In the real-life, time-series data comprise a complicated pattern, hence it may be challenging to increase prediction accuracy rates by using machine learning and conventional statistical methods as single learners. This research outlines and investigates the Stacking Multi-Learning Ensemble (SMLE) model for time series prediction problem over various horizons with a focus on the forecasts accuracy, directions hit-rate, and the average growth rate of total oil demand. This investigation presents a flexible ensemble framework in light of blend heterogeneous models for demonstrating and forecasting nonlinear time series. The proposed SMLE model combines support vector regression (SVR), backpropagation neural network (BPNN), and linear regression (LR) learners, the ensemble architecture consists of four phases: generation, pruning, integration, and ensemble prediction task. We have conducted an empirical study to evaluate and compare the performance of SMLE using Global Oil Consumption (GOC). Thus, the assessment of the proposed model was conducted at single and multistep horizon prediction using unique benchmark techniques. The final results reveal that the proposed SMLE model outperforms all the other benchmark methods listed in this study at various levels such as error rate, similarity, and directional accuracy by 0.74%, 0.020%, and 91.24%, respectively. Therefore, this study demonstrates that the ensemble model is an extremely encouraging methodology for complex time series forecasting.

Object detection in aerial remote sensing images is essential for applications such as traffic management, public security, and ecological monitoring. However, existing methods struggle to handle multi-scale objects, complex backgrounds, and frequent occlusions, resulting in inadequate global feature extraction and unstable multi-scale loss calculations. To address these challenges, we propose the Multi-Feature Attention-Enhanced YOLO (MFAE-YOLO) network. Our approach introduces a Global Feature Fusion Processing (GFFP) module to enhance global feature extraction. The backbone network incorporates Fusion of Channel, Pixel, and Spatial (FCPS) attention modules to strengthen feature representation, while C2F-Feature Pool Extraction Units (C2F-FPEU) optimize pooling-based feature extraction. Additionally, we propose an Accurate IoU (AIoU) loss function to refine bounding box regression. Experiments on NWPU VHR-10, RSOD, and DIOR datasets demonstrate that MFAE-YOLO surpasses state-of-the-art methods, achieving mAP₅₀ values of 94.7%, 94.8%, and 67.0%, respectively. Ablation studies further validate the contributions of each module. The code is available at https://github.com/yiboCode/MFAE-YOLO.

Unmanned surface vehicles (USVs) are becoming increasingly significant in fulfilling integrated sensing, computing, and communication with the emergence of bidirectional computation tasks. However, Quality-of-Service provisioning is still challenging since USVs are restricted with limited onboard resources and direct links between them and shore-based terrestrial base stations (TBSs) are frequently blocked. This article proposes a novel reconfigurable intelligent surface (RIS)-assisted cooperative unmanned aerial vehicle (UAV)–USV mobile-edge computing (MEC) network architecture, where RIS-mounted tethered UAV (TUAV) and rotary-wing UAVs (RUAVs) are collaboratively utilized to serve USVs. RUAVs energy minimization is formulated by jointly considering TUAV hovering altitude, RIS phase-shift vector, RUAV service selection indicator, and RUAVs turning points. A heuristic solution is proposed to tackle the formulated problem, where the original problem is first decoupled into three subproblems, e.g., the joint optimization of RIS phase-shift vector and TUAV hovering altitude subproblem, RUAVs service selection indicator subproblem, and RUAVs turning points subproblem, each of which is solved by the proposed modified alternative direction method of multiplier (ADMM) algorithm, the proposed enhanced simulated annealing (ESA) algorithm and the proposed successive convex approximation (SCA)-based algorithm. In this way, the challenging problem can be efficiently solved iteratively. The results show that the proposed solution can decrease RUAVs energy consumption by nearly 29% compared to numerous selected advanced algorithms. Moreover, the performance of the proposed solution regarding typical penalty coefficients and number of RIS reflecting elements is investigated.

The use of skylights in buildings introduces natural light into the interior space, thereby reducing the reliance on artificial lighting and aligning with the principles of low carbon and environmental sustainability. To ensure optimal indoor lighting quality, it is essential to optimize the arrangement of skylights to strike a balance between high average illumination and uniformity of illumination. Recent initiatives by the Chinese government have emphasized the construction and renovation of numerous gymnasiums. In this research, a novel approach based on optimized algorithms was employed to design skylights and improve the uniformity of indoor illuminance. Simulation results demonstrated that the skylight arrangements derived from the optimization algorithms exhibited significantly higher levels of illumination uniformity, while maintaining comparable average illumination and skylight areas, when compared to conventional designs. Additionally, the study employed genetic algorithms to optimize the skylight arrangement for a specific gymnasium, resulting in a remarkable 32% increase in illumination uniformity. The study also accounted for obstacles and seating in the skylight design, and the genetic algorithm generated desirable skylight arrangements with respective increases of 32% and 21% in illumination uniformity for scenarios involving obstacles and seating. Overall, this study underscores the potential of optimized algorithms in the design of skylights for green buildings, offering valuable insights for future research endeavors in this field.

In modern electronic manufacturing processes, multi-layer Printed Circuit Board (PCB) routing requires connecting more than hundreds of nets with perplexing topology under complex routing constraints and highly limited resources, so that takes intense effort and time of human engineers. PCB fanout as a pre-design of PCB routing has been proved to be an ideal technique to reduce the complexity of PCB routing by pre-allocating resources and pre-routing. However, current PCB fanout design heavily relies on the experience of human engineers, and there is no existing solution for PCB fanout automation in industry, which limits the quality of PCB routing automation. To address the problem, we propose a neuralized PCB fanout method by deep reinforcement learning. To the best of our knowledge, we are the first in the literature to propose the automation method for PCB fanout. We combine with Convolution Neural Network (CNN) and attention-based network to train our fanout policy model and value model. The models learn representations of PCB layout and netlist to make decisions and evaluations in place of human engineers. We employ Proximal Policy Optimization (PPO) to update the parameters of the models. In addition, we apply our PCB fanout method to a PCB router to improve the quality of PCB routing. Extensive experimental results on real-world industrial PCB benchmarks demonstrate that our approach achieves 100% routability in all industrial cases and improves wire length by an average of 6.8%, which makes a significant improvement compared with the state-of-the-art methods.

Joint communication-caching-computing resource allocation in wireless inland waterway communications enables resource-constrained unmanned surface vehicles (USVs) to provision computation-intensive and latencysensitive tasks forward beyond fifth-generation (B5G) and sixth-generation (6G) era. The power of such resource allocation cannot be fully studied unless bidirectional data computation is properly managed. A novel intelligent reflecting surface (IRS)-assisted hybrid UAV-terrestrial network architecture is proposed with bidirectional tasks. The sum of uplink and downlink bandwidth minimization problem is formulated by jointly considering link quality, task execution mode selection, UAVs trajectory, and task execution latency constraints. A heuristic algorithm is proposed to solve the formulated challenging problem. We divide the original challenging problem into two subproblems, i.e., the joint optimization problem of USVs offloading decision, caching decision and task execution mode selection, and the joint optimization problem of UAVs trajectory and IRS phase shift-vector design. The Karush-Kuhn-Tucker conditions are utilized to solve the first subproblem and the enhanced differential evolution algorithm is proposed to solve the latter one. The results show that the proposed solution can significantly decrease bandwidth consumption in comparison with the selected advanced algorithms. The results also prove that the sum of bandwidth can be remarkably decreased by implementing a higher number of IRS elements.