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Memory response on generalized thermoelastic medium in context of dual phase lag thermoelasticity with non-local effect
Theory of non-local continuum is contemporary appraised and is found to be supplementary coherent to capture the impacts of each and every point of the material at its single point. The conviction of memory dependent derivative is also newly appraised and is observed to be more intuitionistic for predicting the realistic character of the real-world obstacles. Attractiveness of the belief of a memory dependent derivative lies in its unique properties such as its significant constituents a kernel function and time-delay are freely selected according to the requirement of a problem. The present study comprises a new meticulous thermoelastic heat conduction model for the homogeneous, isotropic, thermoelastic half space medium concerning memory effects and non-local effects. Governing equations are constructed on the basis of the newly appraised non-local generalized theory of thermoelasticity with two phase lags in the frame of a memory dependent derivative. Exact analytical solutions of the physical fields such as dimensionless temperature, displacement as well as thermal stress are evaluated by using a suitable technique of the Laplace transform. Quantitative results are determined in a time-domain for different values of time by taking the numerical inversion of the Laplace transform. Noteworthy role of the constituents of the memory dependent derivative such as kernel function as well as time-delay factor has been scrutinized on the crucial field variables of the medium through computational outcomes. Moreover, the impact of non-local parameter is examined on the variations of field quantities through the quantitative results. 2022 by IPPT PAN, Warszawa. -
Nonlocal thermoelastic waves inside nanobeam resonator subject to various loadings
The present article focuses on the new meticulous model based on the postulate of memory-dependent derivatives to analyze the thermo-mechanical interactions inside the nano-beam-based machined resonators. Also, the size effect on dynamic responses of thermoelastic vibrations of homogeneous and isotropic nano-beam is considered. The fundamental expressions are formulated in the frame of non-local generalized thermoelasticity with paired relaxation times by operating the results of Euler-Bernoulli beam theory, non-local effect, and memory-dependent derivative. The proposed model is applied to study the nano-beam-based machined resonator subjected to the ramp-type heating and exponentially decaying time-dependent load. Closed-form solutions of the physical fields are examined by applying the Laplace transform mathematical mechanism. However, the coherence of the new thermal conductivity framework, a collation has been bestowed among the results obtained in the presence or absence of the memory-dependent derivative; also, the size effect is analyzed on the significant parameters of nano-beam such as deflection, temperature, displacement as well as bending moment. Moreover, the prominent influence of the distinct affecting parameters such as constituents of memory-dependent derivative (kernel function and time delay) and ramping time parameter with an applied load on the physical fields have been investigated with the help of quantitative results. 2022 Taylor & Francis Group, LLC. -
Moderating Role of Project Innovativeness on Project Flexibility, Project Risk, Project Performance, and Business Success in Financial Services
Project risk management is crucial for project success and for achieving short-term and long-term project goals. This study examines the linkage between the management of project risks and project flexibility for information technology projects in Financial Services. A conceptual framework establishing the link between project risks, project flexibility, project performance, and business success, with project innovativeness as a moderating variable, has been introduced. To test the model, data were collated from over 400 managers working in Financial Services projects. The empirical outcomes through a Ordinal regression analysis demonstrate a substantial association between the management of project risks, project flexibility, and success of projects. Project innovativeness moderates the effects of project risks and project flexibility on project performance. Furthermore, managing project risks is vital to reduce the likelihood of failures in projects. This paper enriches existing research by applying a contingency perspective to project risk management and provides practical guidance for managing risks in projects professionally and also the relevancy of project flexibility. 2021, Global Institute of Flexible Systems Management. -
Moderation effect of flexibility in projects on senior management commitment in achieving success in financial services IT projects
Senior management commitment and flexibility improve project responsiveness to volatile and high-impact scenarios, especially in large projects and programs. The aim of this study is to determine how project flexibility interacts with and affects the relationship between senior management commitment and success in IT projects. A cross-sectional survey of 166 managers was used to derive empirical data from the financial services industry and used to test the conceptual framework based on recent project management literature. Ordinal regression analysis demonstrated a significant relationship between senior management commitment and success in projects which is influenced by significantly positive moderations established through flexibility in projects. The study findings can assist project managers and senior leaders to accomplish their short-term and long-term project goals and achieve success in projects by reducing the chances of failures. This paper adds value to existing research in the context of IT projects and the role of project flexibility on their performance. Copyright 2023 Inderscience Enterprises Ltd. -
Balancing work and life inacademia: unraveling theemployee engagement mystery
Purpose: This study aims to further the understanding of employees engagement by explaining their organizational commitment through their perception of the availability of work-life benefits in the organization. This study also investigates the mediating role of job satisfaction in this context. Design/methodology/approach: The model was tested on the primary data collected in two phases from 270 teaching professionals in higher education institutes in Northern India. Barren and Kennys algorithm and hierarchical regression analysis were used to test the hypotheses. Findings: The results reveal that employees perception of work-life benefits strongly influences their organizational commitment. Also, the results support that employees job satisfaction mediates the above-mentioned relationship. Research limitations/implications: Self-reported data could be considered as a key limitation of this study and for more accurate results supervisors (line managers) perspective could also be included in future studies. Also, in addition to perceived work-life benefits, supervisors support could also have an impact on employees commitment, thus its inclusion in the model could draw a clearer picture. Originality/value: This research has two key contributions: first, it adds to the limited literature examining the employees engagement issues in the academic sector. Second, this research is one of, if not the first, to investigate perceived work-life benefits among third-level teaching staff in India to explain employees commitment to their organizations. 2024, Emerald Publishing Limited. -
A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning
Machine learning (ML) techniques are the backbone of Prediction and Recommendation systems, widely used across banking, medicine, and finance domains. ML techniques effectiveness depends mainly on the amount, distribution, and variety of training data that requires varied participants to contribute data. However, its challenging to combine data from multiple sources due to privacy and security concerns, competitive advantages, and data sovereignty. Therefore, ML techniques must preserve privacy when they aggregate, train, and eventually serve inferences. This survey establishes the meaning of privacy in ML, classifies current privacy threats, and describes state-of-the-art mitigation techniques named Privacy-Preserving Machine Learning (PPML) techniques. The paper compares existing PPML techniques based on relevant parameters, thereby presenting gaps in the existing literature and proposing probable future research drifts. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
CONFIDENTIAL TRAINING AND INFERENCE USING SECURE MULTI-PARTY COMPUTATION ON VERTICALLY PARTITIONED DATASET
Digitalization across all spheres of life has given rise to issues like data ownership and privacy. Privacy-Preserving Machine Learning (PPML), an active area of research, aims to preserve privacy for machine learning (ML) stakeholders like data owners, ML model owners, and inference users. The Paper, CoTraIn-VPD, proposes private ML inference and training of models for vertically partitioned datasets with Secure Multi-Party Computation (SPMC) and Differential Privacy (DP) techniques. The proposed approach addresses complications linked with the privacy of various ML stakeholders dealing with vertically portioned datasets. This technique is implemented in Python using open-source libraries such as SyMPC (SMPC functions), PyDP (DP aggregations), and CrypTen (secure and private training). The paper uses information privacy measures, including mutual information and KL-Divergence, across different privacy budgets to empirically demonstrate privacy preservation with high ML accuracy and minimal performance cost. 2023 SCPE. -
CoInMPro: Confidential Inference and Model Protection Using Secure Multi-Party Computation
In the twenty-first century, machine learning has revolutionized insight generation by using historical data across domains like health care, finance, and pharma. The effectiveness of machine learning solutions depends largely on the collaboration between data owners, model owners, and ML clients, without privacy concerns. The existing privacy-preserving solutions lack efficient and confidential ML inference. This paper addresses this inefficiency by presenting the Confidential Inference and Model Protection, also known as the CoInMPro, to solve the privacy issue faced by model owners and ML clients. The CoInMPro technique is suggested with an aim to boost the privacy of model parameters and client input during ML inference, without affecting the accuracy and by paying a marginal performance cost. Secure multi-party computation (SMPC) techniques were used to calculate inference results confidentially after sharing client input and model parameters privately from different model owners. The technique was implemented in Python language using the open-source SyMPC library to support the SMPC function. The Boston Housing Dataset was used, and the experiments were run on Azure data science VM using Ubuntu OS. The result suggests CoInMPros effectiveness in addressing privacy concerns of model owners and inference clients, with no sizable impact on accuracy and trade-off. A linear impact on performance was noted with an increase of secure nodes in the SMPC cluster. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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E-service quality-impact on customer satisfaction
The paper aims to determine the impact of e-service quality on customer satisfaction. The study utilised data from 252 customers of private and public banks in India through questionnaires. It was found that the e-service quality has significant impact on customer satisfaction in public sector banks as well as private sector banks. 2019 SERSC. -
A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires
Background: Wildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock. Results: The study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10?16), along with a positive correlation (r ? 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10?4kgcarbonh?1), methane (CH4; ~ 200 10?9mol fraction in dry air), carbon monoxide (CO; 2000 1015moleculescm?2 total column), and formaldehyde (HCHO; 3500 1013moleculescm?2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5). Conclusions: We believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies. The Author(s) 2024. -
Technologies in Transportation Engineering
Deteriorating quality of the air, traffic congestions, and rising accident rates have all resulted from an ever-increasing number of vehicles in Indian cities. As a result of a variety of issues, current public transit systems often fall short or are considered unreliable. The present paper deals with multiple ITS architecture and to be specific four major parts of the ITS. These four major parts are Advanced Public Transportation System (APTS), Advanced Traveler Information System (ATIS), Advanced Traffic Management System (ATMS), and Emergency Management System (EMS). Thus, the framework and produced models of four key divisions of ITS have been evaluated in order to conduct a comparative study of the many models currently being developed in respective investigations. 2022 IEEE. -
An AI-enabled research support tool for the classification system of COVID-19
The outbreak of COVID-19, a little more than 2 years ago, drastically affected all segments of society throughout the world. While at one end, the microbiologists, virologists, and medical practitioners were trying to find the cure for the infection; the Governments were laying emphasis on precautionary measures like lockdowns to lower the spread of the virus. This pandemic is perhaps also the first one of its kind in history that has research articles in all possible areas as like: medicine, sociology, psychology, supply chain management, mathematical modeling, etc. A lot of work is still continuing in this area, which is very important also for better preparedness if such a situation arises in future. The objective of the present study is to build a research support tool that will help the researchers swiftly identify the relevant literature on a specific field or topic regarding COVID-19 through a hierarchical classification system. The three main tasks done during this study are data preparation, data annotation and text data classification through bi-directional long short-term memory (bi-LSTM). Copyright 2023 Tiwari, Bhattacharjee, Pant, Srivastava and Snasel. -
In Service Teachers' Diffrentiated Instructional Strategy and Students' Reflective Thinking and Empowered Learning
Every educational program aims at the comprehensive growth and development of learners. Education policymakers and teachers who are part of any education system have a pivotal role in providing an environment that empowers learners. Thinking pervades all spheres of human action and the ability to think reflectively differentiates man from other animals. Psychological theories have proved that, in a classroom, each learner is unique and has different learning profiles, i.e., learning style, intelligence preference, culture and gender. Therefore, one- sized curriculum doesn't fit all. This research was conducted to measure the influence of differentiated instructional strategy of in-service- teachers as a pedagogy on students' reflective thinking and empowered learning. The researcher developed and standardized a module of 16 lesson plans on English grammar and poetry integrating essential components of reflective thinking and empowered learning into differentiated instruction. Randomly selected samples of this research consisted of 100 students of standard 9, boys and girls, from an English medium ICSE school in the urban district of Bangalore. After a try-out of a few lessons on 25 samples, the researcher taught the lessons through differentiated instruction within 3 months. Through control and experimental groups, pre-test and post-test design, data were collected through 2 measuring tools (1) a questionnaire to measure the level of reflective thinking and (2) Learner empowerment measure. Data analysis of the pre and post-test scores of the experiment group shows a significant impact of differentiated instruction on all four components of reflective thinking of students, i.e., Habitual Action, Understanding, Reflection and Critical Reflection; and on the components of empowered learning of students, i.e., Meaningfulness, Competence, Impact and Choice irrespective of the difference in the gender. The results indicate that differentiated instruction could be implemented in schools as an instructional method to include all types of students and respect their diversity. -
Empowered learning in school: A scoping review
A high degree of motivation, a sense of commitment, self-efficacy and the ability to make the right choices are the characteristics of empowered learners. With education being seen as preparation for life, educators are increasingly pressured to develop curriculum and pedagogy that assist learners to become empowered. Based on the theoretical framework developed by Arksey and OMalley, the present study reviewed 16 empowered learning intervention studies at the school level published between the years 1995 and 2021 as well as provides an extensive summary of empowered learning enhancing interventions conducted in schools. This study highlights the concept of empowered learning, features and scope of interventions directed towards empowered learning of students at schools and the role of empowered learning in schools. Notwithstanding varied intervention results, the findings of this study indicate that empowered learning interventions produce highly motivated students with a sense of commitment and self-efficacy. This review also identifies the need for more pure experimental studies and a commonly accepted theory on empowered learning as a single concept. 2023, Institute of Advanced Engineering and Science. All rights reserved. -
Reflective thinking in school: a systematic review
Everything around us changes rapidly and to adapt to these constantly changing conditions and to understand the meaning of our life in the society in which we live, we must reflectively and consciously think about our actions in each given scenario. A school is a miniature form of society where learners are exposed to situations where they need to find solutions for every problem faced. No faultless solution and conclusions can be arrived at without a carefully employed reflective thinking process. In this context, the present study reviewed 19 intervention studies on reflective thinking in schools published between 2010 and 2021 and presents a brief summary. Various theories on reflective thinking, approach of educationists on reflective thinking of students and the relation between reflective thinking and students academic performance, are extensively analyzed. The findings of the study reveal that there are a few generally accepted theories of reflective thinking; reflection is a useful learning strategy and reflective thinking is an essential characteristic of academic excellence. This study recommends future research with a wider scope to accommodate more theoretical perspectives and wide-ranging databases. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Quantum tunneling rotor as a sensitive atomistic probe of guests in a metal-organic framework
Quantum tunneling rotors in a zeolitic imidazolate framework ZIF-8 can provide insights into local gas adsorption sites and local dynamics of porous structure, which are inaccessible to standard physisorption or x-ray diffraction sensitive primarily to long-range order. Using in situ high-resolution inelastic neutron scattering at 3 K, we follow the evolution of methyl tunneling with respect to the number of dosed gas molecules. While nitrogen adsorption decreases the energy of the tunneling peak, and ultimately hinders it completely (0.33 meV to zero), argon substantially increases the energy to 0.42 meV. Ab initio calculations of the rotational barrier of ZIF-8 show an exception to the reported adsorption sites hierarchy, resulting in anomalous adsorption behavior and linker dynamics at subatmospheric pressure. The findings reveal quantum tunneling rotors in metal-organic frameworks as a sensitive atomistic probe of local physicochemical phenomena. 2023 authors. Published by the American Physical Society. -
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Effect of ethnocentrism and attitude towards foreign brands in purchase decision /
Vision, Vol.24, Issue 3, pp. 1-10 -
The effect of celebrity trustworthiness on endorsement effectiveness: A comparison of congruence and hybrid model /
Vision, Vol.23, Issue 3, pp.275-286