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Impact of Social Media on Patience, Anxiety and Stress
Social media has become deeply integrated into everyday life, constantly connecting us to various platforms. While people were once believed to be shaped by those they spent time with physically, the content we consume and individuals we follow online now significantly influence our thoughts, emotions, and behaviours. Despite growing concerns about these effects, limited research has examined the relationship between social media usage patterns and psychological well-being among young adults. This study examined whether social media usage patterns affect patience, stress, and anxiety among young adults. A cross-sectional survey was conducted with 124 participants aged 18-30 years. The survey collected demographic information and assessed social media usage patterns, with scores computed for patience, anxiety, and stress. Results indicated that stress levels were positively correlated with social media usage (r=0.33, p< 0.001), with Instagram emerging as the most widely used platform. A paired t-test revealed that participants significantly underestimated their actual screen time compared to their perceived screen time, t(123) = [insert your t-value], p=0.0002. Correlation analysis also indicated that screen time was positively associated with increased anxiety (r=0.22, p=0.014) and negatively associated with patience (r=-0.19, p=0.035). These findings highlight the importance of developing digital self-awareness, encouraging individuals to maintain control over their social media usage rather than letting these platforms dictate their levels of stress, patience, and anxiety. 2025 IEEE. -
Reducing Systemic Bias in Behavioral Targeting Using Explainable AI: The HARMONIA Complex Systems Approach
Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered latent behavioral signals that affect the relevance of ads based on users' online behavior. The analysis identified four interrelated behavioral dynamics: ad fatigue attenuation, diurnal engagement oscillations, device-driven preference divergence, and category-affinity dominance. These dynamics served as the foundational architectural principles for HARMONIA's design. The method uses gradient boosted prediction models and a multilayer explainability stack that includes SHAP for global interpretability, LIME for local surrogate approximation, and counterfactual reasoning for causal transparency. Quantitative evaluation indicates that HARMONIA maintains relevance accuracy (approximately 1.2% CTR), achieves a 31% enhancement in transparency metrics, and a 27% improvement in user-trust indices, while concurrently reducing systemic entropy by nearly one-third. This research redefines personalization to be self-explanatory and ethically sound AI by incorporating explainability as a regulatory mechanism in the adaptive ecosystem of complex digital advertising. This system takes explainable computational marketing from an idea to a full-scale implementation. 2026 Binghamton University Libraries. All rights reserved. -
Enhancing User Control: A Reinforcement Learning Framework for Breaking Filter Bubbles in Recommender Systems
In an age of information overload, recommendation systems play an important role in providing personalized content to users. However, traditional recommendation systems often create filter bubbles, limiting the types of content users are exposed to. Based on the research presented in the article Breaking the Filter Bubble: A Reinforcement Learning Framework for Controllable Recommender Systems, this article proposes a new approach to further improve the controllability and diversity of recommendations. By using reinforcement learning techniques, the proposed framework aims to break the filter bubble by providing users with more diverse content recommendations while maintaining high recommendation accuracy. Extensive experiments on real-world datasets demonstrate the effectiveness of this approach in suppressing recommendation concentration and improving recommendation diversity. The results of this study contribute to the further development of controllable recommendation systems and provide insights into solving the filter bubble problem in recommendation systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Transparency in Translation: A Deep Dive into Explainable AI Techniques for Bias Mitigation
In an era dominated by artificial intelligence (AI), concerns about bias and discrimination loom large. The quest for fairness and equity in AI-driven decision-making has led to the exploration of Explainable AI (XAI) as a viable solution. This paper undertakes a thorough examination of the bias ingrained within AI systems and posits XAI as a potent antidote. Beginning with an exploration of the origins and aftermath of bias in AI, the analysis traverses the evolution of XAI techniques, including SHAP, LIME, and counterfactual explanations, clearly stating their advantages and drawbacks. With each XAI method thoroughly inspected, the study unravels their applicability across diverse AI models and domains. Furthermore, a compelling case study is presented, showcasing XAI's practical application in a language translation app, where it guarantees transparency and equity in the translation process. This tangible example serves as a testament to XAI's efficacy in mitigating bias within real-world applications. As the analysis concludes, it underscores the pivotal role XAI plays in fostering accountability and trustworthiness in AI systems. By shedding light on how XAI mitigates bias and offering concrete examples of its utility, the paper advocates for its widespread adoption as an imperative step towards the development of ethically robust AI systems. In a landscape filled with concerns about bias, XAI emerges as a beacon of hope, promising a future where AI decisions are transparent, fair, and equitable for all. 2024 IEEE. -
Polymer-Based Nanomaterial as a Bacteriostatic Agent on Gram-Positive Bacteria
The colonization of surfaces by bacteria is a widespread phenomenon that affects environmental processes and human health. Bacterial growth can also be found in materials used in the textile industries, food packaging, and wearable electronics. Moreover, the necessity for replacing traditional antibiotics is relevant due to the increased health risks of antimicrobial resistance from the excessive use of antibiotics. Recently, research is focused more on developing polymer-based antibacterial materials critical to preventing bacterial proliferation. The use of some nanomaterials appears to be very promising in this regard. This work reports the synthesis of a polymer-based nanomaterial derived from polyvinyl alcohol (PVA) via the hydrothermal method and studies its structural and optical properties. It is also observed that these nanoparticles (NPs) display the highest antibacterial potency against gram-positive (Bacillus subtilis) bacteria than their bulk counterpart. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cinnamon A Competent Drug: A Review on Extraction, Analysis and Anticancer Action
Cinnamon, an Indigenous species, is extensively used as a folk medicine in India, China, and other parts of the world due to its therapeutic potential inherited via the latent chemical composition. The vital component presented is cinnamaldehyde, along with cinnamic acid and cinnamate, which contributes to being an anti-inflammatory, antimicrobial, antidiabetic, and anticancer agent together with the capability to control neurological syndromes like Alzheimer's and even Parkinson's diseases. Given the importance of the anticarcinogenic properties of cinnamon on various cell strains concerning the curable effect, this review focuses on evaluating different extraction methods like steam distillation, Soxhlet extraction, microwave-assisted extraction, and more, in addition to a summary of new technologies like gas chromatography, HPLC, DART-MS, and NMR, etc. which paved the way in characterizing the chemical composition of cinnamon. Cinnamaldehyde showed its apoptosis through various mechanistic pathways on an adequate number of cell lines and antineoplastic potential on specific multifaceted cancerous cells, which advocates for continued research and investment in this vital area of drug discovery and suggestions for future scope. 2024 Wiley-VCH GmbH. -
A Survey on 5G Standards, Specifications and Massive MIMO Testbed Including Transceiver Design Models Using QAM Modulation Schemes
Massive MIMO (Multiple Input Multiple Output)is the advanced technology in 5G architecture which improves mobile and data wireless system parameters in multiple folds. The basic idea of this technology is to include huge number of antennas in the base stations serving limited user equipment. This will enhance the parameters like spectral efficiency, data rate, wireless devices connectivity, energy or power efficiency and also, significant reduction in interference and error rates. The Third Generation Partnership Project (3GPP)consortium, International Mobile Telecommunication (IMT)and various partner telecom companies are on the way to develop unified architecture to meet the proposed 5G standards by the year 2020. Initial test beds and field-trials are already in process at various universities and telecom companies considering Long Term Evolution (LTE)releases features in the 5G architecture framework. However, the research is still an open issue on improving the parameters. This research paper provides a detailed overview on 5G standards, specifications and Field trials and test beds implemented by various universities and telecom industry utilizing Massive MIMO technology. This literature survey paper aims to enlighten the researchers working in the area of Massive MIMO to understand the test bed and field trials designs existing till date. This paper also motivates to complete experiments on Bit error rate (BER)estimation in various modulation schemes for single transmitter-receiver as well as in MIMO configuration. The reduction in BER is observed when MIMO models are used for transceiver design. The hardware utilization and simulation work of the field trials and testbed provide different existing techniques to develop a transceiver system which meets 5G standard. 2019 IEEE. -
Study on 5G Massive MIMO Technology Key Parameters for Spectral Efficiency Improvement Including SINR Mapping on Rural Area Test Case
Massive MIMO is one of the key disruptive technologies in 5G which offers significant change in the core network architecture and channel modeling compared to the previous wireless communication standards. There are many research works currently focusing on implementing Massive MIMO network in different channel propagation models. ITU, 3GPP and IMT consortium deliver timely 5G LTE releases and taken as benchmark documents by various telecom companies and universities to set up testing, trials and hardware deployments. However, without optimization on spectral efficiency parameter, the specifications proposed by 5G in terms of improvement in data rate or throughput could be difficult to achieve. This paper initially provides an in-depth study on spectral efficiency estimation and optimization in Massive MIMO by investigating different research papers. From these papers, list of parameters involved in spectral efficiency are identified, such as, fading characteristics, power or energy efficient parameters, standard deviation, angle of arrival factors in antennas installed in base stations and many others. The author however concludes with the best selection of constraint optimization parameters to improve the spectral efficiency taking into account of its simple design and major impact on the improvement in the result by taking downlink scenario of a simulation environment using 5G Massive MIMO network. SINR mapping of standard Rural Macro test scenario adopted from M 2314, LTE release 17 of 5G framework is simulated in this research paper. 2022 IEEE. -
Secure Authenticated Communication Via Digital Signature And Clear List In VANETs
Vehicular ad hoc network (VANET) plays a vital role in the intelligent transportation system(ITS), When a vehicle receives a message through network, the CRL (certificate revocation list) checking process will operate before certificate and signature verification. After successful authentication,a CRL list is created based on authentication. This CRL is used to verify whether a vehicle node can be permitted for communication in the VANET network. But when using CRL, a huge amount of storage space and checking time is needed. So we proposed a method without CRL list, but mentions a key management list to overcome large storage space and checking time even it reduce the access delay too. For the access permission we can do an authentication system based digital novel signature authentication(DNSA) for each vehicles in the vanet with the RSU unit or with other participant node vehicles in the communication as per the Topology.So we can perform an efficient and secured communication in VANET. The Electrochemical Society -
Students Perceptions on the Generative AI Tool ChatGPT: Examining the Interrelationships Between Knowledge, Willingness and Challenges
Generative artificial intelligence (AI) tools are disruptive innovations of recent times that have tranformed numerous sectors, including education. In the realm of management education, platforms such as ChatGPT are redefining teaching methods, tailoring learning pathways and opening new research frontiers. This study examined MBA students perceptions of their knowledge, willingness to use and challenges encountered when engaging with generative AI tools, particularly ChatGPT considering differences by gender and by usage frequency. Through purposive sampling, responses were collected from 179 MBA students at management institutes in Bengaluru, Karnataka, via an online survey adapted from the validated questionnaire by Chan and Hu. Data analysis was conducted using SPSS version 26 for Windows. Bootstrapped univariate General Linear Models showed no gender-based disparities in students knowledge, willingness or perceived challenges; however, usage frequency of AI tools emerged as a strong predictor of willingness to adopt. Multiple regression results indicated that greater knowledge positively influenced both perceived challenges and willingness, while perceived challenges significantly affected willingness. A Sobel mediation test further demonstrated that challenges partially mediated the effect of knowledge on willingnesssuggesting that increased knowledge heightens awareness of potential challenges, which in turn shapes students willingness to embrace these tools. These outcomes offer actionable guidance for educators and decision-makers, highlighting the importance of enhancing AI literacy, minimizing barriers to adoption and providing inclusive, experiential learning environments to support the responsible and confident integration of generative AI in management education. 2026 XLRI Jamshedpur, School of Business Management & Human Resources -
A Narrative Synthesis on the Role of Affective Computing in Fostering Workplace Well-Being Using a Deep Learning Model
Emotional information is more valued in the modern workplaces with increased focus on the need for sensing, recognizing and responding to human emotions. Integrating human emotions as information for communication and decision-making is possible through the computer-based solution called as affective computing. Affective computing is a relatively less explored AI platform though the notion is more than two decades old. The cognitive algorithms employed in affective computing operates in three key areas, viz. context sensitivity, augmented reality, and proactiveness, with outcomes in the fields of emotion management, health, and productivity. Affective computing promises better management of organizational outcomes such as fostering workplace well-being, promoting happiness, productivity, engagement levels, and communication. Further, affective computing can play vital roles in an employees life cycle with applications in functional areas of HRM like employee selection, training and development, and performance management. Even as workplaces are increasingly adopting affective computing, an analysis of its positive effects can help practitioners take informed decisions about its implementation. This paper outlines the theoretical underpinnings of affective computing, discusses the relevance of ResNet50 in image analysis, and proposes a step-by-step methodology for implementing affective computing techniques in the workplace. The potential benefits and challenges of adopting affective computing in fostering workplace well-being are also discussed. Thus, this chapter investigates the role of affective computing in fostering well-being in the workplace usinga deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Education for All: How Schooling Is Creating Social Changes for Lowered-Caste Girls in Rural India
Arguments for the expansion of formal schooling have long focused on individual outcomes from schooling, including increasing income, reducing poverty, delaying marriage, and improving health, particularly for girls and women. For nearly three decades now, global education agendas have supported girls education in an effort to achieve these outcomes. A large body of research analyzes girls individual empowerment from schooling, but less attention is given to how schooling is creating change in families and communities, particularly for lowered-caste girls in India. This article places longitudinal data from a three-year qualitative interview study of schoolgirls in Rajasthan alongside qualitative life-history interviews of girls who completed secondary school in Uttarakhand to understand how schooling affects social changes for lower castes. The analysis, using an intersectional and relational approach, illustrates how girls schooling shifts kin and caste relations connected to marriage and work but in ways that do not transform the stickiness of caste and gender norms. We argue that educational policies and programs must attend to the ways in which caste is implicated in achieving outcomes of delayed marriage and formal employment for lowered-caste girls in Indian communities if schooling is to create positive social change. 2020 by The Author(s). -
Assessing the socio-ecological effects of lakeside gentrificationA case study of two lakes in South India
The global push for sustainability and the urgency of addressing climate change have compelled city governments worldwide to prioritize the upkeep and restoration of urban commons. However, this state-led or private-driven greening often becomes a marker of gentrification. This study examines the ecological conservation policies applied to Periyakulam Lake in Coimbatore, Tamil Nadu, and Hebbal Lake in Bengaluru, Karnataka, aiming to elucidate how these policies exhibit flaws and lack inclusivity. Through the lens of urban political ecology, the research scrutinizes the state-led and state-supported strategies, emphasizing their tendency to favour a minuscule population while perpetuating aspects of gentrification. It critically examines the dual phases of lakeside gentrification, wherein the first phase involves the modification of the lake under the label of rejuvenation. The subsequent phase witnessed a transformation in the surrounding region's social, spatial, and economic characteristics as it became increasingly attractive and subject to investment. Such processes displace and dispossess livelihoods and instil a new urban imaginary where only urban elites become the standpoint for such beautification consumption. This study contributes to the literature on lakeside gentrification and argues how state-led and state-supported extractive policies remove the safety valves for a stable future by negating the linkages between society and nature. 2025 The Author(s) -
Unofficial Bilingualism in English-Only Policy Context: A Postmethod Pedagogy for Difficult Circumstances in Rural Government Schools of Tamil Nadu
This chapter introduces and explains the phenomenon of Unofficial Bilingualism in government-run English-medium schools in rural Tamil Nadu, India. It delineates instances of using the mother tongue to navigate language learning challenges in schools with an official English-only policy. It studies three schools in the Salem district of Tamazil Nadu using a qualitative research framework, with classroom observation and telephonic interviews with teachers as primary research methods. Using thematic analysis of recorded classroom observation of three teachers use of L1 in the English classroom and in-depth, semi-structured telephone interviews with rural government schoolteachers across Tamil Nadu, the study grounds the phenomenon of Unofficial Bilingualism within the conceptual framework of Difficult Circumstances in ELT (West, 1960; Smith, 2011; Anderson, 2021) and explains it as a form of Postmethod Pedagogy (Kumaravadivelu, 2001, 2006). It explains the use of L1 in the classroom, despite official policy mandating teachers not to, and provides theoretical justification to legitimise the use of L1. The chapter argues for making Unofficial Bilingualism official and discusses its implications for classroom policy and teacher education, reconciling classroom practice with official policy. The study has implications for medium of instruction, language policy and teacher education. 2025 selection and editorial matter, Uma Pradhan and Mohini Gupta; individual chapters, the contributors. -
Executive function deficits in autism spectrum disorder analyzed through parental perspectives
Background: Executive function (EF) challenges pose difficulties to everyday functioning and autonomy for autism spectrum disorder (ASD). While research has investigated these impairments, results remain inconsistent regarding which aspects of EF (i.e., response inhibition, working memory, and mental flexibility) are most prominent, particularly in applied contexts. Much research has focused on laboratory settings or clinical assessments that may not fully capture the daily challenges faced by individuals with ASD. Objective: The current study is looking at parental perspectives on how EF deficits manifest in everyday life for individuals with ASD, particularly concerning social communication. Method: Semi-structured interviews were conducted with 25 parents of individuals with ASD (aged 1425years) to understand parental views on the EF challenges faced by their adolescent and young adult offspring. Thematic analysis is employed with ATLAS.ti to identify key themes that reflect the real-life challenges associated with EF deficits. Results: The results showed that response inhibition, especially impulsivity and interruptions, has potential risks on social interactions and academic performance, usually leading to social isolation. Deficits in working memory brought challenging outcomes of their own; the issues of retention, comprehension, and preparation difficulties were more salient. Mental flexibility challenges presented considerable obstacles to both academic and social situations and included task switching and adaptation to changed circumstances. Conclusion: The deficits in response inhibition, working memory, and mental flexibility made a significant contribution to the challenges of social communication and overall functioning in individuals with ASD, highlighting the importance of specific interventions. The Author(s) 2026. -
Ethical AI in HR: Navigating the Data-driven Frontier
The integration of artificial intelligence (AI) in human resources (HRs) presents significant opportunities while raising ethical dilemmas for an organisation. This chapter examines challenges in AI-enhanced HR concerning bias mitigation, data protection, transparency and governance through literature review, and real-life examples. Through a mixture of academic research and industry use cases including Amazon's AI hiring tool, HireVue video interview analysis, IBM Watson Career Coach and Unilever recruitment powered through AI study analyses AI impact on HR functions and approaches to address ethical concerns. This research offers an advanced methodological framework for ethical implementation of AI in HR which is based upon eight foundational components including interdisciplinary collaboration, bias minimisation, transparency and explainability, ethics-based privacy policies governance, continuous monitoring improving engagement with stakeholders, and adaptive trust-enhancing policy. The model weights a set of quantitative suggestion, new metrics such as Team Integration Score (TIS) for assessing cross-functional relationship success. Synthesis of multiple academic sources and case studies mentioned the chapter plan on responsible AI implementation in HR through stakeholder engagement, transparency practices, and review mechanisms. This approach balances technological advancement and ethical considerations within AI-driven HR processes. The significance of this chapter is to bring together academic sources and experts from the industry to provide a complete guide of integrating global best practices for implementing ethical AI. The proposed framework serves as a valuable tool for HR AI practitioners and researchers offering a structured approach to the ethical AI deployment while remaining adaptable to emerging challenges and opportunities in this rapidly growing area. 2026 by A.R. Deepti, B. Manimekala, Farzeen Basith, Vivek K. and Vijayanandh Rajamanickam. All rights reserved. -
Behavioral analysis of malicious activities in AI comprehensive analysis
The chapter provides a detailed overview of behavioral analysis evolved in AI security systems, from rule- based methods to advanced AI- driven approaches with verified threat prediction accuracy. Finance, healthcare, and telecommunications sectors show empirical evidence of modern systems by processing huge data volumes with exceptional threat detection capabilities. Research from Microsoft, IBM, and Google confirms AI- enabled security significantly reduces the time of threat identification compared to traditional approaches. The technical analysis reveals cloud-n ative solutions offer greater cost- efficiency and performance than on-premise alternatives, with measurable ROI improvements. The study examines behavioral analysis integration with machine learning, advanced persistent threat detection challenges, implementation strategies across different organizational contexts, and ethical considerations essential for developing effective security systems. 2026, IGI Global Scientific Publishing. All rights reserved. -
On L? (2, 1)-Edge Coloring Number of Regular Grids
In this paper, we study multi-level distance edge labeling for infinite rectangular, hexagonal and triangular grids. We label the edges with non-negative integers. If the edges are adjacent, then their color difference is at least 2 and if they are separated by exactly a single edge, then their colors must be distinct. We find the edge coloring number of these grids to be 9, 7 and 16, respectively so that we could color the edges of a rectangular, hexagonal and triangular grid with at most 10, 8 and 17 colors, respectively using this coloring technique. Repeating the sequence pattern for different grids, we can color the edges of a grid of larger size. 2019 D. Deepthy et al. -
INDUCED nK2 DECOMPOSITION OF INFINITE SQUARE GRIDS AND INFINITE HEXAGONAL GRIDS
The induced nK2 decomposition of infinite square grids and hexagonal grids are described here. We use the multi-level distance edge labeling as an effective technique in the decomposition of square grids. If the edges are adjacent, then their color difference is at least 2 and if they are separated by exactly a single edge, then their colors must be distinct. Only non-negative integers are used for labeling. The proposed partitioning technique per the edge labels to get the induced nK2 decomposition of the ladder graph is the square grid and the hexagonal grid. 2022, Krasovskii Institute of Mathematics and Mechanics. All rights reserved. -
Tribo-catalytic Dye Degradation Driven by Mechanical Friction Using ZnS Microparticles with Different Morphologies
This work examines the tribocatalytic properties of zinc sulphide (ZnS) microparticles for dye degradation using mechanical energy. ZnS microparticles were synthesized into four distinct morphologies: microrods, spherical aggregates, microflakes, and microflowers using the solvothermal method. These morphologies were characterized using XRD, FESEM, EDS analysis, UVVis spectroscopy, XPS, and BET analysis. The tribocatalytic activity was assessed by degrading methylene blue (MB) dye under magnetic stirring in a dark setting. The experiment was carried out at the neutral pH of MB solution (~ 6.5). Among the prepared ZnS morphologies, the micro flakes displayed the largest surface area (120m/g) and exhibited enhanced dye degradation efficacy, achieving 57% MB elimination after 15h of agitation at 800rpm, corresponding to a pseudo-first-order rate constant of 0.054min?. By analyzing the degradation kinetics as pseudo- first-order kinetics, we elucidated the crucial significance of surface morphology and contact area in facilitating effective electron transfer during tribocatalysis. Additionally, we investigated the influence of PTFE bar size, material concentration, stirring speed and initial dye concentration on degradation efficiency. Reusability test demonstrated stable performance over four consecutive cycles with a minor decrease (~ 5%). The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.