Setif 1 University

The integration of artificial intelligence (AI) into digital platforms has transformed consumer psychology, positioning algorithms as active architects of desire and identity. This conceptual synthesis interrogates the dual role of artificial intelligence (AI) in digital platforms as both a reflector and distorter of human motivation. We propose that AI systems construct "algorithmic selves" by operationalizing fragmented behavioral data, a process we term the Algorithmic Extended Self (AES). This socio-technical process prioritizes corporate objectives over psychological coherence, leading to systemic distortions: "algorithmic alchemy" (the opaque transformation of data into motivational leverage within black-box systems) and "desire dysmorphia" (a failure in sensemaking where users struggle to distinguish authentic desires from algorithmically amplified impulses). Drawing on a synthesis of existential philosophy, consumer psychology, and cognitive systems literature, the paper critiques the quantified self-fallacy and examines the cognitive-work consequences of these mechanisms, such as volitional atrophy and identity fragmentation. Our framework bridges existential concerns with the analysis of recommendation algorithms, highlighting implications for the design of interactive systems. We argue for redefining motivational sovereignty and advocate for coherence-by-design frameworks to address algorithmic interpellation and the commodification of identity in digital capitalism. This work contributes to literature by connecting philosophical critiques of autonomy to concrete issues in human-technology interaction and cognitive work design.

Water reuse represents a critical strategy for alleviating pressure on freshwater resources and reducing reliance on energy-intensive desalination, yet urban wastewater frequently contains pharmaceutical residues, such as 5-aminosalicylic acid (5-ASA), which are resistant to conventional treatment methods. This study evaluates an integrated approach combining coagulation–flocculation, adsorption, and chlorination for the effective removal of 5-ASA from aqueous solutions. Aluminum sulfate was employed as a coagulant, while activated carbons derived from agricultural residues specifically bean peel (ACBP) and pea peel (ACPP) were used as cost-effective adsorbents. The study systematically examined the influence of key operational parameters, including coagulant dose, adsorbent mass, pH, chlorine dose, and contact time, on the removal efficiency of 5-ASA, both individually and in combination. Results demonstrated that the sequential integration of coagulation, adsorption, and chlorination significantly enhances pollutant removal, exceeding the efficiencies achieved by single-step treatments. Mechanistic analysis revealed that coagulation facilitated destabilization and aggregation of organic matter, adsorption provided high-capacity retention via electrostatic and surface interactions, and chlorination selectively degraded residual stable molecules. The findings underscore the potential of this integrated treatment as a sustainable, economical, and scalable strategy for the elimination of persistent organic contaminants from water. Furthermore, the use of agro-waste-derived activated carbons contributes to resource valorization, supporting circular economy principles and offering promising applications for advanced wastewater treatment and water reuse.

Extreme rainfall represents one of the main triggers of shallow landslides in mountainous and hilly environments; however, the rapid identification of rainfall-induced slope failures remains challenging due to the lack of systematic post-event inventories and the limited availability of field data. This gap constrains the development of reliable diagnostic and predictive frameworks, particularly in regions increasingly affected by extreme precipitation events. This study aims to evaluate the capability of satellite-derived spectral indices to detect rainfall-triggered landslides and to interpret their spectral responses within a geological and environmental geochemical context. The analysis focuses on the extreme precipitation event of 1–3 March 2011 in the Marche Region (Central Italy), an area characterized by widespread marly–clayey and Plio–Pleistocene fine-grained formations. Multitemporal Landsat 5 imagery at 30 m spatial resolution acquired between 2007 and 2011 was used to derive seven spectral indices related to vegetation cover, soil exposure, moisture conditions, and surface mineralogy (NDVI, NDWI, NBR, NDSI, BSI, SCI, and CMR). Owing to multicollinearity among predictors, each index was analyzed independently using two complementary statistical approaches: Binary Logistic Regression (BLR), representing a parametric linear framework, and the QUEST decision tree algorithm, designed to capture non-linear relationships and threshold effects. Vegetation-related indices (NDVI and NBR) also show strong predictive capability, with AUC values ranging from 0.94 to 0.96 across both models. Moisture-sensitive indices exhibit a predominantly non-linear response: NDWI performs markedly better under QUEST than BLR (AUC 0.88 vs. 0.58), while NDSI shows the opposite pattern (AUC 0.81 with BLR vs. 0.54 with QUEST), highlighting the complementary strengths of the two modeling frameworks. Importantly, both BLR and QUEST converge on consistent and robust threshold values, providing objective criteria for post-event landslide detection. The identified spectral thresholds are physically interpreted in terms of short-term geochemical weakening processes affecting clay-rich and marly materials. Intense rainfall promotes hydration of expandable clay minerals, partial dissolution of carbonate cement, and rapid soil–water–mineral interactions, leading to reduced soil cohesion and enhanced slope instability. By integrating remote sensing, statistical modeling, and environmental geochemistry, this study offers a practical and transferable approach for rapid post-event landslide mapping. The framework is intended to support hazard assessment in sedimentary terrains prone to rainfall-induced failures, with careful attention to its potential applicability in similar settings. Graphical abstract descriptions: The graphical abstract schematically illustrates the conceptual framework and methodological workflow of the study, integrating geological, geochemical, remote sensing, and statistical components into a unified representation. The upper section depicts the geomorphological and lithological setting of the Marche Region in Central Italy, highlighting marly–clayey formations and Plio–Pleistocene deposits that are highly susceptible to rainfall-triggered slope instability. Intense precipitation is shown to initiate landslide processes through infiltration into fine-grained materials, activating physico-chemical mechanisms such as clay hydration, carbonate dissolution, and cation exchange, which lead to modifications in pore-water chemistry and progressive reduction of soil shear strength. The central portion of the diagram connects these subsurface processes to surface responses detectable by satellite remote sensing, introducing key spectral indices including the Soil Composition Index (SCI), Bare Soil Index (BSI), and Normalized Difference Vegetation Index (NDVI), which capture variations in mineralogical composition, bare soil exposure, and vegetation cover associated with landslide occurrence. The lower section presents the analytical phase, where spectral information is processed through binary logistic regression and decision tree models to quantify landslide probability and identify threshold values distinguishing stable and unstable terrain. Finally, the integration of geochemical interpretation, satellite data, and predictive modeling is linked to landslide hazard assessment, emphasizing the interdisciplinary approach adopted in the study. The graphical abstract thus condenses the entire research structure into a single coherent visual scheme, enabling an immediate perception of the scientific logic, methodological sequence, and interpretative outcomes of the investigation. Rapid post-event detection of rainfall-triggered landslides using Landsat-derived spectral indices. Comparative use of Binary Logistic Regression and QUEST decision tree models. SCI and BSI identified as the most effective indices (AUC up to 0.99). Robust and consistent spectral thresholds for landslide discrimination. Spectral thresholds interpreted in terms of geochemical weakening of clay-rich and marly materials during extreme rainfall.

In this study, we investigated how pressure affects the structural, elastic, electrical, thermodynamic, and optical properties of the BaMg2Ge2 and BaMg2Si2 materials using two complementary ab initio computational methods implemented in the CASTEP and WIEN2k codes. The GGA-PBEsol functional was applied to model exchange–correlation interactions. The calculated equilibrium structural parameters and the counterpart experimental values are almost identical. Under compression, the lattice parameter a decreases more notably than the lattice parameter c in both compounds. The calculated single-crystal elastic constants and related phenomena show that both compounds meet the criteria for mechanical stability at pressures from 0 to 18 GPa, with significant elastic anisotropy and a brittle character. Furthermore, the study considers polycrystalline elastic constants—including the bulk modulus (B), shear modulus (G), Young’s modulus (E), Poisson’s ratio, and related properties—up to 18 GPa. Using the Debye quasi-harmonic model, the temperature- and pressure-dependent behavior of various macroscopic physical phenomena was assessed. Electronic structure calculations reveal that BaMg2Ge2 and BaMg2Si2 are conductors. The spectra of the real and imaginary parts of the dielectric function, the absorption coefficient, the optical conductivity, the loss-energy function, the reflectivity, the refractive index, and the extinction coefficient were determined for two polarization directions of incident radiation.

In this work, a high-performance palladium-based electrocatalyst was synthesized and evaluated for the hydrogen evolution reaction (HER) in an acidic medium. The catalyst consists of Pd nanoparticles electrodeposited onto a three-dimensional gas diffusion electrode (Pd@GDE) using a galvanostatic pulse technique. The deposition time was varied to obtain Pd loadings of 200, 150, 100, and 50 µg/cm2. Electrochemical measurements revealed that Pd loading strongly influences the HER activity, with the highest performance achieved at 100 µg/cm2. In 0.5 M H2SO4, the optimized Pd@GDE exhibited an onset potential of − 4.8 mV vs. RHE, an overpotential of 32.3 mV at –10 mA/cm2, and a Tafel slope of 33.3 mV/dec. Ageing tests revealed remarkable stability of the catalyst, with negligible loss of activity after 1500 potential cycles. Furthermore, the catalyst maintained a stable response at –10 mA/cm2 over 5000 s. This platinum-comparable performance, achieved at significantly lower cost, highlights the strong potential of Pd@GDE as an efficient and cost-effective electrocatalyst for hydrogen production.