University of Bechar

The sustainable management of olive wastes represents an important environmental challenge. Biochars and hydrochars derived from biomass are promising adsorbents for removing emerging pollutants from water. In the present work, olive stone wastes were converted into biochar and hydrochar by using pyrolysis (500 °C for 30 min) and hydrothermal carbonization (HTC) processes (220 °C for 10 h). Then, the obtained materials were physically activated by using CO2 gas (750 °C for 30, 60 and 180 min). Various analytical techniques were applied for the chemical, textural and structural characterization of these carbonaceous materials (i.e., ultimate and proximate analysis, scanning electron microscopy (SEM), BET surface area, Raman spectroscopy, X-ray diffraction, and Fourier transform infrared spectroscopy). Afterwards, the selected activated biochar and hydrochar were applied for the removal of amoxicillin from aqueous solutions. The experimental results show that the generated hydrochar has many microspheres on its surface and inside, while the produced biochar exhibits a porous structure with irregular forms. CO2 physical activation has induced an important improvement of the biochar and hydrochar’s structural, textural, and surface chemistry properties. For instance, the activated biochar samples show a highly porous structure, with large specific surface areas that increase with the burn-off, reaching 1349.3 m2 g−1 following 3 h of activation. Regarding the activated hydrochar samples, they exhibit a spherical morphological structure with an important specific surface area, which increased to 846.7 m2 g−1 after 3 h of activation. Moreover, both activated materials have an amorphous structure with low oxygen surface groups. The selected novel CO2-activated biochar and hydrochar efficiently remove amoxicillin from aqueous solutions under wide experimental conditions, with adsorption capacities of 386.4 and 215.9 mg g−1, respectively. These efficiencies are higher than those reported for various activated biochars derived from lignocellulosic biomass, from sewage sludge, and from animal manure. Future research works are required to assess these materials’ effectiveness in treating real pharmaceutical effluents, to optimize the regeneration of the amoxicillin-loaded materials, and to design full-scale devices for a real application.

This study presents an in-depth experimental investigation into the charging performance of 12-volt photovoltaic (PV) solar panels with lead-acid batteries under diverse atmospheric conditions. The research aims to evaluate how variations in solar irradiance, temperature, and cloud cover influence the charging efficiency and long-term stability of both functional and non-functional 12 V 130 Ah lead-acid batteries. A solar charging system was assembled using four 100 W panels connected in parallel and monitored under real outdoor conditions. Voltage data were recorded at thirty-minute intervals for both types of batteries, enabling a comparative assessment of their performance in clear and cloudy weather. The results demonstrated that the functional battery exhibited steady voltage growth and reached its target charge within two hours under clear skies, while cloudy conditions extended the charging duration to four hours. Conversely, the non-functional battery showed irregular voltage fluctuations and a pronounced decline in charging capacity, confirming its inability to store energy effectively. Mathematical models were formulated to describe the voltage–time relationships for each scenario, revealing charging rates of 0.644 V/h and 0.405 V/h under clear and cloudy skies, respectively. Additionally, post-charging relaxation analysis showed that the functional battery maintained voltage stability, whereas the non-functional battery experienced a continuous drop of 0.342 V/h, indicating internal degradation. The findings underscore the strong dependency of solar charging performance on meteorological conditions and battery health, highlighting the importance of effective charge control strategies and battery maintenance in optimizing solar energy storage and extending battery lifespan.

An inverse problem involves determining unknown physical quantities denoted as u = (u 1 , ..., u np ), which cannot be directly measured but need to be evaluated based on accessible measurements, represented as y = M(u), where M is a mathematical model. Solving such problems often requires mathematical techniques like differential equations or optimization methods such as least squares. Inverse problems can be well-posed (stable, unique solutions) or ill-posed (unstable or non-unique), with ill-posedness often resulting from poor experimental setups or measurement errors. This study addresses the identification of thermophysical parameters - specifically thermal conductivity and heat transfer coefficients—in a 2D steady-state diffusive medium. The proposed method uses a boundary element approach and an iterative descent algorithm to minimize a functional and identify the unknown parameters, validated through simulated thermograms. As a result, the use of sensitivity functions to weight the functional to be minimized makes it possible to avoid selection of the sensors according to the parameter to be identified.

Abstract Peace is an universal phenomenon for sustainable human development which can be established with delivering pure message for the global society. It is only possible after the cultivation of culture of peace in society. Cultivating a culture of peace encompasses elsewhere the nonappearance of conflict to include values, attitudes and behaviors which can foster social harmony, justice, tolerance, patience and mutual understanding among the members of the society. This study opines the role of electronic media in promoting a culture of peace in Nepali society highlighting the potential of television, radio, online news and social media platforms for the purpose of shaping public opinion, social dialogue to support peace education. By adopting inter- and transdisciplinary approaches, to cultivate a culture of peace in society peace education is necessary. Peace education can enhance knowledge from multiple disciplinary genres, including communication, sociology, psychology and technology to create meaningful learning experiences and promote collaborative problem-solving approaches has achieved through institutional programs, classroom practices, personal teaching experiences with the support of electronic media to demonstrate media and educational strategies. It strengthens empathy, tolerance, cooperation and conflict resolution skills. The findings of this study suggest that electronic media can serve as a powerful tool to reinforce everyday peacebuilding among the social members to strengthen social cohesion to maintain social harmony and cultivate a sustainable culture of peace in Nepal. It can diffuse a perception from the land of birth place of Buddha in Nepal, that is " May peace prevail on earth" as well.

This paper presents a body of work (2020-2025) on integrating aquaculture and agricultural fertilization as an innovative model for achieving environmental sustainability and food security. The study discusses the challenges and solutions associated with designing an integrated smart system linking fish farming and plant cultivation, such as maintaining water balance, waste management, food quality control, and climate change adaptation. Advanced farming systems, such as hydroponics and sand farming, have incorporated modern technologies such as AI, IoT, fuzzy logic, and computer vision to reduce labor dependency and improve the efficiency of agricultural and fisheries production. The study demonstrates the effectiveness of smart control systems in water quality, crop nutrient optimization, and irrigation through the use of sensors, microprocessors, and smart applications to improve the conditions of aquaculture and agriculture. This is with the aim of realizing the concept of a circular economy.

This paper introduces the Qudwa Optimizer (QO), a novel metaheuristic algorithm for photovoltaic MPPT, inspired by Islamic principles of Qudwa (guided exploration), Ibada (worship-driven exploitation), and Ilm (knowledge-based diversification). The QO incorporates a unique 'Satanic Repulsion' mechanism, governed by the critical coefficient alpha base, to escape local optima. Through rigorous analysis, we demonstrate that tuning alpha base to 0.0199 optimizes the QO’s performance, achieving a peak power output of 200.0382 W (0.0191% overshoot) compared to 199.5011 W (0% overshoot) at alpha base = 0.02 and 197.7610 W at alpha base = 0.0201. The QO outperforms Interval Type-2 Fuzzy Logic (IT2FL) by 1.5% in steady-state power and surpasses Perturb & Observe (P&O) by 25.117%, while maintaining zero undershoot and superior transient response (rise time = 0.0278 s across all cases). This work establishes alpha base as a pivotal parameter for balancing exploration-exploitation in spiritual-inspired metaheuristics.

This study explores the phenomenon of archaeological vandalism from a sociological perspective, emphasizing its underlying social, cultural, and economic dimensions. Crimes against archaeological monuments are not only acts of physical destruction but also represent deeper patterns of neglect, marginalization, and conflicting values within society. The research highlights the motivations driving individuals and groups to commit such offenses, ranging from economic exploitation and illegal trade in antiquities to expressions of protest or identity struggles. Furthermore, the study examines the consequences of vandalism on cultural heritage, collective memory, and national identity. It also proposes mechanisms to reduce these crimes, focusing on awareness campaigns, stronger legal frameworks, community participation, and the integration of cultural heritage education into social development policies. By adopting a sociological lens, the paper seeks to provide a comprehensive understanding of archaeological vandalism and to suggest sustainable strategies for its prevention.

The segmentation of brain magnetic resonance imaging (MRI) plays a crucial role in neuroimaging analysis. Segmentation is the process of converting inhomogeneous data into homogeneous data. Recently, a significant progress has been reported in brain MRI image segmentation. Among these techniques, finite Gaussian mixture models (GMM) are considered to be more recent and accurate. However, GMM is well-suited when the brain MRI image under consideration is symmetric. In reality, medical brain MRI images are often asymmetric. To overcome this problem, we propose an algorithm for brain MRI image segmentation based on a finite mixture of $$\alpha$$ -stable distributions using the Expectation–Maximization (EM) algorithm. The experimentation is conducted with four different brain MRI images, and the results obtained are evaluated using quality metrics. Additionally, this approach is validated through a comparative analysis with CO $${}_{2}$$ emissions data from fossil fuels and industry, measured in tonnes per person, specifically focusing on the significance of utilizing robust statistical models for real-world data applications. This broader context underscores the versatility and importance of the proposed segmentation method.

Authenticating finger photo images captured using a smartphone camera provides a good alternative solution in place of the traditional method-based sensors. This paper introduces a novel approach to enhancing fingerprint authentication by leveraging images captured via a mobile camera. The method employs a siamese neural network (SNN) combined with a ridge flow attention mechanism and convolutional neural networks (CNN). Our approach begins with collecting a dataset consisting of finger images from two individuals then we apply multiple preprocessing techniques to extract fingerprint images, followed by generating augmented data to improve model robustness, scaling, and normalizing them to form images suitable for model training. Next, we generate positive and negative pairs for training a SNN. We used the SNN with CNN for feature extraction, combined with an attention mechanism that focuses on the ridge flow pattern of fingerprints to improve feature relevance which significantly contributed to the performance enhancement. As for the testing performance, our model has an accuracy of 90%, precision of 89%, recall of 83%, F1 score of 86%, area under the curve (AUC) 95 %, and 13% of equal error rate (EER) when using smartphone-captured images for fingerprint recognition.