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Transforming network security through zero trust architecture: Principles, challenges, and future directions
Given the continued expansion of cyber threats; such perimeter-based resistance traditional security strategies have proven to be inadequate. No one is trusted by default, either inside or outside the network, in a Zero Trust architecture. Zero Trust Architecture (ZTA) is a modern security model that demands consistent authentication of users and devices and denies any presupposition of implicit trust. It should also be of strong authorization, network division, and authentication. This article covers the principles, components, pros, and cons along with zero trust implementation strategies and the impact on network security. 2026, Taru Publications. All rights reserved. -
Entropy diagnostics for cryptographic key material from random circuit sampling
Random Circuit Sampling (RCS) has emerged as a leading paradigm for demonstrating quantum advantage. Beyond computational complexity, RCS provides a high-dimensional, chaotic probability distribution whose structure is characteristic of random unitary dynamics; here, we study its entropy properties in an ideal statevector baseline intended for later hardware validation. In this work, we present a reproducible validation framework for certifying cryptographic keys using a 12-qubit RCS ensemble (N = 4096 states ) within an ideal quantum simulation framework. Unlike standard Quantum Random Number Generators (QRNGs), which often rely on single-qubit optics, our protocol utilises multi-qubit entanglement to ensure nonlocality. We quantify the security of the system using a dual-metric approach: basis-dependent Min-Entropy (H? ? 9.05 bits ) for cryptographic extractability, and basis-independent Subsystem Von Neumann Entropy (S ? 3.96 bits) for quantum certification. We further demonstrate a privacy-amplification pipeline that uses a frequency-preserving, endian-corrected SHA-3 extraction to produce a 256-bit secure key (candidate key material). This study provides a transparent methodological bridge between the theoretical Quantum Supremacy regime and practical cryptographic key generation. 2026, Taru Publications. All rights reserved. -
Invariant intersection graph of a graph
Studies in algebraic graph theory showcase the interplay between group theory and graph theory by defining graphs on groups, investigating their properties, and also by analysing the automorphism groups that emerge from the graphs. In this article, we introduce the idea of constructing an algebraic derived graph; that is, constructing a graph based on the algebraic properties of a graph, by introducing the invariant intersection graph of a graph, constructed based on the automorphism group of a graph. Here, we introduce the graph construction and initiate an investigation on the structure of the invariant intersection graph with respect to the graph and its automorphism group. 2025, Taru Publications. All rights reserved. -
Elliptic curve-based cryptography solutions for strengthening network security in IoT environments
Elliptic Curve-Based Cryptography (ECC) may be a solid way to move forward organize security in Web of Things (IoT) settings, where other cryptography strategies regularly come up short. ECC suggests a tall level of security with moderately little key sizes, which is especially important for Internet of Things devices without any assets. This speed cuts down on preparing squander, memory utilize, and control utilize, which makes it culminate for IoT apps that utilize a part of diverse sorts of equipment. By making beyond any doubt there are secure ways to communicate and verify clients, ECC can lower the dangers of data spills and illicit get to. ECC is additionally great at securing private information over gadgets that are connected to each other since it is safe to modern dangers and can be changed to work with distinctive IoT conventions. Utilizing ECC-based arrangements in IoT systems not as it were makes them more secure, but it moreover moves forward speed, making it conceivable to receive secure and adaptable arrangements in settings that are getting more complicated and spread out. 2025, Taru Publications. All rights reserved. -
Bridging Technology and Consumer Experience: The Role of Augmented Reality and Perceived Usefulness in Digital Retailing
The advent of Augmented Reality (AR) has sparked an interest in the capability of redesigning the experiences of consumers. This paper examines the effects of AR on Consumer Experience (CE) and the mediator of Perceived Usefulness (PU). The study utilizes the Technology Acceptance Model (TAM) to identify the conceptual model based on the Structural Equation Modelling (SEM) with the data obtained in the sample of 311 participants. The results indicate that Augmented Reality has great power over Consumer Experience as elements such as engagement in Augmented Reality inspire a realistic effect on users. The results indicate that Perceived Usefulness plays a minor moderating role in the relationship between Augmented Reality and Consumer Experience, which suggests that the idea of perceived usefulness should be understood even in the environment full of technologies. Therefore, the research contributes to the existing body of literature because it combines AR experience with the main TAM concepts and offers a feasible justification of PU as an enabler in models of consumer experiences driven by technologies. Basically, the findings suggest that to maximize the effect of AR, creators and sellers should not only emphasize on engaging design but also relate the practical utility of the AR tools to the users by encouraging user interaction and awareness. 2026, Iquz Galaxy Publisher. All rights reserved. -
Balancing Beauty and Facts: Examining the Dual Impact of Product Presentation and Information on Consumer Purchase Decisions in the Skincare Products
This paper is a discussion by the authors on effects of the presentation and information of product on consumer purchasing behaviour in skincare industry. In the existing competitive market where there are many products of similar kind and assertions, consumers are usually overwhelmed with excessive information. Product presentation-by designing the packaging, the aesthetics and branding something-develops the first impressions and emotional attraction, whereas product information including ingredients content, safety certification, labelling and others develops confidence and perceived quality. Nevertheless, there are not many studies that contrast their relative impact or investigate their mutual impact. This study bridges that gap by conducting a mixed method study consisting of a focus group visual preference testing (n=170) and an online survey of consumers (n=162). Results also show that information on the product, particularly ingredient disclosure and safety accreditation are a major determinant of purchase among consumers. Design and presentation may evoke immediate interest, but when product information is manipulated they have no statistical power on the ultimate purchase. It is important to note that the synergistic effect of presentation and information is not better than the effects of information alone. The implications of these findings are that although brand recognition increases with the aesthetic appeal, it is factual clarity that eventually leads to purchase intention. The research builds on the consumer decision-making theory because it focuses more on informational factors rather than emotional packaging cues in the ultimate buying of skincare products to provide marketing marketers with an excellent idea on how to increase transparency and authenticity to enhance consumer trust and confidence to buy a product. 2026, Iquz Galaxy Publisher. All rights reserved. -
Idiosyncratic Deals and Motherhood Stress: Understanding the Mediating Effect of Work to Family Enrichment with Moderating Effect of Workplace Dignity
Motherhood stress has developed as a substantial yet underexplored outcome of present-day employment practices, particularly in organizational contexts where flexibility is increasingly individualized. While prior research has examined motherhood stress, flexible work arrangements, and idiosyncratic deals largely in isolation, limited attention has been given to how personalized flexibility shapes the psychological experiences of working mothers. This study attempts to bridge this gap by examining how I-deals influence motherhood stress through psychological and relational mechanisms embedded within organizational and socio-cultural contexts. Drawing on interdisciplinary literature, this study positions I-deals as context-dependent resources whose effects on motherhood stress are mediated by work family enrichment and where workplace dignity has a moderating role. The study adopts a quantitative survey approach across 410 female employees in the Indian IT sector to test the proposed hypotheses among working mothers. The study establishes a direct and negative association between Idiosyncratic deals and work to family enrichment which further plays a mediating role with motherhood stress. It also found that workplace dignity plays a significant role in moderating the association between I-deals and work to family enrichment. The study contributes by integrating organizational behaviour and motherhood research, reconceptualizing flexibility as a psychologically contingent practice, and highlighting the importance of work to family enrichment and dignity in shaping stress outcomes. The findings offer an understanding and practical insights for organizations looking to design flexibility policies that genuinely support working mothers without inadvertently intensifying stress. 2026, Iquz Galaxy Publisher. All rights reserved. -
Discriminated-SDS: A Novel Hybrid Approach for Optimizing EEG Based Brain-Computer Interface Signals Faced by Metaheuristic Algorithms
Brain Computer Interfaces (BCIs) will convert the thoughts of individuals with physical disabilities into commands for devices to enable them autonomous mobility. The Electroencephalogram (EEG) is widely favoured as a control signal due to its ease of acquisition compared to invasive recordings. While the affordability of EEG equipment allows for the use of numerous recording channels, this abundance increases computational complexity, necessitating optimal channel selection strategies to improve efficiency and classification accuracy. Deep Neural Networks (DNNs) often face scalability issues with multidimensional, locally correlated inputs, making them impractical for such applications. Convolutional Neural Networks (CNNs) are efficient for analysing BCI data but require careful hyperparameter tuning to achieve optimal performance. This paper introduces a framework for classifying BCI channel selection using deep learning techniques. The study primarily concentrates on refining the hyper parameters of deep learning algorithms through metaheuristic techniques, specifically employing Discriminated Stochastic Diffusion Search (SDS) to enhance BCI channel selection. The findings indicate that the proposed hyperparameter optimization methods, such as Discriminated-SDS, significantly enhance classification accuracy. The proposed D-SDS balances exploration and exploitation, mitigates the local optima issue, and is especially advantageous for intricate deep learner architectures such as VGGNet, ResNet, and InceptionNet. Hyperparameter optimization in EEG-based BCI systems can substantially improve performance, enhancing their efficiency and reliability. 2026, Iquz Galaxy Publisher. All rights reserved. -
Sridevi's Stardom as A Cultural Vehicle for Women Empowerment and Social Commentary: A Textual Analysis of English Vinglish (2012) and Mom (2017)
In the Indian film sector, stardom is more than mere performance; it operates as a cultural text that produces impacts and negotiates with social values, ideals, and contradictions. Female stardom, in this way, is particularly potent in generating discourses of gender and empowerment, both disrupting patriarchal norms while enacting socially accepted moral orders. Sridevi's stardom carries specific cultural resonance, as the films she stars in offer a blend of popular entertainment while carrying deeper social significance. This study seeks to understand Sridevi's stardom and the potential for her representation of women's empowerment, as well as social commentary by analysing the films English Vinglish (2012) and Mom (2017). The study explores the implications related to Sridevi's star persona as a cultural and ideological site for women's empowerment and social critique in contemporary Indian cinema. It applies a purposive sampling method, and utilises textual analysis to investigate performance style, narrative structures, visual framing and symbolic meaning signifying women's power and resilience. The textual analysis of English Vinglish finds empowerment framed through self-assertion and linguistic competence within familial and social spaces whereas in Mom empowerment emerges in the more ambiguous domain of maternal justice and moral authority. Taken collectively, these films showcase how Sridevi's stardom functioned as a cultural vehicle, entertaining audiences while provoking critical consideration of women's roles, autonomy, justice, and empowerment within contemporary Indian society. 2026, Iquz Galaxy Publisher. All rights reserved. -
A Comparative Study of Gender and Age-Based Differences in Organisational Culture: Evidence from an Empirical Analysis
Organisational culture (OC) plays a significant role in shaping employee attitudes, engagement, and overall effectiveness. However, limited empirical evidence explores how demographic factors, such as age and gender, influence employees perceptions of organisational culture. This study reveals age and gender-based differences in organisational culture among employees from leading Indian-origin IT services companies in Bengaluru. Grounded in the Denison Organizational Culture Model, the study examines four key dimensions: involvement, consistency, adaptability, and mission. Data were collected from employees using a structured questionnaire, and statistical analyses, including ANOVA and Z-tests, were applied to examine differences in cultural perceptions. The results indicated that overall organisational culture scores did not differ significantly among age or gender groups. Specific dimensions, such as capability development, core values, agreement, and vision, exhibited significant age-related differences, with younger employees (2030 years) perceiving a stronger culture than those in the 3140 age group. No significant gender-based differences were observed across any dimension. These findings demonstrate the importance of demographic responsiveness in shaping inclusive organisational practices. The study contributes to organisational behaviour literature and offers practical implications for HR managers and leaders seeking to develop employee engagement and cultural alignment in the IT services sector. Keywords:. 2026, Iquz Galaxy Publisher. All rights reserved. -
Texture-Based DNN for Pneumonia in Thorax X-Rays
This paper introduces an innovative methodology for identifying pneumonia in thoracic X-ray images through the application of neural network classifiers. In our experiment, we employed a comprehensive training regimen involving multiple neural network classifiers, each trained on distinct sets of texture features meticulously extracted from thoracic X-ray images. Four different gray-level matrices and a neighboring gray-tone difference matrix (NGTDM) were used to generate these input features, guaranteeing a reliable depiction of the textural properties found in the X-ray pictures. We carried out an extensive evaluation utilizing a number of performance criteria to gauge the trained classifiers' efficacy. Classifying the thoracic X-ray pictures into two groups pneumonia and healthy state was the assignment assigned to the classifiers. A thorough study of the classifiers' performance was provided by our assessment measures, which comprised accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). The experimental findings showed that the suggested method accomplished a remarkable 91% overall test categorization accuracy, which was encouraging. This degree of precision highlights how well our approach works to accurately diagnose pneumonia from thoracic X-ray images. Furthermore, the consistent performance across different metrics highlights the robustness and generalizability of the proposed strategy. 2025, Iquz Galaxy Publisher. All rights reserved. -
The Influence of Marketing and Awareness Campaigns on Solar Energy Adoption: A Review of Strategies and Effectiveness
The shift to renewable energy sources is picking up pace globally, with solar energy being one of the most significant sustainable solutions. However, with technological advancements and declining costs, solar adoption has been inconsistent among various consumer segments. This review critically examines marketing strategies, awareness campaigns, financial incentives, and socioeconomic factors as drivers of solar energy adoption. The study classifies findings into four key dimensions, namely: consumer awareness, effectiveness of traditional compared to digital marketing, socioeconomic influences, and psychological and behavioural impacts on decision making. Results show that high consumer awareness leads to highly significant increases in adoption rates, while traditional marketing finds relevance in low-digital penetration, but digital marketing is more effective all along. Policies and incentives for economic support also have an immense impact on adoption rates among the lower classes because high-class education and urbanization affect adoption rates strongly. Behavioural factors including consumer trust in providers, environmental causes, installation ease, and social influence further influence consumer adoption readiness. Recommendations emerging from this study point towards awareness campaigns targeted at specific groups, availability of financial incentives, and customized marketing strategies aimed at actual consumption at a scale. This literature review has informed policymakers and marketers on how to tailor their marketing and promotion approach towards solar energy as a mass adoption solution. 2025, Iquz Galaxy Publisher. All rights reserved. -
Factors Influencing Equity Investment Intention: A Behavioral Perspective
Many financial and psychological factors influence equity investment decisions. The present study examines the influence of Personality, risk attitude, and financial literacy on equity investment intention. Questionnaire responses were collected from Bengaluru investors. The present study uses the Big Five Personality Traits (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience) to categorise individual behaviour tendencies. Risk attitude is examined as a mediator variable, and financial literacy (Financial Knowledge, Financial Skill, and Financial Attitude) is examined as a moderator variable. The results show that extraversion, conscientiousness, and openness to experience positively affect equity investment intention, and Neuroticism negatively affects equity investment intention. Risk-taking propensity also moderates the personality-investment intention relationship and shows that individuals with high risk-taking propensity invest in equities. Financial literacy also moderates the relationship and implies that financial knowledge and ability are key determinants of investing. These results have policy and practice implications for investment educators, policymakers, and financial planners and indicate the value of investor-specified advice founded on psychological and financial literacy profiles. Financial literacy programs can assist investors in making effective investment decisions and managing risk. This research contributes to the behavioural finance literature by integrating personality psychology and financial literacy as investment decision-making frameworks. 2025, Iquz Galaxy Publisher. All rights reserved. -
Analysing Young Adults Preferences for AI-Generated and Human-Created Art in India: A Comparative Study Using the Mixed Method Approach
Artificial intelligence (AI) has emerged as a transformative tool in creating art, blending computational precision with creative processes. This study explores the appeal of AI-generated art compared to human-created physical and digital art among young adults in India, particularly focusing on visual art students. Additionally, the research addresses critical questions regarding the aesthetic appreciation and criticism of AI-generated art, its impact on human creativity, and its challenges to traditional art and its future. The research employed a mixed-method approach to understand preferences, motivations, and perceptions regarding these two art forms. The Art Reception Survey (ARS) was utilised to measure individuals engagement with visual aesthetics and their preferences. The qualitative approach using Multimodal Critical Discourse Analysis (MCDA) enabled deeper analysis, which helped examine how meaning, perceptions, and visual cues must have shaped their responses. The findings indicate a strong preference for original works involving creative thought processes and artistic skills-factors that lean towards a preference for traditional artwork. The findings suggest that despite rapid advancements in AI, people still significantly value human effort and creativity. The participants also acknowledged that blending both art forms can open new avenues of opportunity for the artists. The study suggests that traditional art will likely remain highly valued and argues that AI should not be seen in opposition to conventional art but as complementary tools for artistic innovation. While human-created art remains strongly appreciated, embracing AI would be the way forward, as outright rejection may not always be feasible or beneficial. 2025, Iquz Galaxy Publisher. All rights reserved. -
Investment Intentions and Influential Factors among University Students
This study investigates the investment intentions of university students in Delhi NCR and the factors influencing their decision-making, guided by the Theory of Planned Behavior (TPB). Specifically, the research examines how financial attitude, risk tolerance, and academic background contribute to students' intent to invest, alongside demographic factors such as gender, family income, and family structure. A structured questionnaire was administered to 454 university students, and data were analyzed using one-way ANOVA, chi-square tests, and multiple linear regression. Findings indicate that financial attitude and risk appetite significantly influence investment intention, with financial attitude showing the strongest negative effect. While the course of study did not significantly predict general investment intention, it showed a meaningful association with preference for equity investments. Gender differences were statistically significant, with male students more likely to invest both generally and in equities. In contrast, no significant differences were found for family income or family structure. The regression model explained 40.7% of the variance in investment intention, reinforcing TPBs attitudinal and control constructs. The study highlights the importance of integrating behavioral finance elements into education and encourages a shift beyond theoretical literacy toward experiential learning. Although variables such as social influence, financial self-efficacy, and digital platform awareness were not included in this study, their relevance is acknowledged for future research. These insights have practical implications for financial education policies under the NEP 2020 and for designing student-targeted financial awareness programs. 2025, Iquz Galaxy Publisher. All rights reserved. -
Evaluating the Potential of Cordycepin as a Therapeutic Agent for Cancer: In-Silico Analysis of EGFR and VEGFR Interactions
Due to multipotent activity, Cordycepin, a nucleoside isolated from Cordyceps fungi (Cordyceps militaris), has recently attracted considerable interest as a compound for antitumor. Cordycepin is also known as 3-deoxyadenosine, which is known to inhibit tumor growth, but the actual mechanism is not known. The present work aims to evaluate the cordycepin as an anticancer candidate by analyzing its impact on the major oncogene receptors EGFR and VEGFR through an in-silico approach. In the analysis, computational docking was performed with AutoDock Vina 1.5.7, which estimated the binding constants of cordycepin with EGFR and VEGFR and got binding energies of -6.8 kcal/mol and -5.5 kcal/mol, respectively, relative to a reference Leucovorin molecule. In addition, molecular dynamics simulations were also performed for the best complex (Cordycepin-EGFR) to examine the conformational dynamic behavior of the cordycepin-EGFR complex. The functionality and architecture of the cordycepin-EGFR complex were illustrated: their interaction might serve as a base for therapy. Also, ADMET predictions show that cordycepin follows Lipinskis rules, which supports the drug-likeness of cordycepin compounds. Accordingly, the findings presented here will confirm and draw the attention of the scientific community to use the cordycepin as a possible treatment for cancer and its potential use in scientific pharmacology. 2025, Iquz Galaxy Publisher. All rights reserved. -
Featuring Machine Learning Models to Evaluate Employee Attrition: A Comparative Analysis of Workforce Stability Relating Factors
Employee attrition is a problem for most organizations as it affects morale, productivity, and business continuity. In addressing this, the study made use of machine learning techniques such as Clear AI, Random Forest, and logistic regression in designing a prediction model to predict who is the next to leave within an organization. The HR data relating to demographics, performance metrics, job roles, and conditions of work was sourced from publicly available website Kaggle.com for the study. Data preprocessing included scaling, outlier detection, and balancing the dataset using SMOTE. Multiple machine learning models were trained and evaluated by checking on accuracy, F1-score, and the ROC-AUC curve. The best model that was tested was Random Forest, which gave an accuracy of 85.71%. Additional insights from feature importance highlighted the significant effect of overtime, marital status, and stock options on attrition. Among the remaining key drivers are workload, work-life balance, and financial incentives. These findings suggest the need for focused HR strategies, such as reduction of overtime, mentorship programs, and career development opportunities, to reduce attrition rates and improve employee satisfaction. This study provides a robust methodology in predicting attrition and delivers actionable insights into designing interventions that improve workforce stability and organizational efficiency. 2025, Iquz Galaxy Publisher. All rights reserved. -
Global Financial Cycle and Its Determinants: A VECM Approach
The determinants of the global financial cycle are empirically investigated in this study report. The presence of concurrent changes in capital flows, asset prices, and global bank leverage is associated with the Global Financial Cycle (GFCy). According to the research now in publication, the Chicago Board of Exchange's VIX (Volatility Index), which gauges market uncertainty and risk aversion, indicates this cycle. The Federal Reserve's monetary policy decisions are the driving force behind this cycle, and the literature already in existence has examined the ramifications of these decisions. The GFCy and, thus, the financial circumstances of emerging market economies (EMEs) could be impacted by additional global shocks. Other global shocks have the potential to impact the global financial cycle and analysis of the same is required to make the existing literature more robust. Our analysis, which includes a study of identifying the potential global shocks for a period of 23 years data (quarterly), indicates that the global financial cycle is driven by global liquidity and global economic policy uncertainty. VECM, Granger Causality, Impulse Response functions were applied. There is a unidirectional causal relationship between the global financial cycle and global liquidity, as well as a unidirectional relationship between the global financial cycle and global economic policy uncertainty. 2025, Iquz Galaxy Publisher. All rights reserved. -
Coati Optimization Algorithm for Detecting Pediatric Kidney Abnormalities using Ultrasound Images
This study aimed to classify pediatric ultrasound images as normal or abnormal by identifying the optimal number of image texture features for analysis and developing an effective classification system using selected features. The experiment identified a successful feature selection and classification algorithm with a good performance. This study introduced a new approach for computer-assisted ultrasound image classification. Initially, a Gaussian median filter enhances the image quality and removes noise. For feature extraction, various features, including first-order derivatives, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Dependence Matrix (GLDM), Gray Level Size Matrix (GLSZM), and Neighbouring gray tone difference matrix (NGTDM), were extracted using the Pyrandiomics Python package. The Coati optimization algorithm (COA) was employed as a feature selection technique. The Classification was performed using Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-nearest Neighbor (KNN), Nae Bayes (NB), and Extreme Gradient Boosting (XG-Boost) algorithms. Therefore, this study proposed a new machine learning classifier, the Extreme Gradient Neighborhood classifier (XGNC), using NB, KNN, and XG-Boost, with a classification accuracy of 97.91%, which outperformed the other classifiers mentioned in the study. The results indicated that the optimal feature selection and classifier choice yielded the most accurate computer-aided diagnosis of kidney abnormalities. 2025, Iquz Galaxy Publisher. All rights reserved. -
Golden Insights: Analyzing the Influence of Economic Indicators on Sovereign Gold Bond Performance in India
India has been the leading consumers of gold with the consumption of around 774 metric tons in 2022. The demand for gold in India is majorly associated with its culture, tradition, attractiveness, and the source for financial security (GJC,n.d.)The gold market in India plays a vital role in the economy as a stable asset and hedge against inflation due to its ability to hold value over time. In order to limit the import of gold and reduce the countrys current deficit, the Indian Government introduced Sovereign Gold Bonds in 2015 as a substitute to physical gold. As SGBs export-import values are backed by Reserve Bank of India (RBI) they are considered as an inflation hedging tool. The study aims to examine the effectiveness of SGBs, in the changing economy by understanding the impact of key economic indicators Inflation Rate, Exchange Rate, Per Capita Income, Gold Prices, and GDP Growth Rateon the performance of Sovereign Gold Bonds (SGBs) in India. 36 months observations of the selected macroeconomic variables and series wise released prices are collected for a period starting from September 2021 till August 2024 for the analysis. Descriptive statistics is applied to understand the characteristics of the variables. Further, correlation and ordinary least square method is used to check the existing relationship and impact level of macroeconomic variables on SGBs. Lastly, both long run and short run relationships of these variables are analyzed using the Autoregressive Distributed Lag Model (ARDL). 2025, Iquz Galaxy Publisher. All rights reserved.