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Desertification In Irrigated Areas in Mediterranean Environments, Case of Tadla’s Irrigated Perimeter
No full textIncreasingly, desertification within the Mediterranean region's irrigated areas is largely attributable to climate change and unsustainable agronomic, hydrological, and environmental factors. The case study presented in the Tadla irrigated perimeter, in the semi-arid southern central Moroccan region, illustrates this phenomenon. Over the past several decades, a rise in climate variability, increased frequency of drought conditions, and higher overall temperature ranges have profoundly reduced the amount of available water to farmers. There is increasing stress on both surface and groundwater resources. The irrigated perimeter of Tadla suffers from a maj or environmental problem that manifests itself in the phenomenon of climate change which represents a desertification that affects the irrigated area, this is reflected in a very alarming way at the level of the lowering of the piezometric levels either of the groundwater and also the deep groundwater which is not renewable and also the salinity of the soils which becomes very acute; This is leading to an environmental disaster, with a general water shortage threatening agriculture, drinking water supplies and a general reduction in agricultural production. The objective of this study is to identify how desertification is treated within the context of the Tadla irrigated area; to describe how climate change influences vulnerability and resilience in Mediterranean agricultural-hydrology systems through irrigation practices; and to understand how this relationship has developed over time
Numerical Performance Assessment of Uzbekistan Climatic Conditions on a Water Based Flat Plate PVT System
No full textThe exploitation of solar energy in the most efficient way will continue to be the staple of sustainable energy policies, particularly, in solar-rich areas, such as Tashkent, Uzbekistan. Photovoltaic/thermal (PVT) systems are appealing concept of dual-generation having the simultaneous generation of electricity and heat. An in-depth dynamic simulation was done on a calibrated model and compared a normal photovoltaic (PV) panel and a water-cooled PVT system after 100 days with synthetic hourly climate data. The analysis indicates that the cell temperatures of the PV panel were observed to be in excess of 50 0C, a fact that adversely affected its electrical efficiency. Comparatively, there was active cooling in the PVT system-so the cell temperatures were between 30 0C to 45 0C, which led to better electrical performance-as high as 6% better than that of the PV system. Also, the PVT unit discharged high heating capacity with output temperatures of water outlet in excess of 60 0C as well as maximum thermal power production of 1.2 kW. The results indicate the energy savings of the integrated thermal management in solar panels and point out the capability of PVT systems in providing heating and electric power requirements. The findings favour the increased application of the hybrid solar technologies to achieve the improved energy production and sustainability of buildings and infrastructures under various climates
Study of the growth of blue barbel (
No full textThe blue barbel (Pterocapoeta maroccana, Günther 1902) is an endemic Moroccan species classified as Critically Endangered (CE) by the international union for conservation of nature (IUCN) and Data Deficient (DD), [1,6]. This study aims to characterize its growth to support conservation and management efforts. A total of 314 individuals were sampled in the upper Oued Oum Er-Rabia River between November 2022 and May 2024. Growth was analysed using scalimetry and modelled with the Von Bertalanffy Growth Model. Asymptotic length (L∞) and growth coefficient (K) were estimated at 402.8 mm and 0.201 yr−1 for males, 463.7 mm and 0.145 yr−1 for females, and 566.9 mm and 0.106 yr−1 for combined sexes, indicating faster growth in males and larger asymptotic size in females. The length-weight relationship showed isometric growth in males (b = 3.00) and negative allometry in females (b = 2.91) and combined sexes (b = 2.98). These results provide key biological reference parameters for the conservation and sustainable management of this endangered species. These findings provide an important basis for biological knowledge of the species and may contribute to the development of appropriate management and conservation strategies
Arduino Based Voice Conversion System for Dumb Persons Using Touch Sensors
No full textEveryone talks to each other by using sounds to get their point across. But this isn’t good for people who are deaf or dumb. There are several ways for them to get their views over to average people, such as using hand gestures, facial expressions, and body language. Also, people commonly utilize sign language to talk to each other. Each word has a specific action that shows how it is used. Gestures don’t work very well because most individuals don’t know how to use them. So, it makes sense to set up a system that makes it easier for dump people to share information. Sign language can be turned into speech with the help of infrared and touch-based sensors. This method for recognizing gestures uses infrared and touch sensors. The Arduino gets input from the infrared and touch sensors based on their resistance and then makes a voice output. This system reacts faster and makes communication more efficient. One of the key goals of gesture recognition research is to make a system that can recognize human gestures and send data to control devices. Processors utilize hand and body motions to talk to one other. We can understand how people move and utilize gestures to control machines or software by using this strategy
Adaptive QoS-Aware Link Scheduling for Scalable and Interference-Constrained Wireless Networks
No full textStrong Quality of Service (QoS) assurance is necessary in contemporary wireless networks due to the fast growth of data-intensive and low-latency applications, such as live video streaming, industrial automation, and IoT-based smart systems. However, obtaining consistent communication performance is severely hampered by interference-limited environments, dynamic topologies, and dense deployments. This paper suggests an Adaptive QoS-Aware Link Scheduling (AQLS) framework for scalable and interference-constrained wireless networks as a solution to these problems. The proposed approach models the network using a conflict graph and formulates the scheduling process as an Integer Linear Programming (ILP) problem that simultaneously considers QoS priorities and interference constraints. To ensure real-time applicability, adaptive heuristic and learning-based approximations are introduced to efficiently approximate near-optimal scheduling decisions. Simulation results using NS-3 and MATLAB demonstrate that AQLS performs significantly better than greedy schemes and traditional TDMA in terms of throughput, end-to-end delay, packet delivery ratio, and fairness. Because it achieves a special balance between optimization accuracy, scalability, and QoS adaptability, the proposed framework is a strong candidate for 5G/6G, automotive, and large-scale IoT communication systems
Comparative Study of Electrical and Structural Properties of Solar-Sintered and Conventionally Processed Bi-2223 Superconductors
No full textIn contrast to the conventional solid-state preparation, this work investigates the structural and electrical properties of Bi-2223 superconductors made using a sun sintering technique. The X-ray diffraction (XRD) examination revealed that the solar-sintered samples had more sharpness and intensity, with the average peak intensity some 1520 percent higher, which denotes better crystallinity and purity of the phases. The resistivity verses temperature results indicated a higher critical temperature (Tc 110 +- 0.002K) of the solar-sintered material as opposed to 110 +- 0.002K of the conventionally processed material. The sample that had undergone solar-sintering had much lower resistivity above the transition, with R 0.019 m-1 at 300K compared to 0.024m-1 at 300K in the conventional sample. Additionally, I-V measurements at 77 K showed that the solar- sintered sample had a critical current density (Jc) of 20300 A/cm2, which was better than the conventional sample. Such improvements are explained by the fact that, grain connectivity is better, there is less weak links and the flux pinning of the solar-sintered structure is also better. The findings broadly indicate the capabilities of solar-assisted sintering as an efficient and sustainable technique of producing high performance Bi-2223 superconductors, which could be used in high performance in power transmission, magnets and in cryogenic electronics.
Cognitive 6G Architectures: AI, Edge Computing, and Quantum Convergence
No full textIn order to facilitate intelligent, secure, and ultra-low-latency communications, the proposed Cognitive 6G framework combines quantum computing, mobile edge computing, and artificial intelligence (AI) into a single multilayer architecture. While the MEC layer uses lightweight AI models for local inference, caching, and real-time control, heterogeneous IoT nodes provide multimodal data at the device layer. Large-scale model training and collaboration with MEC nodes sustain global intelligence at the cloud AI layer. Complex optimization tasks like resource allocation, beamforming, and quantum key distribution-based security are accelerated by a dedicated quantum layer. Real-time cognition, quantum- safe transmission, and context-aware network adaptation are made possible by this close integration. The AI6G_QoE dataset is subjected to machine learning models in order to assess user-centric performance. Random Forest regression yields an MAE of 0.32, RMSE of 0.47, and a R2 of 0.81. The results show that AI-driven learning has the potential to improve user experience in emerging 6G scenarios, such as Open RAN and blockchain-enabled networks, and they also indicate good QoE prediction capability
Facial Image Analysis for Autism Spectrum Disorder Detection Using Vision Transformer and State of the Art Deep Learning Frameworks
No full textAutism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that can make it difficult for individuals to communicate and connect with others. It touches families and communities everywhere, regardless of geography or background. Those who living with ASD, even everyday tasks can become significant hurdles. Diagnosing autism typically requires a series of medical evaluations, which are not only time-consuming but can also be costly. Unfortunately, many people with ASD go undiagnosed or unsupported. This often happens because there’s still not enough awareness or understanding about autism, and resources like diagnostic services and specialized therapies such as speech or behavioral support .This work conducts an extensive evaluation of automated early Autism Spectrum Disorder (ASD) detection utilizing children facial images. A range of advanced deep learning models including Vision Transformer (ViT), AlexNet, MobileNet V2, MobileNet V3, DenseNet-121, DenseNet-169, and ResNet x-400MF—were implemented and systematically compared. The models were trained on preprocessed datasets, integrating both convolutional and transformer-based models to extract features relevant to ASD specific facial markers. Results from this work indicate that these architectures achieve strong classification performance for identifying ASD in facial images for early, scalable, and non-invasive ASD detection and further used to identify levels of ASD , timely diagnosis and intervention
A Room-Aware Intelligent Framework for Automated Digital Mixer Control Using Acoustic Modeling and OSC-Based Communication
No full textThe growing demand to optimize the spaces like classrooms, auditorium, and conference halls makes it impossible to calibrate sound manually, which is time consuming, unreliable, and to a great extent, specialist expertise reliant. This is a study that will be designed to resolve the issue and present an Intelligent Room-Aware Digital Mixer Control System. This system automatically adjusts the mixer settings with regard to the specific size and acoustic factors of the room. The process involves the measurement or simulation of room dimensions, calculation of the reverberated time (RT60) by Sabine formula, and transmission of the commands to a digital mixer in real-time through the Open Sound Control (OSC) protocol. It is aimed at the maximum quality of sound (music and voice) and the minimum time used on adjustments made manually. The suggested system incorporates the room sensing, acoustic modelling and automatic control of mixers into a unified system. The innovation is in the fact that it combines these factors into a scalable and economical and smart package. The advantage of this method is that it does not require human intervention, and all helps to enhance the quality of audio in both educational and professional and IoT settings
Intelligent Carrier Frequency Offset Estimation in Wireless Sensor Networks Using Fuzzy Logic and Deep Neural Networks
No full textThis work presents an innovative approach to estimating Carrier Frequency Offset (CFO) in Wireless Sensor Networks (WSNs) by integrating deep learning with fuzzy logic. The proposed method combines feature extraction, fuzzy inference, and a deep neural network to deliver highly accurate CFO estimates across diverse channel and noise conditions, significantly outperforming conventional techniques in low-SNR scenarios. CFO estimation is critical for synchronization and reliable wireless communication. While fuzzy logic enhances decision-making and energy efficiency, deep learning offers superior resilience in pattern recognition in WSN Technology. Few existing strategies merge these techniques for real-time CFO estimation under challenging conditions. Following initial CFO detection using the preamble, the method performs multi-parameter feature extraction, integrating the outputs of a GRU-based deep learning network with fuzzy inference for optimal estimation. Simulation and hardware results demonstrate over 70% error reduction, maintaining accuracy above 90% across varying SNRs and channel environments, with strong robustness to mobility and noise