Photovoltaic (PV) systems are one of the promising renewable energy sources that have many industrial applications; one of them is water pumping systems. This paper proposes a new application of a PV system for water pumping using a three-phase induction motor while maximizing the daily quantity of water pumped while considering maximizing both the efficiency of the three-phase induction motor and the harvested power from the PV system. This harvesting is performed through maximum power point tracking (MPPT) of the PV system. The proposed technique is applied to a PV-powered 3 phase induction motor water pumping system (PV-IMWPS) at any operating point. Firstly, an analytical approach is offered to find the optimal firing pattern of the inverter (V-F) for the motor through optimal flux control. This flux control is presented for maximizing the pump flow rate while achieving MPPT for the PV system and maximum efficiency of the motor at any irradiance and temperature. The provided analytical optimal flux control is compared to a fixed flux one to ascertain its effectiveness. The obtained feature of the suggested optimal flux control validates a significant improvement in the system performances, including the daily pumped quantity, motor power factor, and system efficiency. Then converting the data from the first analytical step into an intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS is trained offline with the input (irradiance and temperature) while the output is the inverter pattern to enhance the performance of the proposed pumping system, PV-IMWPS.

The applications regarding the Internet of Things (IoT) demand lightweight and robust cryptographic solutions to ensure authenticated encryption with associated data (AEAD). Also, the lightweight cryptographic solutions that provide data confidentiality, integrity, and authenticity in a single algorithm are critical. In this regard, the CAESAR and NIST lightweight cryptography (LWC) competitions were concluded in 2019 and recently in 2023, respectively, with Ascon selected as the new LWC standard. Ascon has been evaluated (in the literature) for various characteristics, including some limited efforts on field-programmable gate array (FPGA) devices. A comprehensive evaluation of Ascon’s hardware implementation is still needed. Therefore, this work presents a unified hardware implementation of two variants of Ascon, i.e., Ascon-128 and Ascon-128a, to investigate the performance of the AEAD operation on 7-series FPGA devices up to the post-place and route level. For AEAD computations in our work, an iterative design-level approach with a finite-state machine (FSM)-based dedicated controller is employed. The benchmarking results show that Ascon utilizes 1632, 1497, 1904, and 1756 look-up tables on Virtex, Kintex, Spartan, and Artix 7-series FPGA devices, respectively. The operating frequencies on these devices are 335, 331, 309, and 317 MHz, with power consumptions of 239, 236, 219, and 222 mW. Consequently, our evaluation of Ascon demonstrates higher performance in operating frequency on Kintex-7 FPGA.

In the Saudi Arabian construction projects, the transport infrastructure sustainability assessment has become a priority in recent years. The purpose of this research is to assess the sustainability performance of transport infrastructure projects in Saudi Arabia. It specifically seeks to assess the effects of these initiatives on the environment, society, and economy, while highlighting their advantages and disadvantages as well as potential areas for development. A key objective of this evaluation is to provide thoughtful analysis and suggestions for improving Saudi Arabia’s transportation infrastructure development. The study was conducted using a Likert-scale questionnaire survey among 197 professionals in Saudi Arabia. The Cronbach’s alpha test was conducted to assess the validity of the survey. Moreover, statistical analysis using SPSS software 25 was used along with the Spearman correlation test to evaluate the respondent behavior of the survey. The investigation serves as a foundation for devising strategies and policies to promote sustainable transportation practices in Saudi Arabia. The study’s insights can guide effective planning and regulations that prioritize sustainability, environmental preservation, and public support. By focusing on these outcomes, transportation networks can be improved, environmental impacts reduced, construction methods enhanced, and safety ensured for workers and the public. A durable, effective, and environmentally conscious transportation infrastructure for Saudi Arabia’s present and future generations could be achieved by embracing sustainable transportation infrastructure and giving priority to the determined results.

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag.

The Internet of Things (IoT), which is characteristic of the current industrial revolutions, is the connection of physical devices through different protocols and sensors to share information. Even though the IoT provides revolutionary opportunities, its connection to the current Internet for smart cities brings new opportunities for security threats, especially with the appearance of new threats like quantum computing. Current approaches to protect IoT data are not immune to quantum attacks and are not designed to offer the best data management for smart city applications. Thus, post-quantum cryptography (PQC), which is still in its research stage, aims to solve these problems. To this end, this research introduces the Dynamic Galois Reed–Solomon with Quantum Resilience (DGRS-QR) system to improve the secure management and communication of data in IoT smart cities. The data preprocessing includes K-Nearest Neighbors (KNN) and min–max normalization and then applying the Galois Field Adaptive Expansion (GFAE). Optimization of the quantum-resistant keys is accomplished by applying Artificial Bee Colony (ABC) and Moth Flame Optimization (MFO) algorithms. Also, role-based access control provides strong cloud data security, and quantum resistance is maintained by refreshing keys every five minutes of the active session. For error correction, Reed–Solomon (RS) codes are used which provide data reliability. Data management is performed using an attention-based Bidirectional Long Short-Term Memory (Att-Bi-LSTM) model with skip connections to provide optimized city management. The proposed approach was evaluated using key performance metrics: a key generation time of 2.34 s, encryption time of 4.56 s, decryption time of 3.56 s, PSNR of 33 dB, and SSIM of 0.99. The results show that the proposed system is capable of protecting IoT data from quantum threats while also ensuring optimal data management and processing.

In pursuit of a comprehensive understanding of Lean Six Sigma (LSS) adoption within Saudi Arabian maintenance companies, this research aimed to evaluate the prevalence of its utilization and gauge the level of comprehension among practitioners. Furthermore, this study sought to pinpoint the Critical Success Factors (CSFs) crucial for the effective implementation of LSS. Employing an electronically disseminated survey, this investigation delved into the realm of LSS, with a specific focus on identifying and assessing these pivotal CSFs. This study encompassed data from 77 participants employed across diverse maintenance organizations in Saudi Arabia, gathered through a digital questionnaire and analyzed utilizing SPSS 22.0. This study used a variety of statistical techniques, such as descriptive statistics, frequency analysis, t-tests, and one-way ANOVA, to investigate the adoption of LSS and identify key success factors. According to the report, 77.9% of Saudi maintenance businesses have yet to implement LSS, with quality improvement serving as the primary reason for those who have. Comprehensive LSS training, effective people selection, increased awareness, and a strong, data-driven approach are all critical aspects in successful LSS deployment. Furthermore, this study emphasized the importance of training, staff selection, and knowledge of the benefits of LSS in influencing the effectiveness of implementation. Finally, this study provided significant insights into the barriers and enablers of LSS adoption, making both theoretical contributions to the academic area and practical recommendations for firms looking to use LSS for continuous improvement.