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Egypt’s geographic location and natural resources make it an ideal candidate for Renewable Energy (RE) projects. The government has implemented initiatives and policies to promote RE development, resulting in notable projects such as the Benban Solar Park and wind farms in the Gulf of Suez. These endeavors aim to combat climate change, reduce greenhouse gas emissions, and foster sustainable development. Multicriteria decision making techniques are increasingly favored due to their ability to accommodate diverse criteria for selecting RE sources. This paper evaluates numerous RE options in Egypt, including solar, wind, hydropower, and biomass energy, and proposes a hybrid strategy for classifying RE sources for power generation based on criteria such as finance, environment, technology, and community. The study utilizes four multicriteria decision making methods: the analytical hierarchy process, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), Fuzzy Technique for Order Preference by Similarity to an Ideal Solution (FTOPSIS), and fuzzy analytical hierarchy process. The key findings indicate that solar and wind energy are the most viable options for Egypt’s renewable energy strategy, with the fuzzy technique for order preference by similarity to an ideal solution method proving to be the most effective for ranking these sources by providing a comprehensive evaluation framework that aligns well with related studies. These findings have significant implications for policy and investment, as they provide a robust decision-making framework that ensures balanced consideration of financial, environmental, technological, and social factors. The study underscores the value of using advanced multicriteria decision making techniques to guide RE development in Egypt, contributing to the global effort to combat climate change.

There are several infections and diseases that can affect plants. Diagnosis of plant diseases is a challenging task. A fascinating method for identifying plant diseases is computer-based diagnosis using digital images of plant’s leaves. Most of the earlier research in this field has been devoted to feature engineering and traditional machine learning (ML) techniques. Based on hand-crafted features taken from digital photographs of the plant’s leaves, these methods identify various plant diseases. It can be challenging to use feature engineering to extract high-quality features from these digital images. Deep learning (DL) algorithms, have relieved feature engineering by automatically extracting and learning resilient features. However, the high number of parameters in conventional DL models typically leads to overfitting. Therefore, gradient vanishing problems in vast networks intensify learning failure and generalization errors. Additionally, getting a big dataset for starting from scratch to train a deep learning model is also difficult. To solve these issues to detect Dome Galls in Cordia dichotoma G. Forst. early, this research suggests quick and efficient deep transfer learning (DTL) model. In the proposed method, a custom dataset of Cordia dichotoma leaf images is created. 1784 images of Cordia dichotoma leaf are collected in real world environment, and offline augmentation techniques are applied for obtaining the final training dataset of 5400 images. Further, image preprocessing techniques are used to enhance the images. A DL model, Yolov4, has been modified and is trained for the early detection of dome galls in the leaves of Cordia. The model is trained using pre-trained weights, a process called transfer learning (TL). The model is tested on another set of 200 images, and the results show an accuracy of 95% and an F1-score of 95.8%. Experimental results show that the modified Yolov4 performs 3% more accurately than the original Yolov4.

The job of discovering appropriate code snippets against a natural language query is an important task for software developers. Appropriate code retrieval increases software productivity and quality as well. In contrast to traditional information retrieval techniques, code search necessitates bridging the semantic breach between programming languages and natural language to search code fragments. Deep neural networks for search codes have recently been a hot topic in research. The standard neural code quest approaches present source code and query in the form of text as independent embedding, then calculate the semantic similarity between them using vector distance (e.g., using cosine similarity). Although recent research utilized query and code snippets during code search, it overlooked the contained rich semantic information and deep structural features between them. In this study, we are also dealing with the problem of code search by providing a deep neural solution that facilitates software developers during software development. Our proposed model effectively used neural graph matching and a searching approach for semantic code retrieval. It first converts both query and code fragments in graph format and then the semantic matching module is used to facilitate the process of matching that will retrieve the best-matched code snippets. It not only exploits the enriched semantic meanings and features, but it also uses the cross-attention mechanism to learn the fine-grained similarity that exists between query and code. The proposed model’s evaluation is done using the Codesearchnet dataset with six representative programming languages. It provides comparatively good results as compared to existing baselines. It enables users to find required code snippets, and ranking is used to retrieve top 10 results. The accuracy of the proposed system is approximately 97%.

This article presents a cryptographic encryption standard whose model is based on Serpent presented by Eli Biham, Ross Anderson, and Lars Knudsen. The modification lies in the design of the Cipher, we have used power associative (PA) loop and group of permutations. The proposed mathematical structure is superior to Galois Field (GF) in terms of complexity and has the ability to create arbitrary randomness due to a larger key space. The proposed method is simple and speedy in terms of computations, meanwhile it affirms higher security and sensitivity. In contrast to GF, PA-loop are non-isomorphic and have several Cayley table representations. This validates the resistance to cryptanalytic attacks, particularly those targeting mathematical structures. This cryptographic scheme’s full description of encryption and decryption is measured and rigorously assessed to support its multimedia applications. The observed speed of this technique, which uses a key of 256 bits and a block size of 128 bits, is comparable to three-key triple-DES.

Due to the extensive demand for digital images across all fields, the security of multimedia data over insecure networks is a challenging task. The majority of the existing modern encryption schemes are merely developed that ensure the confidentiality of the image data. This manuscript presents a new image encryption scheme that ensures confidentiality, user authentications, and secure key sharing among the communicating parties. Initially, the users share a secret parameter using Diffie-Hellman over the elliptic curve and pass it through SHA-256. Afterwards, the proposed scheme uses the first 128-bits for the confidentiality of the data, while the remaining 128-bits are for authentication. In the encryption algorithm, the confusion module is achieved by affine power affine transformation. At the same time, the diffusion module is attained through highly nonlinear sequences, which are generated through the elliptic curve. Experimental testing and the latest available security tools are used to verify the effectiveness of the proposed algorithm. The simulation findings and the comparison of the proposed scheme with the existing image encryption techniques reveal that the suggested scheme offers a sufficient degree of security. Furthermore, the outcome of the simulation results divulges several advantages of the proposed scheme, including a large key space, resistance to differential attacks, high efficiency, and strong statistical performance.

A significant obstacle to creating efficient machine health monitoring systems is estimating performance degradation in dynamic systems, like aero plane engines. In exceedingly complex systems with many components, states, and parameters, conventional model-based and data-driven methods fall short of producing satisfactory results. While traditional methods had several drawbacks, deep learning has emerged as a viable computational tool for dynamic system prediction. In order to track system deterioration and estimate the EGT, a novel technique based on the Long Short-Term Memory (LSTM) network, (an architecture created to find the hidden patterns hidden in time series data) is provided in this research. The health monitoring information of aircraft turbofan engines is used to assess the effectiveness of the proposed strategy. As a result of this network’s ability to recognize the input data as a real-time series, the output in the following step can be predicted. Results of the suggested study show a significant ability to anticipate the output in the following time step. Additionally, the proposed model has a shorter learning curve and is more accurate.

In the new era of cryptography, Substitution Boxes (S-Boxes) are very important to raise confusion in cipher text and the security of encryption directly depends on the algebraic strength of S-box. To avoid a hacker attack, researchers are focusing on creating dynamic S-boxes that are much stronger. The dominating concept for developing S-boxes is linear fractional transformation. In this article, we proposed a novel technique to generate cryptographically strong S-box by using fractional transformation based on finite field. The substitute box is constructed in two phases. Firstly, general form of dynamic fractional transformation designed which work for odd exponents in the range [1-255]. The S-box is then constructed using quantic fractional transformation as an example. Secondly, in order to increase the unpredictability of proposed S-box, we use the symmetric group’s S256 permutation. The usefulness of the constructed S-box was also tested using several criteria such as nonlinearity, differential uniformity, strict avalanche criteria, linear approximation probability and bit independence criteria. To assess the reliability of S-box, its performance outcomes are compared to those of previously developed S-boxes. Furthermore, we utilized the suggested S-Box to the image encryption approach. Then, to determine the effectiveness of the encryption scheme, use several tests such as contrast, correlation, homogeneity, entropy, and energy. We have compared our results with different algorithms which ensured that the proposed strategy for ciphered image is excellent.

One of the substantial innovations achieved through digitalization is cryptocurrencies, also known as simulated or digital currencies, which have been deliberated in the modern era as a new platform particularly suitable for financiers. Several cryptocurrencies, such as Bitcoin, Ethereum, Binance Coin, and Tether, do not trust a dominant expert. The classification and conduction of insecurity and the confirmation of digital currencies complicate decision-making. q-rung orthopair fuzzy hypersoft sets are an emerging arena of research intended to report the confidential restrictions of q-rung orthopair fuzzy soft sets on multiparameter indefinite functions. Such a function maps a tuple of sub-parameters to a power set of the universe. It emphasizes allocating attributes to their corresponding sub-attribute values in disjoint sets. These structures sort it an innovative systematic tool for addressing the obstacles of hesitancy. The q-rung orthopair fuzzy hypersoft set (q-ROFHSS) expertly compacts with tentative and ambagious facts equated to the existing q- rung orthopair fuzzy soft set and Pythagorean fuzzy hypersoft set (PFHSS). It is the most compelling mode for enlarging imprecise data in decision-making (DM). This investigation’s ultimate impartiality is presenting interactional algebraic operational laws for q-ROFHSS. Furthermore, some interaction aggregation operators (AOs) have been anticipated via our proposed operational laws, such as q-rung orthopair fuzzy hypersoft interactive weighted average (q-ROFHSIWA) and q-rung orthopair fuzzy hypersoft interactive weighted geometric (q-ROFHSIWG) operators with their essential properties. In reality, a mathematical illustration of DM obstacles is pondered to substantiate the proven technique’s dominance. Based on the projected interaction AOs, robust multi-criteria group decision-making (MCGDM) design has been offered, which carries the most practical consequences associated with predominant MCGDM methods. The significance spectacle is that the intentional methodology is more operative and steady in bearing weird facts based on q-ROFHSS.

Substituting conventional energy sources with new renewable sources is crucial issue nowadays in energy generation systems to face climate changes and increased load demands. Due to the increased penetration levels of renewable sources in power systems, the benefits of the high-inertia of conventional sources are being insufficient. The resulting low-inertia power systems introduce several stability, reliability, and coordination problems for power system operation and control. Therefore, this paper tackles the coordination assessment and enhancement between digital frequency relays using a new fractional order load frequency controller equipped with superconducting magnetic energy storage (SMES) virtual inertia system. The improved coordination method is established using optimized fractional order controller based on slime mould optimization algorithm (SMA). The proposed SMA-based design method benefits the adaptive weights of SMA algorithm. The proposed design is generalized to be applied on single area and multi-area interconnected power systems as well. Compared to existing literature, this paper presents an advanced fractional order controller with coordinated operation with existing protection relays. The obtained results show the coordination shortcomings of renewable energy based microgrids with traditional control systems. However, improved design and coordination are obtained using the proposed SMA-optimized fractional order controllers. The superiority and feasibility of the proposed analysis and methods are verified on different case studies using single and multiple interconnected areas.

Recently, electric vehicles (EVs) have proven remarkable impacts on reducing pollution and their suitability for utilizing renewable energy sources (RESs) instead of fossil fuels. The continuously increasing number of installed EVs has encouraged their employment for performing additional functionalities in utility grids. This paper provides study for the impacts of EV integration approaches on enhancing the operation of utility grids. The proposed study considers the targeted renewable energy powered utility grids with installed energy storage devices. Wind energy has been utilized as a case study for the RES with considering its stochastic characteristics and its continuous fluctuations with wind speed. Additionally, the superconducting magnetic energy storage system (SMES) device is chosen due to its promising long-life operation. The various aspects of point of common coupling (PCC) voltage fluctuations, reactive power support, leveling active power of the on-peak period, and power losses are considered in the proposed study. Moreover, fuzzy logic controller (FLC) systems have been proposed for controlling EVs and the SMES device. The energy management between the installed EVs, SMES, wind, and utility grid side is achieved through the coordination of the FLC systems. The results show that reduced peak and mean values of total power losses and line active power are obtained by the proposed coordination method. Moreover, improved profiles of the PCC voltage, reactive power support, active power leveling, and sustainable and reliable power supply are obtained by the proposed method for wide operating ranges and modes of operation.

Due to the enormous increase in the interconnection of electric vehicles (EVs) into utility power grids, more and more challenges are arising in modern power systems. To handle these challenges, many attempts have been proposed to achieve the satisfactory performance of EV charging/discharging, the improvement of utility power grids operation, and an adaptive coordination strategy for the EV–grid connection. Energy storage systems (ESSs) may play a vital role in this process by injecting/absorbing their energy into/from the grid, including EVs. This paper proposes an adaptive coordination method based on fuzzy logic control (FLC) for EVs and superconducting magnetic energy storage (SMES) to enhance the charge and discharge processes of EVs during off and on peak loading periods. FLC is utilized to organize the charging/discharging process for both EVs and SMES. The state of charge (SoC) of SMES and EVs is considered as the input for EVs’ FLC besides the electricity price (EP) signal. To verify the effectiveness of the proposed coordination method, uncertain numbers of EVs, the rate of EP, and daily residential load profile are included in this study. The results demonstrate that the proposed coordination method can improve the performance of EV charging/discharging. Moreover, the cooperative operation with SMES and the reliability improvement of the utility power are preserved using the proposed coordination strategy.

The intermittent property and increased grid restrictions have become the most critical elements for increasing penetration levels of clean renewable energy sources (RESs). Smart inverters with combined RESs integration and reactive power support for utility grids have recently found widespread applications due to their techno-economic benefits. In smart inverters, the distribution-static compensator (DSTATCOM) functionality is inherently integrated to the RESs inverters. However, optimized reactive power-sharing between these inverters has become a big issue for the control systems of utility grids. There are numerous existing attempts presented in the literature for addressing these challenges, although they disadvantage low efficiency, uneven sharing, complex implementations, and/or costly added devices. Therefore, this paper proposes an efficient reactive power dispatch method between hybrid renewable energy generation and energy storage systems. The proposed method enhances the energy efficiency of the utility grid by adopting the reactive power share between interfacing inverters according to the estimated power losses. Besides, the proposed method enhances the reliability of smart inverters by relieving their thermal stresses through adopting their reactive power share according to the estimated power losses. The hybrid photovoltaic (PV) generation with superconducting magnetic energy storage (SMES) systems is selected as a case study for validating the new proposed reactive power dispatch method. The results, comprehensive discussions, and performance comparisons have verified the superior performance of the new proposed reactive power dispatch method.

In an active distribution grid, renewable energy sources (RESs) such as photovoltaic (PV) and energy storage systems (e.g., superconducting magnetic energy storage (SMES)) can be combined with consumers to compose a microgrid (MG). The high penetration of PV causes high fluctuations of tie-line power flow and highly affects power system operations. This can lead to several technical problems such as voltage fluctuations and excessive power losses. In this paper, a fuzzy logic control based SMES method (FSM) and an optimized fuzzy logic control based SMES method (OFSM) are proposed for minimizing the tie-line power flow. Consequently, the fluctuations and transmission power losses are decreased. In FSM, SMES is used with a robust fuzzy logic controller (FLC) for controlling the tie-line power flow. An optimization model is employed in OFSM to simultaneously optimize the input parameters of the FLC and the reactive power of the voltage source converter (VSC) of SMES. The objective function of minimizing the tie-line power flow is incorporated into the optimization model. Particle swarm optimization (PSO) algorithm is utilized to solve the optimization problem while the constraints of the utility power grid, VSC, and SMES are considered. The simulation results demonstrate the effectiveness and robustness of the proposed methods.

The fluctuated nature represents the main obstacle for increasing the penetration level of photovoltaic (PV) generators in utility microgrids. The energy storage systems (ESSs) represent the main solutions to get over these fluctuations. Among various ESSs, the superconducting magnetic energy storage (SMES) systems have proven themselves as an effective solution. The SMES control systems in the literature result in a shortened lifetime, degraded thermal behavior, and high AC losses in the SMES. Thence, reliable operation of SMES systems and microgrids has been given wide concerns due to the large fluctuations in microgrids with high levels of PV penetrations. This paper presents a SMES-based fuzzy logic approach for improving the reliability of SMES and utility microgrids with high PV penetration levels. The proposed SMES controller employs the state of charge (SOC) of SMES as an input. This, in turn, can effectively extend the lifetime of SMES due to eliminating the overcharge and deep discharge operating states. Moreover, the proposed system controls smoothly the SMES current using the fuzzy rules' system. The superiority and effectiveness of the proposed SMES controller are verified using simulation-based case studies of utility grids with high PV penetration levels. The obtained results show the effective smoothing of fluctuated power and constant bus voltages in the studied microgrid under the proposed controller. In addition, the proposed SMES controller provides superior performance regarding the lifetime and reliability of the SMES systems and microgrid components.