Authors - Samira Boulahbel-Bachari, Hind Dib-Slamani Abstract - This study examines digital governance transformation trajectories across fourteen Middle East and North Africa (MENA) countries between 2010 and 2024. Rather than classifying countries as simple “leaders” or “laggards,” it adopts a multidimensional framework covering digital governance, digital infrastructure, inclusion, institutional capacity, and economic capacity. Entropy weighting derives indicator weights, TOPSIS ranks countries according to their proximity to the best observed transformation profile, while hierarchical clustering identifies shared trajectory patterns. Robustness is assessed through VIKOR and principal component analysis. The results reveal marked regional heterogeneity. Saudi Arabia leads the ranking, followed by Türkiye, Oman, and the United Arab Emirates, while Morocco shows a balanced trajectory despite more limited economic resources. Other countries display differentiated progress across connectivity, online services, and institutional conditions, with Tunisia and Lebanon occupying the lowest relative positions. The findings show that progress in aggregate e-government scores does not necessarily reflect coherent digital governance development across all dimensions. The study advances a trajectory-based view of digital governance and offers a practical basis for regional benchmarking and policy prioritization in heterogeneous contexts.
Authors - Mohammad Arafat Ullah Abstract - Fault Detection and Classification (FDC) plays a critical role in semiconductor manufacturing by identifying defective wafers before subsequent processing stages, thereby reducing manufacturing cost, material waste, and production time. Traditional Statistical Process Control (SPC)-based FDC systems are widely used in semiconductor fabrication; however, machine learning techniques can significantly improve defect detection and process monitoring efficiency. In this research, the SECOM semiconductor manufacturing dataset collected from Kaggle was analyzed using multiple machine learning approaches. Several classification techniques including custom Support Vector Machine (SVM), kernel-based SVM, custom K-Nearest Neighbor (KNN), and Random Forest were implemented and compared for defective wafer detection. In addition, pseudo time-series semiconductor signals were reconstructed from static process features. Exponentially Weighted Moving Average (EWMA) smoothing and temporal feature extraction were then applied for signal-based fault analysis. Experimental results show that SVM-based approaches achieved strong classification performance on the SECOM dataset, while temporal signal reconstruction provided additional insight into semiconductor process behavior. The study presents a comparative analysis between conventional feature-based learning and reconstructed temporal feature-based learning for semiconductor fault detection applications.
Authors - M. A. M. P. Wanigaratne, K. B. H. M. T. T. Bandaranayake, C. S. Mohottala, J. V. Pannilage Abstract - Windows Subsystem for Linux (WSL) enables Linux command-line workflows to run directly on Windows endpoints, but this hybrid execution model creates security visibility and interpretation challenges. Host-side monitoring can identify that WSL was launched, but it may not provide sufficient Linux-side command context for threat investigation. This paper presents an explainable WSL command threat detection approach that combines machine learning-based risk scoring with Retrieval-Augmented Large Language Model (LLM) reasoning. The machine learning layer uses wrapper-aware and structure-aware command features to classify WSL-style command activity and convert model output into operational risk scores. The reasoning layer processes suspicious and malicious events using retrieved cybersecurity knowledge to generate analyst-readable explanations, MITRE ATT&CK mappings, confidence reasoning, and suggested defensive actions. The ML component was evaluated using a hybrid command dataset containing 8,028 samples, while the reasoning component was evaluated using 120 sanitized command level scenarios. Results show that the Calibrated SVM achieved 0.96 accuracy and 0.96 malicious class F1-score. Retrieval-augmented reasoning improved MITRE ATT&CK mapping accuracy from 52% to 87% and reduced hallucinated statements from 31% to 12%. The results indicate that combining ML risk scoring with grounded LLM reasoning can improve both alert prioritization and analyst understanding for WSL enabled endpoints.
Authors - Pavel E. Zhukov Abstract - The paper analyzes the problem of growth of public debt in developed countries with the compound interest approach, initially proposed with the Sargent-Wallace model. It is concluded that since 2002, when central banks began to apply the New Keynesian Model in monetary policy, governments have been widely using deficit financing of fiscal expenditures in order to stimulate economic growth. Based on the experience of 2002-2025, the parameters of the exponential growth of the debt-to-GDP ratio and the dangerous values of the budget deficit are assessed. General conclusions are made about the ineffectiveness of the dominant model of fiscal policy and the need to revise it, as well as the need to consider the growth of the money supply in monetary policy. General recommendations proposed. First: it is obvious that the United States and Japan have to introduce the VAT and do not increase customs duties. Second: In order to accelerate economic growth, it is necessary to shift fiscal policy priorities from the development of infrastructure and social programs to R&D, which will increase labor productivity. Third: Generally, all the social programs have to be audited. Specific for the United States, health insurance reform and limiting the growth of the budget deficit due to the Medicare and Medicaid programs are urgently needed. Fourth: Perhaps "national" companies with a high degree of localization should be stimulated with tax incentives for corporate income tax and shareholder income tax.
Authors - Unnati Parmar, Jatin Modh Abstract - Due to the high degree of infusion and morphology, Gujarati is regarded as a low-resource language in the Natural Language Processing field. The key reason why Gujarati can be classified as such is the lack of computing tools and annotated digitized corpus. Every single dialect of the language has its own morphological, lexical, and orthographic peculiarities since the language is extremely diverse. It includes the most diversified dialect – Kutchi – alongside Kathiawadi, Surti, Charotari, and Pattani dialects. The current paper focuses on the evolution in the sphere of Gujarati dialect identification via texts from 2021 till 2025. Morpheme segmentation, parts of speech identification, regional idioms identification, and neural machine translation model adaptation will be analyzed throughout this paper. This study looks at the transition from traditional grammar-based systems to modern deep learning algorithms. Performance metrics from the latest literature are used to identify research gaps. They include excellent results in the detection of idioms and high F1-scores for morphological tagging. Performance metrics of various models, such as transformers and Bidirectional Long Short-Term Memory network, are compared with DFA techniques. A framework for hybrid language models, combining both linguistics and neural networks, is proposed in the conclusion section of this literature review. Neural network models have been found to offer significant improvements in morphology when compared to traditional methods. In this paper, we address an inadequacy in the processing of informal language through the identification of disparities in resources between geographic variations. We propose a combination model that maintains geographic identity in modern-day computerized environments.
Authors - Athanasios Angelakis, Gabriele De Vito, Eleni-Myrto Trifylli, Filomena Ferrucci Abstract - Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely used as a first-line non-invasive test (NIT), but its fixed formula may underuse non-linear diagnostic information contained in age, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and platelet count (PLT). We evaluated whether machine-learning-enhanced NITs (MLE-NITs) can improve advanced fibrosis detection while preserving the clinically accessible FIB-4 variable space. We used three biopsy-validated MASLD cohorts from China, Malaysia, and India (n = 784). The Chinese cohort was split into 486 training and 54 internal validation/tuning patients; final performance was reported only on the Malaysian (n = 147) and Indian (n = 97) external cohorts. Models used five variables: age, FIB-4, AST, PLT, and ALT. We compared FIB-4 with a shallow-deep neural network (s-DNN), TabPFN, and gpt-4o-2024-08-06 in zero-shot and fine-tuned settings. FIB-4 achieved external thresholded ROC-AUCs of 0.75 and 0.60 in Malaysia and India, respectively. TabPFN achieved 0.69 and 0.66, fine-tuned GPT-4o achieved 0.75 and 0.63, and the s-DNN achieved 0.77 and 0.67. The s-DNN contained only 354 trainable parameters, compared with 7,244,554 parameters for TabPFN, and provided the most balanced fixed-threshold operating profile. External diagnostics showed s-DNN Brier scores of 0.18 and 0.22, with AST and FIB-4 as dominant permutation-importance variables. Exploratory decision-curve analysis showed cohort-dependent clinical utility, favoring TabPFN in Malaysia and s-DNN in India.
