Browse Items (14421 total)
Sort by:
-
Shrinking Sizes, Swelling Prices: Evaluating the Ripple Effects of Inflation and Shrinkflation on Economic Growth Using Dynamic Panel Framework
This study used the novel cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model and the JuodisKaraviasSarafidis (JKS) causality test to investigate the intricate nexus between inflation, shrinkflation, and economic growth in 20 countries from 1990 to 2022. The results validated the detrimental effects of inflation and shrinkflation on economic growth, underlining price stability, and reverse or positive shrinkflation as crucial for sustained expansion. Causality analysis further revealed feedback causality between inflation and economic growth. Finally, our findings solidify the quantity-led growth or value-driven growth hypothesis. Reverse shrinkflation, or growth driven by value, drives economic growth unidirectionally. Consumption expenditure increases as the value or quantity of goods and services increase, which boosts consumption, aggregate demand, and economic growth. Hence, to stimulate sustainable economic growth, policymakers should implement prudent monetary policies to control high inflation, a major contributor to shrinkflation. Additionally, industries should be encouraged to enhance productivity and reduce manufacturing costs without sacrificing product size or quality. Lastly, it is essential to monitor pricing changes in critical industries and intervene if unjustified shrinkflation trends emerge. 2025 Emerging Markets Institute, Beijing Normal University -
Segmentation and identification of MRI Brain segment in digital image
Brain image segmentation is important in the area of clinical diagnosis. MRI Brain image segmentation is time consuming and there is always a chance of occurrence of error when the segmentation is done manually. It is always possible to detect the infected tissues easily in the current medical field. However, the accuracy and the characteristics of abnormalities of the tissues are not precise. In the past, many researchers have identified the drawbacks of manual segmentation and hence proposed the semiautomatic and fully automatic segmentation methods in the field of medical imaging. The amount of precision about the detection of defective tissues leads to acceptance of a particular image segmentation method. In this article three segmentation methods are hybridized to get the optimum extraction of the region of interest (ROI) in brain MRI image. Further, the region properties of segment is extracted and stored as knowledgebase. The proposed algorithm integrates multiple segmentation methods and identifies the Brain Outer layer in MRI image. This identification AIDS medical experts for optimum diagnosis of defective tissues in the brain. IAEME Publication. -
Numerical and sensitivity analysis of MHD bioconvective slip flow of nanomaterial with binary chemical reaction and Newtonian heating
The impact of Stefan blowing on the MHD bioconvective slip flow of a nanofluid towards a sheet is explored using numerical and statistical tools. The governing partial differential equations are nondimensionalized and converted to similarity equations using apposite transformations. These transformed equations are solved using the RungeKuttaFehlberg method with the shooting technique. Graphical visualizations are used to scrutinize the effect of the controlling parameters on the flow profiles, skin friction coefficient, local Nusselt, and Sherwood number. Moreover, the sensitivities of the reduced Sherwood and Nusselt number to the input variables of interest are explored by adopting the response surface methodology. The outcomes of the limiting cases are emphatically in corroboration with the outcomes from preceding research. It is found that the heat transfer rate has a positive sensitivity towardsthe haphazard motion of the nanoparticles and a negative sensitivity towardsthe thermomigration. The thermal field is enhanced by the Stefan blowing aspect. Moreover, the fluid velocity can be controlled by the applied magnetic field. 2021 Wiley Periodicals LLC -
Eccentricity splitting graph of a graph
Let G = (V, E) be any connected graph with (Figure presented.) for all uj, uk ? Si if e(uj) = e(uk)(1 ? i ? t) with each | Si |? 2 and (Figure presented.). The eccentricity splitting graph of a graph denoted by ES(G) is obtained by taking a copy of G and adding vertices w 1, w 2, , wt such that wi is adjacent only to the vertices of Si for 1 ? i ? t. We initiate the study on eccentricity splitting graph ES(G) and examine its structural properties. We also analyze diameter, girth and chromatic number of eccentricity splitting graphs of certain classes of graphs. 2021 Taru Publications. -
Distance based properties of the semi splitting block graph of graph
The bounds on the radius and diameter of the semi splitting block graph (SB(G)) of graphs are investigated. The diametral paths and self-centeredness of semi splitting block graph of any connected graph are analyzed. The graphs where the diameter of G and SB(G) are the same are characterized and the number of blocks in the diametral path of such graphs is analyzed. 2023 Author(s). -
ON BLOCK-RELATED DERIVED GRAPHS
This paper introduces and analyses the block-degree of a vertex and the cut-degree of a block. The block-degree of a vertex v is the number of blocks containing v. The cut-degree of a block b is the number of cut vertices of G contained in b. The block-degree sequence of cut vertices of the graph and the cut-degree sequence of the graph are defined. A few characterizations of the block-degree and cut-degree sequence of the graph are established. Given a graph, its block graph (B(G)) is a graph where each vertex represents a block, and two vertices are connected if their blocks intersect. The number of cut vertices of B(G) is determined. Further, an investigation is carried out on the traversability of B(G). A block cutpoint graph (BC(G)) of a graph represents a graph where each vertex corresponds to either a block or a cut vertex, and two vertices are connected if one represents a block and the other represents a cut vertex contained within that block. The properties of BC(G) and its iterations are studied. The graph G for which BC(G) is a perfect m-ary tree is characterized. 2024, Canadian University of Dubai. All rights reserved. -
Digital Watermarking Techniques for Secure Image Distribution
In the contemporary era of digital advancements, it is of utmost importance to prioritize the establishment of robust security measures and traceability protocols for photos. This necessity arises from the inherent risk associated with the effortless diffusion of unlicensed information. Digital watermarking, which implants hidden data into digital photographs to verify their validity, is frequently used. This level emphasizes the need of safe photo distribution, digital platform problems, and unauthorized reproductions. The purpose of this research is to explain digital watermarking fundamentals. It emphasizes verification, IP protection, and digital watermarking monitoring. This research compares spatial and frequency domain watermarking approaches. Direct pixel manipulation in spatial domain techniques is vulnerable to attacks. Integrating watermarks with transform domains like Discrete Cosine Transform improves robustness in frequency domain techniques. The study also studies adaptive watermarking, which adjusts the watermark to the image's content to balance visibility and durability. The purpose of this research is to explore watermark identification methods. These methods use blind and non-blind watermarking. We discuss the security risks that might compromise watermarked photographs and the ways to reduce their likelihood. 2024 IEEE. -
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. -
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). -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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.