As intelligent malware detection systems are deployed in evolving mobile ecosystems, both benign and malicious applications change in structure and behavior, inducing non-stationary data distributions that challenge long-term model reliability. In parallel, adversarial perturbations expose additional vulnerabilities in machine learning–based detectors. Despite extensive work on concept drift and adversarial robustness independently, their interaction in adaptive intelligent systems remains insufficiently characterized.We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device executions. The dataset is organized into yearly slices and evaluated under three deployment protocols that emulate realistic learning scenarios: (1) same-year training and testing, (2) cross-year deployment without model updates, and (3) expanding-window retraining with cumulative historical data. Across multiple classifier families, adversarial examples are generated using FGSM and SPSA under feasibility constraints. We measure clean performance, Adversarial Accuracy (AA), Attack Success Rate (ASR), and introduce temporal linkage metrics—RobustDrop, ΔASR, and Adversarial Amplification Factor (AAF)—to quantify the relationship between distribution shift and robustness degradation. Results show that temporal separation is associated with reductions in both clean accuracy and adversarial accuracy under the evaluated transfer-based feature-space setting. A controlled analysis shows that the residual drift–robustness relationship is configuration-dependent, with a stronger residual temporal association for static features than for dynamic features. Expanding-window retraining mitigates, but does not eliminate, robustness loss under continued distributional evolution. These findings highlight the need for drift-aware robustness assessment frameworks in long-lived adversarial environments.
Riparian zones, situated where land and water environments intersect, rank among the richest and most functionally significant ecosystems on the planet. Yet, despite their importance, they are experiencing escalating deterioration due to human-driven pressures, particularly in Mediterranean landscapes subjected to intense urban and agricultural activities. This research explores the plant composition, biodiversity dynamics, and ecological responses of riparian vegetation along the upper stretch of the Medjerda River in north-eastern Algeria, an area influenced by untreated effluents and agricultural runoff. Field surveys were conducted at ten locations using standardized quadrat sampling to record woody and shrubby taxa. Concurrently, soil samples and Populus alba L. leaf tissues were assessed for concentrations of iron (Fe), zinc (Zn), and copper (Cu). Multiple biodiversity indices were applied, and vegetation community structure was interpreted through the Diversity Evenness and Rarity (DER) algorithm, Principal Component Analysis (PCA), Non-Metric Multidimensional Scaling (NMDS), and Hierarchical Cluster Analysis (HCA). The survey documented 20 species from 11 botanical families, with P. alba L., Tamarix species, and Nerium oleander L. emerging as the most prevalent. Substantial spatial variation was detected: Sites 8 and 9 exhibited diverse and well-balanced plant communities, while Sites 5 and 7 supported species-poor groups dominated by tolerant taxa, signalling ecological decline. PCA results revealed strong inverse relationships between Zn and Cu levels and biodiversity indicators, highlighting the suppressive influence of heavy metals on riparian vegetation. NMDS and DER identified three distinct ecological assemblages—degraded, transitional, and relatively stable—distributed along a continuum of human disturbance. These outcomes reveal pronounced ecological variability and heightened susceptibility of Mediterranean riparian environments to pollution stress. The study emphasizes the necessity of safeguarding biodiversity-rich areas and restoring impacted zones through sustainable management strategies within peri-urban river systems. The graphical abstract provides a visual synthesis of the multiscale ecological assessment conducted along the upper Medjerda River in northeastern Algeria. It highlights the integration of biodiversity surveys, heavy-metal quantification, and multivariate analyses used to characterize riparian ecosystem responses to anthropogenic pressures. The sampling design shows the studied sites along an urban effluent discharge point, with the presence of agricultural pressure nearby. The analysis of heavy metals indicates an elevated presence of zinc (Zn) and copper (Cu), and to a lesser extent, iron (Fe), in the environment. The specific plant species highlighted for study is Populus alba L. (white poplar). The vegetation response observed across the sampling sites varied significantly, reflecting the environmental stress: (i) Impoverished and stressed Tamarix spp assemblages indicate areas subjected to severe environmental stress. (ii) Intermediate assemblages were found in sites 2, 3, 4, 6, and 10. (iii) Stable high-diversity refugia were identified in sites 8 and 9, suggesting areas that are less impacted or more resilient. This pattern is summarized by the sequence: Anthropogenic disturbance → Community simplification → Biodiversity loss.The statistical analyses presented (PCA, NMDS, DER) support the hypothesis that heavy metal contamination (Cu, Zn) and urban/agricultural pressures drive a strong ecological filtering process. This filtering reduces riparian biodiversity by eliminating sensitive species, and instead promotes stress-tolerant species.
In this research article, we highlight the insights and reflections of eight Palestinian English language and literacy teachers as they navigated their ethical and educational responsibilities in the Occupied West Bank of Palestine. In describing the socio-emotional challenges faced by their students and themselves, each participant made reference-either directly or indirectly-to the construct of existence itself as resistance, a familiar theme in Palestine, as well as to others facing adverse circumstances. A primary construct that emerged is the Palestinian Arabic term (sic) (sumud, "steadfastness"), with each participant giving voice to notions of existence, resiliency, and resistance, layered with a personal and collective sense of moral responsibility for their students. Implications of this study will be relevant to educational policy makers and practitioners who are working to strengthen and support the agency and well-being of teachers working in unjust, harsh, oppressive, and/or dangerous contexts.
Purpose This paper aims to examine the relationship between financial inclusion, monetary policy and financial system stability in 15 Middle Eastern and North African (MENA) countries. Design/methodology/approach The study employs a quantitative research design, using a Panel Vector Autoregression model to analyze data from 15 MENA countries over 20 years (2003–2022). Findings Findings reveal a positive relationship between financial inclusion and stability, as inclusion fosters systemic resilience and trust in financial institutions, resulting in a diverse clientele for financial institutions. Financial stability, in turn, enhances inclusion by reducing costs, encouraging private investments, and expanding credit access. While financial inclusion positively impacts money supply, a long-term inverse relationship between money supply and inclusion highlights structural inefficiencies. Practical implications The findings emphasize the importance of adaptive regulatory frameworks and coordinated policy strategies to harness the potential of financial inclusion in fostering economic stability and sustainable development. Policymakers, especially in less-developed MENA countries, are urged to prioritize financial inclusion as a cornerstone for promoting economic resilience and growth. Originality/value This paper contributes to the understanding of the synergies between financial inclusion and macroeconomic stability, offering actionable insights for policymakers in the MENA countries.
Artificial Intelligence (AI)-driven code generation tools are increasingly used throughout the software development lifecycle to accelerate coding tasks. However, the security of AI-generated code using Large Language Models (LLMs) remains underexplored, with studies revealing various risks and weaknesses. This paper analyzes the security of code generated by LLMs across different programming languages. We introduce a dataset of 200 tasks grouped into six categories to evaluate the performance of LLMs in generating secure and maintainable code. Our research shows that while LLMs can automate code creation, their security effectiveness varies by language. Many models fail to utilize modern security features in recent compiler and toolkit updates, such as Java 17. Moreover, outdated methods are still commonly used, particularly in C++. This highlights the need for advancing LLMs to enhance security and quality while incorporating emerging best practices in programming languages.
