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In this study, we investigated reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication systems over Nakagami-m fading channels. To enhance the reliability of RIS-assisted D2D communications, we utilized the rate-splitting multiple access (RSMA) technique to maximize the achievable ergodic rate for our considered systems. Specifically, both devices decoded the common symbol by treating private symbols as interference, and then each private symbol was decoded by treating the other as interference. In order to maximize the achievable ergodic rate at the destination, we analyzed the achievable ergodic rate of the RIS link and the D2D link, and the destination jointly decoded both symbols transmitted from the source and device by involving the maximum ratio combination (MRC). We obtained a closed-form expression for the achievable ergodic rate of the proposed RIS-assisted D2D communication system. Finally, we investigated the influence of power allocation factors and the number of reflective elements on the achievable ergodic rate. As seen by the numerical results, there was a good match between the analysis and simulation results, as well as significant superiority compared with existing works.

Battery voltage plateau characteristics are crucial for designing and controlling battery management systems. Utilising the plateau period attributes to their fullest extent can enable optimal battery control, enhance battery performance, and prolong battery lifespan. This research aimed to investigate the performance of cylindrical ternary lithium batteries at various discharge rates, focusing on the variations in terminal voltage, capacity, and temperature. The battery performance at different discharge rates was meticulously examined through cyclic charge/discharge experiments. The convexity of the voltage curve was used to analyse the voltage plateau characteristics at different rates. The findings revealed significant differences in battery performance under varying discharge rates. Higher discharge rates resulted in shorter discharge times and lower battery voltages at corresponding residual capacities. The discharge time, capacity, and voltage during the plateau phase decreased as the discharge rate increased. At discharge rates of 1 C, 3 C, 5 C, 7 C, 9 C, and 11 C, the proportion of discharged battery capacity ranged from 86.45% to 78.42%. At the same time, voltage and temperature variations during the plateau period decreased significantly compared to those before and after discharge. This research provides a crucial reference point for advancing battery design and thermal management systems.

To enhance the accuracy and robustness of lithium-ion battery state-of-health (SOH) prediction, this study proposes a dual-mode intelligent optimization BP neural network model (AO–AVOA–BP) which integrates the Aquila Optimizer (AO) and the African Vulture Optimization Algorithm (AVOA). The model leverages the global search capabilities of AO and the local exploitation strengths of AVOA to achieve efficient and collaborative optimization of network parameters. In terms of feature construction, eight key health indicators are extracted from voltage, current, and temperature signals during the charging phase, and the optimal input set is selected using gray relational analysis. Experimental results demonstrate that the AO–AVOA–BP model significantly outperforms traditional BP and other improved models on both the NASA and CALCE datasets, with MAE, RMSE, and MAPE maintained within 0.0087, 0.0115, and 1.095%, respectively, indicating outstanding prediction accuracy and strong generalization performance. The proposed method demonstrates strong generalization capability and engineering adaptability, providing reliable support for lifetime prediction and safety warning in battery management systems (BMS). Moreover, it shows great potential for wide application in the health management of electric vehicles and energy storage systems.

Aluminum profiles in the production process will inevitably appear a variety of surface defects, seriously affect the quality of products. The traditional method to detect the surface defects can not meet the actual demand, so it is of great significance to study the efficient detection method. In this paper, digital image processing methods such as rotation, flip, contrast and brightness transformation are used to increase the number of samples and simulate the complex imaging environment. An improved YOLOX-S detection model is proposed. Squeeze-and-Excitation Networks is embedded in the Cross Stage Partial module, and then SECSP module is proposed, and all CSP modules in YOLO-S are replaced with SECSP module, which improves the sensitivity of the network to the feature channel. SCYLLA-IoU loss function is used instead of IoU loss function. The improved model can improve the detection ability of small targets and the ability to resist background interference information. The mAP reaches 91.62, which is 1.82% higher than that of the basic YOLOX-S, and the detection speed reaches 58.67 frames ·s −1 , which can meet the real-time detection requirements. At the same time, the comparison experiment proves that the comprehensive performance of the proposed detection model is the best, and the detection accuracy and speed have reached a good balance. The ablation experiment proves that the two improved schemes can improve the detection accuracy of the network. Graphical Abstract

Tufts Clinical and Translational Science Institute (CTSI) developed an online self-paced course to address the gap identified in critical thinking skills related to peer-reviewed nutrition science publications. Initial engagement was low, prompting the launch of a quality improvement project utilizing Dissemination and Implementation (D&I) science principles to enhance participation. This report details the development and execution of the dissemination strategy, course promotion methods, and outcomes related to participant engagement and feedback. A dissemination plan was designed and implemented using the Value-Added Research Dissemination Framework and the Consolidated Framework for Implementation Research (CFIR). Dissemination efforts targeted registered dietitians and university nutrition program instructors, along with their students. During the active dissemination period from January to May 2023, the cumulative numbers of learners increased from 23 to 118. Instructors from three nutrition degree programs found the course valuable, reporting that it introduced new content or reinforced existing material. Learner participation continued past the active dissemination period into 2024. Findings from the course evaluation survey provided insights to guide future course improvements. This project demonstrates the successful use of D&I frameworks to support the dissemination and implementation of educational innovations such as online learning initiatives.

This paper presents an optical image encryption system based on grating diffraction imaging and interference superposition principle. When the encryption is performed, an image is encrypted into several ciphertexts by vector superposition, and a random phase is added to each ciphertext to prevent the generation of contours in decryption. During the decryption process, the nature of the grating diffraction in the 4f system would be used, and then, all the ciphertexts would be superimposed on the same position at the output plane, so that an accurate decrypted image can be obtained. Since the random phase is especially processed during encryption, only the complete information of the ciphertexts needs to be known at the time of decryption, and the random phase information is not needed to recover the image. If we do not have enough information of the ciphertexts, we cannot recover the correct decrypted image. Computer simulations prove its possibility.

Natural fibers exhibit noticeable variations in their cross-sections, and measurements assuming a circular cross-section can lead to errors in the values of their properties. Providing more accurate geometric information of fiber cross-sections is a key challenge. Based on microscopic images of natural fiber structures, this paper proposes a natural fiber microstructure detection method based on the CA-DeepLabv3+ network model. The study investigates a natural fiber microstructure image segmentation algorithm that uses MobileNetV2 as the feature extraction backbone network, optimizes the Atrous Spatial Pyramid Pooling (ASPP) module through cascading, and embeds an Efficient Multi-scale Attention (EMA) mechanism. The results show that the algorithm proposed in this paper can accurately segment the microstructures of multiple types of natural fibers, achieving an average pixel accuracy (mPA) of 95.2% and a mean Intersection over Union (mIoU) of 90.7%.

In order to further improve the power and fuel economy of hybrid vehicles, this paper proposes a method of hybrid vehicle powertrain matching by combining orthogonal tests with Cruise software, supplemented by the control strategy formulation of critical components of the whole vehicle on the MATLAB/Simulink platform. Considering the influence of vehicle engine, electric motor, battery and overall mass on the powertrain design, the L9(34)-type orthogonal table is selected for the orthogonal test design. After verifying the feasibility and accuracy of each design solution of the powertrain, the different design solutions are simulated for power and economic performance. Finally, the best performance indicators of the vehicle are as follows: the maximum speed is 183.35 km/h, the 0–100 km/h acceleration time is 6.87 s, and the maximum degree of climbing is 39.65 percent. The fuel consumption of 100 km is 3.47 L. The optimal solution was compared with the third-generation Harvard H6 and AITO M5 in terms of fuel saving and emission reduction, and it was found that for every 15,000 km driven, it is expected to save 469.5 L of fuel and 109.5 L of CO2, respectively, which can reduce fuel use and emission by about 1051.21 kg and 245.17 kg CO2, respectively. This simulation experiment can reduce the workload of traditional power system matching. It can provide ideas for power system matching and optimization for Corun CHS Technology Co., Ltd. (Foshan City, Guangdong Province, China) and offer a certain degree of reference for hybrid vehicle power system design and simulation.

Abstract Bacterial infections cause severe health conditions, resulting in a significant economic burden for the public health system. Although natural phytochemicals are considered promising anti-bacterial agents, they suffer from several limitations, such as poor water solubility and low bioavailability in vivo, severely restricting their wide application. Herein, we constructed a near-infrared (NIR)-responsive carrier-free berberine hydrochloride (BH, phytochemicals)/indocyanine green (ICG, photosensitizer) nanoparticles (BI NPs) for synergistic antibacterial of an infected wound. Through electrostatic interaction and π–π stacking, the hydrophobic BH and amphiphilic ICG are initially self-assembled to generate carrier-free nanoparticles. The obtained BI NPs demonstrated NIR-responsive drug release behavior and better photothermal conversion efficiency of up to 36%. In addition, BI NPs stimulated by NIR laser exhibited remarkable antibacterial activity, which realized the synergistic antibacterial treatment and promoted infected wound healing. In summary, the current research results provided a candidate strategy for self-assembling new BI NPs to treat bacterial infections synergistically. Graphical Abstract

In this article, we report the preparation of lignin-derived carbon nanosheet (L-CNS) by direct thermal treatment of lignin without activation operation and the functions of the L-CNS as an adsorbent for rhodamine dye. The L-CNSs are fabricated by freeze-drying (FD) methods of lignin followed by high-temperature carbonization. It is found that lower frozen temperature in FD or lower concentration of lignin aqueous solution renders L-CNSs’ more porous morphology and higher specific surface area (SSA), allowing a promising application of the L-CNSs as an efficient adsorbent for organic pollutants. In particular, the alkaline hydroxide catalyst helps to increase the SSA of carbon products, leading to a further improved adsorption capacity. On the other hand, p-toluenesulfonic acid (TsOH) catalyzed pyrolysis, which dramatically increased the L-CNS product yield, and provided a high-yield approach for the production of pollutant absorbent.