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Effect of substrate on germination, growth, and anthocyanin pigment accumulation in
No full textOptimizing growing conditions is essential for the conservation and promotion of medicinal plant species such as Caralluma tuberculata, a succulent plant of great medicinal importance. This project, this particular research was done at the experimental station of the Faculty of Sciences in Oujda, aims to evaluate the impact of two types of substrates, peat and sand, on the morpho-physiological parameters and anthocyanin pigment accumulation in Caralluma tuberculata. The study compared the effect of these substrates on germination rate and kinetics, as well as aerial and root development and red pigment concentration in transplanted seedlings. The results show that the final germination rate is not significantly different between the two substrates (p=0.358), although peat promotes faster kinetics. On the other hand, peat proved to be significantly superior for growth, resulting in significantly greater stem height (8.49 cm) and root length (8.26 cm). Conversely, sand caused an extremely significant accumulation of anthocyanin pigments, reaching 100.00%, compared to 16.20% for peat. This accumulation is interpreted as a physiological defense response to the more pronounced environmental stress in the sandy substrate. These results suggest that the choice of substrate should be based on the cultivation objective. For biomass production, peat and sand should be used to induce protective secondary metabolites
Effects of Salinity and Water Stress on Seeds Germination of
No full textThis study investigates the effect of salt and water stress on the germination performance of Origanum majorana seeds. Salinity was applied using 0, 50, 100, and 150 mM NaCl, while water stress was simulated, using PEG 6000, at values of 0, -0.1, -0.7, and -1 MPa. In all treatments, germination percentage and rate of germination were measured. Results showed that germination decreased with increasing salt and water stress. However, at 50 mM NaCl (moderate salinity) or -0.1 MPa (moderate water stress), germination remained high (about 82-85%) and comparable to the control. High salinity (>100 mM NaCl) or intense water stress (-0.7 MPa and above) reduced germination to below 40-45%. Germination curves exhibited a typical sigmoid shape due to their longer lag times and lower germination rates under salt and water conditions. The results obtained suggest that Origanum majorana shows moderate tolerance to salinity and is highly sensitive to intense environmental stress. Hence, the current experiment indicates that Origanum majorana is moderately tolerant to salt stress and highly sensitive to stronger stress conditions, confirming that appropriate management is needed if this plant is cultivated in arid and semi-arid conditions
Microalgae-Based Wastewater Treatment as a Circular Solution for Sustainable Resource Recovery
No full textThe need for sustainable and circular wastewater management solutions has increased due to water scarcity and environmental degradation. A promising eco-biotechnological strategy that can lower carbon emissions, recover valuable resources, and purify wastewater is microalgae-based systems. Along with important operational parameters that control process efficiency, like light intensity, temperature, CO2 availability, and hydraulic retention time, the metabolic ability of microalgae to absorb nitrogen, phosphorus, and organic matter is investigated. The energy consumption, carbon footprint, and effluent quality of various cultivation configurations— such as open ponds, photobioreactors, and hybrid systems—are contrasted with conventional wastewater treatment technologies. This chapter examines how microalgae contribute to biomass valorization, nutrient recycling, and pollution removal in order to demonstrate these processes as essential elements of a circular bioeconomy. The benefits of turning energy into biofuels and recovering resources by recycling nutrients into biofertilizers are also highlighted in the chapter. Furthermore, future possibilities for process automation, integration with smart cities, and adherence to international carbon neutrality targets are critically looked at, along with difficulties like biomass collection, contamination hazards, and large-scale feasibility
Impacts of Climate Variability and agriculture on water Resources: Adaptation and Resilience, Case of the Irrigated Perimeter of Tadla, Morocco
No full textMorocco's Socio-Ecological System has been highly impacted by the use of water resources. The decrease in annual volume of water collected and the increase caused by an increased rate of rates of Evapotranspiration result in the decrease of the Available Surface Water and Groundwater Repairs as a result of changes in the frequency and amount of precipitation throughout the year, is a process that produces significant Spatial and Temporal Water Disparities; this is known as Water Inequity. The irrigated perimeter of Tadla is facing a major environmental challenge. This issue is closely linked to climate change, which has generated a form of climatic and hydric imbalance. Its effects are visible through the decline in water levels, as indicated by piezometric measurements of groundwater, particularly the Eocene aquifer, which is non-renewable. A possible environmental crisis characterized by water scarcity that jeopardizes both agricultural productivity and the availability of drinking water is also indicated by the region's rising soil salinity
The Role of TiO₂ and ZnO Nanoparticles in Optimizing the UV Resistance, Thermal Stability, and Mechanical Properties of Polyethylene-Based Composite Materials
No full textPolyethylene (PE) is susceptible to photo-oxidative degradation under ultraviolet (UV) irradiation, exhibits limited thermal/oxidative stability, and faces stiffness-toughness trade-offs. This work examines TiO₂ and ZnO nanoparticles-employed as bare and surface-modified fillers with a PE-g-MA compatibilizer-to optimize the UV resistance, thermal stability, and mechanical performance of PE composites. Composites were produced by melt compounding and solution casting. Accelerated UV weathering was performed, and specimens were characterized by FTIR carbonyl index, UV-Vis, TGA/DSC/DMA, and SEM/TEM; differences were evaluated using ANOVA. Coated TiO2⁄ZnO at 1 − 3 wt% delivered the best balance, increasing oxidative-induction time, raising TGA onset temperature, and elevating crystallinity and storage modulus while preserving tensile strength after aging. Surface modification suppressed photocatalytic discoloration and embrittlement, whereas compatibilization improved interfacial stress transfer. The improvements arise from UV attenuation, heterogeneous nucleation, and strengthened polymer-filler interfaces. The findings demonstrate applicability to industrial packaging and outdoor parts; limitations include agglomeration and viscosity growth at higher loadings
Nanostructured Coatings Approach for Corrosion Protection of Building Steels
No full textCorrosion of steel reinforcement is a persistent durability concern for concrete structures, since conventional coatings often fail to provide lasting protection due to poor adhesion and insufficient barriers in harsh environments. This research focuses on advancing corrosion resistance by introducing nanostructured coatings that combine epoxy, cement, and nano-silica materials. Four distinct coating formulations were developed—epoxy, epoxy–cement, epoxy–nano-silica, and epoxy–cement– nano-silica—and subjected to systematic testing under accelerated corrosion conditions. We measured weight loss to see how well the material was doing, since this shows how much it had broken down. The results reveal that adding nano-silica makes the coating much denser and stronger, which lowers the rate of corrosion. The tests showed that the epoxy-nano-silica coatings were the best at keeping rebar of different sizes safe. These results support the idea that nanostructured hybrid coatings could help reinforced concrete last longer in tough conditions
Run-Sketch: Interactive real-time code visualization and AI narration for DSA learning
No full textLearning Data Structures and Algorithms (DSA) can often be noted to be an important challenge, primarily due to the abstract nature of program execution. To address this pedagogical challenge, the current paper presents RunSketch, an interactive educational application that will help with visualizing code in real-time, highlighting syntax, tracking variables, and providing AI-based narration. RunSketch uses code execution to provide feedback on the code to users, and provides explanatory information in the form of written text and spoken output using Google Text to Speech API. Its front-end architecture is based on React.js, D3.js and Monaco Editor, the back-end is written in Python using Flask and uses the sys.settrace() method to trace execution. RunSketch significantly enhances understanding and interaction of learners, due to the combination of visual and audio feedback systems, thus, becoming an important innovation in the sphere of teaching programming
Deep Learning-Based Lung Segmentation for Multi-Modal Imaging Data using Attention Residual U-Net
No full textPrecise identification of lung regions in CT scans is essential for lung cancer diagnosis, staging, and quantitative assessment. Inaccurate or inconsistent delineation can compromise measurements and affect clinical decisions. Traditional segmentation methods, including standard U-Net architectures, often struggle when confronted with variations in imaging protocols or abnormal lung appearances caused by disease. To address these limitations, this study proposes a deep learning–based framework using the Attention Residual U-Net (ARU-Net) for generating accurate lung masks across diverse DICOM datasets. ARU-Net strengthens feature propagation through residual connections while its attention mechanism enables the network to focus more effectively on relevant lung structures and suppress background interference. The model is initially trained on the Kaggle lung segmentation datasets (LUNA16 and DSB2017), which provide expert -annotated 2D CT slices, and later applied to multi-institutional DICOM scans from The Cancer Imaging Archive (TCIA), including CT and PET -CT studies. Pre-processing steps such as intensity normalization and histogram matching are incorporated to enhance domain consistency. The resulting lung masks are produced as 3D volumes and DICOM SEG overlays to support further clinical tasks, including lesion extraction, TNM staging, and percentile density analysis. Experimental results show that the proposed method outperforms conventional U-Net models in segmentation accuracy, robustness, and downstream clinical applicability
Energy-Efficient IoT-Enabled Smart Street Lighting System with Master-Slave Node Architecture
No full textPublic street illumination plays a vital role in urban safety while accounting for a considerable portion of municipal energy usage and operating expenditure. To find a real-time solution for this challenge, this work presents an Internet of Things (IoT) based smart lighting framework that improves energy efficiency using a centralized and distributed node control strategy. Light intensity is adjusted in a dynamical manner using voltage regulators, relays, and transformers. This results in reducing power consumption by balancing required illumination levels. Ambient illumination levels and motion activity are identified using a combination of light-dependent resistors and passive infrared sensing units. A Raspberry Pi functions as the master node and Arduino boards act as slave nodes and communication is through HC-12 module. Current and potential transformers measure electrical parameters, with real-time data uploaded to a central server for remote monitoring and analysis. This approach offers significant advantages in terms of energy savings, cost efficiency, and environmental sustainability, making it suitable for deployment in diverse locations and contributing to smart city development, sustainability, and the achievement of the United Nations Sustainable Development Goals
Real time drone surveillance system –for detection, distance estimation and automatic target lock using YOLO
No full textThis task presents a practical way to monitor drones and protect aircraft. The system detects and tracks drones in real-time. It uses computer vision and deep learning, employing a Yolov8 model to identify drones reliably. This works well even in poor lighting, background noise, or partial visibility. Besides detection, the system estimates the drone's distance from the monitoring station and checks it against a safety limit. If the limit is exceeded, the system quickly issues a warning and locks onto the target. This design prioritizes efficiency and works well on devices with limited computing power, making it suitable for laptops and larger applications. Possible uses include civil surveillance, industrial security, and defence operations. The proposed YOLOv8 model achieved [email protected] of 0.94, precision of 0.91 and recall as 0.90, with the average inference speed as 3.8 ms per frame, confirming it is suitable for real time operations. Future upgrades may include support for multi-camera setups, swarm identification, and trajectory prediction, enhancing its role in protecting restricted airspace