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Exploring digital age influences on undergraduate students mental health through social media, academic pressure and digital literacy
The research aims to measure the impact of usage of social media, academic pressure, and digital literacy, on mental health. It also aims to measure the mediating role of perceived stress on mental health of undergraduate students. Survey method was used for collecting data from a sample of 565 undergraduate students from state and private universities of Tamil Nadu, Karnataka, Andhra Pradesh, Telangana, and Kerala. EFA and Path analysis was used for testing and validating the conceptual model. The results showed that Social Media Usage increases Perceived Stress and negatively impacts Mental Health Outcomes both directly and indirectly through Perceived Stress. Academic Pressure increases Perceived Stress, which negatively impacts Mental Health Outcomes indirectly. Digital Literacy reduces Perceived Stress and has a positive effect on Mental Health Outcomes both directly and indirectly through reduced stress. Perceived Stress was found to have a significantly negative impact on the Mental Health Outcomes. The demographic variables namely; age, gender, living status, family type, and course type were found to have a significant impact on the usage of social media, academic pressure, digital literacy, perceived stress, and mental health scores of undergraduate students. The study also came up with interventions for managing mental health of under graduate students. The Author(s) 2025. -
Application of distinct motivational types in shaping generative AI (GenAI) adoption behaviour
Differing from AI and GenAI adoption, research on traditional systems emphasised extrinsic factors like utility, social influence and innovativeness as predictors of user behaviour. The role of proximal psychological factors like motivation, however, has been overlooked in this context, which becomes essential with this shift towards AI. In the educational sector, the students use of AI shows the possibility of intrinsic factors like motivation in shaping adoption behaviour. This study uses Self-Determination Theory (SDT) and its Organismic Integration Theory (OIT) extension to propose a conceptual map that examines the role of distinct motivational types in shaping students GenAI adoption behaviour. The adoption behaviour of 348 Indian students pursuing higher education was collected through a cross-sectional survey and analysed using structural equation modelling. Findings indicated that autonomous motivation, including intrinsic, identified, and integrated motivation, significantly predicts students intentions to use GenAI tools. The study further examined the moderating role of perceived compatibility, revealing that alignment between users lifestyles and GenAI usage strengthens the impact of controlled motivations. When students feel that AI fits well with their needs and learning requirements, showing high compatibility, external motivators have a stronger effect on their decision to adopt it. This makes compatibility an important new finding and provides additional insights into the motivational types of GenAI adoption in academic contexts. This study extends the body of knowledge by moving beyond the binary treatment of motivation and empirically distinguishing between specific types of motivation. It emphasises the importance of self-determined motivation while showing how the correlations between various motivation types and GenAI usage intentions are conditioned by perceived compatibility. The study also offers practical insights based on the significant results. The Author(s) 2026. -
Translating artificial intelligence into socio-economic insight: a hybrid deep learning approach to employee financial well-being
This study aims to translate recent advancements in hybrid artificial intelligence (AI) modeling into a functional tool for assessing individual financial well-being. The objective is to develop a system that aids organizations in understanding employees financial stress, with broader implications for enhancing workplace productivity and societal economic resilience. A deep learning pipeline was developed to classify individuals into three financial well-being categories: Financially Secure, Moderately Stable, and Financially At-Risk. The approach utilizes a structured dataset of 20,000 Indian individuals and implements 15 advanced deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Wide & Deep networks. Model performance was assessed using standard evaluation metrics, including validation accuracy and ROC-AUC scores. Among the tested models, the hybrid Wide & Deep + CNN configuration yielded the highest performance, achieving a validation accuracy of 99.44% and a perfect ROC-AUC score of 1.0000. These results validate the models capacity for robust classification and real-world applicability to financial profiling. This study demonstrates a practical application of AI in financial decision support systems and contributes to organizational research by offering a scalable solution to assess and mitigate employee financial stress. The Author(s) 2026. -
Deep learning based classification of microplastic in edible food using optical microscopy images
Microplastics (MPs), a prevalent pollution in food, water, and ecosystems around the world, have become a serious environmental and health concern. The traditional detection and classification techniques are labor-intensive by nature and do not support extensive, large-scale monitoring. The main emphasis of this study is to generate a novel image dataset via a simple extraction method that will be useful for classification applications in high-consumption edible food by integrating with the deep-learning model. This study compares the efficacy of several Deep learning (DL) architectures, including MobileNetV2, ResNet101V2, ResNet50V2, InceptionV3, EfficientNetB0, and a baseline Convolutional Neural Network (CNN) in classification into three groups: threads, beads, and fragments. The best performance was recorded by MobileNetV2, ResNet101V2, and ResNet50 V2, all with 98 percent test accuracy and weighted F1-scores of 0.986 and 0.983, respectively, which is a strong and consistent MPs classification. The outcome indicates that the DL models, especially ResNet101V2 and MobileNetV2, outperform the baseline CNN in terms of classification accuracy (98%). The present study provides strong, scalable opportunities for Artificial Intelligence (AI) based solutions for the assessment and reduction of MPs contamination globally in edible food. The Author(s) 2026. -
3D Printed Skin Graft Scaffolds as Potential Alternative for the Cellulitis-Induced Skin Damages
Cellulitis is a bacterial infection starting from damaging the skin and soft tissue of the body and eventually leading to affect the immune and circulatory system. Demarked erythema, warmth, edema, and tenderness are symptoms of cellulitis affected skin. Unfortunately, due to the symptoms mimicry more than 30% of patients admitted to the hospitals are misdiagnosed as cellulitis. The existing treatment methods for the cellulitis include antibiotics and treatments associated to symptoms. Three-dimensional printing (3D printing) is emerging and innovating technology for the skin and other medical treatments. The combination of antibiotics with hydrogel and their integration with 3D printing technology can be a potential alternative to traditional dressing and solution-based approach. Current review article looks into the feasibility of 3D printing technology to manage the cellulitis-induced skin damages based on existing reports on 3D printed hydrogels for other skin problems. Review article provides insights into the cellulitis-induced skin damage, biomaterials and hydrogels in other skin damages, infections, wounds and scope of integrating 3D printing to treat to treat cellulitis. Review article projects the feasibility of 3D printed hydrogels, biomaterials, dressings, and artificial skin patches as possible solution to cellulitis-based skin damages. Furthermore, this review highlights the safety and regulatory challenges in employing 3D printing technologies for the cellulitis treatment. Current review article is first report proposing the possibility of 3D printing as alternative treatment for the cellulitis-based skin damages. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Compact LoRa Patch Antenna Optimization Using Dual Random Starfish Aggregation Coupled Transformer Network for Vital Sign Detection in Breast Cancer WBANs
The rapid advancement of Wireless Body Area Networks (WBANs) has created a growing demand for compact, efficient, and reliable antenna systems to support continuous health monitoring, particularly for breast cancer applications. Recent methods, including CPW-fed patch antennas, artificial neural network (ANN)-driven models, and wearable textile antennas, have improved antenna design automation and flexibility. However, challenges such as signal distortion from body proximity, gain reduction under bending, Specific Absorption Rate (SAR) compliance, and lack of adaptive tuning continue to limit practical deployment. To overcome these limitations, this study presents a compact LoRa patch antenna optimized using a novel Dual Random Starfish Aggregation Coupled Transformer Network (Dual-Ran-SACTN) framework. This system combines the Starfish Optimization Algorithm (SFOA), a Random-Coupled Neural Network (RCCN), and a Dual-Aggregation Transformer Network (DuAT) to enhance convergence speed and learning efficiency. The antenna, designed in CST Microwave Studio, measures only 80נ60mm2 (0.23??נ0.17??), offering a lightweight and wearable structure for continuous vital sign monitoring. The proposed model exhibits a bidirectional radiation pattern in the E-plane and an omnidirectional pattern in the H-plane, achieving a peak gain of 2.12dBi and a high radiation efficiency of 99.8% at 868MHz. Additionally, the design maintains low SAR and stable performance under bending, making it robust for wearable WBAN applications. This work offers a real-time, energy-efficient solution for intelligent breast cancer monitoring through adaptive antenna optimization. This model supports practical applications such as continuous breast cancer monitoring, wearable health diagnostics, and real-time WBAN-based physiological signal tracking. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Internalized stigma among patients with common mental disorders in South India
Introduction: Common mental disorders (CMDs), include depressive disorders and anxiety disorders, which are highly prevalent. There exists a huge stigma around mental health, and this challenge becomes further magnified in CMDs, especially in LMICs like India. Despite this burden, there is limited scientific evidence on the internalized stigma in CMDs. To address this evidence gap, this study aims to describe internalized stigma and its correlates among patients with CMDs attending the Psychiatry Outpatient Department (OPD) of an academic teaching hospital in South India. Materials and methods: A structured socio-demographic and morbidity questionnaire, along with the Internalized Stigma of Mental Illness (ISMI) Scale, was administered to 119 patients aged 18 years or older who were diagnosed with CMDs according to ICD-10 criteria. Patients with severe mental disorders and psychosis, epilepsy, intellectual disability, organic mental disorders, and those requiring hospital admissions, were excluded from the study. Results: A mild to moderate level of internalized stigma was reported among patients with common mental disorders. Age and history of suicidal thought were significant predictors of internalized stigma. Conclusion: Youth and those who have a history of suicidal thoughts tend to experience greater internalized stigma. A multi-pronged approach is needed to address internalized stigma, which includes a combination of education and awareness programs, peer support programs, psychotherapy, and medication adherence. Addressing stigma can positively influence help-seeking behavior, treatment compliance, and outcomes, thereby improving quality of life. The Author(s) 2025. -
The moderating effect of gender on the relationship between digital intelligence and digital amnesia
Digital amnesia refers to the phenomenon where people tend to forget information that they store digitally, and relying heavily on digital devices to remember the information. The ease of access to digital devices may encourage digital dependence, which could lead to digital amnesia. This study provides a preliminary understanding of the association between digital intelligence and digital amnesia among college students and the moderating role of gender in the relationship. This cross-sectional study has employed a stratified random sampling technique to recruit 1265 students via the survey method. The results revealed a significant negative association between digital amnesia and digital intelligence. Findings also indicated that males reported having higher level of digital amnesia, and females reported higher level of digital intelligence. Furthermore, gender played a significant moderator role between digital amnesia and digital intelligence. Overall, this study has provided a novel finding of the moderating role of gender in relationship between digital amnesia and digital intelligence in the Indian context. Furthermore, the scores of digital amnesia in this study raise a concern over the effectiveness of current sex education in India. The scores may underscore the need for educational initiatives that address the adverse effects of digital amnesia and emphasize the importance of promoting digital intelligence. The Author(s) 2025. -
A review of artificial intelligence enhanced cognitive behavioural therapy using the BECK AI BOT for mental health interventions
The integration of artificial intelligence (AI) and cognitive behaviour therapy (CBT) is a revolutionary solution to the global mental health issue, characterized by increasing need and decreased access to treatment. This research investigates the potential of AI-fortified cognitive behavioural therapy technologies, including chatbots, virtual reality, and adaptive learning modules, to enhance the efficacy, accessibility, and individualization of treatment for anxiety, depression, and PTSD. The study evaluates the scalability, ethical issues, and therapeutic efficacy of the therapies by combining peer-reviewed and experimental data. The suggested methodology combines AI-driven conversational therapy with predictive modelling to deliver individualized, real-time mental health treatment. In this study, a conceptual chatbot prototype, designated BECK-AI BOT, was developed to illustrate the applications interface and functionality, enhancing accessibility for both patients and therapists in the future. This study does not present new clinical trial data. All reported symptom-reduction and engagement findings are drawn from previously published studies of existing AI-driven CBT systems (e.g., Woebot, Wysa, Eleos, Limbic). The present work offers a narrative synthesis of current evidence and introduces a conceptual architecture and prototype (BECK-AI BOT), without evaluating it clinically. Notwithstanding these difficulties, problems persist, including a lack of long-term efficacy statistics, cultural sensitivity issues, and moral reservations about over-reliance on AI during emergencies. The argument comes in the form of AI possibly improving, not replacing, human therapists, emphasizing hybrid systems for fair treatment. Future research needs to advance emotional intelligence within AI, which combines AI-driven conversational therapy and predictive modelling to deliver real-time, personalized mental health services. The Author(s) 2026. -
Traditional beliefs and practices associated with relieving psychological problems of pregnant women of the Zeliang tribe
In Indigenous and resource-limited communities, emotional distress during pregnancy is often understood and managed through culturally grounded belief systems rather than biomedical frameworks. This qualitative study explores how pregnant women of the Zeliang tribe in Benreu village, Nagaland, perceive, interpret, and cope with emotional distress using traditional beliefs and practices. Guided by community psychology, cultural safety frameworks, and Lazarus and Folkmans Transactional Model of Stress and Coping, semi-structured interviews were conducted with ten pregnant women and two traditional healers. Data were analyzed using reflexive thematic analysis. Three interconnected themes were generated. First, emotional vulnerability and cultural conceptions of pregnancy revealed that fear, sadness, and emotional instability were interpreted through spiritual and ancestral meanings rather than psychiatric categories. Second, healing practices as emotional regulation tools illustrated how ritual chanting, fumigation, protective threads, and herbal remedies functioned as embodied coping mechanisms supported by intergenerational kin networks. Third, traditional healers roles in psychosocial support highlighted their function as trusted interpreters of distress who provide narrative explanation, reassurance, and culturally congruent guidance. Participants also described a complementary care pathway in which biomedical services were used for physical monitoring while emotional and spiritual concerns were addressed through traditional systems. The findings indicate that traditional healing within the Zeliang community operates as a culturally embedded model of perinatal emotional care integrating spiritual, relational, and symbolic dimensions of well-being. The study underscores the importance of culturally safe maternal mental health approaches that respect Indigenous explanatory systems and encourage collaboration between biomedical providers and community-based healing structures. The Author(s) 2026. -
The effectiveness of proactive coping intervention for students with learning disabilities
The presence of Learning Disabilities (LD) increases the possibility of psychological distress due to the negative school experiences. Apart from academic difficulties, students with LD are facing social and emotional problems related to their disabilities. If the psychological distress evolves over time without proper management, it may lead to psychosocial maladjustment. Previous research has shown that proactive coping helps minimize stress and maladjustment issues and is a predictor of success in people with LD. In comparison to students without LD, students with LD in Kerala have lower proactive coping and are more maladjusted. Hence, the current study has tailored an intervention to enhance proactive coping for students with LD. The present study followed the quasi-experimental research design to examine the effectiveness of the intervention on students with LD. A total of 200 participants from various schools across Kerala were initially selected using a multistage random sampling method. Subsequently, participants exhibiting the lowest scores in proactive coping were identified, and then 60 of them were randomly assigned to either the experimental or control group for the intervention. Proactive Coping Inventory for Adolescents and Adjustment Inventory for School Students were the tools used in this study. The data collected from the experimental and control groups following the intervention were analysed using Mixed analysis of variance and Repeated Measures of ANOVA. The findings of the study showed that the proactive coping intervention has significantly enhanced the proactive coping and social emotional adjustment of students with LD. Using these proactive coping interventions in remedial instruction will enable the students to develop a healthy coping style that benefits their personal growth. The Author(s) 2025. -
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. -
A qualitative exploration of mental health experiences among IBDP students
The burgeoning concern surrounding mental health has become a prevailing issue. Considering the education sector, cut-throat competition among students has led them to experience concerns with their mental health. Although ample research exists on mental health awareness, escalating academic competition has impacted the holistic well-being of students. Research elucidates that the education sector beckons urgent attention to address the emerging mental health needs of the high school students. Thus, this study aims to probe the specific mental health requirements of students enrolled in the International Baccalaureate Diploma Program (IBDP) in Jaipur, Rajasthan. Conducted using a qualitative research paradigm, this study entailed in-depth interviews of 25 IBDP students. Subsequently, a thematic content analysis was deployed to interpret the emerging themes from the data gathered. The findings illustrated numerous themes such as mental health concerns, self-management, professional development, and other related themes discussed in the paper. This research seeks to enhance the awareness of the IBDP community regarding the mental health challenges experienced by the students. Additionally, it serves as an essential resource for educators, parents and students themselves, empowering them to acknowledge and address these challenges effectively. Furthermore, the results from this study can be utilized to develop a mental health framework tailor made to the needs of IBDP students. The Author(s) 2026. -
Assessing the impact of perceived parenting on the self-concept of college going young women
Self-concept (SC) constitutes the idea and beliefs one has about oneself. As the primary agents of socialization, parents play a crucial role in the development of self-concept of their children. The present study sought to explore the relationship of SC of college going young women in India with perceived parenting. Furthermore, it investigated whether perceived parenting significantly predicted SC. Data was collected from 150 college going young women across Delhi NCR (Mage=19.62, Age Range = 1821 years) using standardized measures of Parenting Scale and Self-Concept Questionnaire. The scales demonstrated high reliability using Cronbachs alpha for the given population. Statistical analyses (correlation and regression analysis) of the data revealed that perceived parenting of mothers and fathers both positively and significantly correlate and predict several dimensions of SC. Particularly, for participants perception of their mothers, carelessness vs. protection significantly predicted the total SC. For fathers, rejection vs. acceptance and freedom vs. discipline were major predictors. For both mothers and fathers, marital conflict vs. marital adjustment was a common predictor. The findings are indicative of the importance of perceived parenting in the development of SC. It also highlights that parenting is a shared responsibility and necessitates a balanced approach as both mothers and fathers play a vital role in shaping an individuals view of self. The Author(s) 2026. -
Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands
The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniqueswere unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP). The Author(s) 2024. -
VAST-GCN: An Attention-Driven Graph Convolutional Network (GCN) for Robust Cluster Head Selection in Vehicular Ad-Hoc Networks
Vehicular Ad-Hoc Networks (VANETs) need smart and flexible communication protocols to deal with fast-moving vehicles and ever-changing network structures. Picking the right cluster head (CH) plays a key role to keep connections stable and reduce routing overhead. This paper presents VAST-GCN (Vehicular Attention-based Spatial-Temporal Graph Convolutional Network), a new model that uses attention to make vehicle grouping and CH selection better across different network sizes. VAST-GCN mixes Graph Convolutional Networks (GCNs) with Spatial, Temporal, and Channel Attention systems. Approach in vehicle settings with 100, 500, and 1000 vehicles has been tested using real-time info like speed and place. The design has transformer blocks to model time-based features and attention modules to improve space and feature relationships leading to better vehicle data. Data have been grouped using the K-Means method and checked with modularity score, silhouette score, and group density. At the time of comparison, it has been observed that VAST-GCN does better than regular GCN and MIXHOP GCN models in cutting down loss making better community structures, and keeping CHs stable when there are few vehicles or theyre moving fast. The proposed VAST-GCN framework exhibits clear advantages over existing spatio-temporal GNNs by delivering superior modularity, silhouette scores, and cluster head stability across diverse vehicular scenarios. Its attention-driven architecture not only improves clustering accuracy but also reduces packet delay and enhances throughput, highlighting its excellence as a robust and scalable solution for dynamic VANET environments. The Author(s) 2025. -
Econometric investigation of sectoral contributions to remittance inflows in Kerala using the VECM framework
The sustained outmigration from Kerala has significantly contributed to the surge in remittance inflows, which have become a critical driver of the states economic advancement. As India maintains its position as the worlds largest recipient of remittances, Kerala remains one of the most prominent subnational beneficiaries. Although remittances have historically played a pivotal role in shaping Keralas developmental trajectory, there is a growing imperative to channel these financial inflows into productive investments across various sectors of the economy. Within this context, an undisturbed system analysis identifies agriculture, industry, and services as key sectors potentially influenced by remittance flows. The present study utilises the Vector Error Correction Model (VECM) framework to investigate the sector-specific contributions to Keralas remittance-induced economic growth. This econometric approach facilitates an examination of both the long-term equilibrium relationships and short-term adjustments among the variables. The empirical findings highlight the differentiated impact of each sector, with particular emphasis on the significant role played by the agricultural and industrial sectors in attracting and sustaining remittance flows. The Author(s) 2026. -
On path-induced signed graphs
The path decomposition of a graph G is the process of decomposing it into edge-disjoint paths. An induced signed graph is a signed graph formed from an ordinary unsigned graph by assigning signs to its edges according to some protocol. In this paper, we introduce the notion of a path-induced signed graph as an induced signed graph whose edges receive a sign according to whether its end vertices are the end vertices of a path in a path decomposition of G. We also discuss some characteristics of this type of signed graph. The Author(s), under exclusive license to Sapientia Hungarian University of Transylvania 2026. -
Enhanced mother optimization algorithm-based optimal reconfiguration to accommodate emerging electric vehicle demand
Radial configuration and high x/r ratio branches in electrical distribution systems (EDSs) result in significant power losses and diminished stability margins. Optimal network reconfiguration (ONR) is a highly flexible solution methodology for addressing these challenges. The identification of optimal branches or tie lines to modify their on/off status in relation to multiple objectives under radial constraints constitutes a complex optimization challenge. This paper presents a novel variant of the mother optimization algorithm (MOA) that incorporates dynamic learning techniques for the optimal placement and sizing of electric vehicle (EV) charging stations to enhance distribution system loadability. The proposed modifications enhanced the overall performance of the algorithm by improving the exploration and exploitation characteristics. This leads to superior global best results and faster convergence than with other competitive algorithms when addressing complex optimization problems. In addition, an enhanced mother optimization algorithm (EMOA) is employed to address the ONR problem in 7-, 12-, 33-, 69-, and 118-bus IEEE radial systems. The losses are reduced by 44.15%, 30.07%, 33.87%, 55.72%, and 33.04% when compared to the base case across the respective test systems. Moreover, the loadability is increased in the 33-bus and 69-bus configurations by 208.75% and 177.07% for the base and optimal configurations, respectively. The results indicate the appropriateness of the ONR for enhancing the loadability to accommodate the rising penetration levels of electric vehicles (EVs) in support of sustainability. The Author(s) 2025. -
Demystifying the 5G-Advanced communication paradigm
With the massive surge in the increasing number of connected wireless devices, the demand for wireless data bandwidth keeps growing exponentially. Further on, in the beginning, many people were using only one computer. Today everyone has his own computer. But in the days ahead, there will be many digital devices to cogently and cognitively identify and deliver real-time and real-world services to a person. That is the world is tending towards a multi-device communication and computing era. Visualizing and realizing context-aware applications mandate for device computing. That is, devices have to be empowered to be computational, communicative, sensitive, vision-enabled, perceptive, decision-making, intelligently responsive, and action-taking. Leading research analysts and market watchers have forecast that there will be billions of IoT devices and trillions of IoT sensors in the years to unfold. Connecting them to instinctively share their unique capabilities and data with one another as well as with nearby and faraway cloud data platforms demands pioneering and powerful communication capabilities. With this projected growth in mind, the cellular industry looks for advanced wireless communication technologies. to other frequency bands that could possibly be utilized in the development of new 5G wireless technologies. This chapter is dedicated to telling all about the unique capabilities and use cases of 5G Advanced releases (Releases 17 and 18). Let us start with release 17 and then jump into the 18th release. 5G is all set to empower consumers and businesses to realise and use advanced applications and it allows a large number of devices to connect and exchange data faster than ever before. 2025