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The r-strategy pests are very challenging to effectively control because of their rapid population growth and strong resurgence potential and are more prone to developing pesticide resistance. As a typical r-strategy pest, the cosmopolitan cotton aphid, Aphis gossypiiGlover, seriously impacts the growth and production of cucurbits and cotton. The present study developed a SPc/double-stranded RNA (dsRNA)/botanical strategy to enhance the control efficacy of A. gossypii. The results demonstrated that the expression of two chitin pathway genes AgCHS2and AgHK2notably changed in A. gossypiiafter treated by three botanical pesticides, 1% azadirachtin, 1% matrine, and 5% eucalyptol. SPc nanocarrier could significantly enhance the environmental stability, cuticle penetration, and interference efficiency of dsRNA products. The SPc/dsRNA/botanical complex could obviously increase the mortality of A. gossypiiin both laboratory and greenhouse conditions. This study provides an eco-friendly control technique for enhanced mortality of A. gossypiiand lower application of chemical pesticides. Given the conservative feature of chitin pathway genes, this strategy would also shed light on the promotion of management strategies against other r-strategy pests using dsRNA/botanical complex nanopesticides.

Cognitive computing endeavors to construct models that emulate brain functions, which can be explored through electroencephalography (EEG). Developing precise and robust EEG classification models is crucial for advancing cognitive computing. Despite the high accuracy of supervised EEG classification models, they are constrained by labor-intensive annotations and poor generalization. Self-supervised models address these issues but encounter difficulties in matching the accuracy of supervised learning. Three challenges persist: 1) capturing temporal dependencies in EEG; 2) adapting loss functions to describe feature similarities in self-supervised models; and 3) addressing the prevalent issue of data imbalance in EEG. This study introduces the DreamCatcher Network (DCNet), a self-supervised EEG classification framework with a two-stage training strategy. The first stage extracts robust representations through contrastive learning, and the second stage transfers the representation encoder to a supervised EEG classification task. DCNet utilizes time-series contrastive learning to autonomously construct representations that comprehensively capture temporal correlations. A novel loss function, SelfDreamCatcherLoss, is proposed to evaluate the similarities between these representations and enhance the performance of DCNet. Additionally, two data augmentation methods are integrated to alleviate class imbalances. Extensive experiments show the superiority of DCNet over the current state-of-the-art models, achieving high accuracy on both the Sleep-EDF and HAR datasets. It holds substantial promise for revolutionizing sleep disorder detection and expediting the development of advanced healthcare systems driven by cognitive computing.

As a component of the Intelligent Transportation System (ITS), Internet of Vehicles (IoV) is becoming increasingly important in the management and construction of urban transportation as it can provide users with a range of applications related to traffic accident warnings, entertainment information, collaborative driving and real-time road information through communication devices on vehicles. However, with the increasing variety of services in the IoV, the growing demand for user traffic and the advances in Unmanned Aerial Vehicle (UAV) technology, UAV is introduced into the IoV as a solution, which can relieve the pressure on the communication infrastructure in the network, provide emergency communication services and improve the performance of network services. Due to the openness of IoV and the high-speed movement of vehicles, authentication and privacy issues are among the most pressing issues in IoV. Therefore, the paper proposes a secure and effective authentication protocol for UAV-assisted IoV. The protocol utilises elliptic curve cryptography to assure the security of the authentication. The protocol undergoes proof of security, Burrows-Abadi-Needham (BAN) logic analysis and informal security analysis to ensure secure and mutual authentication, and have a good resistance to known attacks. Furthermore, performance analysis and comparison are conducted to evaluate the efficiency of our protocol. The results indicate that our protocol has superior advantages in overhead.

Easily synthesized new porous K0.5VOPO4·1.5H2O cathode for aqueous zinc-ion batteries, including high output voltage and intercalation/conversion Zn2+storage mechanism.

Objective: To provide a reference for future policy and measure formulation by conducting a detailed analysis of the burden of vision loss due to cataract by year, age, and gender in China from 1990 to 2019. Methods: Data on the prevalence and disability-adjusted life-years (DALYs) due to cataract in China and neighboring and other G20 countries were extracted from the 2019 Global Burden of Disease (GBD) study to observe the changing trends of vision loss. Results: The number and rate of all-age prevalence and DALYs for cataract in China increased significantly from 1990 to 2019. The age-standardized DALYs rate witnessed a slowly declining trend by 10.16%. And the age-standardized prevalence increased by 14.35% over the 30-year period. Higher prevalence and DALYs were observed in female population from 1990 through 2019, with little improvement over the decades(all p< 0.001). The disease burden of cataract is higher in middle-aged and elderly people. Blindness accounted for the largest proportion of vision impairment burden caused by cataract in China. The age-standardized prevalence and DALY rate of cataract in China were lower than those in India and Pakistan, but higher than those in Russia, South Korea, North Korea, Singapore, and Japan. Conclusions: In the past 30 years, although the age-standardized DALYs rate has decreased slightly in China, the all-age prevalence and DALYs have both increased. This study highlights the importance of reducing cataract burden by providing timely and easily accessible quality care, especially in females and the elderly population.

Data heterogeneity, insufficient scalability, and data privacy protection are the technological challenges of personalized recommendations. This study proposes a new federated learning algorithm (FedSarah) to address low scalability caused by data heterogeneity and uneven computing power in consumer-centric personalized recommendation systems while protecting data privacy of consumers. The algorithm updates the stochastic gradient estimates using a recursive framework on consumer clients. The outer loop calculates the entire gradient for updating global model, and the inner loop calculates the stochastic gradient based on the accumulated stochastic information for updating local models. To increase the stability of convergence, the inner loop modifies intrinsic parameters to change the number of training rounds and the direction of model update on consumer clients. The detailed mathematical analysis and experiments demonstrate that FedSarah has good convergence. In addition, it’s shown that the algorithm can achieve a performance improvement of nearly 5% in terms of accuracy compared to the traditional FedAvg and FedProx algorithms under the condition of heterogeneous data. Furthermore, under the condition of effective privacy protection on consumers’ data, the new algorithm can significantly lessen the impact of data heterogeneity on the real-time service of consumer-centric personalized recommendation systems with low communication latency. The code is available at https://github.com/DashingJ-82/FedSarah.git.

The increasing popularity of 6G communication within the Internet of Vehicles (IoV) ecosystem is expected to induce a surge in both user numbers and data volumes. This expansion will cause substantial challenges in ensuring network security and privacy protection, as well as in addressing the associated issue of inadequate cloud computing resources. In this article, we propose a Quantum Efficient Privacy Protection (QEPP) protocol that leverages reversible information hiding in quantum point clouds. This protocol utilizes quantum communication technology in edge-to-cloud communication of the IoV to transmit sensitive information embedded in quantum state data, thereby ensuring privacy protection. It employs quantum error-correction coding and efficient coding techniques to extract information and recover the carriers. In addition, the protocol utilizes an improved quantum Grover algorithm in the cloud to accelerate the processing speed of quantum data. By addressing security vulnerabilities and improving cloud-computing capabilities, the QEPP can effectively accommodate critical requirements, including precision, timeliness, and robust privacy protection.

Organizations, recognizing tacit knowledge as a key driver of innovation, often create incentives to foster its sharing among members, especially in competitive environments. The challenge lies in the diverse nature of this tacit knowledge and members' varying capabilities, which complicates the design of effective incentives. Acknowledging that innovation value stems from both tacit knowledge and the dynamic efforts of involved parties, we investigate the optimal incentive mechanisms to facilitate the dissemination of tacit knowledge through a differential game model. This model takes into account the influences of competition and member diversity. Our analysis contrasts two incentive approaches to determine the superior strategy under different conditions. The findings suggest that when members' average capability is high, the variance in their capacities can be overlooked in designing incentives. In addition, in heterogeneous settings, organizations tailor incentives to members' abilities. Our results also indicate that heightened competition diminishes members' effort levels and those of the organization, while necessitating increased incentivization. Intriguingly, we discover that organizations adopt a mixed incentive approach when accounting for member heterogeneity.

Over a long time detecting high impedance earth faults (HIEFs) and suppressing the forest fire danger commonly caused by edge-phase-type single phase-earth faults in an ineffectively grounded system are of high concerns. To address this problem, this paper innovatively refines the structure of distribution system via a single-phase power transformer (PT) isolated subnetwork division mode, and optimizes the voltage distribution among three phases by introducing an additional capacitance, which is extremely conducive to control arc ignition commonly occurring in two edge-phases while line-to-line voltage are still equal to the original state. Second, to augment detecting sensitivity in HIEFs, a novel detecting judgement criterion merely using the vector-based variation via pre-fault and post-fault zero-sequence voltage, is theoretically derived, and compared with traditional methodologies it can possess a higher detecting ability. Numerical simulations and real-field tests effectively and fully demonstrate the adaptability and potentials of the proposed structure and approach toward single phase-earth faults.

Recent development in computing power has resulted in performance improvements on holistic (none-occluded) person Re-Identification (ReID) tasks. Nevertheless, the precision of the recent research will diminish when a pedestrian is obstructed by obstacles. Within the realm of 2D space, the loss of information from obstructed objects continues to pose significant challenges in the context of person ReID. Person is a 3D non-grid object, and thus semantic representation learning in only 2D space limits the understanding of occluded person. In the present work, we propose a network based on 3D multi-view learning, allowing it to acquire geometric and shape details of an occluded pedestrian from 3D space. Simultaneously, it capitalizes on advancements in 2D-based networks to extract semantic representations from 3D multi-views. Specifically, the surface random selection strategy is proposed to convert images of 2D RGB into 3D multi-views. Using this strategy, we build four extensive 3D multi-view data collections for person ReID. After that, Pedestrian 3D Shape Understanding for Person Re-Identification via Multi-View Learning (MV-3DSReID), is proposed for identifying the person by learning person geometry and structure representation from the groups of multi-view images. In comparison to alternative data formats (e.g., 2D RGB, 3D point cloud), multi-view images complement each other’s detailed features of the 3D object by adjusting rendering viewpoints, thus facilitating a more comprehensive understanding of the person for both holistic and occluded ReID situations. Experiments on occluded and holistic ReID tasks demonstrate performance levels comparable to state-of-the-art methods, validating the effectiveness of our proposed approach in tackling challenges related to occlusion. The code is available at https://github.com/hangjiaqi1/MV-TransReID.

Person re-identification (Re-ID) has played an extremely crucial role in ensuring social safety and has attracted considerable research attention. 3D shape information is an important clue to understand the posture and shape of pedestrians. However, most existing person Re-ID methods learn pedestrian feature representations from images, ignoring the real 3D human body structure and the spatial relationship between the pedestrians and interferents. To address this problem, our devise a new point cloud Re-ID network (PointReIDNet), designed to obtain 3D shape representations of pedestrians from point clouds of 3D scenes. The model consists of modules, namely global semantic guidance module and local feature extraction module. The global semantic guidance module is designed by enhancing the point cloud feature representation in similar feature neighborhoods and to reduce the interference caused by 3D shape reconstruction or noise. Further, to provide an efficient representation of point clouds, we propose space cover convolution (SC-Conv), which efficiently encodes information on human shapes in local point clouds by constructing anisotropic geometries in the coordinate neighborhoods. Extensive experiments are conducted on four holistic person Re-ID datasets, one occlusion person Re-ID dataset and one point cloud classification dataset. The results exhibit significant improvements over point-cloud-based person Re-ID methods. In particular, the proposed efficient PointReIDNet decreases the number of parameters from 2.30M to 0.35M with an insignificant drop in performance. The source code is available at: https://github.com/changshuowang/PointReIDNet.

With the development of deep learning and super-resolution reconstruction, the performance of single-image super-resolution (SISR) has improved significantly. However, most of them cannot strike a great balance between computational cost and performance, which prevents them from being deployed on edge devices. Additionally, feature fusion and channel mixing are not considered in most lightweight networks, leading to the limited performance of these networks. To solve such problems, we propose a lightweight pyramid feature fusion network (PFFN), which mainly contains the pyramid spatial-adaptive feature extraction module (PSAFEM) and the enhanced channel fusion module (ECFM). They can extract global-to-local feature, build long-range dependence with small parameters increment and realize channel and spatial feature fusion. Finally, some state-of-the-art methods are utilized to compare with our network. Extensive experimental results indicate that our PFFN outperforms these methods in parameters, flops and performance.

The authors focus on the makeup transformation problem, which refers to the transfer of makeup from a reference face to a source face image while maintaining the source makeup‐free face image. In recent years, makeup transformation has become a hot issue and a lot of research has been conducted on this basis, but there are some limitations in the existing methods, mainly due to the lack of consideration of age factor, which makes the final generated face makeup images appear not natural and lack appearance attractiveness. In order to further solve this problem, an age‐compensated makeup transformation framework based on homology continuity is proposed. In order to achieve a stable and controllable age‐compensation effect, the authors design a new coding module that can map the face makeup semantic vector into the higher feature space and achieve age compensation by adjusting the direction of the semantic vector. Finally, in order to comprehensively evaluate the effectiveness of the authors’ proposed method, a large number of qualitative and quantitative experiments have been conducted, and the experimental results show that the authors’ proposed framework outperforms existing methods.

Currently, the use of rich spectral and spatial information of hyperspectral images (HSIs) to classify ground objects is a research hotspot. However, the classification ability of existing models is significantly affected by its high data dimensionality and massive information redundancy. Therefore, we focus on the elimination of redundant information and the mining of promising features and propose a novel Bole convolution (BC) neural network with a tandem three-direction attention (TDA) mechanism (BTA-Net) for the classification of HSI. A new BC is proposed for the first time in this algorithm, whose core idea is to enhance effective features and eliminate redundant features through feature punishment and reward strategies. Considering that traditional attention mechanisms often assign weights in a one-direction manner, leading to a loss of the relationship between the spectra, a novel three-direction (horizontal, vertical, and spatial directions) attention mechanism is proposed, and an addition strategy and a maximization strategy are used to jointly assign weights to improve the context sensitivity of spatial–spectral features. In addition, we also designed a tandem TDA mechanism module and combined it with a multiscale BC output to improve classification accuracy and stability even when training samples are small and unbalanced. We conducted scene classification experiments on four commonly used hyperspectral datasets to demonstrate the superiority of the proposed model. The proposed algorithm achieves competitive performance on small samples and unbalanced data, according to the results of comparison and ablation experiments. The source code for BTA-Net can be found at https://github.com/vivitsai/BTA-Net.

Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the stochastic features underlying complex traffic situations. Currently, Graph Convolutional Network (GCN) methods are among the most successful and promising approaches. However, most GCNs methods rely on a static graph structure, which is generally unable to extract the dynamic spatio-temporal relationships of traffic data and to interpret trip patterns or motivation behind traffic flows. In this paper, we propose a novel Semantics-aware Dynamic Graph Convolutional Network (SDGCN) for traffic flow forecasting. A sparse, state-sharing, hidden Markov model is applied to capture the patterns of traffic flows from sparse trajectory data; this way, latent states, as well as transition matrices that govern the observed trajectory, can be learned. Consequently, we can build dynamic Laplacian matrices adaptively by jointly considering the trip pattern and motivation of traffic flows. Moreover, high-order Laplacian matrices can be obtained by a newly designed forward algorithm of low time complexity. GCN is then employed to exploit spatial features, and Gated Recurrent Unit (GRU) is applied to exploit temporal features. We conduct extensive experiments on three real-world traffic datasets. Experimental results demonstrate that the prediction accuracy of SDGCN outperforms existing traffic flow forecasting methods. In addition, it provides better explanations of the generative Laplace matrices, making it suitable for traffic flow forecasting in large cities and providing insight into the causes of various phenomena such as traffic congestion.

Face editing generates a face image with the target attributes without changing the identity or other information. Current methods have achieved considerable performance; however, they cannot effectively retain the face’s identity and semantic information while controlling the attribute intensity. Inspired by two human cognitive characteristics, namely, the principle of global precedence and the principle of homology continuity, we propose a novel face editing approach called the information retention and intensity control generative adversarial network (IricGAN). It includes a learnable hierarchical feature combination (HFC) function, which can construct a sample’s source space through multiscale feature mixing; it can guarantee the integrity of the source space while significantly compressing the network. Additionally, the attribute regression module (ARM) can decouple different attribute paradigms in the source space to ensure the correct modification of the required attributes and preserve the other areas. The gradual process of modifying the face attributes can be simulated by applying different control strengths in the source space. In face editing experiments, both qualitative and quantitative results demonstrate that IricGAN achieves the best overall results among state-of-the-art alternatives. Target attributes can be continuously modified by refeeding the relationship of the source space and the image, and the independence of each attribute can be retained to the greatest extent. IricGAN:https://github.com/nanfangzhe/IricGAN.

Although convolutional neural networks (CNNs) have shown superior performance to traditional machine learning algorithms for hyperspectral image (HSI) classification tasks, the ability of traditional CNNs to model remote dependencies in the spatial orientation of HSIs is still limited, and they always extract similar low-level features, leading to feature redundancy. To cope with this limitation, this article proposes a novel multiorder statistical representation-guided graph convolution and continuous context threshold-aware network for the classification of HSIs with limited training samples. Initially, the spectral–spatial information is separately modeled using first-order features and second-order pooling operators. Secondly, we graph-structured the patch features, and by employing a random walk transition probability matrix, the graph-structured convolution can mine more discriminative directional features. In addition, a continuous context threshold-aware network is designed to model multidimensional spatial relationships, which enhances the feature representation of graph features. Specifically, the cross-attention mechanism is used to calculate the attention weights in the vertical and horizontal directions, and the features are divided into two levels—important and secondary—by solving the cosine distance between feature vectors; the former is retained and the latter is punished. Extensive experiments on multiple HSI datasets demonstrated that the proposed method delivers competitive performance. The code will be available at https://github.com/vivitsai/GSC-CCTA.

Federated learning is a promising solution in several industries for cotraining models among distributed clients via centralized servers without leaving private user data on the devices. Thus, federated learning can be seen as a stimulus for the edge computing paradigm as it supports collaborative learning and model optimization. In view of the strict requirements for data security and system reliability of hyperspectral classification techniques for surveillance, aerospace, and military missions, this article proposes a novel stereo attention cross-decoupling fusion (CDF)-guided federated neural learning algorithm for hyperspectral image classification, which first trains client devices using a scalable federated learning approach consisting of master server, secure aggregator and edge client devices of a certain size. The distributed devices train local models of the neural network for classifying hyperspectral images and send them to the secure aggregator, which aggregates the local models using a weighted averaging strategy and sends them to the master server for iteration. In addition, the stereo attention CDF module is used to mine the multidimensional spatial details of the hyperspectral images, specifically by first extracting the most discriminative features from different directions (horizontal, vertical, and spatial) using the attention mechanism and then using the decoupling fusion strategy to classify the original feature map into three levels: significant, minor, and redundant, and use them to model the multidimensional spatial relationships, thus strengthening the capability to represent features. Extensive experiments on several public datasets have shown that the proposed method provides competitive performance, and more importantly, is effective in enhancing privacy and reliability for hyperspectral image classification.

Small satellites such as CubeSats pose demanding requirements on the weight, size, and multifunctionality of their structures due to extreme constraints on the payload mass and volume. To address this challenge, we introduce a concept of multifunctional deployable space structures for CubeSats based on ultrathin, elastically foldable, and self-deployable bistable composite structures integrated with flexible electronics. The multifunctional bistable booms can be stored in a coiled configuration and self-deploy into a long structure upon initiation by releasing the stored strain energy. The boom demonstrates the capabilities of delivering power and transmitting data from the CubeSat to the flexible devices on the boom tip. The boom also shows the ability to monitor the dynamics and vibration during and after the deployment. A payload boom has been installed in a 3 U CubeSat as flight hardware for in-space testing and demonstration. This effort combines morphable ultrathin composite structures with flexible electronics.

Protein-DNA interactions play an important role in diverse biological processes. Accurately identifying protein-DNA binding residues is a critical but challenging task for protein function annotations and drug design. Although wet-lab experimental methods are the most accurate way to identify protein-DNA binding residues, they are time consuming and labor intensive. There is an urgent need to develop computational methods to rapidly and accurately predict protein-DNA binding residues. In this study, we propose a novel sequence-based method, named PredDBR, for predicting DNA-binding residues. In PredDBR, for each query protein, its position-specific frequency matrix (PSFM), predicted secondary structure (PSS), and predicted probabilities of ligand-binding residues (PPLBR) are first generated as three feature sources. Secondly, for each feature source, the sliding window technique is employed to extract the matrix-format feature of each residue. Then, we design two strategies, i.e., square root (SR) and average (AVE), to separately transform PSFM-based and two predicted feature source-based, i.e., PSS-based and PPLBR-based, matrix-format features of each residue into three corresponding cube-format features. Finally, after serially combining the three cube-format features, the ensemble classifier is generated via applying bagging strategy to multiple base classifiers built by the framework of 2D convolutional neural network. The computational experimental results demonstrate that the proposed PredDBR achieves an average overall accuracy of 93.7% and a Mathew's correlation coefficient of 0.405 on two independent validation datasets and outperforms several state-of-the-art sequenced-based protein-DNA binding residue predictors. The PredDBR web-server is available at https://jun-csbio.github.io/PredDBR/.

• A multifunctional PLA/CuMOF degradable membrane was fabricated facilely. • Composite membrane displays superb PM capture ability and antibacterial properties. • PLA/CuMOF membrane shows stable oil–water separation performance. Here, a multifunctional poly (lactic acid)/copper-based metal–organic framework (PLA/CuMOF) degradable composite membrane featuring superior antibacterial and self-cleaning properties was fabricated via a simple electrospinning process for high- efficiency filtration/separation. Benefiting from the decrease of fiber diameter, the improved surface roughness and the surface charge of CuMOF, PLA/CuMOF fibrous membrane achieved excellent capture ability for ultra-fine particles and superb purification capability for real PM 2.5 smoke. The differences of filtration capacity between PLA membrane and PLA/CuMOF membrane was further explored using analogue simulation with dynamic particle capture and airflow field distribution. Impressively, PLA/CuMOF fibrous membrane combines robust self-cleaning ability, effective antibacterial effect, and thermal management capability. Moreover, owing to the special selective wettability and chemical stability, PLA/CuMOF membrane possessed the stable oil–water separation performance under harsh environment (e.g., high acid, alkali, and salt). This degradable multifunctional filtration/separation fibrous membrane emerges a broad application prospect ranging from environmental governance, industrial security to personal protection. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy fault-tolerant controller is introduced for a class of kinetic kill vehicle (KKV) with unmodeled dynamics, actuator faults and structural uncertainties. The key point is that the effects of structural uncertainties, actuator faults and unmodeled dynamics existing in KKV systems are universally considered and suppressed by the proposed method. To deal with the unmodeled dynamics and structural uncertainties, a dynamic signal is employed and a fuzzy logic system (FLS) is presented to approximate the multisource uncertainties. In addition, indirect compensation control laws are designed in order to handle the actuator faults caused by fuel consumption and manufacturing errors. Last but not least, benefiting from the adaptive laws, the external disturbances are appropriately compensated. The simulation results show that the proposed algorithm enables the system states of KKV to track the desired trajectories tightly under different conditions and the control performance is better compared with other algorithms.

Modeling, prediction, and recognition tasks depend on the proper representation of the objective curves and surfaces. Polynomial functions have been proved to be a powerful tool for representing curves and surfaces. Until now, various methods have been used for polynomial fitting. With a recent boom in neural networks, researchers have attempted to solve polynomial fitting by using this end-to-end model, which has a powerful fitting ability. However, the current neural network-based methods are poor in stability and slow in convergence speed. In this article, we develop a novel neural network-based method, called Encoder-X, for polynomial fitting, which can solve not only the explicit polynomial fitting but also the implicit polynomial fitting. The method regards polynomial coefficients as the feature value of raw data in a polynomial space expression and therefore polynomial fitting can be achieved by a special autoencoder. The entire model consists of an encoder defined by a neural network and a decoder defined by a polynomial mathematical expression. We input sampling points into an encoder to obtain polynomial coefficients and then input them into a decoder to output the predicted function value. The error between the predicted function value and the true function value can update parameters in the encoder. The results prove that this method is better than the compared methods in terms of stability, convergence, and accuracy. In addition, Encoder-X can be used for solving other mathematical modeling tasks.

Using 1,3,5-triisopropylbenzene (1,3,5-TIPB) and n-octane as the catalytic cracking feedstocks and 1-hexene as the oligomerization feedstock, the coupling mechanism of catalytic cracking reaction and olefin oligomerization reaction over the synthesized hierarchical ZSM-5 zeolite catalyst was evaluated. The results of catalytic cracking reaction of model compounds showed that the catalytic cracking performance of molecules with different sizes was inhibited on the synthesized hierarchical ZSM-5 zeolite. The cracking activity of 1,3,5-TIPB decreased, and the initial activity of n-octane reduced from 70% to 20%. However, enhanced 1-hexene oligomerization activity was observed over the hierarchical ZSM-5 zeolite, with dimer as the main product. The reduction of the strong acid sites in the zeolite can inhibit the catalytic cracking reaction and promote the oligomerization of C6olefin into dimer and trimer (ideal components of jet fuel). Therefore, the designing of the catalyst from the perspective of inhibiting the activity of catalytic cracking can effectively improve the oligomerization performance of the catalyst.

Background:Gestation complications have a recurrence risk and could predispose to each other in the next pregnancy. We aimed to evaluate the relationship between a history of adverse pregnancy and maternal-fetal outcomes in subsequent pregnancy in patients with Immunoglobulin A nephropathy (IgAN). Methods:A retrospective cohort study from a Chinese single center was conducted. Pregnant women with biopsy-proven primary IgAN and aged ≥18 years were enrolled and divided into the 2 groups by a history of adverse pregnancy. The primary outcome was adverse pregnancy outcome, which included maternal-fetal outcomes. Logistical regression model was used to evaluate the association of a history of adverse pregnancy with subsequent adverse maternal and fetal outcomes. Results:Ninety-one women with 100 pregnancies were included, of which 54 (54%) pregnancies had a history of adverse pregnancy. IgAN patients with adverse pregnancy history had more composite maternal outcomes (70.4% vs. 45.7%, p= 0.012), while there was no difference in the composite adverse fetal outcomes between the 2 groups (55.6% vs. 45.7%). IgAN patients with a history of adverse pregnancy were associated with an increased risk of subsequent adverse maternal outcomes (adjusted odds ratio [OR], 2.64; 95% CI, 1.07–6.47). Similar results were shown in those with baseline serum albumin <3.5 g/dL, 24 h proteinuria ≥1 g/day, and a history of hypertension. There was no association between a history of adverse pregnancy and subsequent adverse fetal outcomes in IgAN patients (adjusted OR, 1.56; 95% CI, 0.63–3.87). Conclusion:A history of adverse pregnancy was associated with an increased risk of subsequent adverse maternal outcomes, but not for adverse fetal outcomes in IgAN patients.

•As far as the authors know, this paper propose the first state transformation - based state constrained intelligent control structure for a class of SUS.•In the meantime of guaranteeing the time-varying constraints, the Moreover, the unavoidable actuator failures and the dead-zone nonlinearities of the SUS can also be handled.•By using the BLS and the state transformation technique, the control design complexity can be reduced and the control response speed can be improved.

The cleanup of oily wastewater and crude-oil spills is a global challenge. Traditional membrane materials are inefficient for oil/water separation under harsh conditions and limited by sorption speeds because of the high viscosity of crude oil. Herein, a kind of Graphene-wrapped polyphenylene sulfide fibrous membrane with superior chemical resistance and hydrophobicity for efficient oil/water separation and fast adsorption of crude oil all-weather is reported. The reduced graphene oxide (rGO)@polyphenylene sulfide (PPS) fibrous membrane can be applied in the various harsh conditions with Joule heating and solar heating. In addition, the oil(dichloromethane)/water separation flux of rGO@PPS reached 12 903 L m–2h–1, and the separation efficiency reached 99.99%. After 10 cycles, the rGO@PPS still performed high separation flux and filtration efficiency. More importantly, the rGO@PPS still retained its high conductivity, excellent filtration efficiency, and stable hydrophobicity after acid or alkali treatment. Moreover, the rGO@PPS can be heated by solar energy to absorb viscous crude oil during the day, while at night, the crude oil can be adsorbed by Joule heating. The time to adsorb crude oil can be reduced by 98.6% and 97.3% through Joule heating and solar heating, respectively. This all-weather utilization greatly increases the adsorption efficiency and effectively reduces energy consumption.

The present work evaluated how the incorporation of passion fruit epicarp flour (PFEF), as a source of dietary fiber and polyphenols, influenced the quality, in vitro starch digestibility, and antioxidant activity of cookies. Wheat flour was replaced by PFEF at different levels ranging from 3% to 9%. The cookies containing 6% produced a darken color and a harder texture. The cookies’ consumer acceptance did not substantially change when PFEF addition is 3% and 6%, but when the addition becomes 9%, consumer acceptance significantly deteriorated. As PFEF was added, the acrylamide content of the cookies considerably increased, which might raise health risks for consumers. In vitro starch digestibility suggested that the hydrolysis of starch was inhibited when PFEF level in the cookies increased. More importantly, the addition of PFEF significantly increased antioxidant properties of cookies. Compared with corresponding common products, our findings were the first to demonstrate that it was feasible to produce PFEF‐enriched cookies with nutritional superiority. PFEF, as a by‐product from the passion fruit processing industry, could be utilized for cookie preparation and other food products with improved nutritional and functional properties.

ABSTRACTAt present, the functional recovery after nerve injury is not satisfactory in clinical practice. The aim of this study was to explore the molecular mechanism of miR-21 promoting Schwann cells (SC) proliferation and axon regeneration after peripheral nerve injury, providing a theoretical basis for injured nerve repair. Nerve injury models were constructed to determine the expression of miR-21 in the injured nerve by Quantitative Real-Time PCR (qRT-PCR). After miR-21 over-expression SC (mimic-miR-21) group, control SC (control-miR-21) group and blank SC (RSC96) group were constructed, SC proliferation was determined by CCK-8, cell cycle was analysed by flow cytometry, dorsal root ganglion neuron (DRGn) axon regeneration was observed after DRGn was cultured with SCs for 7 days, the expressions of TGFβI, TIMP3, EPHA4as well as apoptosis-related proteins caspase-3 and caspase-9 were detected by qRT-PCR and Western blot in the three groups, respectively. Target genes were confirmed by dual-luciferase reporter gene assay. The expressions of TGFβI, TIMP3and EPHA4were assessed by immunofluorescence in vivo. qRT-PCR indicated that miR-21 expression was significantly higher in the model group than in the sham operation and blank groups. SC proliferation index (PI) was significantly higher, the apoptosis rate was significantly lower, the axon was significantly longer, and mRNA and protein expressions of TGFβI, TIMP3, EPHA4as well as apoptosis-related proteins caspase-3 and caspase-9 were significantly lower in the mimic-miR-21 group than in the control-miR-21 and RSC96 groups. The double luciferase assay confirmed that TGFβI, TIMP3and EPHA4were potential target genes of miR-21. In vivo immunofluorescence also indicated that expressions of TGFβI, TIMP3, EPHA4were lower in the mimic-miR-21 group than in the control-miR-21 and RSC96 groups. We conclude that during injured peripheral nerve repair, miRNA-21 plays an important role in promoting SC proliferation and axon regeneration by regulating TGFβI, TIMP3and EPHA4target genes.

A programmable multitarget-response electrochemical imaging technique was presented using scanning electrochemical microscopy (SECM) combined with a self-designed waveform. The potential waveform applied to the tip decreased the charging current caused by the potential switch, enhancing the signal-to-noise ratio. This programmable SECM (P-SECM) method was used to scan a metal strip for verifying its feasibility in feedback mode. Since it could achieve simultaneous multitarget imaging during one single imaging process, PC12 cells status was imaged and identified through three different molecules (FcMeOH, Ru(NH3)63+, and oxygen). The FcMeOH image eliminated the error from cell height, and the Ru(NH3)63+image verified the change of membrane permeability. Moreover, the oxygen image demonstrated the bioactivity of the cell via its intensity of respiration. Combining information from these three molecules, the cell status could be determined accurately and also the error caused by time consumption with multiple scans in traditional SECM was eliminated.

Adult mammalian brains have largely lost neuroregeneration capability except for a few niches. Previous studies have converted glial cells into neurons, but the total number of neurons generated is limited and the therapeutic potential is unclear. Here, we demonstrate that NeuroD1-mediated in situastrocyte-to-neuron conversion can regenerate a large number of functional new neurons after ischemic injury. Specifically, using NeuroD1 adeno-associated virus (AAV)-based gene therapy, we were able to regenerate one third of the total lost neurons caused by ischemic injury and simultaneously protect another one third of injured neurons, leading to a significant neuronal recovery. RNA sequencing and immunostaining confirmed neuronal recovery after cell conversion at both the mRNA level and protein level. Brain slice recordings found that the astrocyte-converted neurons showed robust action potentials and synaptic responses at 2 months after NeuroD1 expression. Anterograde and retrograde tracing revealed long-range axonal projections from astrocyte-converted neurons to their target regions in a time-dependent manner. Behavioral analyses showed a significant improvement of both motor and cognitive functions after cell conversion. Together, these results demonstrate that in vivocell conversion technology through NeuroD1-based gene therapy can regenerate a large number of functional new neurons to restore lost neuronal functions after injury.

The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years. Non-cooperative targets exhibit uncertain states and unpredictable behaviors, making collision avoidance significantly more challenging than that for space debris. Much existing research focuses on the continuous thrust model, whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions. Additionally, it is important to minimize the impact on the original mission while avoiding non-cooperative targets. On the other hand, the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer, posing challenges for practical engineering applications. To conquer these difficulties, this paper makes the following key contributions: (A) a turn-based (sequential decision-making) limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time; (B) a novel Selection Probability Learning Adaptive Search-depth Search Tree (SPL-ASST) algorithm is proposed for non-cooperative target avoidance, which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search (MCTS). Numerical simulations confirm the effectiveness and efficiency of the proposed method.