The continuous decline of the Dead Sea water level represents one of the most critical environmental challenges in the Middle East, primarily caused by reduced inflow from the Jordan River basin and increased regional water consumption. Predicting the long-term evolution of the Dead Sea is therefore essential for evaluating the potential effectiveness of large-scale restoration initiatives such as the Red Sea-Dead Sea (RSDS) conveyance project. In this study, a stochastic simulation framework is proposed to model and predict the long-term dynamics of the Dead Sea water level under multiple inflow scenarios. The framework integrates probabilistic increment generation, recursive hydrological updating, and stability-aware monitoring within a unified computational procedure. Annual water-level increments are generated using statistically fitted probability distributions derived from historical observations, enabling the model to capture the inherent variability and uncertainty of natural hydrological processes. The system state is iteratively updated across the simulation horizon, while a stability index is used to evaluate convergence behavior and detect potential equilibrium conditions. Five representative RSDS scenarios are investigated, including baseline recovery, moderate inflow, accelerated recharge, threshold-stabilized inflow, and high-variability environmental conditions. Simulation results indicate that under the baseline scenario the Dead Sea level increases gradually from approximately −434 m to about −60 m within 500 simulation steps, whereas accelerated recharge conditions can raise the level beyond 200 m due to higher early-stage inflow rates. The threshold-stabilized scenario reveals regime transitions in increment dynamics that lead to a near-equilibrium state, while the high-variability scenario exhibits strong stochastic fluctuations with annual increments ranging between 0.4 and 2.2 m. Overall, the results demonstrate that the proposed framework effectively captures diverse hydrological behaviors and provides a flexible and computationally efficient tool for long-term forecasting and policy evaluation of Dead Sea restoration strategies.

The rising malware threats in the past years are due to the proliferation of smart devices and resource-constrained systems, such as the Internet of Things (IoT) and mobile devices. This phenomenon creates great difficulties for conventional defense mechanisms, which frequently lag behind because of changes in the malware landscape. In this work, a lightweight and powerful network combining a single-head attention mechanism with LSTM or GRU for this task, based on the CIC-MalMem-2022 dataset, is introduced. The focus is on learning temporal features retrieved from memory data, considering model efficiency for resource-constrained devices. Results indicate that the generated model can retain an accuracy of 92%, and it can save training time by 30%, which is highly beneficial for time-critical tasks and efficient resource usage in real-life applications. This model contributes to the enhancement of cybersecurity by offering good practice against the growing range of new threats in today's technology-advanced environments.

Researchers have developed different backoff algorithms to help boost how well IEEE 802.11 distributed coordination function (DCF) performs. The standard approach, known as binary exponential backoff (BEB), is commonly used, but alternatives exist. One alternative that has gained attention is the Fibonacci incremental backoff (FIB), mainly because it is shown to be quite effective. This paper introduces a novel backoff method that’s inspired by the Euler spiral curve. To assess its performance, we performed simulations comparing the proposed approach with both BEB and FIB. We focused on key performance measures like network throughput and end-to-end delay, particularly in mobile ad hoc networks. The results are encouraging: our method delivers better throughput than both BEB and FIB. However, it does come with a trade-off. It does exhibit slightly higher end-to-end delay compared to FIB.

Smart home technologies have revolutionized modern living by enhancing convenience, efficiency, and security. In contrast, many interconnected devices introduce significant security and privacy challenges. This comprehensive review investigates the integration of blockchain technology as a robust solution for secure access control in smart home environments. The decentralized and tamper-resistant nature of blockchain technology effectively solves important problems, including device authentication, data integrity, and access management, through the use of cryptography and distributed ledgers. The study synthesizes findings from 52 research papers, categorizing them into three thematic areas: blockchain in access control systems, its applications in IoT, and specific implementations for smart homes. It highlights the transformative potential of blockchain in mitigating vulnerabilities inherent in centralized systems, fostering trust, and enhancing security frameworks. Despite its promising applications, challenges such as scalability, interoperability, and energy consumption persist, warranting further research. This paper stresses the necessity of collaboration to tackle these limitations and enhance blockchain-based access control solutions for smart homes, setting the stage for more secure and user-focused smart environments.

Smart contracts offer automation for various decentralized applications but suffer from vulnerabilities that cause financial losses. Detecting vulnerabilities is critical to safeguarding decentralized applications before deployment. Automatic detection is more efficient than manual auditing of large codebases. Machine learning (ML) has emerged as a suitable technique for vulnerability detection. However, a systematic literature review (SLR) of ML models is lacking, making it difficult to identify research gaps. No published systematic review exists for ML approaches to smart contract vulnerability detection. This research focuses on ML-driven detection mechanisms from various databases. 46 studies were selected and reviewed based on keywords. The contributions address three research questions: vulnerability identification, machine learning model approaches, and data sources. In addition to highlighting gaps that require further investigation, the drawbacks of machine learning are discussed. This study lays the groundwork for improving ML solutions by mapping technical challenges and future directions.

Arabic handwritten script recognition presents an energetic area of study. These types of recognitions face several obstacles, such as vast open databases, boundless diversity in individuals' penmanship, and freestyle writing. Thus, Arabic handwriting requires effective techniques to achieve better recognition results. On the other hand, Multilayer Perceptron (MLP) is one of the most common Artificial Neural Networks (ANNs) which deals with various problems efficiently. Therefore, this study introduces a new technique called Block Density and Location Feature (BDLF) with MLP, namely BDLF-MLP, which aims to extract novel features from letter images and estimate the letter's pixel density and its location for each equal-sized block in the image. In other words, BDLF-MLP can deal with various styles of Arabic handwritten, such as overlapping letters. The BDLF-MLP starts with the Block Feature Extraction (BFE) of the image by dividing the image into sixteen parts. After that, it calculates the density and location of each block (i.e., BDLF) by finding the sum of all values inside blocks. Finally, it determines the position of the greatest pixel density to obtain better recognition accuracy. The dataset containing 720 images is used to evaluate the efficiency of the proposed technique. Also, 1440 letters are used for training and testing divided evenly between them. The experiment results illustrate that BDLF-MLP outperformed the other algorithms in the literature with an accuracy of 97.26 %.