Phytotherapy as a new emerging discipline is considered nowadays as a reference for the elaboration process of new drugs, due in part to its accessibility, affordability, and efficacy, especially in developing countries. This therapy based on natural resource has a long history and large scale of applications. Indeed, it was used in the past by many civilizations as a quick remedy to heal wounds, treat various inflammatory conditions, including fevers, arthritis but plants are also known for their relaxation property and their ability to reduce stress and anxiety. This explains in part why health care professionals are currently using and recommending it. Phenolic acids as a key class of secondary metabolites are well known for their antioxidant and antiinflammatory capacities. This class of phyto-compounds is currently given a new hope in the treatment of pathologies related to neurodegenerative process such as Huntington’s and Alzheimer’s diseases, also amyotrophic lateral sclerosis and Parkinson’s diseases. This neuroprotective effect has been partially explained by scientists by the ability of phenolic acids to interfere with several signaling pathways to downgrade the process of oxidative stress in neurons and glial cells, considered the two key population of nervous system, that is why investigating in depth the pharmacological properties of phenolic acids is necessary in the actual era to allow the establishment of more effective strategies in the elaboration of neuroprotective drugs to face neurological disorders.
Sustainable energy sources like solar and wind speed provide an economically efficient source of energy. Prediction of the output of renewable energy plays a crucial role in shaping decisions concerning electrical system operation and management. Forecasting precision in renewable energy output is essential to ensuring the reliability and stability of the grid, as well as for mitigating risks and minimizing costs within the energy market and power systems. Various statistical techniques were developed to predict solar radiation and wind speed for this purpose and there are two types approaches commonly used: Deep learning and artificial Neural network (ANN). This work propose the used of three statistical methods based in Elman Recurrent Neural Network (ERNN), Long Short Term Memory (LSTM) and Support Vector Machine (SVM) to forecast the output data in different forecasting horizons. Four evaluation deferent metric are used: Forecast skill (FS), Root mean square error (RMSE), Mean Absolute Error (MAE) and coefficient of determination ( R2). These metrics confirm the robustness and accuracy of the LSTM model, validated by its RMSE, MAE, FS, and R² values for both sites. These performances demonstrate the effectiveness of LSTM in capturing temporal patterns, with significant implications for weather forecasting and renewable energy applications.
In an increasingly interconnected world, the intricacies of cross-cultural communication and language acquisition are of a crucial importance. Contrastive Rhetoric theory offers valuable insights into how rhetorical patterns are transferred from L1 to L2. While previous studies acknowledge the influence of culture on rhetoric, they often overlook the reasoning mechanisms shaping rhetorical choices. The present research addresses this gap within the context of Arabic discourse, focusing on Algerian academic corpus, by shifting the focus from surface cultural manifestations to the fundamental reasoning embedded within Arab culture. This study contributes to a deeper understanding of how major Arabic rhetorical patterns, including paraphrase, lexical couplets, and parallelism, are transferred to English compositions by Algerian students.
Given the significant importance of renewable or alternative energies today, extensive research is being conducted to enhance the efficiency and reduce the costs of utilizing these energy sources. Among these studies, solar energy forecasting plays a crucial role in achieving these objectives. Accurate forecasting can optimize energy yield, improve grid management, and facilitate the integration of solar power into existing energy systems, ultimately contributing to more reliable and cost-effective renewable energy solutions. This contribution investigates how data volume influences forecasting accuracy. In particular, the impacts on forecasting accuracy of varying forecast horizons and optimal data splitting for training and testing phases are examined. Additionally, the effects on forecasting Global Horizontal Irradiance (GHI) of clustering the data into 3, 5, or 7 groups using the K-means algorithm are investigated. Five different predictive models are employed— multi layer perceptron (MLP), support vector regression (SVR), random forest (RF), convolutional neural network (CNN), and long short-term memory (LSTM)— alongside the newly proposed kResCLSTM hybrid method. Using GHI observations at an arid site in southern Algeria, it is found that a 10-year time series is optimal, along with a 60%-40% split in it for the training vs. testing periods.
Soil reinforcement encompasses a set of techniques aimed at enhancing its mechanical or physical properties by introducing inclusions that work under tension, compression, or flexion. Some of these techniques include soil nailing, anchor tiebacks, micro-piles, bored piles, and ballasted columns. In this study, we analyze the behavior of a wall anchored by five anchor tiebacks (model of the Ain-Naadja Station 02) subjected to seismic excitations based on the Boumerdes 2003 response spectrum. The analysis is carried out using 2D and 3D finite element methods with the dynamic calculation software Plaxis version 20. The obtained results are presented in terms of horizontal stresses, shear stresses, horizontal displacements, and horizontal deformations over time along the wall (piles) at three different positions: the top, middle, and base of the wall. This is done to anticipate the effect of seismic loading on the stability of the structure.
Onion is an agricultural product widely used in daily life in fresh or dried state where microwave-drying method is one of the exploitable techniques. In such an operation, it would be important to control the effect of the output power in the device on the physicochemical quality of the food. In addition to the water content, the study of the physicochemical quality of onions concerns the color and the shrinkage rate. Monitoring and controlling these parameters is strongly recommended for microwave-dried onions. Onion slices of fixed dimensions (thickness of 10 mm and diameter of 67 mm) are microwave dried, at four different powers (90, 180, 270 and 360 W). The physicochemical quality of the samples is measured at each end of drying and all evaluations are based on minimum values. The results show that the increase of the drying power of the onions accelerates the degradation of their color and increases their shrinkage rate; nevertheless, a reduction in the drying time is quite remarkable. The browning index and shrinkage rate of onion slices are proportional to the microwave drying power. However, the drying time is inversely proportional. Finally, a drying power equal to 90 W and a thickness of the onion slices equal to 10 millimeters are recommended.
Introduction/purpose : This study investigates the seismic response of longspan continuous deck truss bridges under the effect of near-fault vertical ground motions. The primary objective is to assess how near-fault vertical seismic excitation affects the structural safety and performance of these bridges. By exploring the nuanced dynamics induced by near-fault vertical motions, the research aims to improve the understanding of the vulnerabilities and challenges faced by long-span continuous deck truss bridges during seismic events.
Methods : To achieve this objective, the truss bridge was subjected to a series of ground motions, representing natural seismic events. The seismic response of the bridge was investigated by applying the linear time history method to the 3D finite element model. This analysis focused specifically on the evaluation of base shear and displacement. The analysis was extended to include the seismic performance of truss structures. The comparison between the bridge responses with and without consideration of the vertical component of ground motion was made to clarify the effect of vertical excitation.
Results : The results show that there is a significant contribution of vertical excitation, particularly concerning the internal force in the truss elements, where it exceeded 60 % during a severe earthquake, and consequently increased the demand-to-capacity ratio in most elements of the truss bridge structure.
Conclusion : For structural engineers and designers, the results of this research suggest that neglecting to include the vertical ground motion component in the analytical assessments of this type of bridges can lead to a greater degree of uncertainty and risk, particularly in near-fault regions.
The present study aims to evaluate the effects of a cold brine (4 °C) pre-treatment on the quality of camel meat. The studied parameters are moisture content, macronutrient composition, color, pH, and shrinkage, before and after drying. Five groups of 108 camel meat slices with dimensions of 100 ×20 x 4 mm (length x width x thickness) were constituted. The control group (group 1) received no treatment. Groups 2 and 3 were immersed for 30 and 90 minutes respectively in a 19 % sodium chloride solution at 4 °C, then sun-dried. As for groups 4 and 5, they were treated in the same way for 30 and 90 minutes, but oven-dried at 65 °C. Results demonstrate that increasing the soaking time reduced the drying duration from 20 to 16 hours for oven drying and 14–12 hours for sun drying. Moisture content decreased from 73.94±0.31 % to 13.33±0.15 %, while protein levels decreased from 75.76±0.04 % to 74.465±0.02 % and 74.97±0.04 % for oven drying and 74.25±0.07 % to 74.51±0.01 % for sun drying after 30 and 90 minutes of soaking, respectively. A decrease in lipid content from 21.65±0.04 % to 19.10±0.06 % and 19.14±0.08 % was also observed during oven drying and 19.33±0.07 % to 19.12±0.09 % for sun drying. Sodium levels increased from 260.47±1.46 mg/100 g to 1690.36±1.94–1712.11±5.14 mg/100 g for oven drying and 1704.48±7.16 mg/100 g - 1714.89±4.18 mg/100 g for sun drying. Longer soaking times increased total color variation for both drying methods. By using cold brine, the nutrients in the muscle slices are preserved and the final product is lower in salinity.
This paper presents an application of a Region of Interest(ROI)-based compression technique designed to enhance the energy efficiency of visual sensor networks used in wildlife monitoring. By focusing on compressing only the most critical regions within each video frame, the proposed method significantly reduces data volume, leading to substantial energy savings during both compression and transmission stages. The integration of LoRaWAN technology further optimizes energy consumption by providing low-power, long-range communication capabilities. Experimental results demonstrate a compression ratio of 4:1, achieving overall energy savings of approximately 38% for short-range and 40% for long-range transmission compared to traditional non-ROI methods. Despite a slight reduction in image quality, the visual integrity remains acceptable for effective wildlife monitoring, and the method improves transmission success rates over varying distances. These findings highlight the potential of ROI-based compression to extend the operational lifespan of sensor nodes, offering a viable and sustainable solution for long-term environmental monitoring.
