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Response surface optimization and process design for glycidol synthesis using potassium modified rice husk silica
Glycerol, an inexpensive by-product from biodiesel production can be converted into many useful products notably glycidol, which has a wide range of uses. In this study, glycidol synthesis has been done using a biowaste mediated catalyst in a single step process. Silica and potassium incorporated silica were synthesized from biowaste rice husk. These catalysts were characterized by different spectroscopic techniques. Basic sites in the catalysts were estimated using temperature-programmed desorption study. Four operational parameters were optimized using Box Behnken Design (BBD) of response surface methodology (RSM). Potassium incorporated rice husk was found to be one of the best catalysts for glycidol production with 60.8% glycerol conversion and 62.9% selectivity within one hour of reaction time. 2020 Elsevier Ltd. All rights reserved. -
Response of ChatGPT for Humanoid Robots Role in Improving Healthcare and Patient Outcomes
Humanoid robotics is characterized by constant developments, which are supported by several research facilities across the world. Humanoid robots are used in many different industries. In this setting, this letter, written by people, makes use of ChatGPT answers to examine how humanoid robots might be used in the medical industry, particularly in light of the COVID-19 pandemic and in future. Although humanoid robots can help with certain jobs, it is important to recognize the indispensable importance of human healthcare professionals who have knowledge, empathy, and the capacity for critical judgment. Although humanoid robots can complement healthcare initiatives, they shouldnt be viewed as a full-fledged replacement for human care. 2023, The Author(s) under exclusive licence to Biomedical Engineering Society. -
Responding to the pandemic: A case of the indian hotel industry
The chapter presents a case study on how Indian hotel industry was affected by COVID-19. Three hotels-Lemon Tree, Oyo Rooms, and Taj Hotels-were selected to elaborate. The study found that the hotel industry developed various policies to keep running their hotels during the pandemic. Lemon Tree joined various hospitals to provide rooms to COVID patients, provided free food and face masks to individuals. Oyo Rooms gave employee stock ownership plans of Rs 130 crore to its COVID-hit employees. Taj Hotels did not cut down the salaries of their employees and reduced its seating capacity by 50%. The study concluded that as the hospitality sector battled hard to continue during the pandemic, modernization would become an imperative tool in the post-COVID period to beat obstructions and spotlight advancement. So, the companies should minimize fixed costs and maximize variable costs. They should preferably have liquid cash available that could enable them to mitigate the risk. 2022, IGI Global. -
Responding to pandemic challenges: leadership lessons from multinational enterprises (MNEs) in India
Purpose: The business sector plays a major role in achieving comprehensive economic development goals in emerging economies. Consequently, the effects of business responses to the COVID-19 pandemic are receiving increasing research attention from an organizational management development perspective. This article aims to examine the role of leadership in charting the course in an extraordinary crisis context. Design/methodology/approach: Using institutional leadership theory, leadership contingency theory and dynamic leadership capability theory, the authors present a research framework that defines macrochallenges and organizational level responses and outcomes. The article adopts a case study approach, which includes the identification of four target companies and conducting in-depth interviews with senior management professionals within those companies at different time periods. Findings: Based on the interviews, the steps that Indian companies adopted to respond to the COVID-19 challenge are identified. Expanding the insight from the case study, the findings suggest that although feeling overwhelmed at first, organizational leaders combine prudent (i.e. timely and speedy actions for survival first) and bold (i.e. future envisioning for expansion and growth) actions enabling these firms to weather two waves of the COVID-19 pandemic in India. Originality/value: These multiple case studies are unique in exploring MNEs from different industries. This study also highlights the dynamic relationships between leadership practices, risk management strategies and performance outcomes based on a sound theoretical model and rigorous study methods. 2022, Emerald Publishing Limited. -
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Respiratory Motion Prediction of Lung Tumor Using Artificial Intelligence
Managing respiratory motion in radiotherapy for lung cancer presents a formidable and newlinepersistent challenge. The inherent dynamic movement triggered by respiration introduces a notable degree of uncertainty in target delineation, impacting the precision of image-guided radiotherapy. Overlooking the impact of respiratory motion can lead to the emergence of artifacts in images during image acquisition, resulting in inaccuracies in tissue delineation. Moreover, the motion between treatment fractions can induce blurriness in the dose distribution within the treatment process, thereby introducing geometric and dosimetric uncertainties. Additionally, inter-fraction motion can result in the displacement of the distribution of administered doses. Given these complexities, the precise prediction of tumor motion holds the utmost importance in newlineelevating the quality of treatment administration and minimizing radiation exposure to healthy tissues neighboring the pertinent organ during radiotherapy. Nonetheless, achieving the desired level of precision in dose administration remains a formidable task due to the inherent variations in internal patient anatomy across varying time scales and magnitudes. While notable advancements have been witnessed in radiotherapy, attributed to innovations like image guidance tools, which have streamlined treatments, the challenge of accommodating lung tumor motion remains critical, particularly in cases related to newlineradiotherapeutic intervention. Substantial limitations endure despite integrating respiratory-gated techniques in radiation oncology to manage lung tumor motion. Moreover, lung cancer prognosis remains low, irrespective of the recent advancements in radiotherapy. The practice of expanding newlinetreatment margins from the Clinical Treatment Volume (CTV) to encompass the Planning newlineTreatment Volume (PTV) has been adopted as a strategy to amplify treatment outcomes. newlineHowever, this strategy necessitates a trade-off, as it inevitably exposes larger volumes of healthy tissues to radiation. -
Resource Curse - Impact of Renewable Natural Resources on Economic Growth in the U.S. using ARDL Approach
The analyses of the resource paradox in the United States of 29 years are conducted by the econometric model of ARDL. The dataset taken for the study is from the source of World Bank. After testing the stationarity and cointegration of 4 independent variable and one dependent variables of Gross Domestic product, this study will be giving the conclusion of long term and short-term relations of the variables to show the existence of Resource curse in the US within the 29 years of dataset. Causation test shows that there doesn't exist any particular causal relations between the variables and hence there need to be thorough study in this phenomenon. 2024 IEEE. -
Resource Aware Weighted Least Connection Load Balancing Technique in Cloud Computing
Cloud computing became a pivotal for the most of the real time applications. In cloud computing, the customer demands the services with the best performance even when the application is expanding rapidly. Therefore, it is essential to manage the resources effectively because the number of users and services growing proportionately. The main aim of the load balancing technique is to allocate the customers' requests with the large pool of resources efficiently. The problem is how to evenly distribute the load of requests among the compute nodes according to their capacity. Therefore, there is a need for an effective load balancing technique for smooth continuity of operations in a distributed environment with a heterogeneous server configuration. This paper presents a novel load balancing technique, namely, Resource aware weighted least connection load balancing which addresses the above said problem efficiently. The essence of this work is to assign the requests across multiple servers based on the requested resource and the status of the number of connections presently served by each server. This work used standard score technique to enumerate the weight of each node. Experiments were conducted using Cloud Analyst, a famous cloud simulator breed from CloudSim. Appropriate performance parameters were analysed to measure the effectiveness of the proposed technique. Future directions for the extension of the implemented technique also identified. 2023 IEEE. -
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Resilience in Children from Different Socioeconomic Backgrounds: An Exploratory Study
Poverty, violence, substance abuse, family dissonance and illness represent a few potential vulnerabilities in the lives of children who are at risk of failing in their future prospects. It is thus essential to explore resilience in children, owing to the excess or deficit of exposure and access in a childs life. This study aims at exploring the resilience of children of the age group 710years, from different socioeconomic backgrounds. The socioeconomic status was determined using the Kuppuswamy socioeconomic scale and these children had parents with authoritarian and permissive parenting styles which were screened through the Parenting Styles and Dimensions Questionnaire which act as risk factors for the children. Data was collected through individual semi-structured interviews with the participants and was analysed using thematic analysis. For the lower socioeconomic status group, the main themes identified were social interaction and competence, overcoming distress and future focus, and for the upper socioeconomic status group, the main themes identified were social interaction and competence and emotional management. The study paves the way for further exploration of the impact of economic status on childrens wellbeing and might inform changes at a clinical, research and policy level. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
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Residual-Based Statistical Process Control Charts in the Presence of Multicollinearity: an EWMA Framework with (RK) Estimator
Reliability monitoring of financial health requires strong control mechanisms, and the residual chart is an invaluable instrument to perform it. One of the key problems statisticians face while modeling is the problem of multicollinearity which arises when there is a strong correlation between independent variables leading to imprecise coefficient estimates and poor outcomes. To solve this problem and to make sure that the control chart works even with correlated data, we integrated a Weighted Moving Average Exponential smoothing chart within the modeling technique. The theoretical approach assures long-term variability and consistency of the residual control chart. These control charts are used to understand the process and the performances in various sectors. The charts can be used as analytical instruments to help recognize patterns, variations, or anomalies in economic indicators specifically in budget deficit data and facilitate rapid identification of any changes or inconsistencies in the fiscal deficit by policymakers. Further advances in statistical process control are rendered feasible by this study, which deepens the understanding and awareness of the potential uses and implications of the Weighted Moving Average Exponential smoothing chart for fiscal deficit data in the Economic realm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Residual stresses analysis on thermal barrier coatingsndt tool for condition assessment
Improvement in the engine efficiency follows reduction in fuel consumption which is possible by increasing the engine combustion temperature. Coating the piston of diesel engine with a high temperature-resistant material, known as thermal barrier coating, generally 68% Y2O3 stabilized ZrO2, is a popular method to reduce the temperature it experiences in service and to increase engine efficiency. Whether bare or coated component, fabrication and different thermal expansion coefficients of the ceramic coating and piston metal cause generation of residual stresses in them. These hidden residual stresses (tensile or compressive) play a significant role in governing the failure mechanism of the different sections of the components and their important role (also developed in service) is mostly neglected in engineering practices. Residual stresses analysis of components in service may throw light on the condition of the components without destroying them. In this work, portable X-ray residual stress analyzer was used to evaluate the condition of AlSi alloys piston flat plates that were coated with 250-m-thick 68% Y2O3 stabilized ZrO2 and subjected to thermal treatments. The analysis revealed (a) residual stress-free pattern for uncoated AlSi substrate, (b) compressive residual stress at the substrate (AlSi)coating (TBC) interface and (c) tensile residual stress at the substrate (AlSi)coating (TBC) interface of a thermal shocked coated substrate. The analysis method exhibited good possibility for using this as a tool for non-destructive testing for predicting the onset of failure at the coating substrate interface, without destroying the component in service. Springer Nature Singapore Pte Ltd 2020. -
Residual stress analysis on functionally graded 8% Y2O3-ZrO2 and NiCrAlY thermal barrier coatings
Thermal Barrier Coatings (TBCs) protect metallic components that operate in high temperature environments and enhance their service life. The conventional two-layered TBC system consists of a duplex ceramic top coat (TC) fabricated from 8 wt% yttria stabilized zirconia (8-YSZ) and an underlying bond coat (BC) comprised of intermetallic layers such as NiAl or MCrAlY (M = Co, Ni) etc. In the present study, functionally graded material (FGM) TBCs were fabricated by introducing a third blend layer of 8-YSZ and NiCrAlY, in between the BC and TC in order to enhance the thermal fatigue life of the TBC. The blend layer in FGM TBCs provides a smoother transition in thermal expansion properties between the metallic substrate and the top ceramic coat (8YSZ) which have widely different thermal expansion characteristics compared with each other. In service, thermal fatigue introduces severe tensile stresses between the coated layers and the substrates leading to ultimate detachment of the coatings from the substrates. In this work, residual stress analysis by Cos ? method was carried out as a non-destructive assessment tool to foresee the likelihood of onset of failure in the TBCs, well before the damage was visible. The two-layered (conventional) and three-layered (FGM) TBCs were synthesized on Inconel 718 substrates by atmospheric plasma spray (APS) technique. The TBCs were subjected to thermal fatigue tests between 1200? (by using gas flame) and ambient temperature and evaluated for residual stress analysis at different stages of thermal fatigue testing. The goal was to assess if residual stress analysis could be used to determine if the TBC was about to fail well before the delamination occurred and the catastrophic failure could be avoided. The tests conducted and results obtained are presented. 2022 -
Reshaping the Education Sector of Manipur Through Blockchain
The use of technology in education has been over a century, yet blockchain is in its nascent stage in education. Over the years, technology has enhanced the teaching-learning method, and blockchain can improve even in the administrative section of education. The states of North East, India, in general, lag behind the rest of Indian states in almost all sectors, and the lack of transparency in the administrative sector significantly contributed to it. If blockchain is incorporated into the education department at the administrative level, it could pave the way for a faster, more transparent, and smoother administration. Given the harsh reality that transportation is hard and expensive, a standardised blockchain can alleviate the need to be physically present for any academic-related activity. The attempt of this study would be to show how blockchain can be beneficially used even at the institutional level, where unabated printing could be reduced and adopting to e-paper be maximised. Besides the educational institutions, the administrative sector in education could profitably use them in offices, thereby avoiding red tape for the common people. The chapter points out how blockchain can be a trailblazer in reshaping the education sector in Manipur. Educational institutions must take the lead towards a sustainable future, and blockchain can aid in bringing some visible change in the educational sector. This chapter uses an interdisciplinary approach to substantiate the importance and need for blockchain in the context of Manipur to change for a sustainable future. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Research progress of MXenes and MXenes-based catalysts for photocatalytic water splitting: A systematic review
The field of two-dimensional (2D) materials has witnessed remarkable growth over the years, especially on a class of materials known as MXenes. MXenes have garnered significant attention for their exceptional physicochemical properties, which include high electrical and thermal conductivity, large surface area, adjustable bandgap, and hydrophilicity. These characteristics have paved the way for a diverse range of applications, including photocatalysis, electrocatalysis, supercapacitors, sensing, and biomedicine. MXenes have been recognized to be particularly effective in applications such as photocatalytic hydrogen production through water splitting reactions. This involves using MXenes as cocatalysts to enhance the efficiency of the photocatalytic process. In this review, the various synthetic methods for producing MXenes and MXenes-based catalysts are summarized, shedding light on the versatility of their fabrication techniques. The underlying mechanisms of photocatalytic H2 evolution are explored, providing insights into how MXenes function as cocatalysts in these reactions. These mechanisms are crucial for understanding the enhancement of H2 production and improving the overall efficiency of the water splitting process. Furthermore, the review delves into the challenges that researchers face when utilizing MXenes and MXene-based materials for electrocatalytic water splitting. These challenges serve as motivation for further exploration and innovation in the field, driving the development of more efficient and sustainable electrocatalytic systems. In this discussion, the potential future applications of MXenes and their composites in electrocatalytic water splitting and other fields are explored. This suggests that ongoing research and advancements in MXene-based materials have the potential to revolutionize various technological areas, contributing to the development of cleaner energy sources and more efficient catalytic processes. 2024 Elsevier Ltd -
Research Potentials and Future Trends of Digital Sustainability
A once-in-a-century pandemic has left scars on countries already roiled by the increasingly erratic weather patterns induced by the climate crisis, wreaking havoc on sectors as diverse as food security, industrial production, and defence. Countries are torn between ameliorating COVID-19's devastating impact on education, health, and livelihoods of citizens, and finding their footing in a new global order. But from this ferment are emerging technologies, ideas, and solutions that will drive the world of the future; innovation and big ideas are building a vision that is bold and transformative. As the digital technologies evolve, its comprehensive impact on the environment needs to be considered to harness its full potential. Technology is transforming our world, but at the same time it brings new opportunities as well as challenges for sustainability. The unintended negative environmental impacts emerging from technologies are likely to be outweighed by potential of technology to solving it. Advances in technology, coupled with artificial intelligence, innovation in analytics, and data generation, is likely to have positive sustainability impacts. This chapter highlights the research potential and future trends of digital technologies for sustainability purposes. We intend to evaluate the implications of digital technology such as cloud computing, blockchain, Internet of Things, big data analytics, and artificial intelligence on pollution reduction, sustainable farming practices, conservation of biodiversity, and natural disaster management. Using real-life cases, we will investigate how digital technologies can be both an obstacle and enabler to global sustainability, which will enable devising appropriate digitalization strategies geared towards the achievement of sustainability. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE. -
Research on secure workload execution scheme in heterogeneous cloud environment
The increasing demand for the hardware, software and infrastructure is playing a big role in the information technology domain towards the need of customers specific requirements. Cloud computing is a major backbone for providing such services over the internet. It includes the services such as applications, storage, network, scalability, sharing, virtualization, confidentiality, security, authentication, and integrity. A large number of data intensive workflow applications uses heterogeneous cloud environment for communication and computation operation. An intruder/attacker will utilize these environments for their benefit by flooding malicious links, unwanted information and others. In cloud environment, detecting a malicious device/packet during workflow execution is a critical and challenging task. The various workflow method with security, service level agreement (SLA) and quality of service (QoS) have been modelled in recent time; However, these models are not efficient in detecting malicious users and maintaining high level of QoS or workflow applications. This article focus is on addressing research future direction, issues and challenges of work in meeting secure and efficient workflow execution model for heterogeneous cloud environment. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Research Intention Towards Incremental Clustering
Incremental clustering is nothing but a process of grouping new incoming or incremental data into classes or clusters. It mainly clusters the randomly new data into a similar group of clusters. The existing K-means and DBSCAN clustering algorithms are inefficient to handle the large dynamic databases because, for every change in the incremental database, they simply run their algorithms repeatedly, taking lots of time to properly cluster those new ones coming data. It takes too much time and has also been realized that applying the existing algorithm frequently for updated databases may be too costly. So, the existing K-means clustering algorithm is not suitable for a dynamic environment. Thats why incremental versions of K-means and DBSCAN have been introduced in our work to overcome these challenges.To address the aforementioned issue, incremental clustering algorithms were developed to measure new cluster centers by simply computing the distance of new data from the means of current clusters rather than rerunning the entire clustering procedure. Both the K-means and the DBSCANDBSCAN algorithms use a similar approach. As a result, it specifies the delta change in the original database at which incremental K-means or DBSCANDBSCAN clustering outperforms prior techniques. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.