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The present study evaluated the potential of Salvia balansae from the Aures Mountains as a source of natural bioactive compounds. Leaves, flowers, and stems were extracted separately using methanol, ethanol, and acetone. Phenolic compounds were quantified colorimetrically and identified using liquid chromatography coupled with mass spectrometry (LC-MS). Antioxidant capacity was assessed using six different in vitro assays, 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt, 1,1-diphenyl-1-picrylhydrazyl, cupric reducing antioxidant capacity, ferric reducing antioxidant power, ferrous ion chelating, and phenanthroline assay, while the photo-protective capacity was evaluated using the sun protective factor. The methanolic flower extracts revealed the highest O-diphenol and phenolic levels (287.9 ± 0.50 and 147.87 ± 0.21 µg GAE/mg, respectively), whereas the acetonic and ethanolic leaf extracts contained the highest flavonoid (72.17 ± 0.12 μg QE/mg) and flavonol (35.28 ± 0.05 μg QE/mg) levels, respectively. LC-MS was used to identify 18 phenolics, including quinic acid (5.051–69.69 ppm), luteolin-7-o-glucoside (7.802–44.917 ppm), apigenin-7-o-glucoside (3.751–68.507 ppm), and cirsiliol (2.081–15.608 ppm), distinguishing this Aures taxon. Principal component analysis and unweighted pair-group method with arithmetic mean revealed variability in phytochemicals, antioxidant properties, and photo-protective activity influenced by biological activities and the compound content. Overall, S. balansae demonstrated promising photo-protective capacity, the presence of key bioactive compounds, and wide-ranging antioxidant potential, presenting this endemic plant as a valuable source of natural antioxidants and photo-protective agents with pharmaceutical and cosmetic applications.

The olive oil processing industry’s liquid effluents (OMW) have a polluting capacity for the ecosystems. This study aimed to evaluate the physicochemical quality of OMW of two varieties of separated and combined olives cultivated in Khenchela Eastern Algeria, from the cold extraction of the olive oil extraction system. These was to determine their degree of pollution and biodegradability during one year of storage at ambient temperature to recommend the correct treatment for each storage time. Results of the measured parameters pH, EC,TSS percent, H2O percent, lipids, DM, OM, MM, VM, COT percent, NTK percent, C/N, BOD5, COD, BI, TOM, BOD5/COD show that wastewater from olive oil mills has an acid pH, and they are very loaded with organic matter evaluated in terms of COD and BOD5, quite filled with minerals. The storage of olive mill waste can reduce progressively the pollution caused by this waste. Whereas during one year, the reduction rate of COD, BOD5, TOM, BI, is respectively 29.4%, 54.8%, 39.16%, 54.2%, but C/N, BOD5/COD continue to increase as well as pH that continues to decrease during the storage. Accordingly, storing olive mill waste during a year reduces its pollution rate, so it is slowly biodegradable. When disposing of it, an adequate treatment procedure must be required to protect the environment.

Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers' experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field. [ABSTRACT FROM AUTHOR]

The Mediterranean forests, particularly Cork oak (Quercus suber L., 1927), make a major contribution to the fight against climate change through Carbon sequestration. Hence, there is a great interest in the accurate quantification of biomass and carbon stock. In this context, this study aims at assessing the performance of a new approach, based on the combination of Unmanned aerial vehicle airborne Aerial laser scanning (ALS-UAV) and Terrestrial laser scanning (TLS) data, in the determination of dendrometric parameters (Circumference at 1.30 m and Tree Height), and consequently the estimation of biomass and carbon stock, considering field data as reference. This study takes the Maamora forest in Morocco as an example of a Mediterranean Cork oak forest. The methodology consists of collecting data at three levels: the entire area level for an ALS-UAV scan, the plot and tree levels for TLS surveys, as well as field data collection. Afterwards, dendrometric parameters (Circumference at 1.30 m and the Tree height) were estimated using individual tree segmentation and biomass; the carbon stock (aboveground, belowground, and total) was estimated using allometric equations. The comparison of the estimated dendrometric parameters with those measured in the field shows a strong relationship, with a Pearson coefficient of 0.86 and 0.83, a correlation coefficient (R2) of 0.81 and 0.71, and a Root mean square error (RMSE) of 1.84 cm and 0.47 m, respectively. Concerning the biomass and carbon stock estimation, the proposed approach gives a satisfactory accuracy, with a Pearson coefficient of 0.77, an R2 of 0.83, and an RMSE of 36.40 kg for biomass and 20.24 kg for carbon stock.