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6-Bromo-2-[(E)-thiophen-2-ylmethylidene]-2,3,4,9-tetrahydro-1H-carbazol-1- one
In the title compound, C17H12BrNOS, the cyclohexene ring deviates only slightly from planarity (r.m.s. deviation for non-H atoms = 0.047 . In the crystal, the molecules are linked into centrosymmetric R2 2(10) dimers via pairs of N-H?O hydrogen bonds. The thio-phene ring is disordered over two positions rotated by 180and with a site-occupation factor of 0.843 (4) for the major occupied site. -
60CO Gamma irradiation and annealing effects on transport properties of antimony telluride platelets grown by physical vapor deposition
Acta Metail Sinica (English Letters), Vol.28, Issue 5, pp.533-540, ISSN No: 1006-7191. -
60Co gamma irradiation and annealing effects on transport properties of antimony telluride platelets grown by physical vapor deposition
Physical vapor deposition method was employed to deposit antimony telluride (Sb2Te3) crystals in a dual-zone furnace. The microstructure, surface topography and composition of samples were characterized using X-ray diffraction, atomic force and scanning electron microscopy. Seebeck coefficient (S?c), electrical conductivity (??c) as well as power factor (PF) were enhanced for pure Sb2Te3 samples upon annealing, and the samples annealed at 473 K exhibited the highest PF of 3.16 10-3 W m-1K-2 with an enhancement of 22% in the figure of merit (Z). When the delivered dose of 60Co gamma radiation was increased from 0 to 30 kGy in the stoichiometric crystals, ??c decreased due to the decrease in mobility. As a result of the increase in S, PF and Z improved by 12.11 and 13.7%, respectively, in the 30 kGy gamma-irradiated crystals. Both RH (B?c) and S?c were positive, suggesting that the prepared Sb2Te3 crystals retained the p-type semiconductivity after these treatments. The Chinese Society for Metals and Springer-Verlag 2015. -
A Bibliometric Analysis of Asset Allocation for Retirement
Allocation of investment assets is key in attaining a sustainable retirement portfolio. In this research article, the authors analyzed the most recent research publications in the area related to asset allocation for retirement and identified those which have the highest impact. The authors research was conducted using the bibliometric analysis technique of research articles collected from the Scopus database. Most of the research articles were published in reputed journals in the United States, United Kingdom, Australia, and Germany. It was also observed that most of the highly cited research articles in the research area of asset allocation for retirement are focused on financial literacy, increase in retirement age, aging, and pension reforms. The authors findings identified six research themes in asset allocation for retirement such as 1) asset allocation for retirement planning, 2) methods to increase efficiency, 3) investment preferences for retirement savings 4) financial literacy and retirement planning, 5) reforms on retirement savings, and 6) annuities for retirement income. Furthermore, nineteen future research directions are also provided. In conclusion, the authors aim to assist future researchers in identifying highly cited articles, key authors, contributing countries and research themes in asset allocation for retirement. Overall, the analysis provides comprehensive information in addressing research questions in the field of asset allocation for retirement. Copyright 2024 With Intelligence LLC. -
A Bibliometric Analysis of Industry 4.0 and Health-Care Services
A key moment in health care is marked by the Fourth Industrial Revolution, commonly referred to as Industry 4.0. This transformation, driven by the convergence of digital technologies with automation and data driving processes, has led to a paradigm shift in how health care is provided. The integration of the emerging technologies in Industry 4.0, such as Internet of Things, Artificial Intelligence, Big Data Analytics and Advanced Robots, are revolutionizing patient care, improving resource allocation and shaping research's landscape. To learn more about the ever-evolving relationship between Industry 4.0 and health care, this research paper begins with a bibliographic analysis. In this interdisciplinary convergence, our bibliometric analyses serve as a lens through which we can see the key trends, research areas and influential players. The review of literature highlights the profound impact of Industry 4.0 on health care, revealing that Internet of Things technologies for real-time patient tracking, proliferation of artificial intelligence in medical diagnosis and transforming power of big data Analytics are changing health care decision making. Methodologically, we leverage bibliometrics as a quantitative analytical tool, drawing on citation counts, bibliographic coupling, and keyword co-occurrence analysis. The data for this analysis, which covered the period 20152023, was carefully collected from Scopus database. The analysis of the information reveals that, particularly from 2018 onwards, there has been a significant increase in publications concerning Industry 4.0 and health care. In this research landscape India has emerged as a strong contributor, with countries such as the United States and Italy making significant progress. Publication trends and bibliographic coupling among countries and sources shed light on collaborative networks and research focus. The emergence of machine learning, artificial intelligence and data analysis as important themes is illustrated by a co-occurrence analysis of keywords that elucidates evolving research interests. In the complicated terrain of health care converging with Industry 4.0, this research paper serves as a compass. The report highlights this convergence's transformative potential, highlighting the pivotal role that bibliometrics analysis must play in determining future research areas in adopting Industry 4.0 in the health-care sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A bibliometric analysis of sustainability and organizations performance
The incorporation of sustainability into an organizations performance is becoming an emerging topic to work upon. Moreover, conventional economic systems have had significant negative consequences for sustainable management, as well as imbalanced wealth distribution, which has resulted in natural catastrophes and population disparity. Sustainability practices in the current environment represent better quality performances and affect organizations performance. This research highlights the key areas and current evolution in the notion of sustainable development and organizational performance, as well as recommendations for further studies. Using the bibliometric analysis we examine a sample of 1442 articles published in Scopus between 1994 till 2021. The researcher identifies prominent authors, publications, and journals by employing a variety of network analysis techniques such as term co-occurrence, co-citation, and bibliography coupling with the help of VOS viewer. To the best of the authors knowledge, no other study has examined bibliographic data on sustainability and organizations performance; hence, this research is a one-of-a-kind addition to the literature. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification
Heart disease is a serious disease that causes sudden death among 80% of the people around the world. The traditional models performed predictive analytics using machine learning techniques to make a better decision. For better decision making in heart disease prediction, the big data analysis shows the great opportunities to predict the future health status from health parameters and provide best outcomes. However, the traditional decision making models had traffic data or contained noise and uncertainty was unpredictable as the data ambiguity emerged. In order to overcome such an issue, the big data is used to ensure the medical service which is mostly needed in a timely manner and for accurate diagnosis. The pre-processing of the medical data acquired from Cleveland heart disease UCI datasets has a vast number of attributes which helps to predict the heart disease. The data are contaminated with the noise and some of the data are missing, so the pre-processing using Min max Normalization is performed to remove contaminated noise acquired in the data which is taken from the UCI repository dataset. The proposed Fuzzy Deep Convolution Network (FDCN) permits the input features for fuzzification process that uses transformed features. The fuzzification process eliminates the redundant or irrelevant fuzzified features and overcomes the system complexity problems. The proposed FDCN obtains accuracy of 95.56 % and 92 % of F-score shows better results when compared with the existing KNN-DT, Naive Bayes, and Random Forest algorithms. 2021,International Journal of Intelligent Engineering and Systems. All Rights Reserved. -
A Bird's-Eye View on Deep Eutectic Solvent-Mediated Multicomponent Synthesis of N-Heterocycles
Multicomponent reactions are crucial for operating organic synthesis. In today's world, chemists concentrate on greener methodologies that cater to rising environmental concerns. Most conventional organic solvents are harsh on the environment, which can be addressed by environmentally and pocket-friendly Deep eutectic solvents (DES). Organic synthesis via MCR in the presence of DES has resulted in the blend of green methodology and green solvent for organic transformations. Forasmuch as addressing the environmental concerns and developing higher heterocycles, our review focuses on the literature published on the DES-mediated Multicomponent synthesis of N-Heterocyclic compounds. 2023 Wiley-VCH GmbH. -
A Bose horn antenna radio telescope (BHARAT) design for 21 cm hydrogen line experiments for radio astronomy teaching
We have designed a low-cost radio telescope system named the Bose Horn Antenna Radio Telescope (BHARAT) to detect the 21 cm hydrogen line emission from our Galaxy. The system is being used at the Radio Physics Laboratory (RPL) (Radio Physics Lab, IUCAA NCRA-TIFR, , ), Inter-University Centre for Astronomy and Astrophysics (IUCAA), India, for laboratory sessions and training students and teachers. It is also a part of the laboratory curriculum at several universities and colleges. Here, we present the design of a highly efficient, easy to build, and cost-effective dual-mode conical horn used as a radio telescope and describe the calibration procedure. We also present some model observation data acquired using the telescope for facilitating easy incorporation of this experiment in the laboratory curriculum of undergraduate or post-graduate programs. We have named the antenna after Acharya (teacher or an influential mentor) Jagadish Chandra Bose, honoring a pioneer in radio-wave science and an outstanding teacher, who inspired several world renowned scientists. 2022 Author(s). -
A Brief Concept on Machine Learning
Machine learning is a subset of AI. Its a research project aimed at gathering computer programscapable of performing intelligent actions based on prior facts or experiences. Most of us utilize various machine learning techniques every day when we use Netflix, YouTube, Spotify recommendation algorithms, and Google and Yahoo search engines and voice assistants like Google Home and Amazon Alexa. All of the data is labeled, and algorithms learn to anticipate the output from the input. The algorithms learn from the datas underlying structure, which is unlabelled. Because some data is labeled, but not all are, a combination of supervised and unsupervised techniques can be used. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Brief Review of Intelligent Rule Extraction Techniques
Rule extraction is a process of extracting rules which helps in building domain knowledge. Rules plays an important role in reconciling financial transactions. This paper presents a brief study of intelligent methods for rule extraction. The paper touches upon heuristic, regression, fuzzy-based, evolutionary, and dynamic adaptive techniques for rule extraction. This paper also presents the state-of-the-art techniques used in dealing with numerical and linguistic data for rule extraction. The objective of the paper is to provide directional guidance to researchers working on rule extraction. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A brief review of portfolio optimization techniques
Portfolio optimization has always been a challenging proposition in finance and management. Portfolio optimization facilitates in selection of portfolios in a volatile market situation. In this paper, different classical, statistical and intelligent approaches employed for portfolio optimization and management are reviewed. A brief study is performed to understand why portfolio is important for any organization and how recent advances in machine learning and artificial intelligence can help portfolio managers to take right decisions regarding allotment of portfolios. A comparative study of different techniques, first of its kind, is presented in this paper. An effort is also made to compile classical, intelligent, and quantum-inspired techniques that can be employed in portfolio optimization. 2022, The Author(s), under exclusive licence to Springer Nature B.V. -
A Brief Review on the Role of Blockchain in Supply Chain Management
Blockchain is a proficient technology when used in combination with other intelligent technologies which gives an opportunity to an organization to rethink about improvement of their supply chain internal and external processes. It helps in improvement of transparency and provenance by removing shortfalls and building a better organizational control overall. However, blockchain faces numerous challenges, e.g., transaction speed, decentralization, scalability, interoperability, and lack of standardization that could affect its adoption across organizations. However, a greater number of research are required to overcome the governance, standardization, and technological challenges involved within. Concisely, blockchain in supply chain is still in initial phase, many improvements are needed for better adaptation of blockchain using Machine Learning, Neural Network algorithms to make optimized computation decision of blockchain framework. In this paper, we studied and discussed about blockchain and its type, consensus mechanism, blockchain in supply chain, key issues of blockchain and supply chain and intelligence in blockchain. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Brief Review onDifferent Machine Learning-Based Intrusion Detection Systems
In the contemporary cybersecurity landscape, the proliferation of complex and sophisticated cyber threats necessitates the development of robust Intrusion Detection Systems (IDS) for safeguarding network infrastructures. These threats make it more challenging to maintain the communitys availability, integrity, and confidentiality. To ensure a secure network, community administrators should implement multiple intrusion detection systems (IDS) to monitor and detect unauthorized and malicious activities. An intrusion detection system examines the networks traffic by analyzing data flowing through computers to identify potential security threats or malicious activities. It alerts administrators when suspicious activities are detected. IDS generally performs two types of malicious activity detection: misuse or signature-based detection, which entails collecting and comparing information to a database of known attack signatures, and anomaly detection, which detects any behavior that differs from the standard activity and assumes it to be malicious. The proposed paper offers an overview of how different Machine Learning Algorithms like Random forest, k - Nearest Neighbor, Decision tree, Support Vector Machine, Naive Bayes, and K- means are used for IDS and how these algorithms perform on different well-known datasets, and Their accuracy and performance are evaluated and compared, providing valuable insights for future work. kNN shows an accuracy of 90.925% for Denial of Service Attacks and 98.244% for User To Root attacks. The SVM algorithm shows an accuracy of 93.051% for Probe attacks and 80.385% accuracy for remote-to-local attacks. According to our implementation, these two algorithms work better than the others. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A case study of "Parivarthana" - Towards zero waste /
International Journal For Research In Applied Science & Engineering Technology, Vol.4, Issue 6, pp.463-466, ISSN: 2321-9653. -
A case study on a beacon of hope transition: India's renewable energy integration and the Ujwal DISCOM assurance Yojana (UDAY)
This case study examines the transformative impact of the Ujwal DISCOM Assurance Yojana (UDAY) on India's energy landscape, focusing on its role in facilitating renewable energy integration. India's energy sector faced daunting challenges, including financially distressed power distribution companies (DISCOMs) and high aggregate technical and commercial (AT&C) losses. UDAY's financial restructuring and operational efficiency improvements led to remarkable reductions in DISCOMs' debt burdens and AT&C losses, respectively. The policy aligned with India's renewable energy goals, driving DISCOMs to procure renewable energy sources. Consequently, India witnessed significant growth in its renewable energy capacity, environmental benefits through reduced emissions, and economic growth via job creation. This case study offers insights into the challenges faced, technological advancements incentivized, and the long-term sustainability of these reforms. Moreover, it presents broader lessons for energy sector reform and renewable energy integration, both within India and globally. 2024, IGI Global. All rights reserved. -
A Case Study on Zonal Analysis of Cybercrimes Over a Decade in India
Human intelligence has transformed the world through various innovative technologies. One such transformative technology is the internet. The world of the internet, known as cyberspace, though powerful, is also where most crimes occur. Cybercrime is one of the significant factors in cybersecurity, which plays a vital role in information technology and needs to be addressed with high priority. This chapter is a case study where we analyze cybercrimes in India. The data collected from NCRB for 2010 to 2020 are a primary source for the analysis. A detailed analysis of cybercrime across India is done by dividing locations into seven zones: central, east, west, north, south, northeast, and union territories. Cybercrimes reported in each zone are examined to identify which zone requires immediate measures to be taken to provide security. The work also identifies the top ten states which rank high in cybercrime. The main aim of this chapter is to provide a detailed analysis of crimes that occurred and the measures taken to curb them. Along with the primary data, secondary data from CERT-In are also used to provide an analysis of measures taken for handling cybercrime over a decade. The outcome facilitates various stakeholders to better bridge the gap in handling cybercrime incidences, thus helping in incidence prevention and response services as well as security quality management services. 2023 selection and editorial matter, Narasimha Rao Vajjhala and Kenneth David Strang; individual chapters, the contributors. -
A catalytic, one-pot and green synthesis of a-amino nitriles: Cu(BF4)2.x H2O an efficient catalyst
The Strecker reaction is a first reported multicomponent reaction for the preparation of a-aminonitriles. The a-aminonitriles are important intermediates for various aminoacids, 1,2-diazines, heterocycles and biologically active compounds like Saframycin A and Ecteinascidin 746. The preparation of a-aminonitriles by Strecker approach using MCR attracted many research groups owing atom economy to avoid multistep synthesis and to follow Green chemistry principles. Methods: A-aminonitriles have been synthesized using Strecker reaction by treatment of aldehydes, amines, with TMSCN in the presence of Cu(BF4)2.xH2O as a catalyst in one pot under neat conditions. Various aromatic and aliphatic aldehydes have been studied with different primary and secondary amines. Results: The reaction condition has been optimized by choosing a model reaction under various solvents and found good yields under neat conditions. Moreover, various catalytic amounts of Cu(BF4)2.xH2O has also been studied and found 3 mole% providing better yields. The reaction has been studied with different substrates of aldehydes and amines. Some of the products were characterized by comparison of their spectral data (1H NMR, 13C NMR, IR and MS) and physical properties with those of authentic samples reported in the literature. Conclusion: Afacile and efficient one-pot synthesis of a-amino nitriles at ambient temperature using copper(II)tetrafluoroborate as a novel catalyst under solvent-free conditions via Strecker reaction is reported. The process is simple and environmentally benign using the commercially available and inexpensive catalyst. 2017 Bentham Science Publishers. -
A catalytic, one-pot and green synthesis of α-amino nitriles : Cu(BF4)2.X H2O an efficient catalyst /
Letters In Organic Chemistry, Vol.14, Issue 6, pp.440 - 445, ISSN: 1570-1786. -
A census of young stellar population associated with the Herbig Be star HD 200775
The region surrounding the well-known reflection nebula, NGC 7023, illuminated by a Herbig Be star, HD 200775, located in the dark cloud L1174 is studied in this work. Based on the distances and proper motion values from Gaia DR2 of 20 previously known young stellar object (YSO) candidates, we obtained a distance of 335 11 pc to the cloud complex L1172/1174. Using polarization measurements of the stars projected on the cloud complex, we show additional evidence for the cloud to be at ?335 pc distance. Using this distance and proper motion values of the YSO candidates, we searched for additional comoving sources in the vicinity of HD 200775 and found 20 new sources, which show low infrared excess emission and are of age ?1 Myr. Among these, 10 YSO candidates and 4 newly identified comoving sources are found to show X-ray emission. Three of the four new sources for which we have obtained optical spectra show H ? in emission. About 80 per cent of the total sources are found within ?1 pc distance from HD 200775. Spatial correlation of some of the YSO candidates with the Herschel dust column density peaks suggests that star formation is still active in the region and may have been triggered by HD 200775. 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.