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Introduction: Sodium channelopathies are a group of genetic disorders characterized by abnormalities in sodium channels that can cause a wide range of cardiac and neurological symptoms. These disorders can be inherited in an autosomal dominant pattern and can be caused by mutations in various genes, including SCN5A, SCN1B, and SCN2B. A comprehensive literature search was conducted using PubMed, MEDLINE, and Google Methodology: Scholar to identify relevant articles published from March 2005 to November 2021. The following keywords were used: "sodium channelopathies", "sodium channels", "sodium channelopathy genetics", "sodium channelopathies-pathophysiology", "sodium channelopathies- their clinical presentations and management". Only articles written in English and reporting on human studies were included. The clinical presentation of these disorders can vary widely, Clinical features and management: ranging from asymptomatic to sudden cardiac death, and can be inuenced by factors such as age, gender, and comorbidities. Genetic testing can aid in the diagnosis and management of these disorders, and there are several treatment options available, including medications, implantable cardioverter-debrillators, and lifestyle modications. Conclusion: While signicant progress has been made in our understanding of sodium channelopathies in recent years, there is still much to learn about these complex disorders. Further research is needed to fully elucidate the underlying mechanisms of these disorders, identify novel therapeutic targets, and improve clinical outcomes for affected individuals. Nonetheless, the knowledge gained so far has provided valuable insights into the pathogenesis and management of these disorders, and has the potential to greatly improve the lives of those affected by sodium channelopathies.

Text summarization is the process of employing a system to shorten a document or a collection of documents into brief paragraphs or sentences using various approaches. This paper presents text categorization using BERT to improve summarization task which is a state-of-the-art deep learning language processing model that performs significantly better than all other previous language models. Multi-document summarization (MDS) has got its bottleneck due to lack of training data and varied categories of documents. Aiming in this direction, the proposed novel hybrid summarization B-HEATS (Bert based Hybrid Extractive Abstractive Text Summarization)framework is a combination of extractive summary via categorization and abstractive summary using deep learning architecture RNN-LSTM-CNN to fine-tune BERT which results in the qualitative summary for multiple documents and overcomes out of vocabulary (OOV). The output layer of BERT is replaced using RNN-LSTM-CNN architecture to fine tune which improves the summarization model. The proposed automatic text summarization is compared over the existing models in terms of performance measures like ROUGE metrics achieves high scores as R1 score 43.61, R2 score 22.64, R3 score 44.95 and RL score is 44.27 on Benchmark DUC datasets.

Slow evaporation method at constant temperature was used to generate optically better-quality clove and pure doped Potassium Di-hydrogen Phosphate crystals, which were then characterized. If the optimum conditions are adequately attained, the slow evaporation method can be used to produce crystals with low solubility. FTIR spectra are used to assess the reality of crystal efficient groups qualitatively. To investigate the dopants effect on the ideal appearances of Potassium Di-hydrogen Phosphate, an X-ray diffraction investigation was carried out. The tetragonal design of KDP and incapacitated crystals was discovered via powdered X-ray diffraction. The lattice parameter values were determined using X-ray diffraction on a single crystal. UV-Visible spectroscopy was used to examine the generated clove doped (1:1) KDP crystals. The TG and DTA analysis were utilized to examine the constancy and breakdown of unpolluted and clove doped KDP crystals. ND-YAG laser was utilized to notice the SHG. Clove doped KDP crystals outperformed pure KDP crystals in terms of relative generation of second harmonics productivity. The effect of dopants on the exterior morphology of KDP crystals was investigated using a scanning electron microscope (SEM). EDAX was utilized to have a better understanding of the elements found in crystals.

We investigate the protective effects of resveratrol and vitamin-E against 5-flourouracil induced hepatotoxicity in rats. Experimental rats were treated with 5-FU (20 mg/kg b.wt.) i.p. on day 1, 3, 7 to induce hepatotoxicity, evident by significant (p

Decades of research for an ideal sedative agent that could provide both sedation and analgesia with minimal side effects to the patients has failed to find one. Research was then shifted to finding combination of drugs which could suffice the ideal sedative agent. Of these Ketofol showed promising results.Aim of this study is to “Assess the efficacy and safety of propofol and ketamine amalgamation (ketofol) and comparing the superiority over ketamine and midazolam combination for procedural sedation”. In this prospective randomized single blind study, 50 patients between 15 to 60 years of both males and females, ASA Grade I and Grade II were allocated into two groups. Patients in group 1 received ketofol and group 2 patients received ketamine and midazolam. The comparison between the two groups was done using Two-Sample T-Test, With the help of SPSS version 15.0 Statistical calculator, sample size calculated = 5 to 17, and actual power calculated with this sample size was 0.990 to 0.997 which shows significance of the study.: The study showed statistically significant (p Ketofol is safe and efficient sedative combination which can be utilized in emergency department, daycare and OT settings with comfort, ease and without any complications. It is close to ideal sedative agent.

Through-silicon-via (TSV) technology has emerged as the key technology to enable 3D IC integration. 3D integration is seen as the leading candidate, which can help in sustaining Moore’s Law to future IC technology nodes. It clearly set a benchmark to the IC industry due to its tremendous benefits in performance, data bandwidth, and supports heterogeneous integration. In 3D IC structure TSV’s are used to carry the overall electrical signal between the layers. The major drawback in the TSV based 3D integration is noise coupling between aggressive (electrical signal carrying TSV; ETSV) and victim TSV’s (ground). It can be reduced by creating very good isolation between TSV’s and Si substrate by using different liner materials around TSV’s. In this work, we have addressed various TSV models to reduce noise coupling between the TSV’s. The models we have studied are single layer model and stacked layers of liner model. Apart from this we have studied how the core materials in the TSV reduces noise coupling between aggressive and victim TSV’s. Also, we have shown the high frequency study for proposed models. Finally, our work shows single layer model with Crystalline-Germanium is comparably better noise coupling reduction even at higher frequency.

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

Power demand forecasting is one of the fields which is gaining popularity for researchers. Although machine learning models are being used for prediction in various fields, they need to upgrade to increase accuracy and stability. With the rapid development of AI technology, deep learning (DL) is being recommended by many authors in their studies. The core objective of the chapter is to employ the smart meter's data for energy forecasting in the industrial sector. In this chapter, the author will be implementing popular power demand forecasting models from machine learning and compare the results of the best-fitted machine learning (ML) model with a deep learning model, long short-term memory based on RNN (LSTM-RNN). RNN model has vanishing gradient issue, which slows down the training in the early layers of the network. LSTM-RNN is the advanced model which take care of vanishing gradient problem. The performance evaluation metric to compare the superiority of the model will be R2, mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE).

Background: Cisplatin is one of the most remarkable solutions in ‘the war on cancer’. Although cisplatin has been a mainstay for cancer therapy, its use is mainly limited by nephrotoxicity. The current study was aimed to evaluate the ameliorative potential of Terminalia arjuna (TA) in comparison to taurine against cisplatin toxicity. Methods: A total of 36 male Wistar rats were divided into 6 groups of 6 rats in each. Group 1 was normal control. Cisplatin @ 5 mg/kg b.wt was injected on day 1 to groups 2, 5 and 6. Aqueous leaf extract of Terminalia arjuna was administered orally @ 400 mg/kg b. wt to groups 3, 5 and Groups 4, 6 recieved taurine orally @ 1000 mg/kg b.wt for 14 days. Blood samples were collected from animals to assess Kidney function tests, oxidative stress and cytokines and renal tissues were examined for histological changes, if any. Result: Antioxidant profile, serobiochemical and cytokine parameters were significantly (P less than 0.05) increased and histopathological studies revealed degenerative changes and marked infiltration in the kidney of group 2 when compared to groups 1, 3 and 4. However, These changes were reversed in groups 5 and 6 that were administered with Terminalia arjuna and taurine, respectively. In conclusion, the results of the present investigation elucidated that both Terminalia arjuna and taurine have potent nephroprotective activity in cisplatin injected Wistar rats.

Now a day Social Media like Facebook, twitter and Instagram is major Sources for people to share their emotions based on the current situations in society. By knowing the interesting patterns in it, a government/appropriate person for that situation can take good and useful decisions. Sentiment analysis is a method where people can extract the useful information from the text like the emotions (happy, sad, and neutral) of people. Much research work was been underdoing in the area of sentiment analysis. Among that work the Machine learning and Deep learning approaches plays a maximum role. Existing works on sentiment analysis is going in the English language. In this paper, proposed a novel framework that specifically designed to do sentiment analysis of the text data, that available in the telugu language. The proposed framework was integrated with the word embedding model Word2Vec, language translator and deep learning approaches like Recurrent Neural Network and Navie base algorithms to collect and analyse the sentiment in tweeter data that present in telugu language. The results shows effective in terms of accuracy, precision and specificity.

Anomaly is in general defined as deviation or diversion from the normal. The word anomaly came from the Greek word anomalia which means “uneven” or “irregular”. In our day-to-day lives, we have seen many such irregularities or deviations from normalcy. For example, a condition monitoring system beeps an alarm when it detects any value or parameter of the machine away from the minimum value limit to the maximum value limit, or a credit card fraud alerts the bank and the customer immediately. Now, the crucial task here is how we detect anomalies in data streams. When there is streaming data that is continuously generated from any source, it is called a data stream. The task of finding anomalies from such a stream of data will be a challenging job. In this paper, we will discuss elaborately about data streams and anomaly detection in data streams by reviewing several papers and articles written on this topic.

Nowadays, one-day rapid growth in technology has been seen. Within the context of recent healthcare, the utilization of IoT technologies brings their convenience to physicians and patients as they are applied to varied medical areas. The development of new technology in healthcare applications without regard to safety makes patient privacy vulnerable. In this study, we highlighted key security requirements when storing patient information in the cloud. When we consider the hospitality facilities in the villages, they face a lot of problems with the fact that the doctors are not able to visit the rural clinics often; they always want to take all the medical history, medicines, and records with them; and they have to stand in front of the hospitals. Accordingly, providing an honest quality of life for people has become a big social challenge at that point. The rapid proliferation of knowledge and communication technologies has led to the approval of innovative trend solutions and tools that show commitment to meeting challenges. There has been a long struggle to maintain access to quality health services with consulting physicians in rural areas. When we consider the major challenges facing rural public hospitality, such as lack of infrastructure, poor manpower, cost, quality of health services, and time required to provide services to patients, the treatment recommendations of the IoT doctor and patient updated treatment information will be stored directly in the [1] cloud [2]. In order to be effective in storing information on the cloud, we must provide secure communication protocols. We propose a secure IoT based on the sensors used in modern healthcare.

The aim of this paper is to provide the visualization of data in different cloud-based SAAS applications, by choosing ERP System in SAP focusing on cloud technologies. SAP S/4 HANA database is designed to process both structure and relational databases. Integration with these systems is highly challenging, as information is gathered from different modules of an organization data such as sales and distribution, product management and finance accounting. By analysis all these scenarios, proposed comes up with an approach to reach consistency, reliability and reusability of the results.

Infusion shaping is an assembling procedure for creating part for both thermoplastic and thermosetting plastic material. Goal of this undertaking is to decide the parameter impact to the war page and shrinkage of the hand telephone packaging and to decide the enhancement parameter for lessen the war page and shrinkage the most critical issues underway of plastic parts utilizing infusion forming. In this examination, impact of infusion shaping parameters on the shrinkage in polypropylene (PP) and polystyrene (PS) is explored. The connection among information and yield of the procedure is examined utilizing relapse strategy and analysis of variance method. The chosen input parameters are dissolving temperature, infusion weight, pressing weight, and pressing time. Impact of these parameters on the shrinkage of previously mentioned materials is considered utilizing scientific displaying. Thus, efficiency will be diminished while the endeavors are channelized to upgrade quality. To guarantee high caliber and efficiency, it is important to enhance machining parameters. Different reactions of nature of infusion forming process have been examined based on execution parameters and strategies. This paper means to show plastic infusion shaping procedure conditions. The handling conditions fulfilled quality-based item fabricating.

In recent trends, metal matrix composites (MMC) have attracted the newline potential interest in engineering materials due to their superior mechanical newline properties than the conventional materials. The way of selecting the particular newline properties of the constituent materials is to meet the specific demand. In general, MMCs possess newline increased strength, higher service temperature, decreased part weight and improved newline wear resistance, when compare with materials of monolithic. Aluminium and its alloys are the most widely used AMMC. Matrix reinforced with ceramic particles such as oxides and non-oxides (carbides, nitrides, borides) offers a huge variety of mechanical properties depending on the aluminium and its alloy chemical composition. Among the various ceramics, boron carbide (B4C) possesses excellent physical and newline mechanical properties like high melting point and hardness, good impact and wear resistance. In the present work, a combination of Al6061 + B4C metal matrix composites is fabricated with different proportions (1.5, 3, 4.5%) by stir casting technique. Their structural analysis was done by using SEM analysis; wear resistance was found out by using pin-on-disc method; and surface roughness was found out.

Expansive soil deposits mostly appear in arid and semi-arid areas of the world and they pose severe difficulties to engineering constructions as they possess higher tendency to heave in the course of moist season and shrink at summer season. Construction in such highly expansive strata can be done by adopting ground modification techniques like soil stabilization, Vertical drains, inducing reinforcement in to soil etc., On the other hand with rapid industrial growth more quantity of industrial waste will be generated which will trigger several environmental issues when it is dumped in to environment. Utilization of industrial waste in construction industry without compromising in strength criteria is the best possible option for the engineers as the waste is used as construction material there will be no need for dumping yards, biodiversities can be protected, strength properties of the soil can be enhanced and hydraulic properties can also be modified by treating soil with industrial wastes. As the river sand can be partially replaced by some of the industrial wastes requirement for natural sand can be reduced which will not only economise the construction cost but also preserves the natural resources. The present study was carried out to assess the behaviour of coir pith treated black cotton soils. Due to increase in the natural fibres in many industries the waste produced from the coir industry is increasing, if proper care is not taken these waste may create severe effects on environment .To avoid disposal of this waste directly into environment it is better to use the waste in construction industry is a better option. But before adopting such practices proper studies are to be done to check whether these materials are suitable for treating the soils are not .In the present study by adding various proportions of coir pith Swell, strength characteristics of soil where compared for obtaining the optimum Replacement percentage.

Cloud computing is an “Internet-based computing”. It provides many resources to Cloud Users based on demand without buying any infrastructure by making use of CSP (Cloud Service Providers) and the amount is charged based on the usage of data. It made the day to day functions easier “in evolving and making IT utilization for the consumers. The cloud is also used as” a channel to design and deploy user applications including its storage space and database “without worrying about the operating system used. An application can also run without consideration the infrastructure that is used. Also, the Cloud provides huge storage which is available for both data and databases. The storage of data on the Cloud is one of the main activities in Cloud computing which utilizes infrastructure spread across several geographical locations.” This paper gives a study about various type of data stored in a cloud and its security mechanisms.

Deep learning is a subset of machine learning, also known as hierarchical learning. It is based on artificial neural network with various stages of representative transforms. Deep neural networks have been applied in different applications like image processing, speech recognition; market-basket analysis and students’ performance prediction to name a few. Now a day’s education is not limited to only the classroom teaching but it goes beyond that like Online Education System, Web-based Education System, Seminars, Workshops, MOOC courses. It’s a big challenge to extract sentiments from the huge data generated which is stored in the environments of Educational databases. Mining on educational databases can be done to extract the hidden sentiments of the students and their views about the education. Analyzing Students’ sentiments and their learning behavior towards the course, difficulties faced, time spent for the course duration in learning the concepts and worries or fears of students like whether they may pass or fail the Final Exam is of prior importance these days in educational institutes. These factors play a dominant role in reforming education. Tweets are gathered from twitter database and found that the obtained are in unstructured form. Preprocessing methods were applied to clean the data set and later classified tweets based on sentiments into classes namely positive, negative and neutral. In this Paper, sentiments of students are analyzed which can be further considered while making reforms in education. In this paper Educational tweets are extracted from Twitter using twitter API and preprocessed. After Preprocessing, clean data is trained and a Model is attained, on this test data is applied. Results are evaluated on few parameters like Balanced accuracy, Sensitivity and Specificity; Prevalence and Detection rate and found that deep learning technique achieves high performance.

We designed and developed a novel framework DWIRS for Deep Exploration with indexing and ranking. The purpose of DWIRS framework is to explore high quality contents from the quantitative Deep Web, with least possible effort, in the least possible time, irrespective of architectural dependencies and operating system related issues. It is imperative to perform indexing and ranking is the nucleus of the exploration process. The design of the proposed framework is carried out in three phases: Indexing, Standard Search and Ranking Search. In phase 1, the acquired Deep Web data is pre-processed through Tokenization, Stop Word Removal and Stemming. Then, indexing is performed for the Deep Web data. The exploration process is carried out based on the search keyword provided by the user. Cosine similarity measure is used in standard search. The search results are re-ranked by using BRF algorithm. The performance of DWIRS framework has been evaluated by developing a customized application on the collected Deep Web data sets. By comparing the results of standard search with that of ranking search, it is shown that retrieval effectiveness as well as the quality of search results has been improvised. Our experimental results revealed that the proposed framework works effectually.

The term anomaly is derived from a Greek word anomolia meaning uneven or irregular. Anomalies are often referred to as outliers in statistical terminology. For a given set of data if we plot a graph and observe, all the data points that are relative to each other will be plotted densely, whereas some data points which are irrelevant to the data set will be lied away from the rest of the points. We call those points as outliers or anomalies. Anomaly detection is also called as deviation detection, because outlying objects have attribute values that are significantly different from expected or typical attribute values. The anomaly detection is also called as exception mining because anomalies are exceptional in some sense. Anomalous data object is unusual, irregular or in some way, inconsistent with other data objects. In this case, unusual data object or irregular patterns need not be termed as not occurring frequently. If we take a large data set or a continuous data stream, then an unusual data object, that occurs ‘one in a thousand’ times, can occur millions of times in billions of events considered. To find out the anomalies in data sets, we have many approaches like statistical, proximity–based, density–based and cluster–based. Statistical approaches are model-based approaches where a model is created for the data and objects are calculated with respect to how they are relative with all other objects. In this paper, we will be discussing various statistical approaches to detect anomalies. Most statistical approaches to outlier detection are based on developing a probability distribution model and considering how probable objects are under that model.

Data analytics is playing a vital role in the modern industrial era. Electricity is one of the industries that have adapted data analytics techniques to a great extent. The data collected in the smart grid through smart meters and other sensors installed is very huge. The processing of such a huge heterogeneous data is not possible without the use of big data analytics technique. Big data analytics and machine learning algorithms play a vital role in electricity transmission and distribution network for data collection, storage and analysis of the data, prediction for data forecasting, and maintenance of the system. These techniques can help to optimally deliver energy at a lower cost with high quality and can also improve the customer service as well as social welfare. This article will review the use of big data analysis techniques along with the machine learning for various applications that can be mapped for smart grid environment. This article will also discuss the various methods and algorithm to be used for the applications for smart grid. A comparative analysis will also be done to show the best methods and algorithm to be used for a particular application.

Worldwide in the construction industry uses reinforced cement concrete. The different types of structures which were built using reinforced cement concrete served for long period. But due to faulty design, improper quality control and corrosion of reinforcement there are notable premature failure and subsequently sudden collapse of the structure occurs. The presence of aggressive medium such as moisture and chloride ions are the main factors which are induces the steel corrosion. The cost involves for repairing the corroded structure escalate very rapidly which leads property loss to the people, if the problem is not corrected. Columns are the main structural members of R.C bridges when exposed to de-icing salt they are more vulnerable to steel reinforcement corrosion. The thorough knowledge about the corrosion can get only by proper investigation. This experimental work mainly focuses on structural behaviour R.C columns subjected to steel corrosion. Columns with two different size were cast and accelerated corrosion was induced. Structural behaviour of the columns were studied with axial load test and compared with conventional specimen. The ultimate load carrying capacity, axial strain, energy absorption capacity were reduced in all the corroded columns

This paper deals with numerical investigation using finite element software, ABAQUS CAE, for the analysis of precast reinforced concrete (RC) column connected to the foundation containing protruding bar in grouted sleeve subjected to axial, and lateral loads, including the cyclic loading response. The monolithic connection of column and foundation is designed and the same has been used for the design of precast concrete column and foundation. The model focusses on the length of the protruding bar provided in the foundation that is connected to the column through the grouted sleeve. As there is lack of Indian standard codes in the design of precast concrete connections, an attempt has been made by varying the length of protruding bars. The model was subjected to quasi-static loading condition and parameters such as ultimate load carrying capacity, load-displacement hysteresis, ductility and plastic strain were studied and were compared with that of monolithic connection. The results proved that when the length of protruding bars provided was equal to development length of the bar provided, the results were comparable with that of monolithic connection.

Boswellia serrata is an Indian herb known for its potent anti-inflammatory and anti-arthritic properties in ancient folk medicine. The present study was undertaken to evaluate the anti-arthritic potential of Boswellia serrata on radiographic joint damage and histopathology of adjuvant-induced arthritis. Thirty male Wistar rats were randomly divided into 5 groups with six rats each. While group 1 served as normal control, arthritis was induced in animals belonging to groups 2 (arthritic control); 3, 4 and 5 (treatment groups) by injecting 0.1 ml of Freund's complete adjuvant, intradermally into the hind foot pad. Treatment protocol was followed from 3rd to 21st day, with Boswellia serrata given orally as methanolic extract @ 500 mg/kg b.wt. to group 3, meloxicam given subcutaneously @1 mg/kg b.wt. to group 4 and both the drugs given concurrently to group 5. The onset and progression of arthritis were assessed weekly by radiographic interpretation. The drug effects were evaluated on the histopathology after completion of the experiment. The extent of paw inflammation before treatment and its subsequent amelioration after treatment were noticed in groups 3, 4 and 5 Boswellia serrata showed better amelioration compared to meloxicam with a superior curative effect witnessed when both the drugs were administered together. The significant alleviation of joint damage in adjuvant-induced arthritis may be attributed to the pharmacologically active principles present in the extracts of Boswellia serrata.