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Enhancing mobility management in 5G networks using deep residual LSTM model
Mobility management is an essential component of 5G networks to provide mobile users with seamless connectivity and efficient cell transition. However, increasing user mobility, device density, and the diversity of service requirements all pose significant challenges to achieving optimal mobility management. This article describes a novel method for improving mobility management in 5G networks that employs a deep residual Long Short-Term Memory model. Deep learning and LSTM, a type of recurrent neural network, are used in the proposed model to identify temporal dependencies and patterns in user mobility data. The model learns to predict future user locations and mobility patterns by training on historical mobility data, allowing for proactive resource allocation and handover decisions. We incorporate residual connections into the LSTM architecture, inspired by the residual learning framework, to address the inability of traditional LSTM models to capture complex temporal dynamics. This allows the model to effectively incorporate long-term dependencies and improves prediction accuracy. Furthermore, we incorporate the mLSTM model into the mobility management framework of 5G networks. The model continuously obtains real-time user location updates and predicts future user positions, allowing for proactive handover decisions. The network can optimize resource allocation, reduce handover latency, and improve user experience by leveraging anticipated mobility patterns. We test the proposed method by simulating it extensively with real-world mobility traces. The results show that the mLSTM model accurately predicts user mobility and outperforms conventional methods in transition performance. The model is not affected by changing network conditions, user mobility patterns, or service specifications. 2024 Elsevier B.V. -
Study of buoyancy and surface tension driven convention in nanaofluid
This thesis presents a detail study of linear and non-linear analysis of buoyancy and surface tension driven convection in nanofluid. The linear Rayleigh-Bard / Rayleigh- Bard Marangoni convection in nanofluids in the presence of external constraints like magnetic field, rotation and newlineinternal heat generation is investigated. The effect of temperature and volumetric concentration modulation of nanoparticles at the boundary and gravity modulation are studied on the onset of Rayleigh- Bard newlineconvection. The obtained results are discussed qualitatively and presented newlinegraphically. The problem discussed have important applications in the field of oceanography, geophysics, nuclear fuel, astrophysics, geothermal reservoirs, engineering and space situations with g-jitter connected with gravity simulation studies.Given the rising relevance of nanofluid application, we discuss four newlineproblems in this thesis whose detail summary is presented below: (i) LINEAR AND WEAKLY NON-LINEAR ANALYSIS OF GRAVITY MODULATION ON THE ONSET OF RAYLEIGH BARD CONVECTION IN NANOFLUID The effect of modulation of gravity or time-periodic body force on newlinethe onset of Rayleigh-Bard convection in nanofluid is studied using newlinelinear and non-linear analysis. The stability of the fluid layer heated from newlinebelow is analysed by considering time-periodic body acceleration. This newlinehappens generally in the vehicles and satellites associated with studies of newlinemicro gravity simulation. In order to study the effect of gravity modulation newlineon the system stability limit, linear and weakly non-linear analysis is performed. Normal mode technique and perturbation method is applied to study linear stability. The critical Rayleigh number and wave number is newlinecalculated by taking modulation of small amplitude. It is found that the critical thermal Rayleigh number can be increase or decrease by a massive amount depending upon the distribution of nanoparticles.In this thesis we considered bottom heavy distribution of nanoparticles. -
Study of buoyancy and surface tension driven convection in nanofluid
This thesis presents a detail study of linear and non-linear analysis of buoyancy and surface tension driven convection in nanofluid. The linear Rayleigh-Bernard / Rayleigh-Bernard Marangoni convection in nanofluids in the presence of external constraints like mangnetic feild, rotation and internal heat generation is investigated. -
Fuzzy Computational Intelligence in Personalized Medicine and Diagnosis
The development of fuzzy computational intelligence (FCI) has emerged as an effective method for personalized medicine and diagnosis. FCI effectively handles uncertainty and imprecision in medical data, facilitating patient-specific treatment recommendations. Conventional diagnostic and treatment methods typically rely on fixed threshold-based approaches, which fail to account for individual variations in patient responses, leading to suboptimal treatment outcomes. This study proposes the personalized treatment recommendation using fuzzy logic (PTR-FC) framework for diabetes (DB) patients to address these challenges. The framework integrates patient-specific data such as blood glucose levels, diet, exercise, and medication history into the fuzzy inference system (FIS), supporting personalized treatment recommendations. The treatment plans are dynamically adapted based on individual patient outcomes using linguistic factors and fuzzy rules (FR). The proposed method dynamically adjusts recommendations in real time, potentially enhancing personalized treatment and improving decision-making in DB management. Additionally, it promotes lifestyle modifications while reducing the risk of medication-induced complications. The effectiveness of the proposed method was compared to conventional methods, demonstrating improved treatment accuracy, increased patient adherence, and reduced adverse health risks. The PTR-FC framework offers a more adaptive and effective approach to DB management, ensuring better patient outcomes. 2009 Tsinghua University Press. -
Initial Public Offerings (IPOs) Performance: Bibliometric Analysis of Scholarly Articles on Short-Run Performance
Initial Public offerings are used by the companies to raise capital from the public and to enter in to the public markets. To understand the concept of short-run performance and long-run performance of the company is essential for the investors, regulators and market analysts to evaluate market efficiency. there are many researches were conducted on IPO from the year 1991 to till date, shows the importance of the IPOs. This paper conducted a bibliometric analysis on IPO performance landscape. The data were collected using the Dimensions data base. The articles so collected were from the period 1991 to 2024. Total of 126 articles were identified and considered for the current research. The extracted database was analyzed using VOSview software. Using the software, the current research identified the key authors in the field of IPO research, their organizations, countrywide research contributed in the field of IPO performance both short-run and long-run, especially short-run. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Unveiling the Dynamics of Initial Public Offerings: A Comprehensive Review of IPO Pricing, Performance, and Market Trends
Initial Public Offerings (IPOs) serve as pivotal moments in the financial markets, representing a company's transition from private to public ownership. The importance of IPOs lies in their capacity to raise substantial capital, facilitating business expansion and development. This paper conducts an in-depth analysis of Initial Public Offerings (IPOs) in India spanning the period from 2018 to 2022, with a particular focus on their listing day performance. The study categorizes IPOs into various issue price ranges, revealing substantial variability in listing day returns across these categories. It underscores the importance of pricing strategy, emphasizing the need for companies to carefully assess their issue prices to align with market demand. Furthermore, the analysis highlights the varying levels of risk associated with IPO investments based on issue price ranges, advocating for diversification and thorough due diligence. In addition, the paper emphasizes the dynamic nature of IPO markets, influenced by factors beyond pricing, and encourages a balanced approach that considers both potential rewards and challenges. This research provides valuable insights for stakeholders, guiding companies, investors, and analysts in making informed decisions in the dynamic world of IPOs. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Initial Public Offerings (IPOs) Performance: Bibliometric Analysis of Scholarly Articles on Short-Run Performance
Initial Public offerings are used by the companies to raise capital from the public and to enter in to the public markets. To understand the concept of short-run performance and long-run performance of the company is essential for the investors, regulators and market analysts to evaluate market efficiency. there are many researches were conducted on IPO from the year 1991 to till date, shows the importance of the IPOs. This paper conducted a bibliometric analysis on IPO performance landscape. The data were collected using the Dimensions data base. The articles so collected were from the period 1991 to 2024. Total of 126 articles were identified and considered for the current research. The extracted database was analyzed using VOSview software. Using the software, the current research identified the key authors in the field of IPO research, their organizations, countrywide research contributed in the field of IPO performance both short-run and long-run, especially short-run. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Compartmental Mathematical Model of Novel Coronavirus-19 Transmission Dynamics
The COVID-19 pandemic has spread quickly throughout the world, posing a serious threat to human-to-human transmission. The novel coronavirus pandemic is described quantitatively in this paper using a mathematical model of COVID-19 driven by a system of ordinary differential equations. The suggested model is used to provide predictions regarding the behavior of a COVID-19 outbreak over a shorter time frame. It is demonstrated that the system of model equations has a unique and existing solution. Furthermore, the answer is positive and bounded. Thus, it is argued that the model created and discussed in this work is both mathematically and biologically sound. A threshold parameter that controls the disease transmission is used in a qualitative analysis of the model to confirm the existence and stability of disease-free and endemic equilibrium points. Additionally, the key parameters undergo sensitivity analysis to ascertain their relative significance and potential influence on the COVID-19 virus dynamics. 2024 NSP Natural Sciences Publishing Cor. -
AIs Role in Semantic Segmentation for Data-Driven 3D Models of Heritage Structures
Using point cloud data from laser scanning and photogrammetry to create three-dimensional models with scan-to-BIM processes has become increasingly common in heritage conservation. During the processing of point clouds, semantically segmenting data can translate captured spatial information into intelligent data structures, enabling classified, accurate, data-driven digital models of heritage structures. Subsequently, digital models are utilized for analytical tasks like structural tests, energy optimization, etc. Artificial Intelligence (AI) has become a promising solution for automating Three-Dimensional Point Cloud Semantic Segmentation (3DPCSS), enabling a faster and more accurate composition of parametric objects within 3D modeling and management systems. However, implementing 3DPCSS solely with AI presents various technical and theoretical challenges. The geometrical complexities inherent in historical structures often necessitate manual segmentation processes or oversimplified representations that miss the unique characteristics of the building. Therefore, selecting an appropriate AI framework for 3DPCSS is essential to ensure accurate results. Multiple factors determine algorithms selection, making it challenging to categorize universal solutions. The paper highlights the key factors: 1) Data collecting tools and technologies, 2) Types of the dataset, 3) Complexity of geometrical elements, and 4) Computational tasks. AI frameworks are typically selected based on the suitability and significance of these factors relative to the projects intent. Very few studies talk about the choices of algorithms. This papers significant contribution is recognizing trends in effective data acquisition strategies through a case study in India. Additionally, it identifies state-of-the-art AI models from the past decade based on a systematic literature study. The paper infers the extensive use and advancement of hybrid approaches tailored to multi-modal data types and the specific needs of heritage projects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Thermodynamic Modeling of Hashtag Dynamics for Social Media Clustering: A Maxwell-Boltzmann Approach
Social media hashtags function as critical organizational markers in digital discourse, yet traditional weighting methods fail to capture their dynamic significance across temporal and contextual dimensions. This paper presents a novel thermodynamic framework that conceptualizes social network activity as system 'temperature', applying statistical mechanics principles to model hashtag importance as process innovation. We establish mathematical foundations based on the Maxwell-Boltzmann distribution, providing an information-theoretic justification for dynamic hashtag weighting. Our approach incorporates activation thresholds and power-law scaling behaviors through a temperature-dependent function, with Simple Moving Average techniques implemented to stabilize temperature estimation, mathematically reducing variance by a factor of 1/N. Empirical evaluation using Twitter discourse from the US Presidential Election demonstrates unprecedented improvements in clustering performance: Silhouette Scores increased from 0.0126 to 0.9070 for Trump-related content and from 0.0105 to 0.8220 for Biden-related content, while Calinski-Harabasz Scores improved from 65.51 to nearly 98 million. These findings establish a rigorous mathematical bridge between thermodynamic systems and social media behavior, contributing to computational social science by providing a theoretical framework that significantly enhances discourse community detection in politically polarized environments. The approach enables more accurate identification of topic clusters, revealing distinct discourse patterns that conventional methods fail to capture. 2025 The Authors. -
Role of nanomaterials in the development of nanobiosensors for infectious diseases
Transmissible illnesses brought on by viruses, bacteria, fungi, and parasites are referred to as infectious diseases. These can escalate into undesirable pandemic circumstances that disrupt both regular life functions and the world's population. These in turn have an effect on the current global economy, lead to joblessness, induce stress on the body, mind, and emotions, and put human survival in jeopardy. Consequently, in order to avert worldwide life impairment, prompt discovery, treatment, isolation, and control of the spread of pandemic infectious diseases within the town of origin are essential. As of right now, the World Health Organization (WHO) lists 12 infectious diseases that can be fatal: COVID-19, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), human immunodeficiency virus (HIV), human papilloma virus (HPV), influenza, hepatitis, herpes simplex virus (HSV), Zika virus, chikungunya, dengue, and rota virus. Biosensors are becoming more and more potent instruments for diagnosing infectious diseases. Analytical tools that may transform biochemical data into detectable signals such as optical, electrical, magnetic, or thermal signals are referred to as biosensors. The growing need for highly selective, low-concentration sensing of a wide variety of chemicals has spurred the creation of sophisticated instruments known as nanobiosensors, which combine biological components, advanced materials, and nanoscale materials. The design, principle, underlying reasoning, receptor, and molecular features of sensor systems with a focus on the recent COVID-19 pandemic are all covered in this chapter. For critical comparison, electrochemical biosensor systems which included a variety of sophisticated nanostructures like semiconductors, metal organic frameworks (MOFs), MXenes, polymeric nanocomposites, metal and metal oxide nanoparticles, and combinations of biomolecules reported recently were specifically divided into distinct sub-sections. This chapter focuses on the difficulties that exist today in converting lab research into practical device applications, as well as the potential for the future and the commercialization of electrochemical diagnostic devices for the detection of corona viruses. It is anticipated that the background information and overall advancements presented in this study will be instructive for sensor researchers and will make it easier to design and fabricate electrochemical sensors for viruses that pose a threat to human life, with a wider range of applications for any desired pathogen. 2025 Scrivener Publishing LLC. All rights reserved. -
New frontiers in polyphenol analysis: A review of electrochemical sensors and commercial devices enhancing food and beverage analysis
Food safety concerns arise from outbreaks of foodborne illnesses and contamination within the food supply. Polyphenols, naturally occurring compounds in plants, are characterized by multiple phenolic (hydroxyl) groups and are prevalent in fruits, vegetables, tea, coffee, and wine. While beneficial in moderation, excessive polyphenol intake is harmful, and they classified as secondary pollutants in environment. Therefore, accurate quantification of polyphenols is essential for ensuring product safety, quality, and nutritional value, which is the focus of this review. Electrochemical sensors offer a sensitive, selective, and cost-effective method for detecting polyphenols in food and beverages. The review examines advanced voltammetric techniques for identifying polyphenols in various food samples, including beverages and dietary products. Additionally, total antioxidant capacity (TAC) sensors are highlighted as valuable tools for assessing the antioxidant potential of foods, aiding in nutritional analysis and quality control. This review, for the first time, catalogs around ten commercially available devices and twenty assay kits for detecting antioxidant polyphenols, highlighting their significance in advancing food safety, bolstering consumer confidence, and supporting ongoing nutritional research. Additionally, made efforts to bridge a crucial gap between conventional research and industry needs by expanding the existing body of knowledge and providing fresh insights into polyphenol analysis. 2025 Elsevier Inc. -
Recent trends in the electrochemical sensors on ?- and calcium channel blockers for hypertension and angina pectoris: A comprehensive review
Stress, ingrained human behaviors, an inactive lifestyle, and poor dietary decisions are the primary causes of hypertension and the related coronary artery disease (CAD), which is also commonly referred to as angina pectoris. Effective high blood pressure (BP) treatment represents a substantial approach to reducing the burden of hypertension-related cardiovascular and renal diseases. A group of drugs known as ?-blockers and calcium channel blockers (CCBs) are frequently used to treat diseases like hypertension (high blood pressure), cardiac arrhythmias and heart failure. For efficient therapeutic use and to reduce potential side effects, ?-blocker concentration monitoring is essential. Chromatographic techniques are employed in a wide range to detect ?-blockers and CCBs without interference, among other analytical methods that have been described. For the detection of ?-blockers and CCBs, electrochemical sensors provide numerous benefits including sensitivity, selectivity, rapidity, and cost-effectiveness. These sensors can help with patient monitoring in clinical settings, ensuring that the prescription ?-blocker dosage is within the therapeutic range. Since ?-blockers are frequently consumed by people, the contamination can be occurred through discharge of wastewater. The presence and measurement of ?-blockers in water samples enables researchers to evaluate potential risks to aquatic life and public health. In this regard, this review addresses recently developed electrochemical (voltammetric) methodologies and measurement protocols for the determination of both ?-blockers and CCBs in pharmaceuticals, biological fluids, and environmental samples. Additionally, this review also provides an overview of the various advanced nanomaterials such as carbon nanotubes, graphene oxide, metal and metal oxide nanoparticles, polymeric structures, zeolite materials, ionic liquids, perovskite semiconductor-based materials, MXenes, Quantum dots, Nano MIPs and various dimensional materials applied to fabricate chemically modified electrodes/electrochemical sensors to determine the ?-blockers and CCBs. Moreover supplied are tables listing the analyte, modified electrode, measurement method, measuring medium pH, linear detection range (LDR), limit of detection (LOD) and sensitivity as they are cited in the original research. Furthermore, important conclusions are made from the published reports in the last decade and some future perspectives are also suggested. 2023 Elsevier B.V. -
Movie-Induced Tour Guiding: Concepts and Future Implications in South Asian Perspective
Movies have an extensive impact on tourism and its promotion. Movie-induced tourism has been a worldwide phenomenon for the last couple of decades, but this phenomenon is confined to the marketing and promotion of tourism destinations. Here, a new approach has been introduced for co-creating a quality destination experience through traditional tour guiding. Considering the increasing emphasis on tourists experience, satisfaction, and destination imagery over the decades, this concept of movie-induced tour guiding will produce a synergistic value in the overall process of the outdoor leisure tour packages. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Addressing B5G and 6G Network Connectivity Issues and Challenges in Rural Regions of India
The emerging technology of the fifth-generation broadband cellular network is already ruling the market with its efficiency, lower latency, higher connectivity, and many more features. In contrast, the sixthgeneration broadband cellular network is yet in its research and development stage. These technologies cannot only revolutionize the world with their features, such as high speed and enhanced cybersecurity but also empower it to reach greater heights. To understand the network requirements of the rural and under-developed areas, it is important to understand all those challenges in the way ahead.. Launching such efficient and effective technologies in rural areas would benefit the country as well as its economic growth. The large markets of these cellular networks are at constant growth and are expected to be booming in the future of the Telecom Regulatory Authority of India. (2023, September 29).. 2025 by IGI Global Scientific Publishing. All rights reserved. -
5G Technology Empowering Wireless Technology
Wireless Communication is the means of transferring data from one point to another without the use of any wired means. With reference to wireless communication, wireless sensor Networks (WSN) have also developed in recent times. It can be referred as an infrastructure-less system of wireless devices which can gather and exchange information with the help of a wireless link. The information which is gathered is sent respectively to the base stations and sinks for further developments. Recently, the 5G generation network, the latest Wireless Communication Network operates at a higher frequency range than its predecessor. In this paper, a detailed analysis on the 5G generation cellular network, which is expected to be a key instrument of wireless technologies in the near future is outlined. Also a comparative analysis of different kinds of networks in context to wireless scenario is discussed. It was found that 5G provides the best outcome in terms of high speed and network spectrum bandwidth. 2023 IEEE. -
Machine Learning Models for SMS Spam Detection
With the increasing reliance on mobile communication, detecting spam messages sent via Short Messaging Service (SMS) has become more important. This advent has created a new era for spam in peoples lives, one that calls for quick attention and automatization in categorizing messages. This study analyzes three machine learning algorithmsLogistic Regression, Naive Bayes, and Decision Tree resulting in the binary classification of SMS messages into either spam or not spam (ham). To achieve effective spam detection, the study highlights the significance of feature engineering, model selection, and evaluation metrics such as accuracy, precision, recall, and F1-score. The research challenges, including unbalanced data, changing spam strategies, and the requirement for scalable solutions, are handled in this study. During experimentation, it was observed that Logistic Regression increased performance by 98.07% accuracy. The results also showed the advantages and disadvantages of each model, providing guidance on which strategy, is best for SMS spam filtering apps in the real world. This analysis aims to give readers a thorough grasp of existing approaches and how they might be used to improve the effectiveness and security of mobile communication systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Predictors of Sleep Quality Among Emerging Adults in India: Exploring the Role of FoMO, Nomophobia and Evening Chronotype
Background: The increasing integration of mobile technology into daily life has raised concerns about its effects on sleep quality and mental health, particularly among emerging adults. The interplay between evening chronotype, nomophobia (no mobile phone phobia), and FoMO is crucial to understanding these impacts, especially in the digital age. The current study investigated whether nomophobia mediates the relationship between evening chronotype and sleep quality and between chronotype and FoMO with sleep quality in emerging adults. Methods: A cross-sectional survey was conducted among N = 501 emerging adults (Males = 144, Females = 356), aged 1825 (21.2 1.85 years), after approval from the Institutional Review Board. The participants completed measures of demographic information, sleep quality, FoMO, nomophobia and chronotype. Data were analysed using Jamovi and Statistical Package for the Social Sciences (SPSS). Results: Significant negative associations were found between evening chronotype, FoMO, and sleep quality, indicating that individuals with an evening chronotype and those with higher FoMO tend to experience poorer sleep. Nomophobia significantly mediated the relationships between evening chronotype and sleep quality (Indirect estimate = ?0.00896, p < .05), and between FoMO and sleep quality (Indirect estimate = 0.0185, p < .05), amplifying these negative impacts. Conclusion: The study highlights nomophobias critical role in exacerbating the effects of evening chronotype and FoMO on sleep. Interventions targeting nomophobia and digital habits could improve sleep and mental health among emerging adults. The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
Influence of media messages on voters behaviour, a study on people's perception of the Loksabha election 2014 /
Including, all private, public, regional and national news media there are more than 100 news, channels are available in India. Compare to previous Loksabha elections; on the 16thLoksabha election the role of media was exceptional. On Loksabha election 2014, the effect of media messages and the news representation was very much visible. -
Hybrid Model Using Interacted-ARIMA andANN Models forEfficient Forecasting
When two models applied to the same dataset produce two different sets of forecasts, it is a good practice to combine the forecasts rather than using the better one and discarding the other. Alternatively, the models can also be combined to have a hybrid model to obtain better forecasts than the individual forecasts. In this paper, an efficient hybrid model with interacted ARIMA (INTARIMA) and ANN models is proposed for forecasting. Whenever interactions among the lagged variables exist, the INTARIMA model performs better than the traditional ARIMA model. This is validated through simulation studies. The proposed hybrid model combines forecasts obtained through the INTARIMA model from the dataset, and those through the ANN model from the residuals of INTARIMA, and produces better forecasts than the individual models. The quality of the forecasts is evaluated using three error metrics viz., Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Empirical results from the application of the proposed model on the real dataset - lynx - suggest that the proposed hybrid model gives superior forecasts than either of the individual models when applied separately. The methodology is replicable to any dataset having interactions among the lagged variables.. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.