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A study on the perception of MOOC (Massive Open Online Course) amongst the students of Christ University, Bengaluru /
Massive open online courses (MOOC) are a recent innovation in the field of online learning. Several top-tier universities around the world have started offering MOOC programmes in a wide array of professional, technical as well as creative fields. Top MOOC providers such as Coursera, Udacity and edX have a student fellowship from all across the world, pursuing one or more from the thousands of courses offered by these MOOC giants. -
Predictive value of IL-6, IL-1?, TNF-?, and vaginal pH in diagnosing vaginal microbial infections: A host-inflammatory axis perspective
Microbial-associated vaginal infections are common among women of reproductive age and are linked to alterations in the local immune environment. Inflammatory biomarkers such as IL-6, IL-?, and TNF-?, along with vaginal pH have emerged as potential indicators of microbial dysbiosis. This study aimed to statistically evaluate the ability of these specific inflammatory cytokines and vaginal pH to identify infection status. Cytokine concentrations and vaginal pH were measured in clinically characterized samples. The group differences were analyzed using Mann-Whitney U tests and Cliff's Delta for effect size. ROC-AUC analysis was also performed to assess the discriminative power, and correlation heatmaps explored marker synergy. The infected individuals showed increased levels of all cytokines (p < 0.001), with large effect size (? > 0.9 for IL-6, IL-1?, TNF-?). Vaginal pH also differed significantly (? = 0.60). In addition, the combination of IL-6 and vaginal pH achieved excellent discriminative performance (AUC = 0.98). These findings suggest that IL-6, IL-1?, and TNF-?, when combined with vaginal pH, can function as reliable non-invasive biomarkers for the early detection and improved diagnostic triaging of vaginal microbial infections. 2024 -
Counterfactual Demand Forecasting Using Multivariate LSTM
Demand forecasting is a key part of running operations efficiently in the fast-changing retail and online shopping industries. Regular methods that use statistics often have trouble handling the complex, changing, and time-based patterns found in actual sales data. This study introduces a new way to predict demand that uses multivariate Long Short-Term Memory (LSTM) models. The models take both the order of sales over time and other factors like prices and weather into account. Three model designs were tested: a simple straightforward model, a pure LSTM model, and a new hybrid LSTM model that mixes time-based data with steady economic factors. The combined hybrid model worked the best, by successfully balancing learning from sequences with keeping things stable. The study did experiments to see what would happen if weather conditions changed, like extreme heat, cold, storms, or dry spells and compared normal forecasts with these changed scenarios to see how demand would shift for products and overall sales. The results show that this new framework not only makes better predictions but also gives useful information on how weather events can affect store sales. By linking prediction with 'what if' analysis, this research moves demand forecasting from just predicting what will happen to helping make better decisions. 2025 IEEE. -
Quantum Convolutional Neural Network for Medical Image Classification: A Hybrid Model
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) in the realm of image classification, particularly focusing on datasets with a highly reduced number of features. We investigate the potential quantum computing holds in processing and classifying image data efficiently, even with limited feature availability. This research investigates QCNNs' application within a highly constrained feature environment, using chest X-ray images to distinguish between normal and pneumonia cases. Our findings demonstrate QCNNs' utility in classifying images from the dataset with drastically reduced feature dimensions, highlighting QCNNs' robustness and their promising future in machine learning and computer vision. Additionally, this study sheds light on the scalability of QCNNs and their adaptability across various training-test splits, emphasizing their potential to enhance computational efficiency in machine learning tasks. This suggests a possibility of paradigm shift in how we approach data-intensive challenges in the era of quantum computing. We are looking into quantum paradigms like Quantum Support Vector Machine (QSVM) going forward so that we can explore trade offs effectiveness of different classical and quantum computing techniques. 2024 IEEE. -
Gut Homeostasis; Microbial Cross Talks In Health and Disease Management
The human gut is a densely populated region comprising a diverse collection of microorganisms. The number, type and function of the diverse gut microbiota vary at different sites along the entire gastrointestinal tract. Gut microbes regulate signaling and metabolic pathways through microbial cross talks. Host and microbial interactions mutually contribute for intestinal homeostasis. Rapid shift or imbalance in the microbial community disrupts the equilibrium or homeostatic state leading to dysbiosis and causes many gastrointestinal diseases viz., Inflammatory Bowel Disease, Obesity, Type 2 diabetes, Metabolic endotoxemia, Parkinsons disease and Fatty liver disease etc. Intestinal homeostasis has been confounded by factors that disturb the balance between eubiosis and dysbiosis. This review correlates the consequences of dysbiosis with the incidence of various diseases. Impact of microbiome and its metabolites on various organs such as liver, brain, kidney, large intestine, pancreas etc are discussed. Furthermore, the role of therapeutic approaches such as ingestion of nutraceuticals (probiotics, prebiotics and synbiotics), Fecal Microbial Treatment, Phage therapy and Bacterial consortium treatment in restoring the eubiotic state is elaborately reviewed. 2021 The Author(s). Published by Enviro Research Publishers. -
Assessing the Efficacy of Artificial Intelligence (AI) Applications in Predictive Policing: A Systematic Review Method
Artificial intelligence (AI) has gained attention for its potential to improve law enforcement operations through proactive policing. Advancements in data science have shown the potential benefits of applying machine learning (ML) in the criminal justice sector. Therefore, research in improving methods to forecast the likelihood of criminal reoffending is quickly growing. Creating a cutting-edge model for using ML to predict recidivism is challenging. We picked 12 out of 79 studies from Scopus and PubMed online databases in a comprehensive review that ensures the models can be replicated across various datasets and are suitable for predicting recidivism. Using two specific measures, the 12 research compared different datasets and machine learning algorithms. This study demonstrates that each approach achieves strong performance, with an average accuracy score of 0.81 and an average area-under-the-curve score of 0.74. This systematic research emphasizes essential factors that could enable criminal justice professionals to consistently utilize forecasts of recidivism risk generated by machine learning approaches. The factors include performance indicators, transparent algorithms or explainable AI approaches, and high-quality input data. 2026 Sofia Khatun, K. Sivananda Kumar. All rights reserved. -
Through Gendered Lens: Addressing the Health Inequities of Women in Prison
The chapter delves into a nuanced examination of the epidemiological factors associated with the physical and mental health challenges faced by women in incarceration. The study represents the inaugural thorough synthesis of evidence regarding the prevalence of these conditions among the prison population, presented through an umbrella review of meta-analysis. A comprehensive overview of the overall burden of disease among prisoners is notably limited, as much of the existing literature tends to concentrate on one or two specific health issues. Data were extracted to ascertain the prevalence of serious illness among inmates, uncovering levels of illness that frequently surpassed those found in the general population, along with notable gender disparities. The researchers documented a notable prevalence of mental health disorders, including major depression and other maladies. The initiative fosters beneficial communities by controlling disease reservoirs, enhancing the management of chronic conditions, and promoting rehabilitation and reintegration into society. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Strategising Algorithm: The Prospects and Perils of Artificial Intelligence (Al) in Criminal Justice Reformation
The criminal justice system relies significantly on human decision-making, with the parole system primarily responsible for addressing convicted criminals' rehabilitation. A paradigm change in prisoner rehabilitation and reintegration is underway with the introduction of artificial intelligence ( Al) into correctional institutions. A specific approach to alleviate the effects of human error is by utilising artificial intelligence to enhance human decision-making. Algorithms are being utilised in several jurisdictions to offer judges guidance on the appropriate type and level of punishment that should be imposed on convicted criminals. While human judgement has long played a crucial part in criminal justice systems, technological advancements are progressively augmenting the ability to make decisions. This paper examines the necessity of establishing broad restrictions on the application of algorithms in sentencing determinations. Critique plays a vital role in criminal sentencing; however, the implementation of algorithms in advisory capacities may compromise this significance. To uphold condemnatory sentencing, it is essential to recognise a principle of 'meaningful public control', which necessitates ethical accountability from representatives of the wider political community. This principle does not prohibit the use of algorithms; still it does impose restrictions on their implementation. The review posits that Al has the potential to improve fairness and efficiency in pretrial and jail systems within the criminal justice framework through the application of risk assessment software. The research envisages Al's potential to enhance the rehabilitative, compassionate, and effective aspects of the penal system, thereby facilitating societal reintegration and decreasing rates of recidivism. 2025 Sofia Khatun and Sivananda Kumar K. All rights reserved. -
Masculinities, tlawmngaihna, and mizo nationalism: Why soft, pretty Mizo men are perceived as a threat
Masculinities are social and cultural attributes, roles and performances typically associated with being men. Through ethnography, this article explores the complicated position of soft masculinity in mizo cultural space and nationalist discourse. It looks at tlawmngaihna (mizo code of conduct) performances as hierarchies that are gendered and explains why mens tlawmngaihna are considered to be more visible and valuable. Using hegemonic masculinity theory, this article argues that mizo nationalism is masculinised which is fuelled by homophobia and anti-femininity. Ultimately, it explains the complexity of soft mizo masculinitys position and how they are involved but are non-visible in mizo cultural space. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
From my research to our research: moving toward titi as an Indigenous method in Mizo research
Mizos (an Indigenous community in North-East India) have a form of communication called titi (conversation based on looking out for one another and laughing together). In Mizo academia, there have not been attempts made to establish titi as a Mizo Indigenous method. This article aims to situate titi as a Mizo Indigenous method by locating it within the Mizo Indigenous Paradigm through the Mizo Indigenous Standpoint. Indigenous scholars have stated how relationality sums up the Indigenous Paradigm. This article further looks at the roots of relationality by exploring the values and ethics of Indigenous communities as something that creates a special bond in the research process through titi. In doing this, it also looks at the Mizo Indigenous worldview through humor. In this way, we argue that Mizo Indigenous peoples feel accountable to the research, thereby making participants feel like the research belongs to them and acting like researchers themselves. The Author(s) 2025 -
Author profiling: Age prediction of blog authors and identifying blog sentiment
Authorship profiling is about finding out different characteristic of an author like age, gender, native languages, education background etc., by finding out the patterns in their writing. Blog authors write about a lot of topics like purchase decisions, digital advertising, personality development, fitness, technology updates etc., and these authors play an influential role on its readers. In this paper, we are categorizing the blog authors in three different age groups based on the content available from the blog. Natural Language Toolkit (NLTK) is a set of libraries used for natural language processing to distinguish among the different writing pattern of the author based on the different age groups. NLTK helps to make analysis on the words of the blogs which is an important feature in our research. We also wanted to conduct sentiment analysis on the blog in order to understand the insight on how the author feels about the blog topic. Thus, we have used Nae Bayes Classifier for doing the analysis and considered two sentiments for the same: positive and negative. An average accuracy of 66.78% was achieved in predicting the age of authors. From the sentiment analysis we figured out that elder authors tend to have more positivity in their blogs as compared to younger authors. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Hierarchical Mapping-Partitioning-Search with Attention-Weighted Communication for UAV Swarms in Search and Rescue Operations
UAV swarm Search and Rescue (SAR) operations demand intelligent coordination to function efficiently in unfamiliar terrains while maintaining communication under bandwidth limitations. To address this, we propose a Hierarchical Mapping-Partitioning-Search (HMPS) framework that combines quadtree-based adaptive partitioning of the search area with deep reinforcement learning for region selection, together with an Attention-Weighted Flooding (AWF) communication protocol to enhance swarm coordination. The HMPS framework adapts search granularity to uncertainty and obstacle density, uses a Deep Q-Network (DQN) to learn a region-selection policy, and employs a lightweight local coverage planner to improve exploration efficiency. The AWF protocol prioritizes message relays based on content and link quality, reducing bandwidth while preserving essential information flow. This paper presents HMPS as a practical option for autonomous swarm SAR operations, and reports encouraging preliminary results in GPS-denied terrains. 2025 IEEE. -
A Green Inventory Model for Growing Items with Mortality and Permissible Delay in Payment
With rapid industrial growth, environmental concerns have become increasingly important. The expanding market and rising greenhouse gas emissions are significantly contributing to environmental degradation, pushing the Air Quality Index to high levels. Simultaneously, the growing global population is driving up the demand for livestock, which heavily relies on natural resources. This paper proposes an inventory model that incorporates key environmental factors, including carbon emission reduction through optimal investment, payment delays and mortality. The model aims to determine the optimal solution while taking into account the environmental impacts. An analytical discussion on the concavity of the objective function in relation to the decision variable is included. The paper outlines a solution methodology to obtain the optimal result, supported by a numerical example. Sensitivity analysis reveals that the selling price and investment in carbon emission reduction are the most influential parameters. 2025 IEEE. -
Complicated Grief during COVID-19: An International Perspective
Cultures across the globe have evolved time-tested rituals to honor those who die and offer solace and support to survivors with the goal of helping them to accept the reality of the death, cope with the feelings of loss, adjust to life without the deceased, and find ways to maintain a connection to the memory of the deceased. The COVID-19 pandemic has disrupted these rituals and brought significant changes to the way we mourn. Specifically, public health responses to COVID-19 such as social distancing or isolation, delays or cancellations of traditional religious and cultural rituals, and shifts from in-person to online ceremonies have disrupted rituals and thus made it more difficult to access support and complete the psychological tasks typically associated with bereavement. This paper conceptualizes the common bereavement tasks including emotion-focused coping, maintaining a connection to the deceased, disengagement and reframing death and loss, and problemfocused coping. It provides examples of how the COVID-19 pandemic has altered mourning rituals across several cultures and religions and contributed to prolonged grief disorder as defined by the ICD-11 that includes depressive symptoms and post-traumatic stress. Early evidence suggested that the suddenness of loss, the social isolation, and the lack of social support often associated with COVID-19-related death are salient risk factors for complicated grief. As a consequence, psychological assessments, grief counseling, and mental health support are needed by families of patients who died from COVID-19. These services must be essential components of any comprehensive public health response to the pandemic. 2022 Hogrefe Publishing. -
On estimation of extropy for non-negative data with application on uniformity testing
{Poisson weights-based density estimator is used to estimate the extropy function to the non-negative data}. The traditional class of nonparametric extropy estimators, typically constructed using kernel density estimators with symmetric kernels, is not well suited for non-negative data. To address this limitation, we propose two Poisson-weights-based density estimators that are naturally adapted to the non-negative domain. The asymptotic properties of the proposed estimators are rigorously established, providing theoretical support for their use. A comprehensive simulation study demonstrates that both estimators outperform their conventional kernel-based counterparts in terms of bias and mean squared error. Furthermore, we introduce uniformity tests based on extropy and obtain their critical values through simulation. The practical utility of the proposed methods is illustrated through analyses of real data sets. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Big data analytics in tourism development and marketing: Theoretical perspectives on big data analytics in tourism marketing
The title of the suggested book chapter is " Theoretical Perspectives on Big Data Analytics in Tourism Marketing" and it is about the influence of big data analytics in the growth and promotion of tourism. It just shows how the AI and Metaverse can strategically use big data for better Market Segmentation and Customer behaviour analysis. This chapter looks at how metaverse technology allows tourists to participate in virtual experiences. Tourism companies can refine their marketing strategies, streamline operations, and provide value added experiences to their consumers by utilizing big data analytics. This Chapter underlines the power that big data has to change the tourism industry by enhancing decision making and spurring innovation in service provision. 2025 by IGI Global Scientific Publishing. All rights reserved. -
An empirical analysis of android permission system based on user activities
In today's world there has been an exponential growth among smart-phone users which has led to the unbridled growth of smart-phone apps available in Google play store, app store etc., In case of android application, there are many free applications for which the user need not shell out a penny to use the services. Here the magic word is "free" which entices millions of pliant people into installing those apps and giving unnecessary access to their data and device control. Current studies have shown that over 70% of the apps in market, request to gather data digressive to the most functions of apps that might cause seeping of personal data or inefficient use of mobile resources. Of late, couple of malignant applications gather unobtrusive information of the user through third-party applications by increasing their permissions to high-level on the Android Operating System. Android permission system provides, the user access to the third party apps and in return based on the permissions granted by the user, an app can access the related resource from the user's mobile. A user is bound to grant or deny permits during the installation of the application. For the most part, users don't focus on the asked permissions, or sometimes users do not understand the meaning of the permission and install the app on their device. They allow a way for attackers to perform the malicious task by demanding for more than expected set of permissions. These extra permissions permit the attacker to exploit the device and also retrieve sensitive information from it. In this research paper we describe how permission system security can create an awareness among the users that would assist them in deciding on permission grants. This improved and responsible user activities in Android OS can help the users in utilizing their device securely. 2018 Ankur Rameshbhai Khunt and P. Prabu. -
Inhibiting extracellular cathepsin d reduces hepatic steatosis in spraguedawley rats y
Dietary and lifestyle changes are leading to an increased occurrence of non-alcoholic fatty liver disease (NAFLD). Using a hyperlipidemic murine model for non-alcoholic steatohepatitis (NASH), we have previously demonstrated that the lysosomal protease cathepsin D (CTSD) is involved with lipid dysregulation and inflammation. However, despite identifying CTSD as a major player in NAFLD pathogenesis, the specific role of extracellular CTSD in NAFLD has not yet been investigated. Given that inhibition of intracellular CTSD is highly unfavorable due to its fundamental physiological function, we here investigated the impact of a highly specific and potent small-molecule inhibitor of extracellular CTSD (CTD-002) in the context of NAFLD. Treatment of bone marrow-derived macrophages with CTD-002, and incubation of hepatic HepG2 cells with a conditioned medium derived from CTD-002-treated macrophages, resulted in reduced levels of inflammation and improved cholesterol metabolism. Treatment with CTD-002 improved hepatic steatosis in high fat diet-fed rats. Additionally, plasma levels of insulin and hepatic transaminases were significantly reduced upon CTD-002 administration. Collectively, our findings demonstrate for the first time that modulation of extracellular CTSD can serve as a novel therapeutic modality for NAFLD. 2019 by the authors. -
Self-Control and Decision-Making Skills as Predictors of College Enrollment: Role of Parental Influences
Self-control and planful decision-making can play a critical role in promoting academic outcomes. Nonetheless, little is known about how parental influences impact these noncognitive skills in promoting college enrollment. Using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examined adolescent self-control and decision-making skills (at wave 1) as predictors of college enrollment (at wave 3). Further, we assessed if the effect of parental influences (i.e., maternal academic involvement, maternal academic expectations, parental control/limit-setting, and parental education) on college enrollment was indirect and operated through the associations of parenting variables with adolescent self-control and planful decision making. Both self-control and decision-making skills significantly predicted college enrollment, controlling for age, gender, family income, and cognitive ability. Parental control/limit-setting and educational level had significant direct effects on college enrollment and were not significantly related to adolescent self-control or planful decision making skills. The effect of maternal academic involvement on college enrollment was indirect and operated through its associations with adolescent self-control and decision-making skills. The effect of maternal academic expectations on college enrollment was both direct and indirect, through its association with adolescent decision-making skills. Our findings suggest that individual and family-based interventions that target critical noncognitive skills, such as self-control and planful decision making, hold promise in promoting college enrollment. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Associations Between Early Life Adversity, Moral Development, and Psychopathology in Children and Adolescents: A Cross-Sectional Study
Introduction: Moral psychological development is shaped by socio-cultural and neurobiological factors, with the formation of conscience central to this process. Early Adverse Childhood Experiences (ACEs) have been linked to delays in moral development and increased risk of psychiatric disorders. This study examined how adversity affects conscience functioning, specifically the association between Psychopathological Interference (PI) and delays in Conscience Stages (CS) compared to youth raised in relative advantage. Methods: We analyzed 125 conscience-sensitive psychiatric interviews with youth admitted to a Psychiatric Residential Treatment Facility (PRTF). CS scores were compared with expected stages from community youth, using the Conscience Development Quotient (CDQ = CS attained CS expected 100). PI was rated on a Likert scale, incorporating full psychiatric evaluations, behavioral ratings, and DSM diagnoses. Multiple regression models examined the associations between CDQ, PI, and Clinical Global Assessment of Functioning (CGAF) scores, controlling for six covariates. Results: Participants (mean age, 14.2 years; 59% male, 41% female) exhibited significantly greater distress signals across conscience domains compared to community youth. No differences emerged by age at the onset of ACE. However, lower CDQ was associated with higher PI, earlier ACE onset, DSM Axis II disorders, and lower CGAF. Legal history and ACE count were not significant predictors. The model explained 22.7% of the variance in CDQ (p = 0.00018). Discussion: Findings highlight CDQ as a sensitive measure of developmental impact, beyond simply identifying red flags, consistent with prior ACE research. Retrospective design may limit sensitivity to ACE characteristics. Conclusion: Systematic conscience-sensitive interviewing, attuned to cultural and developmental contexts, may enhance clinical assessment of moral functioning. 2025, Bentham Science Publishers