Fatin A. Elhaj

Audio forgery has emerged as a significant security and forensic challenge, driven by rapid advances in generative artificial intelligence and the widespread availability of audio editing tools, which enable the creation of highly realistic manipulated speech with minimal technical expertise. Existing approaches predominantly operate at the file level, providing only coarse binary decisions without identifying when or where manipulation occurs. This study addresses fine-grained temporal localization through a unified frame-level localization framework. We introduce a controlled forgery generation framework derived from the TIMIT speech corpus, applying atomic, localized manipulations under strict temporal constraints and producing precise frame-level annotations across diverse manipulation types. Building on this dataset, we then propose a transform-agnostic localization-driven detection approach using temporal inconsistency modeling, enabling unified analysis across heterogeneous manipulations at frame-level resolution. To analyze forensic evidence, we present an evidence-stratified modeling paradigm comparing three complementary strategies: a handcrafted anomaly-based method, a deep localization model leveraging pretrained wav2vec 2.0 representations, and a hybrid approach combining both through confidence-aware fusion and temporal consistency reinforcement. A systematic experimental analysis evaluates the effects of representation adaptation, hybrid fusion, and manipulation type on detection and localization performance. Results show that handcrafted features are insufficient for reliable frame-level localization, while task-adapted wav2vec 2.0 achieves strong and consistent performance. The hybrid approach does not consistently improve frame-level accuracy but yields substantial gains in segment-level localization by enforcing temporal coherence. Per-transform analysis confirms robust performance across most manipulations, with deletion-based operations remaining the most challenging.

As tourist contexts become more complex and the need for personalized, real-time requests grows within smart destinations, such needs call for innovative technology solutions. Service robotics is a creative way to do so by using autonomous navigation, contextual awareness and interactive services. This paper applies a Design Science Research methodology to design and analyze a service robotics framework for automated tourist assistance in smart destination ecosystems. The research begins with the identification of operational and informational gaps in existing digital tourist services, followed by the definition of design objectives focused on adaptability, accessibility, and scalability. Based on these objectives, a modular robotic assistance artifact is developed, integrating mobile robotic agents, cloudbased knowledge management, and context-aware interaction mechanisms. The proposed artifact is demonstrated through representative tourism scenarios, including urban navigation, transport hub assistance, and event-based visitor support. The evaluation is conducted using scenario-based analysis, focusing on functional coverage, service responsiveness, and system integration capabilities. The evaluation uses quantitative serviceperformance indicators (SCR, RTRI, CSQI, SLRC) to compare human-only versus robotic-assisted assistance across representative scenarios. The results show improved coverage, reduced response time, extended availability, and measurable workload redistribution, providing an actionable KPI toolkit for smartdestination planning. Abbreviations and notation - SCR - Service Coverage Ratio; RTRI - Response Time Reduction Index; CSQI - Composite Service Quality Index; SLRC - Service Load Redistribution Coefficient; Vd - daily assistance requests; Th - mean duration of human-assisted service; Ch - cost per human interaction; Lh - number of supported languages (human-based); Ah - service availability (hours/day); Tr - average robotic assistance time; Ar - robotic service availability; Cr - cost per robotic interaction; Ns - successfully serviced requests; Nh - requests handled exclusively by human staff.

Purpose: This study examines the role of information and communication technology (ICT) in fostering resilient infrastructure and sustainable industrialisation (SI) in the Arab region. It highlights the contributions of Public-Private Partnership (PPP) investments in ICT towards economic diversification, industrial resilience, and sustainable development amidst global challenges. Design/Methodology/Approach: Quantitative analysis is conducted from the data accessed from the World Bank on Arab PPP investments in ICT and qualitative insights are discussed based on the policies of digital initiatives. The study evaluates investment impacts, enablers, and barriers to SI. Findings: The research reveals that strategic ICT investments significantly improve infrastructure efficiency, accelerate industrial digitisation, and advance sustainability goals. Policy gaps, digital divides, and limited regional coordination hinder broader impact. Effective regional collaboration and targeted policies are critical to leveraging ICT's potential. Originality/Value: This paper provides a novel analysis of ICT's transformative potential in the Arab region, its ability to bridge developmental gaps and enhance resilience. Research Limitations: The study focuses on data from selected Arab countries, which may limit generalisation. Future research should explore broader datasets and longitudinal analyses to provide deeper insights into ICT-driven industrial growth. Practical Implications: Policymakers and stakeholders are advised to address policy gaps, enhance regional cooperation, and invest in bridging digital divides to maximise the benefits of ICT-driven transformation. Strategies that prioritise inclusivity and sustainability can amplify economic diversification and industrial resilience. Keywords: Infrastructure Efficiency; Economic Diversification; Public-Private Partnership; Information and Communication Technology; Sustainable Industrialisation; Sustainable Development. Citation: Naim, A., Elhaj, F. and Magd, H. (2026): Applications of Information and Communication Technology in Structuring Resilient Infrastructure and Promoting Sustainable Industrialisation in Arab Region. World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 22, No. 3, pp. 245-261.

Digital twin systems are becoming an important tool in intelligent transportation management, as they provide simulation-based environments for monitoring, analyzing, and predicting traffic behavior. However, the predictive performance of traffic digital twins is often limited by the quality and temporal consistency of sensor-level data generated from microscopic simulations. Most current calibration methods focus mainly on matching macroscopic traffic indicators, such as vehicle count and speed, without explicitly addressing the requirements of multi-horizon forecasting. This creates a gap between achieving realistic simulations and building reliable predictive models. This research proposes a forecasting-aware digital traffic twin framework that integrates microscopic SUMO simulation, controlled sensor-level observation modeling through geometric misalignment and noise injection, behavioral calibration, and deep temporal forecasting within a unified end-to-end structure. Unlike traditional calibration approaches, the proposed Genetic Algorithm (GA) reformulates calibration as a multi-step predictive optimization task. Simulation parameters are optimized by minimizing forecasting error produced by a lightweight proxy sequence model embedded within the calibration loop. In this way, calibration moves beyond simple statistical matching and instead emphasizes temporal learnability and forecasting stability, enabling the digital twin to generate traffic patterns more suitable for long-term prediction. Based on the calibrated traffic time series, both convolutional and recurrent deep learning models are evaluated under single-step and multi-step forecasting scenarios. To further examine generalizability, external validation is performed using the real-world PEMS-BAY dataset. The experimental findings demonstrate that forecasting-aware calibration reduces macroscopic traffic signal errors by around 50% for vehicle count and around 40% for average speed, improves temporal stability, and significantly enhances forecasting accuracy across both short-term and long-term horizons.

Intracranial hypertension (ICH) is a critical complication of traumatic brain injury (TBI), associated with poor outcomes. AI shows promise for early ICH prediction, but its clinical integration remains uncertain. This systematic review evaluates the performance, clinical applicability, and limitations of AI models for ICH prediction in TBI. We searched PubMed, Embase, IEEE Xplore, and Scopus, identifying 250 records. After removing duplicates and screening titles and abstracts, 37 full-text articles were assessed, with 9 studies meeting the inclusion criteria. Risk of bias was evaluated using PROBAST, and data on algorithms, performance metrics, and clinical integration were extracted. The included studies demonstrated strong predictive performance, with ensemble models achieving the highest accuracy. However, reliance on invasive monitoring, small sample sizes, and retrospective designs limited generalizability. Only one non-AI study reported clinical integration, highlighting a translational gap. While AI models show potential for ICH prediction, methodological heterogeneity and the lack of prospective validation hinder clinical adoption. Future research should prioritize standardized outcomes, model explainability, and real-world testing to bridge this gap.