The University of Gafsa (Arabic: جامعة قفصة) is a public university located in Gafsa, Tunisia. The university is oriented primarily toward sciences and information technology.
Solar still (SS) technology offers a sustainable solution for freshwater production, especially in arid regions such as southern Tunisia with high solar irradiance. In the present paper, different SS designs were experimentally investigated to enhance their performance compared with a conventional solar still (CSS). The novelty of this work lies in the combined integration of a stepped basin design, an external reflector, and a fan-assisted airflow system to intensify evaporation and condensation processes under real arid climatic conditions. Five configurations with identical geometric dimensions were evaluated. The first one is a conventional solar still, while the others are modified configurations: a basic stepped solar still (BSSS), a fan-assisted solar still (FSSS), a reflector-assisted solar still (RSSS), and a hybrid configuration integrating both a fan and reflector (FRSSS). Experiments were conducted over four consecutive summer days under the arid climatic conditions of Gafsa City, southern Tunisia. Various performance parameters were evaluated including glass temperature, basin water temperature, solar irradiance, and cumulative freshwater yield. An improvement in productivity of 52
A comprehensive electrical, vibrational and optical investigation of the Cs3Bi2Br9 single crystals emphasizes their unique light-responsive behavior. Strong electron–phonon coupling and thermally-activated exciton delocalization are shown through a narrowing of the full width at half maximum (FWHM) from 0.080 to 0.063 eV while, with an emission peak shifting from 2.48 eV at 77 K to 2.53 eV at 300 K, the broad self-trapped exciton emission is revealed by temperature-dependent photoluminescence (PL). A redshift of 0.005–0.015 eV and a pronounced photoluminescence quenching exceeding 50
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 research, we have focused to examine en detail the microstructural, magneto-optical and photocatalytic properties of the spinel chromite Ni0.5Cd0.5Cr2O4. SEM analysis revealed an average crystallite size of approximately 73 µm. Magnetic characterization, based on hysteresis loop measurements, enabled the determination of key parameters including the saturation magnetization (MS), coercivity (Hc), anisotropy constant (K), and squareness ratio (SQ). The band gap energy (Eg) was estimated to be 2.52 eV using multiple methods, including the Tauc plot and the derivative method. The superposition of the absorbance and reflectance spectra reveals an optical singularity around 895 nm. Cauchy dispersion parameters were derived from the variation of the refractive index with wavelength, and the dispersion energy was evaluated using the Wemple–DiDomenico relation. Additional optical parameters—such as penetration depth, extinction coefficient, electrical conductivity, and plasma frequency—were analyzed as functions of wavelength. Finally, the photocatalytic activity of Ni0.5Cd0.5Cr2O4 was assessed through the degradation of methylene blue, confirming its potential for environmental remediation. Overall, this comprehensive study provides valuable insights into the multifaceted physicochemical behavior of Ni0.5Cd0.5Cr2O4 and underscores its applicability in functional and environmental applications.
Many plants act as natural anti-constipation agents due to their specific compounds that work as laxatives and promote bowel movements. This study focuses on the investigation into the Globularia alypum plant’s ability to relieve constipation induced by loperamide (Lop) in rats. The aqueous extract of G. alypum leaves (GA) was subjected to physico-chemical analyses and in vitro anti-oxidative assays. GA in vivo effects on intestinal transit, oxidative stress markers and tissues’ structure were evaluated. Dominant chemicals of GA were selectively tested for their interaction to inflammatory factors (IL-6 and TNF- α) receptors through in silico study. Our results revealed that GA contains 19 bioactive components. At 20 µg/ml, GA did reduce the half of both DPPH radical and ferric ions. In vivo experimentation, proved that GA ameliorates intestinal transit and restores the oxidative stress equilibrium in both kidney and colon organs. Among the studied components of GA, oleuropein and verbascoside have the best binding affinities to both tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) receptors, with Gibb’s free energies ranging from – 6.6 and – 6.8 kcal. They present interactions with 4 or more hydrogen bonds into the site of action of IL-6 receptor. Furthermore, no relevant histological abnormalities were induced by both doses of the extract. Overall, these findings support the potential use of GA extract as a laxative and antioxidant agent, as well as a modulator of inflammatory pathways.
