In this study, a water pumping photovoltaic system (WPPVS) provided with a water storage tank is introduced to supply freshwater for drip irrigation in Farafra oasis, Egypt. A multi-objective K-means clustering based on non-sorting genetic algorithm is utilized to minimize both the loss of water supply probability and the total annual costs including capital and running costs. Due to the variability of the water flow rate, a techno-economic energy coordination is developed with particular attention to avoid vibration of water pumps. The economic feasibility is based on the tomato crop yield against the total cost of the drip irrigation system. Sensitivity analysis for dynamic head, crop price, and interest and inflation rates is conducted. At specific dynamic head of 6 m, the results revealed that the optimal design of the WPPVS pumps 10220 cubic meter of water for irrigation through the developed energy coordination against 18575 cubic meter with pump vibration which saves 45 % of the pumped water. This reflects its effect on a decrease of the pumped water for all dynamic head values. Furthermore, the vibration avoidance strategy allows the pump to operate at lower dynamic heads above the water threshold level in the well, which is not possible with the presence of vibrations.

The static charges and induced voltages from extra-high-voltage alternating current transmission lines (EHVACTLs) on parallel oil pipelines (POPLs) raise the risk levels for people and animals. Thus, the objective of this paper was to reduce and/or mitigate the electric field which is concentrated on POPLs by using grounded shield wires under EHVACTLs. Three techniques are employed to reduce the electric field effects on POPLs of two distinct types of transmission lines (TLs), 500 kV and 220 kV. The first technique involves raising the tower’s height to improve the clearance space between the POPLs and the TL conductors. The second technique is increasing the horizontal distance between the POPLs and the nearest stressed conductors of the TL. The third technique involves placing shield wires beneath the stressed conductors of the EHVACTLs. The electric field under the EHVACTLs is calculated with and without the grounded shield wires using charge simulation method. The results of the first technique revealed that with increasing the tower height from 10 m to 15, 20, 25, and 30 m, the electric field decreased by 43.75%, 62.5%, 68.75%, and 75%, respectively. Herein, employing the second technique, the electric field intensity is reduced by 20% and 21% depending on the POPL placed at a distance from the right stressed conductor equal to the horizontal clearance between conductors of 500 kV and 220 kV, respectively. Besides, the results of the third technique proved that the shield wires under the EHVACTLs reduced the electric field intensity on the POPLs by 17.65% and 24.71% for 500-kV and 220-kV TLs, respectively.

Traditional techniques are used for fault location detection in high-voltage transmission lines that
mostly depend on traveling waves and impedance-based techniques suffer from large errors
owing to the intricacy of fault modeling for various types of faults. Although single-line to ground
faults are dominant in high-voltage transmission lines, fault resistance as well as fault inception
angle might distort the current fault detection techniques. In addition, other types of faults exist
and that raises the need to develop an accurate fault detection technique to minimize the recovery
time. The current paper introduces a fuzzy and neuro-fuzzy algorithm to detect, analyze, and locate
different faults taking place in high-voltage transmission lines. A MATLAB Simulink Model is used
for analyzing different fault cases; fault detection and classification are done by the Fuzzy Interface
System (FIS), while fault location detection is done using the Adaptive Neuro-Fuzzy Interface
System (ANFIS). The introduced algorithm is evaluated via the Mean Square Error (MSE) technique.
The results showed full success in detecting and identifying different fault types, with a 0.0042
validity performance factor for fault location detection using ANFIS.

The main target of this research is to allow solar PV to contribute economically to an on-grid energy-efficient building where the dust accumulation is a significant factor. Self-cleaning coatings such as hydrophobic or hydrophilic materials have recently been introduced to reduce dust deposition on building-integrated PV (BIPV) panels. The hydrophilic Nano-coated material is examined as a solution to decrease the impact of the dust on the BIPV panels and harvest more solar energy. An impartial comparison of the BIPV panels performance under natural dust conditions, manual cleaning, and hydrophilic nanomaterial coating is performed. Through an exhaustive and qualitative experimental analysis, the anti-reflection and anti-static properties of the utilized Nano-coated material are examined. The experimental results show that the hydrophilic Nano-coated material significantly improves the gathered maximum output power by 18% compared to the manually wiped panel. The calculated efficiencies of the Nano-coated, manual cleaning, and dusty panels are 11%, 9%, and 6%, respectively, which highlights the future proofing of the Nano-coated solar panel. Compared to the dusty panels, the ecological and economical results show that the BIPV carbon emissions are desirably dropped by 11% while using Nano-coated PV panels and the payback period is reduced to 3.9 years, which is approximately 12.8% faster.

The main aim of present work is to investigate the dynamics of the chaotic nonlinear distributed order Lü model (DOLM). The distributed order (DO) derivative is used for describing the viscoelasticity of various technical models and materials. The modified spectral numerical method is used to evaluate the numerical solutions for DOLM. Using nonlinear feedback control and the Lyapunov direct approach, the adaptive synchronization of two chaotic distributed order models (DOMs) is presented. We state a theorem to drive analytical controllers which are used to achieve our synchronization. The DOLM is introduced as an example of DOMs to verify the validity of our analytical results. Numerical computations are displayed to show the agreement between both analytical and numerical results. The DOMs appear in many applications in engineering and physics, e.g., image encryption and electronic circuits (ECs). Based on our proposed synchronization, the encryption and decryption of color images are studied. Information entropy, visual analysis and histograms are calculated, together with the experimental results of image encryption and decryption. We design the EC of the DOLM using the Multisim circuit simulator for the first time to our knowledge. Using electronic circuit simulation, we achieved the same results for the numerical treatment of our synchronization. Other ECs can be similarly designed for other DOMs.

The use of reclaimed asphalt pavement (RAP) represents a recycling method with environmental benefits along with cost savings. RAP in new combinations of asphalt mixtures has benefits such as lowering the amount of virgin material, reducing cost and natural resources, and causing less environmental harm. In order to improve the physical and rheological characteristics of aged asphalt binders found in RAP, rejuvenators have been used. There are many types of rejuvenators for RAP binders, such as bio-oil, waste engine oil, and waste cooking oil. Foamed and emulsified asphalt have been widely used for their energy-saving and emission-reducing properties for cold mix-in-place (CIR) production. Hot in-place (HIR) recycling does not necessitate the transportation of significant amounts of new materials to the working site, there are fewer traffic noise and delays caused by cars coming and going from the work area. Finally utilizing cement to recycle the surface, base, and subgrade (full-depth reclamation (FDR)) to enhance the structural strength and durability of pavements.

Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network’s traffic flow. This article presents residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network’s O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model’s performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors.

The dynamic modulus (∣E*∣) of hot-mix asphalt mixes is one of the most time-consuming and labour-intensive material metrics to evaluate in the laboratory. This study introduces a novel paradigm for assessing the ∣E*∣'s most influential elements by employing widely accepted literature models. Witczak 1-37A, Witczak 1-40D, and Modified Hirsch Models are selected for analysing the asphalt dynamic modulus. First, a thorough laboratory database of Arizona State University is used to account for all major input factors, such as mixture gradation, binder qualities, volumetric properties, and testing conditions parameters, during models' validation. Second, each model's performance is evaluated using standard measures to build confidence levels in the subsequent analysis stage. Finally, with the aid of Latin Hypercube Simulation, a comprehensive global sensitivity analysis (GSA) is performed. Three unique GSA approaches are used; namely, elementary effects, variance-based, and PAWN methods, to highlight the effect of each input variable on the magnitude of ∣E*∣. Different GSA tools are strongly recommended since there is no analytical tool for validating the findings with the complex formulations of the selected mathematical models. The GSA demonstrates that the voids ratio in total mix, binder shear modulus, viscosity, phase angle, and binder quantity are the most significant factors.