Arabian Journal for Science and Engineering最新文献

The stability of the power system and efficient allocation of resources depend on precise predictions of short-term electrical load. Traditional forecasting methods often struggle with the inherent complexities of load data, such as multi-frequency patterns and non-stationarity. This study presents a novel ensemble approach that integrates the strengths of three advanced techniques: complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), temporal convolutional networks (TCNs), and long short-term memory networks with automatic hyperparameter tuning (AutoLSTM). CEEMDAN decomposes the load data into various frequency components, enabling the model to capture both short-term and long-term patterns. TCNs extract temporal characteristics from the decomposed components, while AutoLSTM identifies long-term dependencies within the data. To further enhance the performance of the ensemble model, Cross-Stitch networks are employed to facilitate information exchange between the individual models. The empirical findings demonstrate the robust performance of the ensemble model, with a training MAE of 0.0148, RMSE of 0.02529, and R² of 0.9945, alongside a test MAE of 0.01491, RMSE of 0.02570, and R² of 0.9943. Due to the stochastic nature of deep learning models, which can lead to variability in predictions, we further perform the Diebold-Mariano (DM) test to rigorously verify the consistency and statistical significance of our results. The DM test results confirm the superiority of the Cross-Stitch model over individual TCN and LSTM models, highlighting statistically significant improvements in forecast accuracy with p values of 0.007, which is less than the commonly accepted threshold of 0.05.

Characterizing quantum dynamics is critical in quantum physics, quantum information science, and computation, where the precision of quantum gates plays a key role. We present a comprehensive experimental analysis of the SQSCZ gate–a novel universal two-qubit entangling gate combining (sqrt{text {SWAP}}) and (sqrt{text {CZ}}) operations–on superconducting quantum hardware. Leveraging quantum process tomography via the Choi-Jamiołkowski isomorphism, we benchmark the gate’s performance across different noise environments. Experimental results demonstrate high process fidelities of (97.27%) (quantum simulator) and (88.99%) (quantum hardware), revealing remarkable noise resilience. Owing to its hybrid architecture, circuit depth reduction capabilities, and hardware-efficient decomposition into only two CNOT gates, the SQSCZ gate holds strong potential for near-term quantum applications, including the Quantum Fourier Transform and Variational Quantum Eigensolvers for molecular simulations. These findings establish the SQSCZ gate as a promising primitive for NISQ-era quantum algorithms, while providing key insights into gate-level error processes in superconducting quantum processors.

This study presents a comprehensive kinetic model and reaction network for simulating the FCC unit of the Abadan refinery, using industrial five- and six-lump catalyst systems. The model is developed based on actual process data and simulates the industrial riser and regenerator through heterogeneous modeling, incorporating energy and mass balance equations. The model operates under steady-state conditions. A sensitivity analysis was conducted to examine how variations in operational parameters, such as temperature and flow rate of the input feed and air, influence the production of key products like gasoline, coke, CO, and CO₂. The results indicated that increasing both the temperature and flow rate of the input feed and catalyst leads to improved conversion and feed utilization. To estimate kinetic parameters for both the riser and regenerator, a genetic algorithm optimization framework was applied. Among the configurations tested, the five-lump kinetic model yielded superior agreement with actual industrial performance. The Average Absolute Relative Error (AARE) between simulation and real data in the riser was 3.097% for the five-lump model, compared to 4.86% for the six-lump model. For the regenerator, the Mean Absolute Error (MAE) was 0.77 for the five-lump model and 4.88 for the six-lump one. The regenerator was modeled by dividing it into dense and dilute phases, and specific coke-burning reaction equations were solved for each region, enabling a more accurate representation of catalyst regeneration behavior.

In this study, we analyze a vulnerable road protection approach utilizing reinforcement learning to extract reward functions, enabling proficient agents to optimize the motion of trajectory objects in real-world scenarios. By leveraging reinforcement learning in simulation, we propose a predictive framework where a reward function guides an agent in decision-making for object movement. Our findings enhance the understanding of grounded simulation learning and control strategies for safeguarding vulnerable road users. Particularly in self-driving cars, service robots, and advanced surveillance systems, accurate trajectory prediction of dynamic agents is critical for planning and safety. This study focuses on predicting the motion trajectory of moving cars, synthesizing and categorizing existing methodologies based on motion modeling strategies and contextual information. Additionally, we summarize key performance metrics and datasets while identifying current safety limitations. Finally, we propose future research directions to improve road safety through advanced trajectory prediction and decision-making models.

Integrating carbon dioxide (CO₂)-based chemical enhanced oil recovery (CEOR) with CO₂ sequestration offers a dual solution for both decarbonization and addressing global energy demands. This review critically evaluates advanced CO₂-based CEOR techniques, including nanoparticle-enhanced CO₂ flooding, CO₂ foams, and water alternating gas, emphasizing their effectiveness in enhancing oil recovery through mechanisms such as mobility control, wettability alteration, and interfacial tension reduction. Additionally, the potential of geological CO₂ storage is explored, with a focus on its role in mitigating CO₂ emissions via mineral, structural, and solubility trapping in various geological formations. The successful application of CO₂-based CEOR requires comprehensive investigation, mitigation strategies, a robust policy framework, and ongoing monitoring, thus emphasizing the challenges. This review highlights the challenges and aims to bridge technological gaps between CO₂-based CEOR and geological CO₂ sequestration by examining recent advancement, identifying integration challenges, and outlining opportunities for sustainable deployment. Emphasizing the collective potential of these technologies, this review underscores their capacity to extend reservoir productivity, optimize oil recovery, and contribute to global climate goals. The findings offer actionable insights to improve environmental safety and economic feasibility, paving the way for innovative solutions in sustainable energy production.

With the increasing depletion of natural sand and gravel resources, construction and other businesses have gradually realized the seriousness of the situation. As a potential waste resource, tailings sand (TS) has huge reserves in countries all over the world, but it has not been well recycled. For this reason, this review introduces the basic characteristics of TS and systematically analyzes the applied research progress of TS in cement-based materials (CBMs) over recent years based on CiteSpace software. Based on the above results, the influence of TS on the mechanical properties, durability, and microscopic characteristics, as well as the economic and environmental effects of CBM (e.g., concrete, mortar), is primarily detailed. The existing research has indicated that the moderate content of TS can enhance the mechanical strength and durability of CBM by improving its microstructure. However, challenges such as reduced fluidity and variability in TS properties due to different ore sources remain significant barriers to widespread adoption. For example, studies show that 20–40% TS replacement improves compressive strength by 12 ~ 18% (e.g., ITS: + 15.7% at 30% dosage; CTS: + 9.2% at 20%), while 60% replacement reduces slump by 26–34%. The incorporation of TS into CBM is conducive to promoting sustainable production with favorable economic and environmental benefits. Environmental assessments indicate 18–22% lower CO₂ emissions compared to conventional concrete. Overall, the utilization of TS as a substitute material for fine aggregate or cement has great application value and development potential in CBMs. Finally, this review discusses the feasibility of TS-containing CBM in practical applications and gives some suggestions for its future research focus. The results of this research have important theoretical and engineering implications for the application of TS in construction materials.

In this study, the application of enhanced inerter-based control devices is considered to improve broadband vibration control, including optimizing the design of a base-isolated structure featuring a supplementary tuned mass damper inerter (TMDI). The primary response of an undamped single degree of freedom (SDOF), considering relative displacement, velocity, relative accelerations, and force transmitted to the ground, is investigated. Optimal TMDI parameters are evaluated using the hybrid optimization technique, employing numerical search methods. Subsequently, a curve-fitting approach is utilized to derive explicit formulations for TMDI parameters based on the numerical search results and optimal parameters determined through fixed-point theory. The proposed equations demonstrate minimal inaccuracies, rendering them valuable for damped system design. In addition, the primary system’s damping minimally impacts the TMDI’s optimal damping ratio but significantly affects its optimum tuning frequency. Furthermore, the optimally developed TMDI is applied to an SDOF-damped system with both fixed and flexible bases as a supplemental damper. The study extends to incorporate multi-degree-of-freedom (MDOF) systems, considering three different case study structural models equipped with TMDIs. The analysis of twenty ground motion excitations on these models highlights the efficiency of the optimal TMDI in controlling seismic behaviors. Particularly, the maximum responses are observed in a nine-story benchmark structure under twenty real ground motion seismic excitations, underscoring the utility of the optimized TMDI in seismic control applications.

Satellite communication, or SATCOM, is expanding quickly and piques the interest of researchers due to the growing need for greater data transmission speeds. In satellite communication, the Ku/Ka-band is regarded as the major frequency range due to its broad bandwidth capacity. Radio receivers in the Ku/Ka-band for satellite television require the low-noise amplifier (LNA), a crucial building piece at the receiver end. In this work, several high out-of-the-band rejection LNAs are thoroughly analyzed. Key parameters and design topologies for Ku- and Ka-band SATCOM systems and applications form the basis of this review. Comprehensive examples are also provided for the high out-of-band active and passive rejection/filtering techniques.

In this paper, the theory for cost optimization of heat exchanger inventory in power cycles is developed for any number of closed and open feedwater heaters (CFH and OFH). For this purpose, endoreversible power cycles are initially analyzed for the following four cases: (i) one CFH with a trap, (ii) one CFH with a mixing chamber, (iii) one CFH before the OFH, and (iv) one CFH after the OFH. Only the most practical scenario of specified power output is studied. The non-dimensional cost function was then generalized, which resulted in the determination of generalized cost optimal equations with respect to the high-side absolute temperature ratio and the fluid temperature ratio along with unit to total cost ratios for all heat exchangers in the system. Based upon these cases, general instructions are provided that can be used to write all relevant cost equations for any number and combination of feedwater heaters in order to remove the need for detailed derivations. A fifth, more complicated, case is studied after this to show the applicability of these guidelines.

The growing demand for computation-intensive and delay-sensitive services in internet of things (IoT) networks is constrained by the limited computing capacity and battery life of device users, as well as bandwidth limitations in shared communication channels. Mobile-edge computing (MEC) emerges as a promising solution to address these resource limitations by offloading tasks. However, many existing offloading approaches may restrict performance gains due to the overloaded communication channels among multiple users. To tackle these issues, this research aims to develop an energy-efficient task offloading framework for multi-IoT, multi-server edge computing systems. This framework integrates a load balancing algorithm for optimal device distribution, a compression layer to reduce data transmission overhead, and a deep reinforcement learning technique to dynamically make offloading and compression decisions. Additionally, the proposed solution jointly formulates load balancing, task offloading, compression, and communication allocation, aiming to minimize the energy consumption of the entire system. Given the NP-hard nature of this problem, an efficient deep learning-based technique is developed to achieve a near-optimum solution. Finally, experimental results reveal that the model achieves significant energy savings, with reductions of up to 63.96% and 61.87% in local execution and offloading scenarios, respectively, in scenarios with low channel bandwidth availability. These findings confirm the effectiveness of the proposed solution in enhancing system efficiency and scalability in real-world MEC environments.