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An Empirical Analysis of Security and Privacy Issues Associated with Selling and Purchasing of Secondhand Storage Devices
No full textThe increase in use of secondhand storage devices, such as USB drives has created a progressive market that is affordable and offers several environmental benefits. However, these devices often contain residual sensitive data due to improper sanitization and pose significant privacy and security risks. Legal frameworks like GDPR aim to enforce secure data handling, but gaps in enforcement and awareness further increase the problem. This study systematically evaluated the effectiveness of low-level formatting and shredding as data sanitization techniques. A 16 GB USB flash drive was formatted using BureauSoft\u27s low-level formatting tool, and shredding has also been performed using File Shredder (DOD S220.22-M standard. USB drive was analyzed for residual data using forensic recovery tools such as PhotoRec and Scalpel. Despite the sanitization efforts, 431 files were recovered post-formatting and 391 files post-shredding using PhotoRec. Furthermore, scalpel was used as secondary tool for enforced data recovery and recovered 1946 files. This analysis revealed that neither method fully prevented data recovery. There is a need for advanced methods like cryptographic erasure and stricter regulatory enforcement. Future work will explore improved tools and methods to enhance data security in secondhand storage devices
PViT: A Hybrid Model for Deepfake Face Detection using Patch Vision Transformers and Deep Learning
No full textThe proliferation of AI-generated deepfakes, particularly facial image forgeries, poses a significant threat to digital security by facilitating misinformation, identity theft, and privacy breaches. Traditional detection approaches, primarily based on Convolutional Neural Networks (CNNs), often exhibit limited effectiveness when confronted with highly refined or subtle manipulations, leading to compromised detection performance. To address this challenge, this study explores the application of Vision Transformers (ViTs), which leverage self-attention mechanisms to capture fine-grained inconsistencies in visual patterns. This research proposed a hybrid deepfake detection model that integrates patch-oriented ViTs with CNN architectures to improve discriminative feature extraction. Experimental evaluation on benchmark datasets demonstrates that the proposed model achieved a detection accuracy 99%, precision 99%, recall 99%, F1-Score 99% on a validation set comprising 76,161 facial images, outperforming conventional CNN-based methods. These results highlight the potential of transformer-based architectures in advancing the robustness and reliability of deepfake detection systems, thereby contributing to the protection of digital authenticity and information integrity
ICAIMT: ART-LSTM: Augmented Reverse Training for Data-Efficient Time Series Forecasting
No full textFinancial time series forecasting faces significant challenges due to data scarcity, high volatility, and inherent nonlinearities. Complex deep learning models, such as transformers, typically require extensive datasets and computational resources, making them prone to overfitting in financial contexts where datasets are limited. To address this, we propose Augmented Reverse Training LSTM (ART-LSTM), a novel data augmentation strategy for time series forecasting using a straightforward unidirectional LSTM architecture. ART-LSTM leverages both forward and reversed sequences during training, effectively doubling the available training data without increasing architectural complexity. Our approach maintains computational simplicity while enhancing model robustness and generalisation. Empirical evaluations on challenging datasets, including daily S&P 500 index prices and USD/EUR exchange rates, demonstrate that ART-LSTM consistently outperforms traditional statistical methods (ARIMA), standard recurrent neural networks (RNN, GRU, and LSTM), and multi-layer perceptrons (MLPs), achieving substantial reductions in Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Overall, ART-LSTM provides a practical and data-efficient solution for financial forecasting tasks characterised by limited data availability and volatile dynamics
The Effect of Biochar as Partial Cement Replacement on Mechanical, Durability, Thermal, and Microstructural Properties of Concrete - Comparison with Biomass
Get PDFThis thesis paper examines the use of 1% cement replacement with date palm derived biochar and compares it to 1% replacement of biomass. The main aim of this idea is to reduce the carbon emissions from concrete production factories and utilize the abundant waste from date palm plants, which is prevalent in the UAE. Three samples of concrete were used: 1% biochar mix, 1% biomass mix, and a control mix. Mechanical tests, including compressive, tensile, and flexural strengths, were evaluated. Thermal conductivity, chloride penetration, water absorption, and characterization testing by XRD, TGA, SEM, and FTIR were conducted. As for compressive strength, results showed that both additives were lower than the control, with biochar performing better than biomass. In flexural and tensile strength, the biomass sample performed better than biochar. However, biochar was found to be a better performer regarding thermal insulation and chloride resistance, as seen in the FTIR and TGA results. Structure-wise, SEM analysis showed biochar to have a denser matrix than biomass, which exhibited more pores. It is evident that biochar replaced concrete would deem beneficial in applications where durability and thermal stability is needed. This research sheds light on the structure and environmental benefits that could come with utilizing sustainable biochar to replace concrete
DTPA-chitosan mushroom waste biochar for efficient removal of tetracycline from wastewater
No full textTetracycline (TC), a widely used antibiotic, has emerged as a major aquatic contaminant, posing significant environmental and health risks. This study presents a novel DTPA-chitosan-modified biochar derived from mushroom waste for the efficient removal of TC from wastewater. The biochar was produced via pyrolysis at 400 °C and modified at an optimal DTPA-chitosan-to-biochar ratio of 0.35. The material exhibited a high adsorption capacity of 130 mg/g, which was obtained at an initial tetracycline concentration of 50 mg/L (at pH 7). The Langmuir isotherm model predicted a theoretical maximum of 238.1 mg/g, indicating monolayer adsorption. Adsorption kinetics studies showed that the adsorption process of the DTPA-chitosan-modified biochar better fits the pseudo-second-order kinetic model, which suggests that the adsorption rate is controlled by chemisorption. The rate constant for the modified biochar was significantly higher than that of the unmodified biochar (0.6884 g·mg⁻¹·min⁻¹ vs. 0.1425 g·mg⁻¹·min⁻¹), indicating a faster adsorption rate after modification. Characterization results confirmed that surface functional groups, including carboxyl, hydroxyl, and amine, facilitated TC binding through chelation, electrostatic attraction, and hydrogen bonding. Furthermore, the modified biochar maintained approximately 85 % of its initial adsorption capacity after five regeneration cycles and showed stable performance in simulated wastewater conditions. These findings highlight the potential of DTPA-chitosan-functionalized mushroom biochar as a sustainable and high-performance adsorbent for practical wastewater treatment applications
Deploying explainable AI in entrepreneurial organizations: Role of the human-AI interface
No full textThe current advancement of artificial intelligence (AI) is the culmination of a prolonged effort to endow machines with human cognitive capabilities. Scholars and practitioners agree that AI has the potential to revolutionize decision-making in uncertain environments, with the potential role of AI in shaping entrepreneurial decision-making. Simultaneously, AI presents novel challenges, such as explainability, privacy, and data security, and may induce mistakes and ethical issues. As organizations and individuals expect AI decision-making processes to be transparent and understandable, the question of how entrepreneurial organizations adopt AI technologies remains unanswered. There is a lack of clarity on the implications of AI in the context of entrepreneurial organizations. To answer our research question, we conduct a qualitative study and use an interpretive research paradigm with an abductive approach to enrich the current understanding of the role of Explainable AI in shaping organizational processes and accomplishing organizational goals. The finding reveals that Explainable AI enables entrepreneurial organizations to align their decision-making. The role of the human-AI interface is crucial to leverage AI recommendations. We conclude with a discussion of future research on Explainable AI
The effectiveness of a proposed guide to improve the role of parents in developing their kindergarten children English language skills
Get PDFThe aim of this study is to explore United Arab Emirates (UAE) parents’ perceptions of their involvement in their kindergarten children’s English language learning as a second language (L2) and the challenges they face. The study consisted of two phases: the first investigated the role of 329 parents using a questionnaire, revealing a strong need for guidance on how to support their children’s English learning. The second phase assessed the effectiveness of a structured educational guide. Pre/post-test results showed that the guide enhanced parents’ abilities to teach English as an L2 and apply strategies aligned with their children’s learning styles
Public country-by-country reporting, tax avoidance and the cost of equity capital: pan-European evidence
No full textPurpose – The purpose of this paper is to investigate the role of tax avoidance in multinational corporations’ management decisions to voluntarily disclose country-by-country (CbC) information in annual reports and examine investors’ perceptions of these disclosures. Design/methodology/approach – The authors use robust cluster standard errors pooled regression and a sample of 3,243 firm-year observations of European multinational corporations (MNCs) between 2007 and 2018. CbC reporting data are hand-collected from MNCs’ annual reports, whereas the firm-level financial variables are obtained from the Thomson Reuters DataStream and IBES databases. Data for the Financial Secrecy Index are obtained from the Tax Justice Network website. Findings – This study demonstrates that firms engaging in higher levels of tax avoidance tend to disclose less CbC information. Furthermore, the authors find that investors reward increased transparency and tax-responsible behavior by lowering the cost of equity capital. The analysis also shows that the impact of CbC reporting on the cost of equity is more pronounced for firms with lower tax avoidance. Additionally, the authors find that multinational corporations with high tax avoidance operating in countries with high financial secrecy are less likely to disclose CbC information. Originality/value – This study contributes to the growing discourse on corporate tax behavior by offering policy-relevant insights for regulators, policymakers and accounting standard-setters in support of mandatory public CbC reporting for non-financial multinational corporations
Dynamic Framework for Collaborative Learning: Leveraging Advanced LLM with Adaptive Feedback Mechanisms
No full textThis paper presents a framework for integrating LLM into collaborative learning platforms to enhance student engagement, critical thinking, and inclusivity. The framework employs advanced LLMs as dynamic moderators to facilitate real-time discussions and adapt to learners’ evolving needs, ensuring diverse and inclusive educational experiences. Key innovations include robust feedback mechanisms that refine AI moderation, promote reflective learning, and balance participation among users. The system’s modular architecture featuring ReactJS for the frontend, Flask for backend operations, and efficient question retrieval supports personalized and engaging interactions through dynamic adjustments to prompts and discussion flows. Testing demonstrates that the framework significantly improves student collaboration, fosters deeper comprehension, and scales effectively across various subjects and user groups. By addressing limitations in static moderation and personalization in existing systems, this work establishes a strong foundation for next-generation AI-driven educational tools, advancing equitable and impactful learning outcomes
Therapeutic Applications of Microbial Enzymes Recent Innovations and Future Prospects
No full textMicrobial enzymes are proteins synthesized by microorganisms that catalyze specific biochemical reactions and have garnered significant attention as potential therapeutic agents, owing to their diverse applications and unique properties. Various techniques have been developed for manipulating microbial enzymes, including protein engineering, directed evolution, rational design, immobilization, enzyme fusion, and encapsulation. However, several limitations persist, including immunogenicity, stability, specificity, production costs, delivery challenges, and regulatory hurdles. The field of enzyme engineering and production is experiencing a surge in innovative approaches to overcome the existing limitations. This chapter endeavors to explore the diverse array of cutting-edge methodologies currently employed to enhance enzyme performance and production efficiency. This research elucidates the potential of microbial enzymes as therapeutic agents and delineates various techniques for their manipulation, while acknowledging persistent challenges. The ongoing advancements in enzyme engineering and production methods hold promise for overcoming current limitations, potentially revolutionizing the field of enzyme-based therapeutics. These include the application of machine learning algorithms for enzyme design optimization, in silico screening techniques for rapid candidate identification, and CRISPR-Cas9 technology for precise genetic modifications. Researchers are also exploring innovative formulation strategies, including the use of nanoparticles, to improve enzyme delivery and efficacy in various applications. These advancements in enzyme engineering and production techniques have the potential to revolutionize industries ranging from pharmaceuticals to biofuels. By enhancing enzyme stability, activity, and delivery, these innovations could lead to more efficient and cost-effective processes in biotechnology and healthcare. Furthermore, the development of novel enzymatic pathways and improved production methods may pave the way for new therapeutic approaches and sustainable manufacturing solutions