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Optimizing Earth observation nanosatellite missions requires careful orbit selection to ensure global coverage and high image quality. The main challenge is to define an optimal orbit that maximizes image quality while achieving comprehensive Earth coverage. To address this, an orbit definition model that accounts for the J2 perturbation was developed, evaluating key parameters such as swath width, spatial resolution, time resolution, and tilt angle. The model was applied within the context of a 3U university nanosatellite equipped with the Gecko imager. The analysis identified 551 km as the most suitable altitude for achieving comprehensive coverage. At this altitude, the swath width is 88.16 km, and the distance between adjacent ground tracks is 86.33 km, satisfying the condition for global coverage with nadir pointing. The revisit time is 31 days, ensuring regular coverage of the Earth's surface. Results were validated through Systems Tool Kit simulations, showing minimal differences compared to the MATLAB model, underscoring the model's accuracy and reliability. To further optimize the mission's effectiveness and bandwidth utilization, images captured in this orbit can be processed onboard to ensure that only relevant and useful data are transmitted to the ground station. To support this, a dataset of 900 images was generated at the optimal orbit altitude, specifically tailored for a cloud detection application using the Gecko imager. The findings validate the effectiveness of the proposed orbit design model and demonstrate the feasibility of pre-launch dataset preparation, paving the way for future onboard implementations and in-orbit real-time applications.

Over the past decade, many types of waste have been exploited as feedstocks in different industries. Recycled plastics are among the waste sought for several civil engineering applications. In this work, various plastic-bonded sand composites based on polypropylene waste and silica sand were produced to serve as building materials in many construction applications. Many tests and analysis were carried out in this investigation. First of all, two initial used compounds (waste PP and various silica sand) were analyzed by using ATR-FTIR, XRF, and grain size distribution. In the second time, the different plastic-bonded sand composites were analyzed by using ATR-FTIR to assess their composition. On the other hand, mechanical, and physical tests such as three-point flexural strength, compressive strength, water absorption, and optical observation were applied on different produced composite samples, then the results were examined and analyzed. The results showed that the developed composites exhibit commendable mechanical properties, especially flexural and compression resistance, and minimal water absorption. It is worth noting that the plastic-bonded sand containing Khobana sand showed the highest flexural and compressive strength at 11.56 ± 0.36 and 26.19 ± 0.27 MPa, respectively, along with the lowest water absorption rate of 0.46%. This study confirms its contribution to enhancing sustainability and promoting the principles of the circular economy.

Abstract Autonomous driving is an exciting research field that has received growing attention in recent years. One of the most challenging and safety‐critical driving situations is highway on‐ramp merging. Most decision‐making strategies that perform highway on‐ramp merging are designed, firstly, to reduce the risk of crashes and improve the safety metrics. However, even with the development of such advanced driving systems, human drivers will still be involved in road traffic. Human drivers have various driving styles and different reactions to other traffic participants on the highway on‐ramp. Understanding driver behaviors is essential for designing safe and efficient real‐world driving strategies. Therefore, this paper provides a unique systematic review of existing techniques for modelling driver behaviors at highway on‐ramps, which are critical locations for traffic safety and efficiency. The novelty of this review is that it proposes a new classification of current state‐of‐the art techniques. Each category of techniques involves a unique paradigm. For each category of approaches, fundamental concepts are examined together with their challenges and limitations, and an overview on practical implementation. Furthermore, and based on the classification and chronological order, current research trend is identified, i.e. “data‐driven approaches”. Some future research avenues and disparities are also discussed.

Cement manufacturing significantly impacts global CO₂ emissions, contributing around 8% due to its high energy demand and the calcination process in clinker production. To reduce this environmental impact, the industry is exploring sustainable alternatives by integrating supplementary cementitious materials (SCMs), which enhance cement properties and lower its carbon footprint, aligning with sustainable construction goals. One such material is bentonite, a naturally occurring clay with high plasticity and excellent water retention properties. When added to cement mixtures, bentonite improves essential properties like decreasing permeability, enhancing workability, and boosting chemical resistance. Its pozzolanic activity aids in long-term strength development, making it valuable for standard and specialized applications, including geotechnical engineering and waste containment. Bentonite supports environmental sustainability by reducing cement demand thus lowering CO₂ emissions and by increasing durability, which extends the lifespan of structures and cuts down on maintenance costs. This discussion will explore the various environmental and mechanical benefits of bentonite as a sustainable additive in cementitious systems and highlight studies that have demonstrated its effectiveness in promoting more eco-friendly construction practices.

Grass pea (Lathyrus sativus L.) is a nutritious legume crop well-adapted to fragile agro-ecosystems that can survive under challenging climatic conditions. The cultivation of grass pea faces stigma primarily due to the presence of β-N-Oxalyl-L-α, β-diaminopropionic acid (β-ODAP), which is associated with a risk of inducing neurolathyrism upon prolonged consumption of its grains as a staple diet. The grass pea improvement program of the International Center for Agricultural Research in the Dry Areas (ICARDA) aims to reduce β-ODAP content to a safe level along with improving yield potential and nutritional quality of grass pea. In this study, 183 germplasm accessions representing 13 different Lathyrus species and 11 L. sativus breeding lines were evaluated for β-ODAP content based on Rao protocol and mineral concentration using ICP-OES. Significant variability was observed among the accessions for the studied traits. The results showed low β-ODAP content and high mineral concentration in 25 accessions of crop wild relatives, which included L. cicera, L. ochrus, and L. cassius, with one accession IG65277 of L. cassius, in addition to two lines, IG117034 and ACC1335, of L. sativus having very low β-ODAP content. Furthermore, some accessions of L. pseudocicera, L. aphaca, L. cicera, L. marmoratus, L. gorgoni, and L. tingitanus also showed low β-ODAP content. The results showed significant positive correlations among different trait combinations, viz., K and P (r = 0.193 ***), K and Fe (r = 0.177 ***), Mn and Fe (r = 0.210 ***), Mn and Se (r = 0.137 ***), β-ODAP and Mg (r = 0.158 **), and β-ODAP and Ca (r = 0.140 **). L. cicera, L. ochrus, and L. cassius were identified as a great source for improving the mineral concentration and reducing β-ODAP content in the cultivated grass pea.

This research uses Artificial Neural Network (ANN) as a soft computing technique to predict the carbonation depth and service life of cementitious materials with low clinker content. For this purpose, different mortars were prepared with 0, 10, 15, 20, 25 and 30% replacement levels of cement by metakaolin (MK), brick powder (BP) and calcined sediments (CS). The experimental results of the carbonation depth were obtained under natural and accelerated carbonation conditions for exposure periods of 12 months and 28 days respectively. ANN was utilized taking into account the main influential factors on mortars carbonation, including mix proportions and environmental conditions. For the ANN model, seven datasets were considered as inputs, covering mineral admixture content, cement content, curing time, CO2 concentration, relative humidity, temperature and CO2 exposure time, in addition to one output parameter which is the carbonation depth. The results show that the resistance to carbonation of the mortars decreases with the increase of cement substitution by MK, BP or CS. The network model gives good performance values in the validation and testing set with a lower mean square error (MSE) and a higher determination coefficient (R). The predicted carbonation depths are in good agreement with the experimental measurements of carbonation depths, confirming the efficiency of the developed ANN model to be applied to correctly estimate the carbonation depth of cementitious materials with low clinker content.

The main objective of this study is destined to combine the Analytical Hierarchy Process (AHP), Weight of Evidence (WOE), Logistic Regression (LR) methods and geographic information system (GIS) to predict landslide susceptibility of the Echorfa region (northwestern of Algeria). Nine factors such as slope, aspect, lithology, distance to faults, lineaments density, distance to the streams, precipitations, land use and altitude are included in landslide susceptibility evaluation process. A detailed landslide inventory map was established by satellite images and filed surveys. Three landslide susceptibility maps are established using the different statistical models. Five landslide susceptibility categories are generated by the GSI classification nil, low, moderate, high and very high susceptibility. The performance of the different models in landslide susceptibility is calculated based in the area under curve of the Receiver Operating Characteristic (ROC) which give a satisfactory result. The results showed that the WOE is more performance than the two other techniques. The produced landslide susceptibility maps provide important spatial information about landslide prone area, where the constructed map’s content will help the decision makers in land use planning.

This paper shows that computing the n-queen solution of the chessboard n-by-n from the chessboard (n-1)-by-(n-1) can be used in polynomial time O(n2) using symbolic computation on the complement of the n queen Graph. Nevertheless, continuing further in an incremental approach, besides the n-1 solution, which represents the maximum cliques of size n-1 on the complement of the graph corresponding to the chessboard n-1, we need the maximum cliques of size n-2,and below. By doing so, we need to eliminate the anti-chain problem, which increases the algorithm’s complexity.

Orobanche crenata parasitism on lentil ( Lens culinaris Medik) is one of the most destructive factors for this crop in Morocco. Field and pot assays were performed to study the mitigation of O. crenata stress on two lentil genotypes, Bakria (partially resistant to O. crenata) and Zaaria (susceptible), using salicylic acid (SA) and indole acetic acid (IAA). These two hormones were applied separately at concentrations of 1 mM and 0.09 mM, respectively, using seed pre-treatment and/or foliar spray methods. SA and IAA seed pre-treatment for the susceptible genotype Zaaria and foliar spray for the resistant genotype Bakria gave the best control of O. crenata under field and controlled conditions. This control reached ~91% in Zaaria and 83% in Bakria and was sometimes accompanied by an increase in plant growth and seed yield compared to the untreated plants. Biochemical assays showed that SA and IAA reduced O. crenata infestation in lentil through induction of systemic acquired resistance characterized by increasing activities of phenol metabolizing enzymes (phenylalanine ammonia-lyase, peroxidase, and polyphenol oxidase) implicated in natural defense systems of plants. Treatment of plants with SA or IAA could be an alternative strategy of crop protection with more satisfactory preservation of the environment.

Argania spinosa (L.) is one of the most economically and culturally important indigenous species in Morocco. Its seeds contain a vegetable oil, internationally known as argan oil, which is widely used in edible, cosmetic and pharmaceutical sectors. The aim of this study was to assess the levels of variation in oil content and fatty acid composition of eighteen trees to identify genotypes with desirable traits in terms of oil quantity, quality and industrial utilization. Oil yield of eighteen genotypes was determined after mechanical pressing, and fatty acid methyl ester analysis was carried out using gas liquid chromatography. Obtained results show that oil yields ranged between 37.2 to 43.8% and major fatty acids in the extracted oil were oleic (47.15%), followed by linoleic (31.57%), palmitic (14.24%) and stearic (5.8%) acid. Fatty acids composition was significantly different among genotypes tested. Significant correlations, both positive and negative were located between some fatty acids. The high variability observed between genotypes represents a very promising base to develop a new argan variety with high oil quality.