methods, system electrical parameters at specific weather conditions will be considered. These methods gives results for given loading condition and have no provision to incorporate weather condition of a specific geographical location. If the load flow has the provision to incorporate the dynamic electrical characteristics of the conductors based on the operating weather condition, the results of the methods are going to be more accurate or in other words the results are close to the real values. In view of the above, in this paper a modified load flow algorithm incorporating the thermal modeling of the conductors with ambient temperature, wind velocity, conductor surface absorptivity, global solar radiation, Wind direction factor etc. is proposed. To verify the effectiveness of the proposed algorithm, the thermal mathematical model of transmission lines available in IEEE Std. 738™ -2006, IEEE Std. 738™ -2012 and CIGRE WG 22.12 are considered. In the proposed algorithm, new variables corresponding to Conductor Temperature are incorporated. The proposed algorithm is tested with the real time weather data of India and demonstrated on IEEE 30 and 118 bus systems. • An algorithm for power flow including thermal models of the transmission lines. • Variable weather and ZIP load models are incorporated in the proposed algorithm. • It estimates the transmission line temperature using the field measurements. • Seasonal variations of the critical bus voltages are estimated using proposed method. • Impact of renewable generation on system performance is evaluated. [ABSTRACT FROM AUTHOR]

Background Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers. Methods The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker. Results Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB. Conclusions The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance. [ABSTRACT FROM AUTHOR]

Purpose: Non-contrast CT head scans provide rapid and accurate diagnosis of acute head injury; however, increased utilisation of CT head scans makes it difficult to prioritise acutely unwell patients and places pressure on busy emergency departments (EDs). This study validates an AI algorithm to triage patients presenting with Intracranial Haemorrhage (ICH) or Acute Infarct whilst also identifying a subset of patients as Normal, with the potential to function as a rule-out test. Methods: In total, 390 CT head scans were collected from 3 institutions in the UK, US and India. Ground-truth labels were assigned by 3 FRCR consultant radiologists. AI performance, as well as the performance of 3 independent radiologists, was measured against ground-truth labels. Results: The algorithm showed AUC values of 0.988 (0.978–0.994), 0.933 (0.901–0.961) and 0.939 (0.919–0.958) for ICH, Acute Infarct and Normal, respectively. Sensitivity/specificity for ICH and Acute Infarct were 0.988/0.925 and 0.833/0.927, respectively, compared to 0.907/0.991 and 0.618/0.977 for radiologists. AI rule-out of Normal scans achieved 0.93% negative predictive value (NPV) for the removal of 54.3% of Normal cases, compared to 86.8% NPV for radiologists. Conclusion: We show our algorithm can provide effective triage of ICH and Acute Infarct to prioritise acutely unwell patients. AI can also benefit clinical accuracy, with the algorithm identifying 91.3% of radiologist false negatives for ICH and 69.1% for Acute Infarct. Rule-out of Normal scans has huge potential for workload management in busy EDs, in this case removing 27.4% of all scans with no acute findings missed. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive approach to improved lithofacies characterization using the simultaneous pre-stack inversion, Bayesian classification, and multiple-point geostatistics. This integrated workflow provides an enhanced lithofacies characterization by reproducing the continuity of geological facies in the meandering-fluvial sandstone reservoir from Upper Assam, India. The simultaneous pre-stack inversion provides the rock elastic properties of the reservoir, such as P- impedance, s-impedance, density, and V P /V S ratio. Next, the probabilistic modeling approach of Baye's theorem is used to integrates seismic elastic properties with well log lithofacies identified from the cross-plot analysis for probability volumes of lithofacies. Finally, the multiple-point geostatistics based on the Single Normal equation Simulation algorithm is used to generate the improved lithofacies model by constraining of the lithofacies probability volumes (soft data) and well logging data (hard data). These results were compared with the Bayesian lithofacies classification and proved that the adopted method had successfully improved the continuation of the reservoir lithofacies by reproducing the lithofacies, hence an improvement in the reservoir characterization. • An integrated workflow is used that provides better lithofacies characterization. • Estimation of seismic elastic properties using simultaneous prestack inversion. • Prediction probability volumes of lithofacies by Bayesian classification. • The SNESIM algorithm applied with constrained by probability volumes and well data. • Improved lithofacies volume generated by the multiple-point geostatistics. [ABSTRACT FROM AUTHOR]

The article addresses the void in developing analytical methods concerning to design urban configurations that could reduce fire risks, and, thus, could help in achieving sustainable goals. A novel algorithm is developed to generate alternative Urban Built Form (UBF) models that could be less susceptible to fire compared to the existing built-form. Fire susceptibility of a generated UBF is predicted using a developed linear regression model. The algorithm considers existing regulations to derive rules and develop scenarios that might be effective in building fire-resilient cities. The outcomes of the simulations showed a significant decrease in the fire susceptibility of the southern region of Mumbai city. Moreover, for a certain simulated scenario the predicted UBF could accommodate twice the current population while being less susceptible than the existing UBF. The proposed techniques and methods can act as a decision-making tool in taking pre-emptive planning measures to develop fire resilient cities. [ABSTRACT FROM AUTHOR]

The firefly algorithm and cuckoo search are the meta-heuristic algorithms efficient to determine the solution for the searching and optimization problems. The current work proposes an integrated concept of quantum-inspired firefly algorithm with cuckoo search (IQFACS) that adapts both algorithms' expedient attributes to optimize the solution set. In the IQFACS algorithm, the quantum-inspired firefly algorithm (QFA) ensures the diversification of fireflies-based generated solution set using the superstitions quantum states of the quantum computing concept. The cuckoo search (CS) algorithm uses the Lévy flight attribute to escape the QFA from the premature convergence and stagnation stage more effectively than the quantum principles. Here, the proposed algorithm is applied for the application of optimal path planning. Before using the proposed algorithm for path planning, the algorithm is tested on different optimization benchmark functions to determine the efficacy of the proposed IQFACS algorithm than the firefly algorithm (FA), CS, and hybrid FA and CS algorithm. Using the proposed IQFACS algorithm, path planning is performed on the satellite images with vegetation as the focused region. These satellite images are captured from Google Earth and belong to the different areas of India. Here, satellite images are converted into morphologically processed binary images and considered as maps for path planning. The path planning process is also executed with the FA, CS, and QFA algorithms. The performance of the proposed algorithm and other algorithms are accessed with the evaluation of simulation time and the number of cycles to attain the shortest path from defined source to destination. The error rate measure is also incorporated to analyze the overall performance of the proposed IQFACS algorithm over the other algorithms. [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]

• Studied the effect of catchment size and simulation time steps on parameter sensitivity. • Hydrological model is developed for 10 different watersheds of Narmada River basin. • Two Global Sensitivity Analysis methods used to identify the sensitive parameters. • To avoid over parameterization, most and least sensitive parameters are identified. • Identifying a set of sensitive parameters that covers all the hydrological processes. Hydrological models have many parameters representing various hydrological processes. These parameters are effective at different spatial and temporal resolution. Most of the parameters can be measured such as slope, elevation, area, vegetation type etc., but others cannot be estimated from the available data. Thus, they remain unknown in a hydrological simulation. Local and Global Sensitivity Analyses (LSA and GSA respectively) are therefore used to reduce the number of parameters that need to be equipped with input–output data. GSA also improves the efficiency of the model calibration and validation which in turn increases the reliability of the model. In this paper, we investigate into the effect of size of a catchment (small, medium, and large) time steps of simulation (Daily and Monthly), and the choice of GSA method in performing sensitivity analysis. A Soil and Water Assessment Tool (SWAT) hydrologic model is developed for 10 subbasins of Narmada River basin at daily and monthly temporal simulations from January 1991 to December 2000. The sensitivity of 29 input parameters have been analysed with two GSA methods: the multilinear regression SA used by sequential uncertainty fitting algorithm (SUFI-2) in SWAT – Calibration and Uncertainty procedure (SWAT-CUP), and the first order sensitivity indices driven by Fourier Amplitude Sensitivity Test (FAST). Our results show that a hydrological model set up with only sensitive parameters can also produce similar results as when all the parameters are considered. We concluded that the selection of catchment size and simulation time step strongly influence the parameter sensitivity. Our results show that the sensitivity of some of the parameters like Curve number for moisture condition (CN2.mgt), baseflow alpha factor for bank storage (ALPHA_BNK.rte) and lateral flow travel time (LAT_TTIME.hru) are independent of catchment size and simulation time step. Moreover, baseflow alpha factor for the recession constant (ALPHA_BF.gw), manning's value for overland flow (OV_N.hru) and soil erodibility factor (USLE_K(..).sol) are not sensitive for large and medium range watersheds but those are sensitive for small watershed at a monthly time step. Ground water delay (GW_DELAY.gw), average slope steepness (HRU_SLP.hru) and support practice (p) factor of USLE soil equation (USLE_P.mgt) are not sensitive for large watersheds but sensitive for small and medium range watersheds at a daily time step. Our results strongly recommends that a detailed parameter sensitivity analysis is an important step in setting up any hydrological model to reduce the number of parameters while addressing all relevant hydrological processes. [ABSTRACT FROM AUTHOR]

Summary: A vast community of developing countries, like India, is still deprived of a proper water supply system. Such communities often depend on hand‐pumps, ponds, and ravines for their daily water needs. Government‐subsidized PV‐based water pumps have emerged as a boon for such communities. But intermittent nature of solar irradiation and unavailability at night time poses a big challenge. To mitigate this challenge, the present article proposes a stand‐alone PV‐based induction motor‐driven water pumping system serving a dual‐head, that is, an overhead storage tank and ground level supply. A single‐stage three‐phase high‐gain resonant inverter (HGRI) is proposed for driving the induction motor. A back‐ended wide bandgap (WBG) device‐based boost converter is used to extract maximum power from PV by employing the MPPT algorithm. HGRI injects sinusoidal current to the induction motor while operating it through PV power matched modified V/f control algorithm, thus minimizing the torque ripple. HGRI coupled with boost converter provides a high‐gain voltage boost at the output; enabling wide input voltage operation, which increases the operability of the system in high as well as low irradiation level. The practicality of the system is validated on a laboratory‐developed hardware test bed with a 1.5HP induction motor. [ABSTRACT FROM AUTHOR]

The Autism Dysmorphology Measure is designed for non-expert clinicians. It uses an algorithm to assess 12 body regions and categorizes Autism on the number of dysmorphic regions identified; Essential (≤ 3), Equivocal (4–5) or Complex (≥ 6). We evaluated 200 Indian children with Autism (mean age 3.7 years) in a hospital-based cross-sectional study and compared inter-group profiles. We found 31% Essential, 49% Equivocal and 20% Complex Autism. On comparing results with existing literature, it appeared that genetic ancestry and age significantly influenced dysmorphism and hence categorization. No significant differences were observed between complex and essential autism in epilepsy, severity of autism or development, as reported earlier. These shortcomings make the present tool unsuitable for use in young Indian children with Autism. [ABSTRACT FROM AUTHOR]