University of Sciences and Technology Houari Boumediene

Nickel ferrite (NiFe2O4) was synthesized via the co-precipitation method and thoroughly characterized to investigate its structural, optical, and dielectric properties for photocatalytic and optoelectronic applications. X-ray diffraction confirmed a highly crystalline spinel structure, while SEM and EDX analyses revealed a porous morphology that enhances surface-active sites. XPS analysis verified the oxidation states of Ni2+ and Fe3+, which are crucial for charge transfer and catalytic activity. Diffuse reflectance spectroscopy determined a direct bandgap of 1.78 eV, with a strong extinction coefficient (k) in the visible range and a high refractive index (n) peaking at 7 around 550 nm. These properties indicate robust light absorption, efficient photon trapping, and enhanced optical path length, all of which are beneficial for photocatalytic applications. The dielectric analysis showed a high real dielectric constant (εr) exceeding 50 at low photon energies, suggesting strong polarization capacity, while the imaginary dielectric constant (εi) peaked around 2.5 eV, confirming significant energy absorption and storage potential. Optical and electrical conductivity studies further demonstrated the material’s ability to transport charge efficiently, reducing recombination losses and improving photocatalytic performance. The dissipation factor (tan δ) indicated minimal energy loss, while the relaxation time (τ) analysis showed prolonged carrier lifetimes, ensuring sustained charge separation and enhanced catalytic efficiency. The photocatalytic activity of NiFe2O4 was evaluated through the degradation of Rhodamine B under visible light, achieving a significant degradation efficiency with a rate constant of 0.00934 min−1, markedly higher than that of photolysis alone. Scavenger tests revealed that photogenerated holes and electrons are the dominant active species, driving the degradation process through efficient redox reactions, while reusability studies confirmed the material's stability over multiple cycles. These findings establish NiFe2O4 as a highly efficient material for photocatalysis and optoelectronics, with strong light-matter interactions, high charge mobility, and stable dielectric properties. Its ability to harness visible light effectively, sustain charge separation, and minimize energy loss makes it an excellent candidate for environmental remediation and energy conversion applications.

This paper investigates the characteristics of surface acoustic waves (SAWs) in piezoelectric ZnO thin films deposited on various bilayer substrates composed of silicon-derived materials. Finite element analysis is used to study the phase velocities and electromechanical coupling factors K² of the first two fundamental SAW modes, propagating in multilayered ZnO/Si, ZnO/SiC/Si, ZnO/SiO2/Si, and ZnO/SiN/Si structures. ZnO thin films are grown on these substrates and subsequently characterized using atomic force microscopy (AFM) and X-ray diffraction (XRD) to assess film quality. Among the studied configurations, ZnO/SiO2/Si demonstrates the best performance, exhibiting well-defined SAW modes, superior crystallinity and enhanced K². ZnO/SiO2/Si SAW delay line is then fabricated using conventional photolithography and electrically characterized by network analyzer. The response of the device confirms the presence of both Rayleigh and Sezawa modes, aligning with numerical predictions. The findings of this study contribute to the development of high-performance SAW sensors and resonators compatible with silicon and silicon-based technologies.

Accurate predictions of international trade flows form the basis for informed decision-making and strategic planning at both national and global levels. By offering reliable forecasts of market trends, these predictions allow policymakers, businesses, and economic institutions to anticipate shifts in supply and demand, adapt to evolving economic conditions, and mitigate potential risks. We applied a temporal fusion transformer-based (TFT) model with improved precision to predict international raw material trade flows. Our goal is to enhance prediction accuracy and robustness by leveraging the strengths of TFT in handling complex time series data, surpassing the performance of conventional machine learning techniques.Using an enriched dataset from the UN Comtrade database, the CEPII Gravity dataset, and the World Bank, our model achieves a 17% increase in $R^{2}$ compared to baseline models random forest and graph attention networks. Furthermore, the proposed model offers improved interpretability regarding feature importance, providing clearer insights into trade flow predictions.Our analysis demonstrates the TFT model’s ability to cope with economic disruptions such as COVID-19 and the Ukraine conflict, proving its reliability in volatile trade conditions. This work represents the first application of transformer-based methods to multi-horizon forecasting in raw material trade, offering novel insights into global economic trends.

This study investigates the use of natural pozzolana (PZ) and glass powder (GP) as binary and ternary cementitious materials to improve the sustainability of self-compacting mortar (SCM) for construction applications. The study assessed the effects of PZ and GP on the flowability, mechanical strength, and durability of SCM. Artificial Neural Networks (ANNs) were employed to simulate and predict the performance of these materials, while a statistical ternary mixture design approach was used to explore the interactions between the components and optimize the mixture proportions. This integrated methodology yielded comprehensive insights into the impact of these novel materials on the characteristics of the mortar. The findings indicated that the inclusion of GP enhanced the workability and filling capacity of the mortars by roughly 30% in comparison to the control mixture. A ternary mix containing 5% GP and 10% PZ increased compressive strength by 16%, concurrently achieving a 40% reduction in water absorption and a 50% decrease in porosity. This resulted in a denser microstructure that reduces moisture-induced degradation and improves resistance to environmental conditions. The ANN models effectively predicted the behavior of the mortars and their impact on the mixture parameters. Substituting PZ and GP with sustainable cementitious additives improved the structural properties of the mortar and decreased energy consumption during production, resulting in a 14% reduction in CO₂ emissions.

Obtaining the classical solutions of quasi-linear differential equations is a complex mathematical problem usually solved by a limited number of mathematical techniques. Burgers’s equation is offered for modeling the dynamics of a viscous medium while studying the quantitative properties and classical solutions. An iterative method with new appropriate topological properties is presented to show one classical solution for the problem (1.1) and at least two non-negative classical solutions.

This study examined the viability of column method utilizing the immobilized zinc oxide-loaded activated carbon obtained from corncob (ZnO@CB) to treat the landfill leachate. Instrumental techniques like BET, FTIR, SEM–EDX, and XRD were applied for the characterization of the adsorbents. The break through curve (BTC) was evaluated by altering the flow rate, bed height, and initial concentration of NH3-N and COD. At 35 cm bed height with an initial level of 3264 mg-COD/L, the optimal adsorption capacity was observed to be 35.44 mg-COD/g. Meanwhile, the optimal NH3-N adsorption capacity was 4.81 mg-NH3-N/g at a flow @ 1 mL/min, with an initial concentration of 460 mg-NH3-N/L, and a bed height of 35 cm. Both NH3-N and COD adsorption exhibited a correlation coefficient higher than 0.98 as calculated by linear plots of bed depth service time (BDST) equations, indicating that the column structure model was appropriate. The results reveal that the performance of the adsorption process could be well predicted by artificial neural network (ANN) at 4, 7, and 1 neuron for input, middle, and output layers, with a mean absolute error of 0.0096 and 0.0093 for COD and NH3-N reduction, respectively. In the RF model, higher values of R² (0.9876 for COD and 0.9874 for NH3-N) indicate the model accuracy. The regenerated adsorbent achieved 54.2% and 54.1% removal of COD and NH3-N and adsorbent usage was feasible for up to three cycles. Results of BDST, ANN, and RF models revealed that packed column with immobilized ZnO@CB adsorbent is an efficient method for treating landfill leachate, highlighting the potential of ZnO@CB for industrial applications. Graphical Abstract

A biologically active palladium (II) complex was prepared with a 5‐pyrazolone substituted‐thiourea derivative acting as a κ‐N,S bidentate ligand. The complex was characterized by mass, ¹H nuclear magnetic resonance, infrared and electronic absorption spectrometry. The experimental results indicate a square planar geometry in which there are two molecules of the chelating ligand that forms five‐membered chelate rings. The structure of the complex was optimized and its properties investigated by the Density Functional Theory (DFT). The biological profile of the Pd (II) complex was fully investigated. The interaction of the complex with the desoxyribonucleic acid (DNA) was conducted by UV–Vis absorption spectroscopy. With a binding constant of 2.7 × 10⁴, the DNA binding is strong, and intercalative in nature. The cytostatic and cytotoxic activities of the complex were evaluated by the Sulphorhodamine B chromogenic assay on two tumor cell lines: HT29 and PC3. The complex cytostatic activity is good and comparable to that of Cisplatin as the total growth inhibition is respectively 9 and 11 μM. The assessment of the complex acute oral toxicity conducted in vivo on mice did not result to mortality or major adverse effects after treatment of NMRI mice with the palladium complex, this latter is safe at a dose level of 2000 mg/kg of body weight, and the LD50 is considered to be higher than 2000 mg/kg. The complex anti‐inflammatory activity has been assessed in vivo on mice by the λ‐carrageenan‐induced paw edema. The complex displays a good anti‐inflammatory potential, as 200 mg/Kg of the latter results in an inhibition of 57%.

This study presents a novel approach consisting of integrating experimental mechanics and machine learning (ML) to predict the dynamic compressive strength of plain and steel fibre reinforced concrete (SFRC) under high strain rates. It addresses key challenges of conventional Hopkinson bar experiments, including high costs, limited accessibility to specialized equipment, and difficulties in replicating extreme conditions. A comprehensive database of 157 experimental datasets was compiled to develop robust predictive models, including random forest, gradient boosting (GB), extreme gradient boosting, and categorical boosting. Among these, GB demonstrated the highest predictive accuracy, emphasizing the dominant influence of strain rate. A key contribution of this study is the development of a user-friendly graphical user interface, which transforms these ML models into a practical tool for researchers and civil engineers, enabling cost-effective and time-efficient estimation of SFRC’s compressive strength under dynamic loading. This work highlights the transformative potential of ML-driven approaches in civil engineering, offering innovative solutions to long-standing experimental challenges.

The goal of the agricultural chemicals manufacturing sector is to use the least quantity of pesticide necessary to achieve the best possible effects; the majority of chiral pesticides are synthesized and sold as racemates. In order to guarantee the proper active pesticide application rates, chiral separation is required for precise assessment of the stereoisomers in the designed product. In the present work, the chiral separation of six chiral synthetic pyrethroid insecticides (alpha-cypermethrin, lambda-cyhalothrin, fenvalerate, permethrin, tetramethrin, and cyfluthrin) was studied. The effect of the chiral stationary phase (CSP) (chiralcel OD-H, nucleocel alpha and nucleocel delta), the structure of analytes, the mobile phase proportion and the organic phase type under HPLC normal mode were estimated to assess the good resolution of its stereoisomers. The results indicated that when using the mobile phase of Heptane/2-propanol (98/2, v/v) with the Chiralcel OD-H column allowed the separation of six cyfluthrin stereoisomers, four α-Cypermethrin stereoisomers, four Fenvalerate stereoisomers, two tetramethrin stereoisomers, three permethrin stereoisomers and two λ-cyhalothrin stereoisomers. However, on Nucleocel alpha, λ-cyhalothrin stereoisomers in all the chromatographic conditions studied in the insecticides were not separated except for seven stereoisomers for cyfluthrin were separated and two stereoisomers for permethrin.

In this dissertation, we investigate a class of perturbed interconnected mean-field systems, commonly referred to as coupled systems. Under suitable assumptions, we establish the existence of an invariant open set under the flow of the perturbed system. In other words, we prove that the distance between the components of an orbit remains uniformly bounded, a property known as synchronization. The main tool used is the perturbation method. Notably, the synchronization result does not hold trivially in the unperturbed system. We also apply a fixed point theorem to demonstrate the existence of periodic orbits on the torus. Furthermore, we analyze both stability and exponential stability of such systems by studying the dynamics of associated linear systems.

Geopolymer technology is widely recognized and extensively tested as a sustainable alternative to conventional cement, with considerable environmental and economic benefits through waste management; however, it remains largely unstudied and underutilized in Algeria. Despite the abundant availability of aluminosilicate materials, there is only limited and incomplete research on pozzolans and metakaolins in the region. This article aims to address this gap by investigating the use of Algerian ground-granulated blast furnace slag (GGBFS) to develop an optimal formulation for producing high-performance geopolymers. To determine the optimal combination of alkaline activators compatible with GGBFS and sand content, a series of experiments were conducted on fresh and hardened GGBFS-based geopolymer mortars (GPMs) to verify properties such as workability, setting time, water absorption, efflorescence stability, and mechanical strength. Techniques used to characterize the microstructure of a subset of geopolymer samples included attenuated total reflectance – Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy – energy dispersive X-ray spectroscopy. This research not only emphasizes the environmental benefits of repurposing waste materials but also advances the development of more sustainable and durable GPMs, presenting a promising approach to improving environmental stewardship in material science practices.

This paper presents impact tracking and assessment of disruptions on business processes. Although disruption, in the literature, is known for being negative since it suspends business processes, which means potential delays and penalties, this paper demonstrates that disruption could also be positive when better outcomes are obtained after resuming suspended processes. Assessing disruptions’ impacts also depends on policies controlling resources assigned to business processes. Depending on these policies, a disruption’s impact is either worsened in the case of a negative impact or solidified in the case of a positive impact. A system implementing how to specify policies in Open Digital Rights Language (ODRL) and how to track and assess impacts of disruptions on business processes according to these policies is also presented in the paper.

Hydrogen is a clean and sustainable energy source, making the development of efficient production technologies essential for meeting global energy demands and mitigating carbon emissions. In this study, the optical and photo-electrochemical properties of CoFe2O4 (CFO) nanoparticles have been investigated, highlighting their effectiveness in hydrogen generation. CFO was synthesized using the wet method and formed at 700 °C, the spinel was identified by X-ray diffraction (XRD), FTIR spectroscopy, Raman, and X-ray photoelectron spectroscopy (XPS). The XRD pattern revealed a cubic phase, with a lattice constant of 8.0374 Å and a crystallite size of 36 nm. The morphology was examined by scanning electron microscopy/energy-dispersive X-ray analysis (SEM/EDX). The gap of CFO, obtained from UV–VIS diffuse reflectance spectroscopy, was 1.46 eV. The flat band potential (Efb = 0.06 VSCE) was obtained from the capacitance-potential (C⁻²—E) characteristic in NaOH (0.1 M) solution with p-type behavior. The cyclic voltammetry of CFO indicated favorable hydrogen generation under visible light irradiation, with a potential of -0.7 VSCE. An H2 liberation rate of 48 μmol min⁻¹g⁻¹ was reached under optimal conditions: 1g/L of catalyst, basic medium NaOH at a temperature of 50 °C in the presence of hole scavenger SO3²⁻ (10–3 M).

Titanium alloys, renowned for their exceptional strength, corrosion resistance, and lightweight properties, play a crucial role in numerous mechanical engineering applications. This study addresses the machining complexities of titanium alloy Ti6Al4V by focusing on dry turning operations under varying hardness conditions. Two distinct hardness levels 32 HRC and 38 HRC are investigated using uncoated carbide tools. Experimental parameters including cutting speed, feed rate, and depth of cut are systematically varied to assess their effects on machining performance metrics such as surface roughness, cutting forces, power consumption, and tool wear. Through comprehensive experimentation and mathematical modeling, this research aims to explain the influence of material hardness on machining behavior and optimize process parameters for enhanced efficiency and quality. Ultimately, a multi-objective optimization was conducted and discussed regarding multi-objective artificial Vultures optimization algorithm “MOAVOA” and multi-objective optimization genetic algorithm “GA” methods. The MOAVOA algorithm has demonstrated highly satisfactory results in addressing multi-objective optimization problems and has outperformed the genetic algorithm. The results from the MOAVOA algorithm optimization indicate that the optimal cutting conditions, which achieve a balance between surface roughness (Ra), cutting force (Fz), and cutting power (Pc), fall within the following ranges: cutting speed (Vc) of 90.5–115.32 m/min, feed rate (f) of 0.08–0.81 mm/rev, and depth of cut (ap) of 0.103–0.166 mm for the treated workpieces examined. The findings offer valuable insights into the machinability of Ti6Al4V and provide practical recommendations for improving machining processes in mechanical engineering applications.

We study the asymptotic behavior of the electric field in the transverse magnetic (TM) mode, propagating in a domain consisting of a two-dimensional ball surrounded by a thin ring of thickness $\varepsilon$ (detined to tend to 0), with contrast coefficients tending to infinity, and embedded in an ambient medium. We derive and justify an asymptotic expansion of the solution $u_{\varepsilon }$ to the Helmholtz problem with respect to the thickness $\varepsilon$. Subsequently, we establish Ventcel transmission conditions on the limiting interface G modeling the effect of the thin layer with accuracy up to $O(\varepsilon ^{3})$.