Blockchain’s decentralized, immutable, and transparent nature offers a promising solution to enhance security, trust, and efficiency in supply chains. While integrating blockchain into the SCM process poses significant challenges, including technical, operational, and regulatory issues, this review analyzes blockchain’s potential in SCM with a focus on the key challenges encountered when applying blockchain in this domain—such as scalability limitations, interoperability barriers, high implementation costs, and privacy as well as data security concerns. The key contributions are as follows: (1) applications of blockchain across major SCM domains—including pharmaceuticals, healthcare, logistics, and agri-food; (2) SCM functions that benefit from blockchain integration; (3) how blockchain’s properties is reshaping modern SCM processes; (4) the challenges faced by businesses while integrating blockchain into supply chains; (5) a critical evaluation of existing solutions and their limitations, categorized into three main domains; (6) unresolved issues highlighted in dedicated “Critical Issues to Consider” sections; (7) synergies with big data, IoT, and AI for secure and intelligent supply chains, along with challenges of emerging solutions; and (8) unexplored domains for blockchain in SCM. By synthesizing current research and industry insights, this study offers practical guidance and outlines future directions for building scalable and resilient global trade networks.

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.

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.

Cloud computing has revolutionized modern education by enabling students to access, store, and share academic resources efficiently. However, the level of cloud literacy among first-semester students, particularly in developing countries, remains uncertain. This study investigates the awareness, usage patterns, and challenges faced by first-semester students in Malaysian Polytechnics and Community Colleges when utilizing cloud services. A quantitative survey was conducted among 148 students from various disciplines. The results reveal that while most students are familiar with basic cloud functionalities, many lack proficiencies in advanced features such as data security, multi-platform integration, and real-time collaboration. Key challenges include limited internet connectivity, insufficient training, and privacy concerns. The findings emphasize the need for structured cloud literacy training programs to enhance students' digital competencies and prepare them for technology-driven workplaces. This study concludes by recommending the integration of cloud computing into educational curricula and the provision of subsidized internet access to bridge the digital divide.