Oracle (India)

Electricity prosumer growth is increasing rapidly across the world and particularly in regions where government initiatives are actively promoting residential roof top solar in pursuit of green growth and meeting net zero targets. Availability and affordability of technology have also aided and abetted this rapid growth. Simultaneously Electric Vehicle (EV) growth has picked up momentum with common charging standards adoption by nations and suppliers actively promoting new and affordable models. These developments coupled with national mandates to backdown fossil fuel generation distribution utilities manage supply/demand situation more dynamically than ever.(p)(p)Distribution utilities were slow to implement automation solutions to manage distribution grid assets and operations. These solutions often lack full visibility of the grid network as compared to transmission grids. Typically, utilities design distribution, automation to focus on automating distribution grid operations as well as moving from analog controls to digital controls. However the design does not consider the significant developments at the consumer/prosumer end. Utilities often treat these as separate initiatives. This cannot go far. With rapid growth of DERs (Distributed Energy Resources),, more utilities are likely to face "Duck Curve" challenges unless they establish flexible energy grids which optimally leverage front of the meter (FTM) and behind the meter (BTM) consumer/prosumer/third-party owned resources to operate the grid with stability and efficiency.In this context, a consumer centric approach with right policy and regulatory frame works, understanding consumer behavior, analysis of consumer load patterns, capability to monitor and manage assets beyond the traditional grid control zones, and suitable technology adoption to seamlessly integrate grid edge consumer/prosumer owned resources in to the distribution grid operations function is an imperative to realize a flexibly energy grid providing stability, resilience, and affordability —making it a win-win proposition for the utility and the consumer.This paper will delve deeper into the challenges faced by electricity distribution utilities in supply/demand optimization in the context of DER growth, issues faced by customers in Grid-tied as well Off -grid options, and battery storage affordability. The paper intends to propose a solution approach to designing consumer centric flexible and sustainable energy grid for future.

Mathematical creativity is the focus of most curriculum in that students with this ability are prone to solving problems in society from a multidimensional perspective. However, studies of this nature, especially in Africa, are limited, and this poses a challenge to policy development. To cover this gap, the study sought to model the predictive effect of mathematics self-efficacy, motivation for mathematics, and attitude towards mathematics on mathematical creativity from a multidimensional perspective. The study adopted a cross-sectional survey using 654 mathematics students for the study. Two instruments–the mathematics self-efficacy, attitude towards mathematics, and mathematical motivation scales and the mathematical creativity test were used for data analysis after they had undergone a qualitative and quantitatively rigorous validation process. This was done using exploratory and confirmatory factor analysis with the average variance extracted and Fornell-Larcker criterion for convergent and divergent analysis, respectively, while Cronbach’s alpha was used to determine the stability of the scales. Analysis was performed using hierarchical regression and the result showed that mathematics self-efficacy and attitude towards mathematics, when taken individually, are the only strong predictors of mathematics creativity from the perspective of fluency, flexibility, and originality components. Collectively, the three predictors were significant in contributing to the variance in mathematics creatively from the three dimensions assessed. However, mathematics self-efficacy was the strongest predictor, followed by attitude towards mathematics. The implications of the study were discussed for policy and curriculum redesign in mathematics education.

Numerous machine learning algorithms and procedures were used on the acquired data in the current paper. The methods covered include classification, statistical analysis, assessment, and unsupervised learning methods. Data on PD and non-PD patients are displayed using a visualization technique. Techniques include the Operating Characteristic (ROC), Sieve Multi-gram, and Self-Organizing Map (SOM), among others. The information focuses a lot on sick classes. Decision Tree, Logistic Regression, SVM, Linear Regression, KNN, Dimensionality Reduction Algorithms, Random Forest, K-Means, Naive Bayes, Gradient Boost, and Ada-boost are a few additional algorithms. These algorithms can be applied to any data issue.

We study the time-dependent flow behavior of gravity-driven free surface granular flows using the discrete element method and continuum modeling. Discrete element method (DEM) simulations of slightly polydisperse disks flowing over a periodic chute with a bumpy base are performed. A simple numerical solution based on a continuum approach with the inertial number based µ − I rheology has been proposed to predict the flow dynamics. The results of the continuum model are compared with the DEM simulation results for a wide range of chute inclinations. Solutions for the constitutive model described by the popular JFP model as well as the recently proposed modified rheological model using a non-monotonic variation of µ − I are obtained. Our results demonstrate that the popular JFP model reliably predicts the flow at low to moderate inclination angles (i.e. for I 0.5). However, it fails to predict the flow properties at high inclinations. The modified rheological model, on the other hand, is very well able to predict the time-averaged flow properties for all the inclination angles considered in this study. Accounting for the presence of the slip velocity, layer dilation, and stress anisotropy are found to be crucial for accurate predictions of transient flows at high inertial numbers (i.e. for I 1).

In healthcare industry, it is a standard practice to assign a set of International Classification of Diseases (ICD) to a clinical note (which can be a patient visit, a discharge summary and the like) as part of medical coding process mandated by medical care and patient billing. A supervised framework is adopted for most of the automated ICD coding assignment methods in which a subset of the clinical notes are a-priori labeled with ICD codes. But in lot of cases enough labeled texts are not available. These call for an unsupervised assignment of ICD codes. However, the quality of the data plays an important role in the performance of unsupervised coding, - low quality data leads to degradation of performance. In this paper, we explore a transfer learning approach for ICD coding using a combination of pre-training and supervised fine-tuning. We use a hierarchical BERT model comprising of a Bi-LSTM layered on top of BERT (this removes the restriction on the size of clinical texts)) as part of model architecture, and pre-train it on the total corpus (which include both labeled and unlabeled data). Next we transfer its weights to fine tune the model with labeled data (MIMIC data) in a supervised framework and then use this model to predict ICD code for unlabeled data using token similarity. This is the first use of using transfer learning in ICD prediction to our knowledge. Finally we show the efficacy of our transfer learning approach through rigorous experimentation, - there is 20% gain of sensitivity (recall) and 6% lift in specificity in ICD prediction compared to direct unsupervised prediction.

SummaryCloud computing aids users for storing and recovering their information everywhere in the world. Security and efficiency are the two main issues in cloud service. Numerous intrusion detection techniques for the cloud computing environment were proposed, but those techniques do not effectively and accurately detect the attacks. Hence, an efficient and secure key management using extended convolutional neural network, that is, hybrid Enhanced Elman Spike convolutional Neural Network optimized with improved COOT optimization algorithm (Hyb EESCCNN) is proposed for intrusion detection in cloud system. Furthermore, the novel Adaptive Tangent Brakerski‐Gentry Vaikuntanathan Homomorphic Encryption (ATBGVHE) method is proposed for providing the security of the system. At first, SHA‐512 is used for authenticating cloud users to store its own information in to the cloud server. Then for Intrusion Detection (ID) the input data from the NSL‐KDD, UNSWNB15, CICIDS2018, and ToN‐IoT datasets are pre‐processed. The most relevant features are extracted using Fast Independent Component Analysis (Fast ICA) from the pre‐processed output. These extracted data are classified into malicious and non‐malicious data using Hyb EESCCNN. After classification, the non‐malicious data is secured using an ATBGVHE technique. The outcomes of the proposed methods shows that the NSL‐KDD datasets attains 99.9% higher accuracy, UNSW‐NB15 datasets offer 99.89% higher accuracy, CSE‐CIC‐IDS2018 datasets attains 99.8% higher accuracy, ToN‐IoT datasets attains 99.8% higher accuracy and 0.02 s lower encryption time compared with existing methods. Finally, case study with real time applications is also analyzed to prove the efficiency of the proposed method.

Over the last year, the evolution in Robotic Process Automation (RPA) has been staggering. The automationAutomation it brings to applications has yielded efficiency, reduced operating costs, and decreased the time of research, development, and production. Industries have already integrated RPA into their workflow and are profoundly transforming into an intelligent automated industry with minimum human interventionHuman intervention, calling this the fourth industrial revolutionIndustrial revolution. In this race of transformation, the healthcare industry is quite ahead of many other industries. It stood the test of time when COVIDCOVID-19COVID-19 was spreading rapidly and was also resilient against all odds. The system did experience an unprecedented crisis that depicted its weakness, fragility, and unpreparedness. The healthcare system was forced to adapt to a new paradigm. And though there was the loss of life and economy, we learned to evolve as a community to tackle this crisis. This chapter sheds light on the role of RPA and covers how these technologies can assist healthcare workers in their day-to-today activities, reviewing what the fourth industrial revolutionIndustrial revolution would look like in the healthcare sector. The intelligent, automated system would provide a seamless experience of gathering information by various means, processing, and assisting healthcare workers to deliver quality treatment.

Utility computing (cloud-computing) has turned into a general service provider. The security challenges related to utility computing in the literature, (cloud computing) has received the most of the attention. The goal of this analysis is to look at the safety, challenges, and current developments in computing services (cloud). This SLR examined the dispersal focus of the investigation, which is between the years 2010 and 2020, within the well-known specialized collections. The results of this with seven significant security dangers to utility (cloud) computing (CC) administrations. The outcome is that the information altering, and spillage were among the profoundly talked about subjects in the picked writing. Other recognized security changes were related to the information interruption and information stockpiling in the utility computing (cloud-computing) climate. This SLR's outcomes additionally demonstrated that customers' information revaluating remains a test for both Utility (cloud) service providers and utility (cloud) clients. Our study paper recognized the blockchain as banding together innovation to reduce security concerns. The SLR discoveries uncover a few ideas to be done in future attempts to bring information classification, information respectability, and accessibility.