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The Role of the Gut Microbiota in Neurodegenerative Diseases
The human gut has a rich and dynamic microbial population that plays an important role in many physiological activities. This review explores the complex interaction between the gut microbiota and human health, with an emphasis on its effect on neurodegenerative illnesses. The makeup of the gut microbiome and its impact on brain function through the gutbrain axis is highlighted. Dysbiosis, characterized by changes in the gut microbiotas composition, has been linked to the development of neurodegenerative diseases such as Alzheimers, Parkinsons, Huntingtons, and amyotrophic lateral sclerosis. A Bidirectional communication between the stomach and the brain takes place via a variety of channels, including neurotransmitters and metabolites generated by gut bacteria. We investigate the processes through which dysbiosis causes neuroinflammation, oxidative stress, and neuronal damage, which drive disease development. Potential therapeutic approaches that focus on the gut microbiota, such as antibiotics, probiotics, prebiotics, and fecal microbiota transplantation, are reviewed, with promising preclinical and clinical findings. Overall, this study emphasizes the relevance of gut microbiota to neurodegenerative illnesses, as well as the need to understand and target the gut-brain axis for future treatment options. 2024 by the authors. -
Impact of language-based diversity on affective, normative and continuance commitments level amid techs
Language-based diversity is a relatively understudied area within diversity research. The present paper examines the effects of language-based diversity among IT employees on levels of affective, normative and continuance commitments in Bengaluru, Cosmopolitan city. In primary data, responses are collected through well framed questionnaires and direct interaction with the employees to selected sample of 550 respondents of 10 Information Technology organisations in Bengaluru city. The independence of the two characteristics mother tongue and continuance commitment was tested using Cramers V statistic and the asymptotic significance value of 0.000 resulted into the conclusion that the two characteristics are not independent. 51.9% of the employees with mother tongue Tamil have high level continuance commitment. Cramers V statistic was used to test the independence of the two attributes namely, mother tongue and total commitment, and it was found that the asymptotic significance value is 0.000 which is less than 0.05. Hence it was found that the characteristics are not independent. IAEME Publication. -
Connect among employee engagement and three key of organisational commitment level - An empirical exploration AMID techs
The main objective of this paper is to gravely examine the link between employee engagement level and affective, continuance and normative commitment level in information technology (IT) organisations in Bangalore city. In primary data, responses are collected through well framed questionnaires and direct interaction with the employees to selected sample of 550 respondents of 10 Information Technology organisations in Bangalore city. The result revealed that employee engagement level explained 32% of the variation in total commitment. Since the P value is less than 0.01, it can be inferred that the linkage between employee engagement level and total commitment is statistically significant. The study identified a strong, positive correlation between employee's engagement level and affective commitment (d = 0.347, p =0.000) and employee's engagement level and normative commitment (d =.265, p =0.000), which were statistically significant. The study also revealed a positive correlation between employee engagement level and continuance commitment, which was not statistically significant (d =.072, p =0.096). The current study adds to the research pointing at employee's engagement level as a promising underlying mechanism to improve employee's organisational commitment level. IAEME Publication. -
Precursor to employee engagement AMID knowledge workers
The main objective of this study is to critically analyze the precursor to employee engagement. The research methodology used in this research is descriptive research. In primary data, responses are collected through well framed questionnaires and direct interaction with the employees to selected sample of 550 respondents of information technology organisations in Bengaluru City. The questionnaire consists of 20 questions based on employee engagement precursor. To reduce the dimension of this an exploratory factor analysis was carried out and 3 factors explaining 65.26% of the variance were derived. The 3 precursors identified as professional contentment (Cronbach's alpha 0.940) career development (Cronbach's alpha 0.836) and job enrichment (Cronbach's alpha 0.826). The current study adds to the research pointing at precursors to employee's engagement among knowledge researcher. Medwell Journals, 2017. -
Planned fashion obsolescence in the light of supply chain uncertainty
Fast fashion has popularised the phenomenon of perceived obsolescence whereby customers try to stay in line with the current fashion trends in the market even though the apparel they own are in perfect condition. This has ultimately led the fashion industry to become the second largest polluter in the world. The primary objective of this research paper is to comprehend how the media manoeuvres customers to indulge in fast fashion and how that in turn leads to uncertainty in the supply chain. To understand this, a maximum variation sampling method was adopted which consisted of customers, supply chain partners and marketers. In order to draw a parallel between the variables researched in the past and the present day scenario, an interview schedule was employed. Through the variables selected with the help of Dedoose, a model was created to identify the hurdles faced by suppliers as well as the customer in the fast fashion cycle. The results found that the power to break the fast fashion phenomenon lay in the hands of the media as it is through them that customers' perception can be altered. The importance of artificial intelligence in SCM and the modern tools used in industry 4.0 have also been discussed. 2020 Allied Business Academies. -
Journey through spiritual lens
This research is a comparative study of the Journey of Santiago in 'The Alchemist' who is after a personal legend and Christian in 'The Pilgrim's Progress' who sets his journey to attain Salvation. The aim of the study is to understand the similarities and diff erences of the terms such as Spiritual Quest, Sin, Purpose of Life, Truth and Sin. The growth of the two journeys has been analysed by highlighting the similarities and differences between the two. Such comparison not only helps in better understanding but it also helps to find new insights. Through the observations one states that though material treasure brings happiness but it is momentarily but a journey of trusting God will bring better understanding of the world. IJSTR 2019. -
Advancing the Evaluation of Oral Fluency in English for Specific Classrooms: Harnessing Natural Language Processing Tools for Enhanced Assessment
A crucial component of language learning and teaching is evaluating students' speaking abilities. Natural language processing (NLP) techniques have been employed recently in language assessment to automate the evaluation process and produce more impartial and reliable findings. In this study, we offer a speaking evaluation tool based on Natural Language Processing (NLP) that assesses a learner's speaking ability in real-time using cutting-edge algorithms. The instrument is altered to assess the fundamental facet of speaking skills - Fluency. As a result of the tool's immediate feedback, learners may pinpoint their areas of weakness and focus on honing their language abilities. The usefulness of the instrument was assessed through an intervention with a sample size of 30 students of the post-graduate students of a college in Pune, India. Python libraries, including random and re, were utilized to implement the algorithm. Data preprocessing involved accurate transcription of videos using an online tool and manual checking for corrections. Despite acknowledging limitations, such as potential biases in manually inserted hesitation markers, the study serves as a pivotal step toward automated fluency assessment, presenting exciting prospects for NLP and language education advancements. 2024 IEEE. -
A Multi-Layer Complex Adaptive System Framework for AI-Driven Robo-Advisory Services
The rapid integration of Artificial Intelligence (AI) into investment advisory services has changed financial decision-making, giving rise to adaptive robo-advisory systems capable of real-time analysis, personal recommendations, and autonomous portfolio optimization. Existing research evaluates these systems primarily through technological performance or investor adoption, overlooking the complex feedback-driven interactions that emerge when AI analytics, data environments, and human behavior operate together. This study addresses this gap by conceptualizing AI-enabled robo-advisors as a multi-layered Complex Adaptive System comprising historical data, real-time data, AI analytics, investor perception, and decision-making layers. A simulation model grounded in machine learning dynamics, behavioral finance, and complexity theory is developed to capture nonlinear interactions, adaptive learning, and emergent investor responses. Results show that historical data acts as a stabilizing memory, real-time data amplifies short-term volatility, AI analytics self-organize toward performance equilibrium, and investor perception evolves through nonlinear trust thresholds that ultimately drive decision lock-in. Complexity measures reveal that adaptive intelligence is concentrated in the historical and perception layers, while the decision layer becomes increasingly deterministic as feedback loops strengthen. The findings provide a unified system-level understanding of robo-advisory ecosystems and highlight the need for governance structures that incorporate transparency, behavioral dynamics, and adaptive model monitoring. This framework offers a foundation for designing more resilient, trustworthy, and sustainable AI-driven financial advisory systems. 2026 Binghamton University Libraries. All rights reserved. -
Independent partial domination
For p ? (0, 1], a set S ? V is said to p-dominate or par-tially dominate a graph G = (V, E) if|N[S]| |V | ? p. The minimum cardinality among all p-dominating sets is called the p-domination number and it is denoted by ?p(G). Analogously, the independent partial domination (ip(G)) is introduced and studied here independently and in re-lation with the classical domination. Further, the partial independent set and the partial independence number ?p(G) are defined and some of their properties are pre-sented. Finally, the partial domination chain is established as ?p(G) ? ip(G) ? ?p(G) ? ?p(G). L. Philo Nithya et al. -
History of gestational diabetes mellitus, self-efficacy and coping in postpartum women: A pilot study
The present study investigates whether the history of gestational diabetes mellitus (GDM) influences self-efficacy and coping among postpartum women. Purposive sampling technique was used to collect data from 100 postpartum women, 50 with a history of GDM and 50 without. The General Perceived Self-Efficacy Scale was used to measure the self-efficacy of the participants. The Brief COPE developed by Carver was used to measure coping. A Mann-Whitney U-test showed postpartum women with a history of GDM are higher in self-efficacy and coping than those without such a history. Even though self-efficacy showed a relationship to coping, the two groups differed in the use of coping strategies. 2018 by the Research Institute of Asian Women, Sookmyung Women's University. All rights reserved. -
Assessing factors influencing intentions to use cryptocurrency payments in the hospitality sector
Purpose: The emergence of cryptocurrency has developed a new payment system that is changing how financial transactions happen in hospitality. Consumers/travelers have started experimenting with cryptocurrency payments in hotels and restaurants. However, extant research is lacking in understanding the consumer adoption intention of cryptocurrency payments. This study investigates the intention to use cryptocurrency payments in the hospitality industry. Design/methodology/approach: The conceptual model in this study is based on the Behavioral Reasoning Theory, and it explores the motivating and deterring factors influencing the adoption of cryptocurrency payments in the hospitality industry. A quantitative survey was conducted among 1,080 consumers to examine and confirm the model, with data being analyzed through the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. Findings: The outcome of this work showed that the reasons for positively influence and reasons against negatively influence consumers attitudes and use intentions. Consumers values of openness to change positively influence the reasons for and do not influence the reasons against and attitude toward the use of cryptocurrency payments. Practical implications: This work contributes to practice by providing insights to customers (users/payee), hospitality managers (investors) and organizations/firms (receiving crypto payments) as well as to financial firms and the government. Originality/value: This research contributes to cryptocurrency payment adoption and behavioral finance literature. The research uniquely provides the adoption and inhibiting factors for cryptocurrency payment in an integrated framework in the hospitality sector. 2024, Emerald Publishing Limited. -
Unveiling virtual interactive marketplaces: Shopping motivations in the Metaverse through the lens of uses and gratifications theory
The emergence of Metaverse has transformed the consumer shopping experience. This novel e-commerce platform offers a fresh approach to shopping, with Generation Z primarily exploring this innovative technology. Our research examines shopping within the Metaverse by developing a model based on the Uses and Gratifications Theory and Metaverse-related factors. A total of 1220 Gen Z consumers were surveyed, and data was collected using a structured questionnaire. Further, analysis of collected data was done using PLS-SEM. The results reveal that information seeking, perceived enjoyment, escapism, social interaction, sense of immersion, and personalization influence the shopping intention in the Metaverse, and perceived risk negatively influences the shopping intention of consumers. Further, shopping intention influences the potential use of Metaverse for shopping, and this relationship is moderated by technological innovativeness. This investigation into the adoption of the Metaverse for retail purposes augments the current Metaverse research and enhances the uses and gratifications theory within the Metaverse domain. Metaverse e-commerce professionals, including managers and developers, can acquire valuable perspectives on consumer shopping tendencies in the Metaverse from this study. 2025 The Author(s) -
Assessing the Determinants of Metaverse Adoption for E-Commerce Retailing
The advent of metaverse technology has impacted the retail sector, shaping e-commerce platforms into a new form of metaverse-based online shopping environments. The metaverse e-commerce experience is new to shoppers, making it crucial to comprehend consumer reactions to this technology in the context of retail. This study explores the shopping intention and potential use of the metaverse for shopping using the UTAUT2 model and metaverse-based context-specific antecedents. Using a structured questionnaire, data from 1340 consumers were collected and analyzed through PLS-SEM. The findings indicated that factors such as performance expectancy, effort expectancy, social influence, hedonic motivation, and facilitating conditions influence shopping intention in the e-commerce metaverse. The metaverse-related antecedents, namely, a sense of immersion and imagination, have a positive influence, whereas technological anxiety and perceived security and privacy concerns have a negative impact on e-commerce shopping intention in the metaverse. It was also found that shopping intention influences the potential use of metaverse for shopping and that stickiness to traditional shopping negatively moderates this relationship. This unique research explores consumer buying behavior in the metaverse. It provides marketers, e-commerce managers, designers, and developers of metaverse platforms with the antecedents of the potential use of the metaverse for shopping insights. Consumer policymakers can also draw insights from this study. 2024 The Author(s). Published with license by Taylor & Francis Group, LLC. -
Early detection of breast cancer using ER specific novel NIR fluorescent dye conjugate: A phantom study using FD-f-DOT system
Fluorescence diffuse optical tomography (f-DOT) is an imaging technique that can quantify the spatial distribution of fluorescent tracers in small animals and human soft tissues. Efficacy of f-DOT imaging can be improved by tagging a functional group to the dye. A novel estrogen receptor (ER) specific near-infrared (NIR) fluorescent dye conjugate was synthesized which can be effectively used for detecting breast cancer tissues at an early stage. Our novel dye, Near Infrared Dye Conjugate-2 (NIRDC-2), is a conjugate of 17?-estradiol with an analogue of Indocyanine Green dye, bis1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. Our present study focuses on imaging cylindrical silicone phantoms using Frequency Domain f-DOT system. Background absorption and scattering coefficients were 0.01mm-1 and 1mm-1 respectively. 10?M concentration of NIRDC-2 and Indocyanine Green (ICG) were administered separately into a cylindrical hole (target) of size 8mm diameter in the phantom. In-silico studies were performed to analyze the properties of dyes using experimental data. Absorption coefficient of 0.0002 mm-1 was recovered for the background. Fluorophore absorption coefficient at the target recovered were 0.000173 mm-1 and 0.000408 mm-1 for ICG and NIRDC-2 respectively. In comparison with ICG, our novel dye had a two fold higher target to background contrast. Recovered target position was accurate but size altered. In concurrence with the recovered fluorescent property and the cell lines studies carried out earlier, binding properties of NIRDC-2 makes it a potential probe for the early tumor detection using f-DOT system. COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. -
SUSTAINABLE CHOICES, GENERATIONAL VOICES: UNRAVELING THE GREEN BUYING BEHAVIOR OF MILLENNIALS AND ZOOMERS USING THE THEORY OF PLANNED BEHAVIOR
Younger generations have begun to change their purchasing behaviour in response to growing environmental concerns and global sustainability efforts. This research evaluates the eco-friendly shopping habits of Zoomers (born 19972012) and Millennials (born 19811996) in India. The research employs the Theory of Planned Behaviour (TPB) to analyse how the stewardship model, subjective norms, perceived consumer effectiveness, environmental attitudes and ecological values influence green product buying behaviour. A quantitative research design was adopted, and data were collected from 391 respondents across urban, semi-urban, and rural regions using a structured questionnaire. The relationship between the constructs were examined using structural equation modelling (SEM). The results indicate that environmental awareness (? = 0.558) has a strong positive influence on green purchasing behaviour, followed by social influence (? = 0.225). Environmental awareness is significantly driven by attitude towards the environment (? = 0.430) and ecological values (? = 0.356). Social influence is primarily driven by subjective norms (? = 0.338) and perceived consumer effectiveness (? = 0.187), whereas stewardship orientation has a negligible effect (? = 0.025). The model explains 50.7% of the variance in green purchasing behaviour (R = 0.507), 51.1% in environmental awareness (R = 0.511), and 28.4% in social influence (R = 0.284). The findings confirm the presence of an attitudebehaviour gap, where positive environmental attitudes do not consistently translate into actual purchasing behaviour because of various influences such as price sensitivity and the non-existence of sustainable products. This study contributes to the literature by extending TPB through the inclusion of ecological and social mediators and by providing comparative insights into generational differences in an emerging market context. The results provide useful implications for marketers and policymakers to create focused strategies that encourage sustainable consumption. 2026, School of Environmental Science, Universitas Indonesia. All rights reserved. -
Assessing factors influencing intentions to use cryptocurrency payments in the hospitality sector
Purpose The emergence of cryptocurrency has developed a new payment system that is changing how financial transactions happen in hospitality. Consumers/travelers have started experimenting with cryptocurrency payments in hotels and restaurants. However, extant research is lacking in understanding the consumer adoption intention of cryptocurrency payments. This study investigates the intention to use cryptocurrency payments in the hospitality industry. Design/methodology/approach The conceptual model in this study is based on the Behavioral Reasoning Theory, and it explores the motivating and deterring factors influencing the adoption of cryptocurrency payments in the hospitality industry. A quantitative survey was conducted among 1, 080 consumers to examine and confirm the model, with data being analyzed through the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. Findings The outcome of this work showed that the reasons for positively influence and reasons against negatively influence consumers attitudes and use intentions. Consumers values of openness to change positively influence the reasons for and do not influence the reasons against and attitude toward the use of cryptocurrency payments. Practical implications This work contributes to practice by providing insights to customers (users/payee), hospitality managers (investors) and organizations/firms (receiving crypto payments) as well as to financial firms and the government. Originality/value This research contributes to cryptocurrency payment adoption and behavioral finance literature. The research uniquely provides the adoption and inhibiting factors for cryptocurrency payment in an integrated framework in the hospitality sector. 2024 Emerald Publishing Limited -
Unveiling virtual interactive marketplaces: Shopping motivations in the Metaverse through the lens of uses and gratifications theory
The emergence of Metaverse has transformed the consumer shopping experience. This novel e-commerce platform offers a fresh approach to shopping, with Generation Z primarily exploring this innovative technology. Our research examines shopping within the Metaverse by developing a model based on the Uses and Gratifications Theory and Metaverse-related factors. A total of 1220 Gen Z consumers were surveyed, and data was collected using a structured questionnaire. Further, analysis of collected data was done using PLS-SEM. The results reveal that information seeking, perceived enjoyment, escapism, social interaction, sense of immersion, and personalization influence the shopping intention in the Metaverse, and perceived risk negatively influences the shopping intention of consumers. Further, shopping intention influences the potential use of Metaverse for shopping, and this relationship is moderated by technological innovativeness. This investigation into the adoption of the Metaverse for retail purposes augments the current Metaverse research and enhances the uses and gratifications theory within the Metaverse domain. Metaverse e-commerce professionals, including managers and developers, can acquire valuable perspectives on consumer shopping tendencies in the Metaverse from this study. 2025 The Author(s) -
Optimizing resource management using hybrid metaheuristic algorithm for fog layer design in edge computing
The growing complexity of management in fog computing environments necessitates more efficient algorithms capable of optimizing resource allocation, minimizing latency, and maximizing throughput and energy efficiency. Existing techniques, consisting of the Multi-Objective Crow Search Algorithm (MOCSA) and Fuzzy Meta-Heuristics Optimization (FMHO), regularly suffer from suboptimal performance due to constrained exploration abilities and slower convergence fees. To overcome with these demanding situations, this paper proposes a singular Hybrid Metaheuristic Algorithm (HMA) that mixes the strengths of more than one metaheuristic techniques, along with genetic algorithms, simulated annealing, and gray wolf optimization (GA-SA-GWO). The HMA is specifically designed to enhance useful resource control in fog computing by optimizing useful resource allocation, lowering latency, and enhancing usual gadget performance. Experimental results exhibit that the proposed HMA significantly outperforms existing solutions, with 26.98 % improved latency, 90.64 % resource utilization, 96.05 % throughput, 37.06 % reduced energy utilization, and 93.85 % energy utilization. These outcomes spotlight the HMA's potential to successfully manage sources in dynamic and unpredictable fog computing environments, providing a greater scalable and robust solution for actual-time applications. 2025 -
On the Hermite and Mathieu Special Characterizations to the Logarithmic ZakharovKuznetsov Equations
In this paper, we find the new travelling wave solutions for several aspects of logarithmic ZakharovKuznetsov (ZK) equations using an efficient technique called the special function method which is composed of Hermite and Mathieu differential equations being novel and special functions. In order to illustrate the efficiency of the projected scheme, we considered four different examples with different cases, namely, logarithmic ZK (log-ZK) equation, logarithmic modified ZK (log-mZK) equation, and logarithmic ZK modified equal width (log-ZK-mEW) equation and logarithmic ZKBenjaminBonaMahony (log-ZKBBM) equation. The behaviour of the obtained results and corresponding consequences are illustrated and captured. Finally, the obtained results confirm that the considered solution procedure can be widely employed to find the solution and also capture some interesting and stimulating consequences. 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Machine Learning-Based Imputation Techniques Analysis and Study
Missing values are a significant problem in data analysis and machine learning applications. This study looks at the efficacy of machine learning (ML) - based imputation strategies for dealing with missing data. K-nearest Neighbours (KNN), Random Forest, Support Vector Machines (SVM), and Median/Mean Imputation were among the techniques explored. To address the issue of missing data, the study employs k-nearest neighbors, Random Forests, and SVM algorithms. The dataset's imbalance is considered, and the mean F1 score is employed as an evaluation criterion, using cross-validation to ensure consistent results. The study aims to identify the most effective imputation strategy within ML models, offering crucial insights about their adaptability across various scenarios. The study aims to determine the best plan for data preprocessing in machine learning by comparing approaches. Finally, the findings help to improve our knowledge and application of imputation techniques in real-world data analysis and machine learning. 2024 IEEE.