Takaful is an alternative Shariah compliant insurance product which is being offered by more than fifty takaful companies in Pakistan. Currently takaful market is facing low penetration due to many challenges including regulatory or compliance, payment efficiency, fraud prevention, transparency. Blockchain technology, a decentralized, transparent and trust-based system, which could address these issues efficiently and effectively by offering smart contracts.

This paper examines Blockchain's feasibility and its impact on Pakistan’s insurance market in general and takaful sector in particular, using a systematic literature review (SLR) and case studies from Malaysia, the UAE, and Indonesia. In Malaysia and the UAE, the success of using Blockchain in Islamic finance highlights potential efficiency and security benefits. However, in Pakistan's regulatory ambiguity, lack of Shariah-compliant frameworks, limited human expertise, and low industry readiness are few factors which needs to look at, by the Government of Pakistan, and this could lead to sustainable growth in Pakistan’s digital financial sector including takaful industry. The Policymakers, Ministry of science and technology and State of bank of Pakistan could benefits from this study by creating a regulatory sandbox and offer current takaful operators full IT and regulatory support to develop Shariah-compliant smart contracts. The results reveal that, Takaful operators should develop and test pilot digital projects focusing on cost reduction, fraud prevention, automation of standards claims where possible, streamline the insurance industry and takaful operations and this leads to not only increase takaful penetration but also help Pakistani takaful market to align with global digital trends.

The digital transformation has fundamentally reshaped brand management, moving from traditional mass communication to data-driven, interactive, and highly personalized strategies. With emerging technologies such as artificial intelligence (AI), augmented reality, and digital ecosystems, brands are now engaging consumers in innovative ways to enhance loyalty and gain a competitive advantage. This study examines how leading brands, such as Nike, Apple, and Coca-Cola, employ digital brand management strategies to enhance brand equity, boost consumer engagement, and maintain market leadership. A multiple-case study approach was employed to analyse this. Data was collected through archival research, social media analytics, and consumer sentiment analysis to assess the impact and effectiveness of these strategies. The study examines key digital branding elements, including direct-to-consumer (DTC) models, experiential marketing, and interactive campaigns. The findings reveal that Nike's DTC strategy fosters direct consumer relationships and strengthens brand equity. Apple's experiential marketing and storytelling foster emotional brand loyalty, while Coca-Cola's personalized and interactive digital campaigns drive consumer engagement and social media virality. These strategies demonstrate the growing importance of AI-driven personalization, omnichannel consistency, and consumer-centric engagement.

The study concludes that brands prioritizing AI-powered personalization and immersive digital experiences achieve stronger consumer engagement and long-term brand growth. Practical implications suggest businesses integrate AI-driven analytics, invest in emerging technologies, and adopt consumer-focused digital strategies. Future research should investigate the long-term effects of AI-driven brand interactions and examine the role of Web3 and the Metaverse in shaping the future of digital brand management.

The rise of distributed applications and cloud computing has created a demand for scalable, high-performance key-value storage systems. This paper presents a performance evaluation of three prominent NoSQL key-value stores: Redis, Aerospike, and Dragonfly, using the Yahoo! Cloud Serving Benchmark (YCSB) framework. We conducted extensive experiments across three distinct workload patterns (read-heavy, write-heavy), and balanced while systematically varying client concurrency from 1 to 32 clients. Our evaluation methodology captures both latency, throughput, and memory characteristics under realistic operational conditions, providing insights into the performance trade-offs and scalability behaviour of each system.

Sovereign large language models (LLMs), emerging as strategic assets in global information ecosystems, represent advanced AI system developed under distinct national governance regimes. This study examines how model origin and governance context influence AI-generated narratives on international territorial disputes. The study compares outputs from three prominent sovereign LLMs - OpenAI’s GPT-4o (United States), DeepSeek-R1 (China), and Mistral (European Union), across 12 high-profile territorial conflicts. Statistically significant differences in each model's sentiment distribution and geopolitical framing are identified using a mixed-methods approach that combines sentiment analysis with statistical evaluation (chi-square tests and analysis of variance, ANOVA) on responses to 300 standardized prompts.

The findings indicate model provenance substantially shapes the tone and stance of outputs, with each LLM reflecting distinct biases aligned with its national context. These disparities carry important policy and societal implications: reliance on a single sovereign model could inadvertently bias public discourse and decision-making toward that model's native perspective. The study highlights ethical considerations such as transparency and fairness and calls for robust governance frameworks. It underscores the need for careful oversight and international cooperation to ensure that sovereign LLMs are deployed in a manner that supports informed and balanced geopolitical dialogue.

The proliferation of Generative AI (GenAI) tools has introduced new dynamics in user behaviour, environmental perception, and digital sustainability. This study, based on a primary questionnaire survey of 1,005 GenAI users aged 18 and above from India, investigates the frequency of GenAI usage and its relationship with climate change awareness, environmental concern, and willingness to adopt energy-efficient digital practices. Using regression-based models, the research reveals a pattern of indirect dependence: lower GenAI usage is related with a greater inclination toward environmentally responsible behaviours, such as transitioning from non- sustainable platforms and adopting energy-efficient digital services. In contrast, frequent GenAI users tend to perceive climate change as temporally distant and of lower immediate importance.

The study also examines how the frequency and nature of social media usage influence users’ attitudes toward sustainable technology choices. These findings provide valuable insights for policymakers, AI educators, digital strategists, and sustainability advocates aiming to foster environmentally conscious technology adoption in emerging economies like India.

Accurately diagnosing emotional and psychological disorders is essential for prompt mental health interventions, especially in intelligent healthcare systems. This paper proposes a deep learning model that uses convolutional neural networks (CNN) and long short-term memory (LSTM) networks to classify emotional states based on physiological inputs like EEG and ECG. Bayesian optimisation improves the model's learning efficacy and generalisation ability by adjusting hyperparameters. In comparison to conventional machine learning models such as Support Vector Machines (SVM), random forest, and standalone deep learning models (CNN and LSTM), the proposed CNN-LSTM architecture increases classification accuracy by 25%, to 92.1%. Its exceptional performance is demonstrated by its AUC-ROC score of 0.96, accuracy of 0.93, recall of 0.91, and F1-score of 0.92. These results show that the model can distinguish between several emotional states, including neutral, tense, and concerned. A real-time application is used to investigate the potential of wearable EEG-based brain-computer interface (BCI) devices for continuous emotional monitoring. The findings indicate that the proposed framework might be a helpful tool for the early detection and tailored management of mental health conditions in intricate healthcare environments.

This research aims to explore the impact of digital virtual anchors, such as virtual presenters and singers, on the entertainment industry, with a focus on user perceptions and market changes. The study analysed data on the behaviour of Chinese youth, including their perceptions of virtual presenters on platforms such as Bilibili, and their influence on preferences and consumer decisions. The methodology included surveys and statistical analysis to assess the degree of engagement, users’ willingness to interact with virtual anchors and their influence on the overall growth of interest in virtual platforms. The results showed that 78% of respondents had a positive perception of virtual anchors, and 62% said that such technologies increased their interest in platforms. The analysis also revealed a significant impact of virtual anchors on market structure, including revenue growth in streaming, virtual concerts and e-commerce. Study participants also noted increased interest in augmented reality (AR) technologies and their integration with virtual anchors. The study’s findings emphasize the importance of the industry adapting to new technologies to attract audiences and remain competitive. The long-term potential of virtual anchors includes opportunities to expand business models, introduce personalized solutions and develop new products, creating significant prospects for their continued use in the entertainment industry.

The purpose of this study is to analyse the effectiveness of implementing adaptive AI learning models in decision support systems to optimise the functioning of smart regions. The study provides a detailed examination of the application of machine learning algorithms, deep learning, and reinforcement learning across various sectors, such as urban management, energy resources, and security. The results revealed that the implementation of these models enhances the efficiency of urban system management, reduces costs, and increases the flexibility of decision-making processes. In particular, adaptive models in energy resource management optimise decision-making processes, leading to more rational resource use and substantial cost reductions. In the security field, adaptive AI models show improvements in predicting and preventing incidents, ensuring more reliable and stable system performance. Moreover, the results include the implementation of adaptive models based on programming languages such as TypeScript and JavaScript. The study demonstrated that the use of TypeScript reduces errors and improves system scalability due to strict typing, as shown in the implementation of a reinforcement learning model. Meanwhile, the use of JavaScript enabled the effective adaptation of models to new data through dynamic updates of regression coefficients, leading to improved prediction accuracy.

The aim of this study is to evaluate the performance of a proposed model utilizing the Technology Acceptance Model (TAM) to forecast student perceptions of statistics education with advanced technology. A total of 379 undergraduate students from Malaysia’s East Coast region were recruited using a simple random sampling technique. This study incorporates six main constructs that are tested simultaneously, namely social influence, self-efficacy, perceived usefulness, perceived ease of use, attitude toward using, and behavioural intention. The Pooled Confirmatory Factor Analysis (PCFA) was employed to assess the factor loadings and fitness of the model being tested. Moreover, the Composite Reliability (CR) and Average Variance Extracted (AVE) were established to assess their reliability and validity. The results of the Confirmatory Factor Analysis (CFA) demonstrated that all six constructs achieved satisfactory levels of model fit, reliability, and validity. These findings confirm that the measurement model is statistically robust and that each construct is well-defined and appropriate for further analysis. Given their strong psychometric properties, these constructs provide a solid foundation for future research and should be considered for further investigation by examining the structural relationships among them, particularly in the context of technology adoption in statistics education.

Speaker identification among identical twins remains a significant challenge in voice-based biometric systems, particularly under emotional variability. Emotions dynamically alter speech characteristics, reducing the effectiveness of conventional identification algorithms. To address this, we propose a hybrid deep learning architecture that integrates gender and emotion classification with speaker Identification, tailored specifically to the complexity of identical twin voices. The system combines Emphasized Channel Attention Propagation and Aggregation in Time Delay Neural Network (ECAPA-TDNN) embeddings for speaker-specific representations, Power Normalized Cepstral Coefficients (PNCC) for noise-robust spectral features, and Maximal Overlap Discrete Wavelet Transform (MODWT) for effective time-frequency denoising. A Radial Basis Function Neural Network (RBFNN) is employed to refine and fuse feature vectors, enhancing the discrimination of emotion-related cues. An attention mechanism further emphasizes emotionally salient patterns, followed by a Multi-Layer Perceptron (MLP) for final classification. The model is evaluated on speech datasets from RAVDESS, Google Research, and a proprietary corpus of identical twin voices. Results demonstrate significant improvements in speaker and emotion recognition accuracy, especially under low signal-to-noise ratio (SNR) conditions, outperforming traditional Mel Cepstral-based methods. The proposed system’s integration of robust audio fingerprinting, feature refinement, and attention-guided.

This paper presents a conceptual framework for enhancing knowledge management (KM) processes in virtual organizations through the integration of mobile agents. With the growing digitization of workplaces and the proliferation of distributed teams, managing and leveraging knowledge efficiently has become critical. Mobile agents offer promising features such as autonomy, adaptability, and mobility, making them suitable for dynamic knowledge environments. The paper outlines the architecture of a multi-agent system for KM and discusses its potential impact on organizational performance. Emphasis is placed on the role of intelligent agents in collecting, filtering, and disseminating relevant knowledge across virtual settings. The proposed model aims to support decision-making, reduce information overload, and facilitate knowledge sharing among members of decentralized organizations.

The domination of cyberspace technologies in inter-human communications is obvious because of their ‎ultra-rapidness and enormous data capacity. Human-intensive ‎use of cyberspace increased the magnitude of streamed data through its nodes, created by two sources: human users and AI. While humans can control their generated data, ‎it proves impossible to control AI due to its super intelligence along with their self-developing ‎abilities, enabling it to produce unlimited volumes of data. It is known that cyberspace depends on physical infrastructure, which is inherently limited. Despite investments to expand capacity, overloading this infrastructure with unlimited data creates critical functionality issues. Additionally, the presence of uncontrollable AI elements leads to unpredictable outcomes. Ultimately, this results in AI dominating cyberspace, a phenomenon known as cyber singularity.

The ultimate consequences of AI cyber singularity motivated the study to recall a similar phenomenon in astrophysics: gravitational singularity. Using general relativity theory, the ‎research analyses the dilemma of data overload in cyberspace and its effects, drawing parallels ‎between outer space and cyberspace‎. It aims to illustrate AI's acquisition of cyber singularity according to astrophysics laws on gravitational singularity, providing an innovative perspective for scientists and scholars studying cyberspace.

This article aims to discuss the evolution over the centuries of the role and social position of those mastering the technologies of their time. We suggest that the Industrial Revolution, the rationalization of technical and managerial processes, then the rise of IT, the ascent of cryptocurrencies and finally the emergence of the neoliberal state have lifted a fringe of these individuals to the top of the social hierarchy. Among the “technology masters”, we distinguish three families: those who remain at the service of the State and the established order, those who have exploited, consciously or not, the withdrawal of the neoliberal State to offer services and innovations formerly assumed by the public sector, and finally those who have consciously taken advantage of this same withdrawal and the recognition they enjoy in society to propose other models (free software, open source, crypto anarchism, technical alternatives, etc.).