University of Gabès

This paper aims to manufacture a low-cost activated carbon (AC) from date palm fiber material via chemical activation with H3PO4. Various acid/carbon impregnation ratios (30%, 60%, 100%, and 150%) were prepared and characterized using BET, FTIR, SEM, and TGA/DTG analysis. The optimal impregnation ratio obtained was 150%. AC150% was used as an adsorbent for the MB dye. The Box-Benken design (BBD) was utilized to optimize retention parameters such as pH, dye concentration, temperature, and contact time. Twenty-nine experiments were carried out using the Box-Behnken design. With an optimal R² score of 0.9954 and an appropriate link between the variables and response, the quadratic model was determined to be the best fit. The optimal conditions for MB dye adsorption onto AC150% were identified. The results indicate that the retention conditions had a significant influence on MB color removal. The ideal parameters for 96.99% MB elimination were obtained at an initial concentration of 400 mg.L⁻¹, a pH of 5.65, a contact time of 60 min, and a temperature of 35 °C. Kinetic and equilibrium analyses were also performed to assess the efficiency of the generated AC under optimal MB elimination conditions. The pseudo-second-order model and the Langmuir isotherm provided a good fit to the experimental data, with an elevated adsorption capacity of 763.35 mg.g⁻¹. According to the experimental results, AC150% has the potential to be used as an adsorbent in wastewater treatment processes to remove organic pollutants.

The management of heavy metal-contamined mining wastes is one of the critical global challenges for the present and future generations. In the mediterranean context, Jebel Ressas (JR) tailings (Northern Tunisia) present a serious pollution risk due to their high concentration of heavy metals, in particular lead (Pb) and zinc (Zn), with an heterogeneous mixture of sulphide and oxidized minerals. To address this issue, a modified-froth flotation method was developed and optimized for the specific characteristics of JR tailings to enhance heavy metals recovery. Representative samples of JR mine waste were collected and fully characterized using various techniques. Mineralogical analysis identified the presence of galena, sphalerite, cerussite, smithsonite, hemimorphite, hydrozincite and willemite. Chemical analysis of raw materials indicated a total Pb and Zn contents of 2.25% and 2.41%, respectively. After optimization of grinding time and determination of the mesh release, the froth flotation process, involving two steps, was applied. The floatability of both sulphide and oxidized minerals was significantly improved. A final treated product with substantially reduced heavy metals concentrations (0.08% Pb and 0.05% Zn) was obtained. The separation of sulfide minerals was achieved by xanthate collector under alkaline pH, while the flotation of oxidized minerals was facilitated by a combination of sulphidization, anionic collector and primary alkylamine-type cationic collector under alkaline pH.

Comorbidity, the simultaneous existence of multiple medical conditions in a patient, is a major challenge in healthcare. Comorbidity is highly threatening for healthcare systems, which requires innovative solutions over traditional methods. The medical field is challenged by accurately diagnosing these intertwined diseases of coexisting ailments and anticipating their rise. The current diagnostic approaches are time-consuming and inaccurate, hinder effective treatment, and delay accurate results for the patient. Artificial intelligence can provide an effective method for early prediction of comorbidity risks. In this study, various artificial intelligence models are used, and a clinical dataset of 271 patients is utilized to diagnose comorbidity. In which a hybrid diagnosis model is proposed based on the intersection between machine learning (ML) and feature selection techniques for the detection of comorbidity. Fuzzy decision by opinion score method is utilized as a sophisticated tool to select the most representative ML for prediction. Extensive simulation results showed an accuracy rate of 91.463 using AdaBoost ML. Furthermore, utilizing the fuzzy decision by opinion score technique, we were able to confirm that the best model using all features as well as the chi square and KBest features is the AdaBoost, which scored the smallest value of 0.204 and hence confirm that it is the best selected ML model for comorbidity.

The Dunkl-Bessel wavelet transform (DBWT) is a novel addition to the class of wavelet transforms. Knowing the fact that the study of the time-frequency analysis is both theoretically interesting and practically useful, the first aim of this article is to explore the main theorems of harmonic analysis of this novel transformation. The second aim is to study some quantitative uncertainty principles associated with the proposed transformation. Our third endeavour is to study the $L^{p}$ boundedness and compactness of localization operators associated with the DBWT. Further, we study their trace class properties and we prove that they are in the Schatten-von Neumann.

Induction motors (IMs), as essential components in industrial operations, are subject to various operational abnormalities, such as voltage unbalance, harmonic distortions, under/over voltage supply, and ambient temperature variations. These factors necessitate the de-rating of torque to ensure motor reliability, efficiency, and safe operation within rated power loss limits. Traditional methods for estimating de-rated torque often involve complex and time-intensive calculations, creating challenges in real-time applications. To address these limitations, this manuscript introduces the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a robust predictive tool for de-rated torque estimation under abnormal conditions. This study defines and quantifies main de-rating factors (Dfs), including voltage unbalance, harmonic distortions, and temperature rise, employing MATLAB/Simulink simulations for performance analysis. The proposed ANFIS controller integrates neural networks and fuzzy logic, enabling efficient evaluation of de-rated torque by dynamically adjusting to real-time operating conditions. Validation of the ANFIS predictions against Simulink outcomes highlights its reliability and accuracy, with minimal deviations observed. Results reveal the significant impact of DFs on induction motor (IM) performance. Voltage unbalance and harmonic distortions emerged as primary contributors to reduced torque output, while temperature rise exacerbates power losses and thermal stress on IM components. By mitigating the need for extensive calculations, ANFIS simplifies the process of assessing torque de-rating and ensures rapid, precise predictions. ANFIS controller is trained offline to assess the de-rated torque of the IM under different operating conditions. The results from this training have been validated against Simulink outcomes, confirming the reliability and accuracy of the ANFIS technique. This research advances the understanding of IM performance under non-ideal conditions, offering a practical framework for de-rating torque evaluation and management. The integration of ANFIS as a control mechanism not only optimizes motor efficiency but also extends operational longevity, underscoring its potential for real-world industrial applications.

This paper presents a novel maximum power point tracking (MPPT) controller for photovoltaic systems, named the artificial neural network double integral sliding mode controller (ANN-DISM MPPT). The proposed controller combines the intelligent capabilities of an artificial neural network (ANN) to address partial shading challenges with the robustness of a double integral sliding mode (DISM) approach, ensuring reliable operation across various environmental conditions. The ANN component effectively tracks the global maximum power point, while the DISM component incorporates a novel sliding surface design with power and current error expressions to eliminate steady-state errors and enhance reliability. Additionally, a new nonlinear switching control strategy, uDISMC, is introduced to improve the system's stability and robustness. The controller is implemented with a two-phase interleaved boost converter (IBC), offering superior performance over conventional boost converters. Simulation results in MATLAB demonstrate the effectiveness of the ANN-DISM MPPT controller and IBC under both uniform and partial shading conditions, showing significant improvements in tracking accuracy, response time, and overall efficiency compared to the traditional perturb and observe algorithm.

The objective of this study is to produce porous, sustainable ceramic adsorptive aggregates for the improvement of water quality and water treatment applications. Three types of clay, Natural Zeolite (NZ), and Spent Coffee Grounds (SCG) are used in the synthesis of aggregates with high adsorptive capacities. Various characterizations are conducted, encompassing measurements of bulk density, Specific Surface Area (SSA), total porosity, and Scanning Electron Microscopy (SEM) analysis. Additionally, the study examines the influence of initial pH, reaction time, and initial zinc concentration on adsorption performance. The experimental findings indicate that the bulk density ranges between 0.5 and 0.71 g/cm³, while the total porosity varies from 44 to 62%. In batch adsorption experiments, the kinetics are best described by a pseudo-second-order (PSO) model. Furthermore, the Redlich-Peterson and Freundlich isotherm models offer a more accurate representation of the adsorption data when compared to the Langmuir model. The quantity of adsorbed material ranges from 14 to 30.5 mg/g and is significantly influenced by physicochemical and mineralogical properties. In conclusion, this research contributes a viable approach for the recovery and recycling of various materials, transforming them into Ceramic Adsorptive Aggregates (CAA). These aggregates present a cost-effective, efficient, and environmentally friendly adsorption medium. Graphical Abstract

This study comprehensively investigates the structural, morphological, and electrical properties of Sr2TiZrO6 (SrTZr) double perovskite synthesized via a solid-state reaction method. X-ray diffraction (XRD) analysis confirmed the formation of a single-phase tetragonal structure (space group P4mm) with high crystallinity. Scanning electron microscopy (SEM) revealed a dense microstructure with uniform grain distribution. Crystallite size, calculated using the Scherrer formula, ranges from 35 to 40 nm, influencing grain boundary area and charge transport. Dielectric measurements show significant interfacial polarization, indicating potential for high-frequency applications. Impedance spectroscopy and electrical modulus analysis revealed non-Debye behavior, thermally activated conductivity, and hopping conduction. The calculated activation energy suggests a thermally induced hopping process, with values Ea1=832 meV (high temperature) and Ea2=503 meV (low temperature). Dielectric loss increases with temperature, likely due to enhanced conductivity. These findings highlight the promising potential of SrTZr double perovskite for applications in advanced electronics, particularly where low dielectric loss and efficient energy storage are required.

The use of plastics and other anthropogenic debris (AD) as nesting materials by the yellow-legged gull Larus michahellis (YLG) has been previously reported in different north Mediterranean and Atlantic breeding colonies. This behavior is also suspected to widely occur in south-Mediterranean areas, and possibly to a greater extent because of high AD availability related to inefficient waste management systems, but data are lacking. The aim of our study was therefore to investigate AD incorporation into YLG nests in a southern Tunisian breeding colony and determine to what degree this integration was associated with debris availability in the environment. We examined nest materials from 232 nests and evaluated AD pollution within the colony area using transect sampling. Eighty-eight percent of sampled nests contained AD which accounted for 3% of the weight of nest materials. Although plastics were the most common AD category, representing 78% of recorded AD items and 71% of AD weight, there was no evidence of a preference for plastics over the other categories. Indeed, AD of different categories were incorporated into nests in accordance with their availability in the surroundings. However, our results did suggest a preference towards white-clear AD, a pattern that could be driven by the probable dietary origin of these materials. Overall, we report a level of AD in YLG nests that is among the highest found in the Mediterranean, and we suggest that the YLG may be a facilitator of Mediterranean coastal pollution by carrying human waste elements into natural areas.

This manuscript presents an innovative control strategy for the Hybrid Excitation Permanent Magnet Synchronous Motor (HEPMSM) designed for electric vehicle (EV) applications. The strategy combines Maximum Torque Point Tracking (MTPT) and Maximum Torque Per Ampere (MTPA) techniques to track the ideal torque-speed profile, ensuring maximum torque at low speeds for starting and climbing, and high power at higher speeds for cruising. A novel unidirectional excitation current method is proposed to replace traditional bidirectional field current control, eliminating the risk of permanent magnet demagnetization, reducing copper losses, and increasing efficiency. This approach extends the constant power (CP) region by a 4.2:1 ratio. The manuscript also introduces a detailed mathematical model, considering both iron core losses and their impact on the EV profile. Additionally, the Multi-Objective Ant Lion Optimizer (MOALO) algorithm is used in two stages: first to optimize the hybridization ratio (HR) and base speed (Nb), and second to analyze the effect of varying the hybridization ratio while maintaining constrained output power. The proposed strategy is validated through MATLAB simulations, demonstrating its effectiveness in achieving high acceleration, efficiency, and reliability for EV applications.

In assent with the environmental stimuli, the germination cascade is partially mediated by hormone regulation, viz. the level and the tissue sensitivity, particularly by gibberellins (GAs) and abscisic acid (ABA). Moreover, water scarcity and quality are a prevalent impediment to its successful accomplishment. Seed sensitivity to such interconnected factors could be quantified via the population-based threshold (PBT) approach. Germination of Capsicum annuum L. across a wide range of water potentials (Ѱ) induced by NaCl, GA3 concentrations, and their combinations, was appraised to address hydrotime, GA3-time, and Ψ-GA3 time models. Seeds showed relatively low sensitivity to Ѱ (median threshold water potential Ψb(50%) = − 2.187 MPa). Meanwhile, a prominent effect was noted for GA3 in the germination percentage, primarily at − 1.4 MPa, and in the speed, except with 200 and 500 µM GA3. Exogenous GA3 improves stress tolerance, i.e. reduces Ѱ sensitivity, mainly by a decrease in Ψb(50%) (shift to − 4.91 MPa with 200 µM GA3) and a continuous increase in the hydrotime constant (θH) (from 211.55 to 937.17 MPa h) which, unexpectedly, did not resonate slower germination. On the other hand, there is an increase in GA3 sensitivity until − 0.6 MPa (median threshold GA concentration log[GAb(50%)]: from − 2.00 to − 2.16 log M at − 0.4 MPa), then a decrease, and an increment in the standard deviation (σGA) which reflects a wide variation in GA sensitivity among individual seeds. Thus, by quantifying changing sensitivity, PBT models could accentuate the underpinning physiological mechanisms that help to understand how GA3, Ѱ, and NaCl conjointly may regulate germination.

Widespread plant species exhibit considerable intraspecific variation due to phenotypic plasticity and genetic adaptation. Portulaca oleracea, a ubiquitous invasive annual C4 weed, poses a challenge to agriculture worldwide. Its resilience to climate change, demonstrated by its ability to tolerate extreme temperatures and high salinity, suggests an increasing invasiveness that threatens agricultural yields. Despite its short life cycle, P. oleracea produces numerous seeds that can thrive in extreme environments. In this study, eight Tunisian wild populations of P. oleracea were analyzed to understand seed morphology and germination under different salinity conditions. Under control conditions, a large variability in the studied parameters and a geographical differentiation primarily related to temperature and latitude were found, indicating a clinal adaptation across an aridity gradient. In addition, our results showed that populations from Saharan and arid regions exhibited greater salt resistance compared to populations from semi-arid regions, demonstrated by larger seeds and robust germination. These results emphasize the joint role of clinal adaptation and phenotypic plasticity in the invasiveness of P. oleracea in different environments. Utilizing these findings could lead to more effective strategies for managing the spread of the plant in agricultural landscapes.

Heterogeneous communication modes in 5G demand integrated device connections, resource availability, and high capacity for meeting user demands. The radio resource allocation and usage for massive users results in interference between the device-to-device (D2D) uplink channels. This issue is addressed using a Non-orthogonal Convex Optimization Problem (NCOP) that identifies the chances of self-interference cancellations. This technique classifies interference and non-interference allocations in the rate of uplink communications. The channel reassignment is addressed as an NCOP based on the available interference levels. The interference levels before and after allocation and reallocation are analyzed under convex optimization. The interference cancellation convergence is computed for both channels wherein the transfer switching is performed. The convergence rate is estimated using the interference level and the number of channels reassigned for the uplink devices. Hence, the self-interference cancellation relies on non-convex channel allocations across various switching in this case. This feature is revisited if the D2D channels exceed their capacity for communication. Therefore, the 5G communication features coexist with the D2D uplinks for interference cancellations to improve channel allocation. For the SNR = 45dBm, the proposed NCOP reduces 12.4% of channel reassignment by augmenting 9.24% of interference cancellation.

This study reports the green synthesis of novel iron oxide nanoparticles (α-Fe2O3NPs) with high dye removal efficiency using cactus cladode extract as a reducing agent. The synthesized α-Fe2O3NPs were characterized using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, dynamic light scattering (DLS), and X-ray fluorescence (XRF) analyses to confirm their structure and properties. The influence of various parameters, including solution pH, initial methylene blue (MB) concentration, adsorbent dose, and salt content, on the adsorption process was investigated. The equilibrium adsorption data closely followed the Langmuir isotherm model with a high correlation coefficient (R² = 0.991). The maximum adsorption capacity of MB dye reached an impressive 1666 mg.g⁻¹. Kinetic studies revealed that the adsorption process was well-described by the pseudo-second-order kinetic model (R² = 0.980). Thermodynamic analysis indicated that the dye adsorption process was exothermic and spontaneous in nature. Optimization of the MB adsorption process was carried out using response surface methodology (RSM) with the application of Box–Behnken design (BBD). Under the optimal conditions of an adsorbent dose of 10 mg, solution pH of 10.92, initial MB concentration of 131.92 mg.L⁻¹, and salt concentration of 0.02 g.L⁻¹, a maximum adsorption capacity of 358.48 mg.g⁻¹ was achieved. Finally, the nanoparticles maintained high efficiency over three successive cycles of MB dye removal, demonstrating their potential as a sustainable and effective adsorbent for wastewater treatment applications.

With rising energy demands and mounting environmental challenges, the push for sustainable energy systems has increased interest in developing advanced energy storage materials. This study aimed to create and evaluate the properties of a novel latent heat storage bio-composite (LHSB) using cost-effective and eco-friendly materials. A bio-based phase-change material (PCM), coconut oil, was added to a bio-support derived from pomegranate peels (PGPs) waste in two forms: raw peels (unwashed and washed) and biochar (physically and physicochemically activated carbon). Pellets of the raw PGPs and activated carbon were fabricated and subsequently impregnated with coconut oil under vacuum conditions. The highest loading capacities were observed to be 60.44% and 58.02% for washed PGPs and physicochemically activated carbon, respectively. Fourier transform infrared spectroscopy (FTIR) analysis corroborated the absence of chemical reactions between coconut oil and raw or modified PGPs. Scanning electron microscopy (SEM) micrographs provided visual evidence of successful coconut oil impregnation. Thermal gravimetric analysis (TGA) revealed that the operational temperatures of all synthesized PCM composites were considerably lower than their respective thermal degradation temperature limits. The encapsulation efficiencies were determined to be 47.69%, 61.62%, 43.97%, and 59.45% for unwashed peels, washed peels, physical biochar, and physicochemical biochar, respectively. Differential scanning calorimetry (DSC) analysis indicated that the coconut oil/unwashed peels composite exhibited the highest latent heat of melting and freezing, with values of 52.51 and 56.11 kJ/kg, respectively. These findings collectively demonstrate that the prepared LHSBs possess several desirable properties, including leak-proof nature, environmental friendliness, energy efficiency, and suitability for temperature regulation in diverse energy storage applications.

This research focuses on non-Newtonian stagnation-bioconvective point flow near a stretched cylinder along the Reiner-Rivlin model. The study incorporates thermal and mass transfers, considering thermodynamic diffusion, bioconvection, and viscous heating. Entropy production analysis is included to assess the inherent uncertainty in transport processes. The computational framework is developed under prescribed wall temperature and concentration conditions, which are essential for achieving self-similar solutions. Numerical findings are collected using MATLAB's "bvp4c" technique. The numerical outcomes are validated by comparing them with solutions for specific parameter values. The impact of curvature on boundary layer behavior is investigated for a range of governing parameters. For Reiner-Rivlin fluids, the skin friction coefficient is calculated to determine the force exerted by the straining cylinder. Additionally, a rise in the Reiner-Rivlin fluid factor causes a reduction in the surface cooling rate. Mainly the flow patterns observed by considering the quantity of parameters as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on all profiles.

This paper presents a comprehensive study on utilizing artificial intelligence (AI) and advanced detection techniques for the study and monitoring of ships. The primary objective is to prevent various issues, such as ship intrusion detection, ship detection in satellite images, and ship detection in river images. To achieve this, the study proposes innovative methods; including enhancing the capabilities of YOLOv3 and YOLOv8 neural networks to improve the accuracy of ship detection. Additionally, the study leverages IoT technology for real-time tracking and integrates feature fusion modules for more effective information integration. A crucial aspect highlighted in this study is the necessity of controlling pollution caused by ships. By addressing this environmental concern, the study aims to contribute to the preservation of marine ecosystems and enhance maritime safety. The results of the study demonstrate a significant enhancement in detection accuracy, showcasing the potential of these advanced methods for efficient and reliable ship monitoring systems.

Metal-free photocatalysts, especially through the use of semi-conductors g-C3N4 (graphitic carbon nitride, CN) have become a prominent topic due to their sustainable advantages and promising effectiveness in hydrogen (H2) production. However, CN material requires specific modifications, since its efficacy under visible light suffers from fast recombination of electron/hole pairs (e‒/h+), slow charge transfer and limited surface area. In this study, we present the synthesis of CN via the thermal treatment of urea and melamine mixture. To enhance its crystallinity and photocatalytic performance, Pt nanoparticles were loaded onto CN by simple incipient wetness impregnation method. The H2 production was investigated through the potential application of aromatic alcohols including anisyl (AA), benzyl (BA), piperonol (PA), and methanol (M) alcohols, as sacrificial reagents. H2 production was achieved using the hybrid Pt–CN system with the added benefit of value-added organic synthesis under visible light exposure. The Pt–CN photocatalyst exhibited varying H2 evolution rates on the alcohol used as sacrificial reagent, with the PA yielding to the highest rate of 503.5 µmol·g–1·h–1. Stability assessments confirmed the robustness of the synthesized Pt–CN photocatalyst across three consecutive visible light driven experiments. Notably, piperonal (P) synthesis occurred along with H2 production under visible light. Comprehensive structural, textural, morphologic, optoelectronic and electrochemical characterizations were performed correlating the Pt–CN’s properties with its visible photocatalytic performance.

Using of Aloe vera showed as alternative feed a positive influence on the ruminant’s animals production, health and well-being. In addition, its corporation in animal feed enhanced animal performance and reproduction, as well as the nutritional quality of produced food such as meat and milk. This trial was carried out to introduce Aloe vera gel (AV gel) in dairy cows ration and determine its effects on intake, digestibility in vivo, and on quantity and quality of produced milk. The experimental animals were12 cows (mean age = 4.4 years and mean weight = 669.68 kg). They were assigned into two groups: control (C), animals were fed a basic ration and a concentrate and AV gel group (AV); the cows received the same C feed with addition of 10 g of AV gel. The dry matter intake, feed digestibility, milk yield, (feed/diet) chemical composition, and the fatty acids profile were determined. The incorporation of AV gel did not influence (P > 0.05) both feed dry matter intake and digestibility. However, the milk yield was (3.05 l/d) higher in AV lot (18.99 l/d) compared to C group (15.94 l/d) (P < 0.05). Similarly, value of protein, lactose, and fat were higher (P < 0.05) in AV group in comparison with C group. Concentration of saturated fatty acids and Omega ω − 3 and Omega ω − 6 were similar (P > 0.05) in both groups. The ω − 3 /ω − 6 ratio was lower (P < 0.05) in the AV group compared to C group (2.12 and 4.87, respectively). In addition, the concentrations of linoleic acid were (P < 0.05) greater in the AV group in comparison with C group. Use of alternative resources in cow feeding, such as Aloe vera can increase the milk product and improve the disease quality of milk specially the beneficial fatty acid and decrease the costs of animal feed.