Catalytic reduction reactions using isopropanol(IPA) as a transfer hydrogenating agent are gaining significant attention due to the low cost and large-scale production of IPA. Traditional methods for carbon-carbon(C — C) bond construction often rely on expensive and scarce transition metal catalysts, raising concerns about sustainability and environmental impact. To address these challenges, we develop a bifunctional photocatalyst, phloroglucinol carbon quantum dot(PG-CQD). It facilitates catalytic transfer hydrogenation(CTH) with IPA as the hydrogen donor. PG-CQDs exhibit both dehydrogenation and reduction activities, enabling the formation of vicinal diols under mild conditions with visible light irradiation. We propose a CTH mechanism that has been successfully validated through experiments. The catalytic system demonstrates remarkable versatility, enabling the synthesis of various vicinal diols from diverse α-keto ester substrates with good or excellent yields. These findings offer a sustainable synthetic strategy that aligns with green chemistry principles and establish a promising pathway for the development of environmentally benign and energy-efficient organic transformations.

Conventional polyethylene(PE) fibers face limitations in large-scale industrial applications due to their poor thermal stability and inherent hydrophobicity, which restrict processing temperatures and dyeability, especially in blended fabric production. In this research, a one-step ultraviolet(UV) irradiation technology was employed to modify medium molecular weight PE fibers through simultaneous crosslinking and grafting modifications, aiming to enhance their thermal stability and hydrophilicity. The modification employed a cost-effective, UV-initiated crosslinking system consisting of benzophenone(BP) as the photoinitiator and triallyl isocyanurate(TAIC) as the co- crosslinker. Acrylic acid(AA) was selected as the grafting monomer. These modifiers were thoroughly mixed with the PE matrix in a liquid-phase environment, and the mixture was melt-spun into fibers. The resulting fibers were then subjected to UV irradiation, which triggered the crosslinking and grafting reactions. The effects of the mass fraction of each component and irradiation parameters on modification efficacy were systematically investigated, followed by a comprehensive characterization of the modified PE fibers. The modified PE fibers achieved optimal thermal stability under the following conditions:2. 0% mass fractions for both BP and TAIC, a UV irradiation intensity of 2 000 mW/ cm ², and an equivalent irradiation time of 60 s. This synergistic modification approach enables the fibers to maintain superior morphological integrity and mechanical performance when exposed to elevated temperatures ranging from 130 to 150^°C. Meanwhile, an AA grafting mass fraction of 2. 0% maximizes hydrophilicity with minimal impact on other properties, as evidenced by a dramatic reduction in the water contact angle(WCA) from 105. 0^° (hydrophobic) to 48. 4^°(hydrophilic). These improvements confirm the effectiveness of the modification strategy in synergistically enhancing both thermal stability and hydrophilicity of PE fibers.

As the annual production of industrial hemp in China increases and its global market share grows, its multipurpose development has become an important trend for future development. The cellulose mass fraction of industrial hemp was found to be as high as 59. 36% by chemical composition determination, providing a possibility for the production of nanocellulose. To broaden the application field of industrial hemp, the 2, 2, 6, 6-tetramethylpiperidine-1-oxyl radical(TEMPO)-oxidized nanocellulose(TCNF), sulfuric acid hydrolyzed nanocellulose(SCNC), and lignin-containing hydrolyzed nanocellulose(LCNC) were prepared by multi-step chemical purification pretreatment combined with TEMPO oxidation and sulfuric acid hydrolysis, respectively. They were characterized by Fourier transform infrared(FTIR) spectroscopy, X-ray diffraction(XRD), and thermogravimetric analysis(TGA). The effects of the sodium hypochlorite volume, sodium hydroxide mass fraction in the pretreatment process, and acid hydrolysis reaction time on the Zeta potential and particle size of the prepared nanocellulose were investigated. The absolute value of the Zeta potential of SCNC could reach 29. 59 mV, and the particle size was small. The suspension could still maintain good dispersion stability after standing for 24. 0 h under the same dispersion conditions. The basic functional group composition and crystal morphology of TCNF, SCNC, and LCNC did not change compared with the raw hemp, and the highest crystallinity increased from 24. 6% to 68. 1%. Due to the introduction of ester and carboxyl groups, the initial degradation temperature and the temperature at the maximum mass loss rate of the nanocellulose were lower than those of the raw hemp, but the nanocellulose still maintained the thermal stability for practical applications.

The colorectal cancer is one of the most common and lethal cancers, and colorectal polyps, as precancerous lesions, can lead to diagnostic oversight or misdiagnosis due to their varied shapes and sizes, thereby promoting the irreversible progression of colorectal cancer. We propose a YOLO based model and name it EF-YOLO. It incorporates transformer to extract contextual information about the colorectal polyps. Simultaneously, leveraging the morphological characteristics of colorectal polyps, we design a brand-new module, namely advanced multi-scale aggregation(AMSA), to replace the traditional multi-scale module. The backbone adopts deformable convolutional network-maxpool(DCN-MP) to enhance feature extraction while adaptively sampling points to better match the shapes of colorectal polyps. By combining coordinate attention(CA), this model maximizes the use of positional and channel information, more effectively extracting features of colorectal polyps, directing the model’s attention toward the colorectal polyp region. EF-YOLO has made advancement on the merged Kvasir-SEG and CVC-ClinicDB dataset. Compared to the original model, the mean average precision(mAP) of EF-YOLO increases and reaches 96.60%, meeting automated colorectal polyp detection requirements.

In non-independent and identically distributed(non-IID) data environments, model performance often degrades significantly. To address this issue, two improvement methods are proposed:FedReg and FedReg^∗. FedReg is a method based on hybrid regularization aimed at enhancing federated learning in non-IID scenarios. It introduces hybrid regularization to replace traditional L2 regularization, combining the advantages of L1 and L2 regularization to enable feature selection while preventing overfitting. This method better adapts to the diverse data distributions of different clients, improving the overall model performance. FedReg^∗ combines hybrid regularization with weighted model aggregation. In addition to the benefits of hybrid regularization, FedReg^∗ applies a weighted averaging method in the model aggregation process, calculating weights based on the cosine similarity between each client gradient and the global gradient to more reasonably distribute client contributions. By considering variations in data quality and quantity among clients, FedReg^∗ highlights the importance of key clients and enhances the model’s generalization performance. These improvement methods enhance model accuracy and communication efficiency.

In federated learning, backdoor attacks have become an important research topic with their wide application in processing sensitive datasets. Since federated learning detects or modifies local models through defense mechanisms during aggregation, it is difficult to conduct effective backdoor attacks. In addition, existing backdoor attack methods are faced with challenges, such as low backdoor accuracy, poor ability to evade anomaly detection, and unstable model training. To address these challenges, a method called adaptive simulation backdoor attack(ASBA) is proposed. Specifically, ASBA improves the stability of model training by manipulating the local training process and using an adaptive mechanism, the ability of the malicious model to evade anomaly detection by combing large simulation training and clipping, and the backdoor accuracy by introducing a stimulus model to amplify the impact of the backdoor in the global model. Extensive comparative experiments under five advanced defense scenarios show that ASBA can effectively evade anomaly detection and achieve high backdoor accuracy in the global model. Furthermore, it exhibits excellent stability and effectiveness after multiple rounds of attacks, outperforming state-of-the-art backdoor attack methods.

The integration of the intelligent reflecting surface(IRS) with simultaneous wireless information and power transfer(SWIPT) has emerged as a cost-effective and efficient solution to enhance the performance of information and energy transfer. In this research, a hybrid active/ passive IRS-assisted SWIPT system is proposed. Specifically, an active IRS(AIRS) and a passive IRS(PIRS) are deployed in the SWIPT system to facilitate a multiantenna base station(BS) in simultaneously delivering information and energy to multiple information users(IUs) and energy users(EUs). The objective is to maximize the sum throughput by jointly optimizing the transmitter beamforming and the reflection coefficient matrices of the AIRS and the PIRS while satisfying the transmitter power constraints, the energy harvesting(EH) requirements of EUs, and the AIRS amplification power limitations. However, the optimization variables are highly coupled and cannot be solved directly. To tackle this complex problem, we propose an efficient algorithm based on alternating optimization(AO) and semi-definite relaxation(SDR) techniques to obtain high-quality solutions. Simulation results demonstrate that the hybrid active/ passive IRS- assisted SWIPT system significantly enhances throughput performance and outperforms benchmark systems.

Vehicular communication systems rely on secure vehicle-to-vehicle(V2V) communications for safety-critical information exchange. However, the presence of eavesdropping vehicles poses a significant challenge. This paper investigates the security of V2V communications in reconfigurable intelligent surface(RIS)-assisted vehicular communication systems with spectrum sharing. It proposes a three-stage alternating optimization(TSAO) algorithm to address the complex problem of multiple eavesdropped V2V links that reuse the spectrum already occupied by vehicle-to-infrastructure(V2I) links. To solve the mixed-integer and non-convex optimization problem due to coupled variables and complex constraints, the algorithm decomposes the original problem into three easily solvable sub-problems:RIS reflection coefficient optimization, vehicle transmission power optimization, and spectrum sharing optimization. First, the RIS reflection coefficients are optimized by using the penalty convex-concave procedure(CCP) method. Second, the optimal power points are determined in the power optimization sub-problem. Finally, the spectrum sharing optimization sub-problem is constructed as a weighted bipartite graph matching problem and solved by using the optimal matching algorithm. The TSAO algorithm not only maximizes the sum V2V secrecy rate but also ensures the quality-of-service(QoS) requirements of the V2I links. Simulation results validate the superiority of the proposed algorithm and highlight the improvement in the sum V2V secrecy rate achieved by utilizing RIS technology in vehicular communication systems with spectrum sharing.

The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs) within multi-group networks under signed digraphs is investigated, where the first-order and second-order nonlinear dynamic agents belong to distinct groups. Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups. Firstly, a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders, respectively. As an extension, the bipartite control problem with time-variant formation for the multi-agent system(MAS) is also considered, and a corresponding control protocol with formation compensation vectors is given. Finally, in view of Lyapunov stability theory and matrix inequality, the sufficient conditions for realizing the bipartite containment control are obtained, and several simulations are provided to verify the validity of the above methods.

Conversational recommender systems(CRSs) focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history. Large language models(LLMs) have shown outstanding performance across various domains, thereby prompting researchers to investigate their applicability in recommendation systems. However, due to the lack of task-specific knowledge and an inefficient feature extraction process, LLMs still have suboptimal performance in recommendation tasks. Therefore, external knowledge sources, such as knowledge graphs(KGs) and knowledge bases(KBs), are often introduced to address the issue of data sparsity. Compared to KGs, KBs possess higher retrieval efficiency, making them more suitable for scenarios where LLMs serve as recommenders. To this end, we introduce a novel framework integrating LLMs with KBs for enhanced retrieval generation, namely LLMKB. LLMKB initially leverages structured knowledge to create mapping dictionaries, extracting entity-relation information from heterogeneous knowledge to construct KBs. Then, LLMKB achieves the embedding calibration between user information representations and documents in KBs through retrieval model fine-tuning. Finally, LLMKB employs retrieval-augmented generation to produce recommendations based on fused text inputs, followed by post-processing. Experiment results on two public CRS datasets demonstrate the effectiveness of our framework. Our code is publicly available at the link:https:/ / anonymous. 4open. science/ r/ LLMKB-6FD0.

During the image generation phase, the parser-free Flow-Style-VTON model(PF-Flow-Style-VTON), which utilizes distilled appearance flows, faces two main challenges:blurring, deformation, occlusion, or loss of the arm or palm regions in the generated image when these regions of the person occlude the garment; blurring and deformation in the generated image when the person performs large pose movements and the target garment is complex with detailed patterns. To solve these two problems, an improved virtual try-on network model, denoted as IPF-Flow-Style-VTON, is proposed. Firstly, a target warped garment mask refinement module(M-RM) is introduced to refine the warped garment mask and remove erroneous information in the arm and palm regions, thereby improving the quality of subsequent image generation. Secondly, an improved global attention module(GAM) is integrated into the original image generation network, enhancing the ResUNet’s understanding of global context and optimizing the fusion of local features and global information, thereby further improving image generation quality. Finally, the UniPose model is used to provide the pose keypoint information of the target person image, guiding the task execution during the image generation phase. Experiments conducted on the VITON dataset show that the proposed method outperforms the original method, Flow-Style-VTON, by 5. 4%, 0. 3%, 6. 7%, and 2. 2% in Fréchet inception distance(FID), structural similarity index measure(SSIM), learned perceptual image patch similarity(LPIPS), and peak signal-to-noise ratio(PSNR), respectively. Overall, the proposed method effectively improves upon the shortcomings of the original network and achieves better visual results.

In reliability analyses, the absence of a priori information on the most probable point of failure(MPP) may result in overlooking critical points, thereby leading to biased assessment outcomes. Moreover, second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios. To overcome these challenges, a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed. Initially, the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task. Subsequently, a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs. Finally, a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs, and the failure probability of the structure is calculated by using the hypersphere integration method. The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples, with accuracy comparisons of failure probabilities against the first-order reliability method(FORM) and the second-order reliability method(SORM). The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision.

In order to eliminate the meshing interference between the flexspline and circular spline after the taper deformation of the flexspline, the radial deformation difference method, major and minor axis fitting method, and ellipse fitting method are used to modify the tooth thickness of the flexspline and analyze the performance indexes such as the assembly stress, transmission error, and fatigue life. Firstly, the conjugate tooth profile is solved based on the quadruple-circular-arc tooth profile and modified kinematic method. Then, based on the finite element radial deformation of the flexspline, the principle and characteristics of three modification methods are analyzed, and the modification amount of each section of the flexspline tooth is calculated. Finally, the influence of the three modification methods on the performance of the harmonic drive is compared. The results show that the radial deformation difference method can initially determine the modification amount. The minimum static assembly stress is 406. 22 MPa by the major and minor axis fitting method. The ellipse fitting method has the best dynamic performance, small transmission error fluctuation, a peak-to-peak value of 3. 060", and a maximum fatigue life of 107. 558 cycles.

In modern industrial production, foreign object detection in complex environments is crucial to ensure product quality and production safety. Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds. To address these issues, this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions, viewing angles, and object scales. The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU). A dataset consisting of complex backgrounds, diverse lighting conditions, and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments. Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC) of 0. 92 and an average F1 score of 0. 85. Combined with data augmentation, the proposed model exhibits improvements in AUROC by 0. 06 and F1 score by 0. 03, demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings. In addition, the effects of key factors on detection performance are systematically analyzed, providing practical guidance for parameter selection in real industrial applications.

In order to optimize the reaming process of the type IV composite hydrogen storage cylinder, the netting theory was employed for the design of stacking sequences, and the thickness in the head section was predicted. A finite element model of the plastic-lined composite hydrogen storage cylinder, designed to withstand a working pressure of 70.0 MPa, was established by using the wound composite modeler(WCM) in the Abaqus software to analyze the forces acting on the winding layer. The Hashin failure criterion was utilized as the standard for assessing composite failure, and a progressive failure analysis of the cylinder was conducted to predict both the bursting pressure and the failure location of the composite hydrogen storage cylinder. The results indicate that the reaming process can effectively reduce the maximum filament winding thickness in the head section and promote a more uniform transition. At the bursting pressure, the stress within the head liner decreases, thereby enhancing the ultimate bearing capacity of the cylinder. A control system for a four-axis winding machine was designed by utilizing an industrial computer and a programmable multi-axis controller(PMAC). The winding line pattern is designed and the G-code trajectory is generated by the industrial computer. The numerical control system, composed of the PMAC and servo motor, executes the four-axis interpolation motion.