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Best unbiased estimation and CAN property in the stable M/M/1 queue
The Uniform Minimum Variance Unbiased (UMVU) estimators of ??, the probability of having ? or more customers, L, the expected system size, Lq, the expected number of customers in the queue, and, the expected number of customers in a non empty queue, are derived based on a random sample of fixed size n on system size at departure points from the geometric distribution on the support {0, 1, 2,.} with mean, which is the distribution of system size in M/M/1 queueing system in equilibrium. The derivations are based on application of Lehmann-Scheffe theorem. Also, CAN estimators of performance measures are derived. In addition the probability distribution of UMVU estimators are obtained. 2014 Copyright Taylor and Francis Group, LLC. -
Best HR practices in an organized retail sector /
Patent Number: 202111056332, Applicant: Dr. S. Pramila.
The achievement of an organization's goals is impossible without effective human resource management. The research focuses on the human resource practices of retail organisations, which have an impact on both employee and organizational performance. The primary goal of this paper is to investigate the human resource management practices used in India's organised retail sector. A questionnaire is used to collect the most important data. -
BERT-Enhanced Bi-LSTM with weighted cross-entropy for multilingual sentiment classification
With the increasing volume of multilingual user-generated content across social media platforms, effective sentiment analysis (SA) becomes crucial, especially for low-resource languages. However, traditional models relying on context-independent embeddings, such as Word2Vec, GloVe, and fastText, struggle to handle the complexity of multilingual sentiment classification. To address this, we propose an Automatic Multilingual Sentiment Detection (AMSD) framework that leverages the contextual capabilities of BERT for feature extraction and a Bidirectional Long Short-Term Memory (Bi-LSTM) network for classification. Our method, termed Elite Opposition Cross-Entropy Weighted Bi-LSTM (EOCEWBi-LSTM), integrates elite opposition-based learning to optimize hyperparameters and enhance classification accuracy. A weighted cross-entropy loss function further refines the model's sensitivity to class imbalance, thereby improving its performance. The model is trained and evaluated on the NEP_EDUSET corpus, comprising 45,434 tweets in English, Hindi, and Tamil. Experimental results demonstrate notable improvements in precision, recall, F1-score, and accuracy, highlighting the effectiveness of EOCEWBi-LSTM in multilingual sentiment analysis, especially across both high-resource and low-resource languages. The experimental results show that the proposed EOCEWBi-LSTM achieves a high F1-score ratio of 93.83% and an accuracy ratio of 93.83% compared to other existing methods. EOCEWBi-LSTM provides an effective solution for multilingual sentiment analysis, especially for languages with limited resources. 2025 The Author(s). -
BERT-Based Secure and Smart Management System for Processing Software Development Requirements from Security Perspective
Software requirements management is the first and essential stage for software development practices, from all perspectives, including the security of software systems. Work here focuses on enabling software requirements managers with all the information to help build streamlined software requirements. The focus is on ensuring security which is addressed in the requirements management phase rather than leaving it late in the software development phases. The approach is proposed to combine useful knowledge sources like customer conversation, industry best practices, and knowledge hidden within the software development processes. The financial domain and agile models of development are considered as the focus area for the study. Bidirectional encoder representation from transformers (BERT) is used in the proposed architecture to utilize its language understanding capabilities. Knowledge graph capabilities are explored to bind together the knowledge around industry sources for security practices and vulnerabilities. These information sources are being used to ensure that the requirements management team is updated with critical information. The architecture proposed is validated in light of the financial domain that is scoped for this proposal. Transfer learning is also explored to manage and reduce the need for expensive learning expected by these machine learning and deep learning models. This work will pave the way to integrate software requirements management practices with the data science practices leveraging the information available in the software development ecosystem for better requirements management. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Benzoyl hydrazine-anchored graphene oxide as supercapacitor electrodes
In this study, benzoyl-hydrazine anchored graphene oxide (BHGO) is synthesised using graphene oxide (GO) and benzoyl hydrazine (BH) via a simple, cost effective ultrasonic assisted chemical route. BH acted as a nitrogen source, reducing agent, and morphology modifier resulting in good electrochemical performance of BHGO. The supercapacitor behaviour of BHGO is investigated in different aqueous electrolytes and it exhibits a specific capacitance of 170 F g?1 at a current density of 1 A g?1 in 1 M H2SO4 and capacitive retention of 85% over 5000 cycles at 5 A g?1. This high performance is attributed to the enrichment of electroactive sites of GO through nitrogen moieties enhancing faradaic redox reactions and thereby the polarization at the electrode surface. 2020 Elsevier B.V. -
Benzimidazole and benzothiazole fluorophores with large Stokes shift and intense sky-blue emission in aggregation as Al3+ and Pb2+ sensors
New fluorophores based on 2-(2?-hydroxyphenyl)benzimidazole (HBZ) and 2-(2?-hydroxyphenyl)benzothiazole (HBT) for metal ion sensing were designed and synthesized using a simple method. The photophysical behaviour of these fluorophores were investigated in various solvents using UVvisible and fluorescence spectra. All the heterocycles showed strong excited state intramolecular proton transfer (ESIPT) characteristics with remarkably large Stokes shift (190252 nm). Spatial charge distribution in the frontier molecular orbitals also demonstrated the ESIPT mechanism through intramolecular charge transfer. Time resolved fluorescence measurements for these heterocycles showed two long-life decay mechanisms which may be attributed to excited state enol and keto emission. These intense sky-blue emitters also exhibited aggregation induced blue shifted emissions due to restriction of intramolecular rotation processes. Fluorescence sensing studies for metal ions revealed the good selectivity of these fluorophores towards Al3+ and Pb2+. Theoretical computations performed using density functional theory methods showed two possible geometric configurations for Al3+ binding. 2019 Elsevier B.V. -
Benzimidazole and benzothiazole conjugated Schiff base as fluorescent sensors for Al3+ and Zn2+
Two benzimidazole/benzothiazole based azomethines, (E)-2-(1H-benzo[d]imidazol-2-yl)-4-(4-(diethylamino)-2-hydroxybenzylideneamino)phenol (HBZA) and (E)2-(benzo[d]thiazol2-yl)4-(4-(diethylamino)2-hydroxybenzylideneamino)phenol (HBTA) were designed and synthesised. Investigations of solvatochromic behaviour of these fluorophores in solvents of varying polarities showed large Stokes shift of 134210 nm. Time resolved Laser induced fluorescence measurements revealed the excited state dynamics of the fluorophores. Molecules were also found to be emissive in aggregated state as seen from the aggregation induced emission studies. Appreciable absorption and emission spectral changes upon co-ordination of HBZA with Al3+/Zn2+ and HBTA with Al3+, as well as good sensitivity and selectivity, indicated their capability of detecting the two analytes. The binding stoichiometry was determined using electrospray ionization mass spectrometry (ESI-MS) and the binding mechanism was studied using density functional theory. 2019 Elsevier B.V. -
Benefits of cross training: Scale development and validity
Studies related to benefits of cross - training were mainly done either in the context of qualitative research or as comprehension of desk research. The literature scarcely covered the measurement issues, and thus, it became vital to quantify and develop a scale to measure the benefits of cross - training (BCT). Cross -training means training that covers multiple tasks within a department This training technique keeps employees prepared to handle more than a single Job for which they have been Initially hired. This concept Is also called 'worker multlfunctlonallty'. The study aimed to propose and validate an Instrument to measure BCT. The nrst section of the study was exploratory factor analysis (EFA) establishing the benefits of cross training through four dimensions namely Job Stability, Career Advancement, Networking, and Idle lime Management. Confirmatory factor analysis (CFA) was used in the second section to verify the factor structure of the observed variables. The results indicated that cross training the employees in an organization could help practitioners to adopt the same as a strategy in retaining the employees by saving on the costs of recruitment, selection, and staffing. The findings also suggested that cross training helped in securing a job, progressing in one's career, enabling better interaction among the employees, and efficiently managing the idle time in the organization. 2019, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Benefits of AI in the Food Industry
Artificial Intelligence is increasingly becoming a transformative force in the food industry, reshaping how food is produced, processed, and delivered. The current research on the impact of Artificial Intelligence on improving operational effectiveness in the food industry. Firstly, it emphasizes how AI technologies enhance production processes and optimize supply chain management while prioritizing food safety. By employing predictive analytics and automation, AI contributes to minimizing waste, maximizing resource use, and upholding high-quality standards throughout the production cycle. Secondly, the study investigates how AI boosts customer interaction by offering personalized experiences and streamlined service delivery. By evaluating consumer preferences and behaviors, AI allows food enterprises to customize their offerings, ultimately resulting in heightened customer satisfaction and loyalty. Through this investigation, the research seeks to provide practical insights and best practices for food sector stakeholders aiming to exploit AI for better operational results. 2026, IGI Global Scientific Publishing. All rights reserved. -
Benchmarking Ensemble Methods: Stacking, Hard Voting, and Soft Voting
This study evaluates three ensemble techniquesbasic stacking, hard voting, and soft votingfor predicting diabetes onset using the Pima Indians Diabetes dataset. While traditional methods often focus on single models, this research emphasizes the benefits of combining models like Cat Boost, random forest, logistic regression, linear discriminant analysis, and gradient boosting classifier (LightGBM) within ensemble frameworks. The models were rigorously assessed using metrics for evaluation such as AUC-ROC curves, confusion matrices, F1 scores, etc. The advanced calibrated model achieved the highest performance, with an accuracy of 90.10%, precision of 90.32%, recall of 81.16%, and an F1 score of 85.50%. The soft voting model also delivered strong results, with an accuracy of 89.06%, precision of 87.50%, recall of 81.16%, and F1 score of 84.21%. In comparison, the hard voting model recorded an accuracy of 88.02%, precision of 85.94%, recall of 79.71%, and F1 score of 82.71%. These results highlight the potential of advanced ensemble methods to enhance predictive accuracy. Future work could involve integrating these models with real-time monitoring systems for improved healthcare diagnostics and applying them to diverse datasets and medical conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Benchmarking Ensemble Methods: Stacking, Hard Voting, and Soft Voting
This study evaluates three ensemble techniquesbasic stacking, hard voting, and soft votingfor predicting diabetes onset using the Pima Indians Diabetes dataset. While traditional methods often focus on single models, this research emphasizes the benefits of combining models like Cat Boost, random forest, logistic regression, linear discriminant analysis, and gradient boosting classifier (LightGBM) within ensemble frameworks. The models were rigorously assessed using metrics for evaluation such as AUC-ROC curves, confusion matrices, F1 scores, etc. The advanced calibrated model achieved the highest performance, with an accuracy of 90.10%, precision of 90.32%, recall of 81.16%, and an F1 score of 85.50%. The soft voting model also delivered strong results, with an accuracy of 89.06%, precision of 87.50%, recall of 81.16%, and F1 score of 84.21%. In comparison, the hard voting model recorded an accuracy of 88.02%, precision of 85.94%, recall of 79.71%, and F1 score of 82.71%. These results highlight the potential of advanced ensemble methods to enhance predictive accuracy. Future work could involve integrating these models with real-time monitoring systems for improved healthcare diagnostics and applying them to diverse datasets and medical conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Beliefs of secondary school teachers towards education for sustainable development: a statistical research
Educators are the architects of sustainable development (SD), transforming society and balancing development and sustainability. They enhance education for sustainable development (ESD) and societal transformation, driving innovative evolution and future-oriented development within the community. ESD, a millennium, and sustainable development goal (SDG), need to be implemented globally. Teachers are vital in transmitting knowledge, beliefs, and skills required for sustainability in the changing environment. This study examined secondary school teachers beliefs about ESD based on their professional qualifications, teaching experience, and position. The authors used a survey approach and collected the data using a belief assessment tool, i.e., the ESD beliefs scale. The respondents were 400 secondary school teachers in Kerala, India. The study used an item-based evaluation to achieve these objectives and calculated t-values, F-values, and percentages. The research findings indicated that teachers hold constructive opinions towards ESD. The positional status of teachers did not alter beliefs regarding ESD among secondary school teachers. In contrast, professional qualifications and years of teaching experience significantly influenced these ESD beliefs. The findings from this study enable education stakeholders to amend the current secondary education system for SD. 2026 Institute of Advanced Engineering and Science. All rights reserved. -
Being socially responsible: How green self-identity and locus of control impact green purchasing intentions?
This paper investigates the influence of green self-identity (GSI) and two attributes of locus of control, namely external environmental locus of control (ExLOC) and pro-environmental locus of control (PELOC), to predict perceived consumers effectiveness (PCE) on green purchase intentions (GPI) using attribution theory. For this study, data from 391 Indian consumers were analyzed using PLS-SEM via SMARTPLS version 3.2.9. Results show that GSI positively influences both ExLOC and PELOC. Furthermore, both aspects of locus of control are significant positive predictors of PCE and have partial mediation roles. The results not only imply comprehensively expound the process of green buying intentions of consumers through self-identity but also addresses the process of attribution. The study applied the Importance Performance Map Analysis (IPMA) to compare the relative importance and performance of three antecedents (i.e., ELOC, GSI, and PCE). The finding is of utmost importance for practitioners and public authorities to design more focused strategies to increase GPI among the masses to enhance the sales of green products. 2022 The Authors -
Being a therapeutic clown- an exploration of their lived experiences and well-being
Therapeutic clowning uses humor and play to minimize the stress for patients and their families during hospitalization. This study aims to explore the subjective meaning of therapeutic clowning through clowns perspective, understand why they continue clowning and interpret how it has impacted them. The research design takes a qualitative approach using phenomenological paradigm. Nine therapeutic clowns between 20 and 60years with clowning experience of 6months-4years from Compassionate Clowns, located at Bangalore were interviewed. The results reflected that the journey of being a therapeutic clown has been equally therapeutic for the clowns. Based on the thematic data analysis network, it was found that clowning has instilled many values in the way they think. It has given them a platform to learn new things from the children they clown. Therefore, looking at these results it could be said that therapeutic clowning serves as a medium for community service and in maintaining personal wellbeing. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
Behind the Fallout: Environmental Strategy and Innovation Gone Awry
Innovation and environmental strategy play vital roles in addressing the issues of ecological preservation and sustainability. This chapter explores the complicated nature of these concepts, along with their benefits and risks. It also aims to uncover practical lessons from its identified failures. The chapter provides an overview of innovation and environmental strategy, emphasizing their importance in today's environmental and business landscapes. It also explores the central theme of the study: the failure of innovation and environmental strategy to address the challenges of sustainability. Secondly, the chapter explores the various causes of environmental strategy malfunctions. Through a combination of case studies and analysis, it is possible to learn about the common traps, such as poor execution and resource limitations. Thirdly, the chapter focuses on the relationship between innovation and the environment, shedding light on its potential and also the obstacles it encounters in case studies of unsuccessful approaches. The impact of regulation and environmental policy on corporate strategy is explored in the fourth section, which considers how such changes can affect existing approaches, offering practical insights through case studies. Next, the importance of collaboration and communication is emphasized, in which case studies show how poor stakeholder engagement can affect the outcome of an environmental strategy. The sixth section of the chapter tackles the technological issues that can arise when implementing an environmental strategy. It delves into the cases where technological obstacles have resulted in failures. Next, the effect of culture on environmental initiatives is explored. This shows how short-term thinking and resistance to change can either hinder or support initiatives. The eighth section focuses on improving environmental strategies. It offers suggestions on identifying and rectifying issues with such approaches, emphasizing the significance of learning from failures and continuous improvement. Finally, there is a summary of the chapter's findings and a comprehensive overview. This emphasizes how important it is to learn from failures in environmental approaches, offering suggestions for future research. 2026 selection and editorial matter, Sonal Trivedi, Balamurugan Balusamy, Krishnaraj Nagappan, Dinesh Krishnan Subramaniam and Daniel Arockiam; individual chapters, the contributors. All rights reserved. -
Behavioural nudges and maternal diet: Results from a cluster-randomised pilot trial among pregnant women in India
Micronutrient shortfalls pose a significant threat to maternal health across India. We evaluate whether brief, low-intensity informational nudges can improve short-run maternal diet quality during pregnancy. We conducted a pilot cluster-randomised controlled trial across 22 primary health centre (PHC) catchments in Karnataka, assigning catchments to one of three behavioural interventions (printed pamphlets, Accredited Social Health Activist (ASHA) home visits, or research-team phone calls) or to routine-care control. A panel of 440 pregnant women was surveyed at baseline and again four weeks later. Primary outcomes were small meal frequency and two 24-hour dietary diversity measures: a continuous score and the binary Minimum Dietary Diversity for Women. Using multi-arm difference-in-differences models with pooled specifications, we find modest improvements over time across all arms. However, the interventions did not improve meal frequency or dietary diversity relative to the control group. These inferences were robust to 100 control-group subsampling iterations. Over this four week pilot period, low intensity, information only nudges did not improve meal frequency or dietary diversity beyond standard care by policy relevant amounts, helping bound the short run impacts of brief informational messaging in this setting. 2026 Elsevier Inc. -
Behavioural Intention towards adoption of Robotic Accounting for a profitable leading digital transformation
Leading digital transformation accelerates impactful changes in business environments and work places and helps them thrive in this age dominated by physical, emotional, and financial disruptions. This is very much evident during the pandemic-induced current economic climate; the Robotic Process Automation (RPA) industry has been found to grow at an exponentially increasing rate throughout 2020, and based on the response towards it, it can be logically predicted that this trend will continue to be in vogue for several years into the future. The use of RPA technology enables auditing firms to not only automate business processes but also significantly improve the way the company currently completes tasks. In view of the above, the present study focuses on the nature of digital automation of business processes in auditing firms using RPA and its impact on revenue management and client engagement. The study proposes to make use of qualitative research methods and also aims to theorize the role of various antecedents that develop a strong intention among the auditing firms to adopt RPA for the purposes of accounting and auditing. 2022 IEEE. -
Behavioural drivers of access-based consumption among millennial and generation Z in India
The world of consumerism is very dynamic, and technology driven changes in the field of consumerism are unavoidable especially among new generation customers millennial and generation Z. The customers, especially in urban areas, gradually move from ownership-based consumption to access-based consumption. The purpose of this study is to explore the behavioural drivers of new generation customers towards access-based consumption. The study is descriptive in nature and employed a survey method for data collection. The drivers identified are tested through a quantitative study and the primary data are collected using online questionnaires. The study has also analysed the impact of behavioural drivers on current usage of access-based consumption as well as on willingness to use access-based consumption in the future. The study has found that sustainability is the only driver that significantly motivates access-based consumption in Indian urban areas. Copyright 2022 Inderscience Enterprises Ltd. -
Behavioral Time Management Analysis: Clustering Productivity Patterns using K-Means
This paper focuses on investigating the efficiency profile through the three-time management behaviors using the K-Means clustering method. In the case of the study, the data gathered from digital time management tools for 100 participants for one month was preprocessed to distil features surrounding productivity, including daily working hours, focus time, break duration and frequency, and task completion ratios. The four groups that were agreed upon through K-Means clustering differed in terms of time management behaviours and productivity. Insert table 6 IT cluster 1 worked long hours with high productivity owing to the fact that they are IT professionals but had a tendency of multitasking. Employment Cluster 2 (marketing and sales professionals) achieved both personal and work-related self-care but identified the need for more concentrated time per task. As for the differences in the breaks, it can be noted that cluster 3 (management and administration personnel) had significantly higher task completion times and focus times, but their break intervals needed to be optimized. Hypothesis 2 stated that there will be many hours of leisure for Cluster 4 (students and interns) imply that their work hours should be adjusted to several small tasks a day, and their rates of task completion should be increased. From the study, it is possible to stress that time management should be considered as an individual activity that requires specific approaches to the given subject area and to the learner in particular. Specifically, demographic profiling identified the roles that age and occupational status may play in averting or exacerbating productivity deficiencies: insights that could be actionable in specific scenarios. The implications of this research offer practical insights into individual and organizational time management, as the usability aspects of machine learning techniques were considered and their applicability established, which further extends the scope of time management by revealing patterns and improving time management plans and practices. 2024 IEEE.