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Abstract Polymers capable of depolymerizing back to their own monomers offer a promising solution to address the challenges in polymer sustainability. Despite significant progress has been achieved in plastics circularity, chemical recycling of thermoplastic elastomers is relatively less concerned, largely because of their intrinsic complex multicomponents. This work creates a homopolymer-based platform towards chemically recyclable but tough thermoplastic elastomers. It is enabled by a semicrystalline polymer with high molecular weight but low crystallinity, which is prepared through ring-opening metathesis polymerization of a fully biobased cyclic olefin. By shifting the ring−chain equilibrium, quantitative conversions were achieved for both forward polymerization and reverse depolymerization. This simple circular, high-performance thermoplastic elastomer platform based on biomass highlights the importance of monomer design in addressing three challenges in sustainable polymers: the feedstock renewability, depolymerization selectivity, and performance trade-offs.

In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. The VAGCN framework employs a deep-stacked autoencoder to convert the input image’s high-dimensional raw CNN visual features into more manageable low-dimensional representations. In addition to improving feature representation, the GCN can also reason more intelligently about predictions, which would not be possible without this compression. The GCN encoder processes nodes in the graph to capture structural and feature correlation. Following the GCN encoder, the refined embeddings are input to a multi-layer perceptron (MLP) to calculate compatibility scores. The approach extends to using neighborhood information only during the testing phase to help with training efficiency and generalizability in practical scenarios, a key characteristic of our model. By leveraging its ability to capture latent visual features and neighborhood-based learning, VAGCN thoroughly investigates item compatibility across various categories. This method significantly improves predictive accuracy, consistently outperforming existing benchmarks. These contributions tackle significant scalability and computational efficiency challenges, showcasing the potential transformation of recommendation systems through enhanced feature representation, paving the way for further innovations in the fashion domain.

Abstract Printed Circuit Boards (PCBs) are key devices for the modern-day electronic technologies. During the production of these boards, defects may occur. Several methods have been proposed to detect PCB defects. However, detecting significantly smaller and visually unrecognizable defects has been a long-standing challenge. The existing two-stage and multi-stage object detectors that use only one layer of the backbone, such as Resnet’s third layer ( $$C_4$$ C 4 ) or fourth layer ( $$C_5$$ C 5 ), suffer from low accuracy, and those that use multi-layer feature maps extractors, such as Feature Pyramid Network (FPN), incur higher computational cost. Founded by these challenges, we propose a robust, less computationally intensive, and plug-and-play Attentive Context and Semantic Enhancement Module (ACASEM) for two-stage and multi-stage detectors to enhance PCB defects detection. This module consists of two main parts, namely adaptable feature fusion and attention sub-modules. The proposed model, ACASEM, takes in feature maps from different layers of the backbone and fuses them in a way that enriches the resulting feature maps with more context and semantic information. We test our module with state-of-the-art two-stage object detectors, Faster R-CNN and Double-Head R-CNN, and with multi-stage Cascade R-CNN detector on DeepPCB and Augmented PCB Defect datasets. Empirical results demonstrate improvement in the accuracy of defect detection.

SimRank is a widely used metric for evaluating vertex similarity based on graph topology, with diverse applications such as large-scale graph mining and natural language processing. The objective of the single-source and top-k SimRank query problem is to retrieve the kvertices with the largest SimRank to the source vertex. However, existing algorithms suffer from inefficiency as they require computing SimRank for all vertices to retrieve the top-k results. To address this issue, we propose an algorithm named HitSimthat utilizes a branch and bound strategy for the single-source and top-k query. HitSim initially partitions vertices into distinct sets based on their shortest-meeting lengths to the source vertex. Subsequently, it computes an upper bound of SimRank for each set. If the upper bound of a set is no larger than the minimum value of the current top-k results, HitSim efficiently batch-prunes the unpromising vertices within the set. However, in scenarios where the graph becomes dense, certain sets with large upper bounds may contain numerous vertices with small SimRank, leading to redundant overhead when processing these vertices. To address this issue, we propose an optimized algorithm named HitSim-OPT that computes the upper bound of SimRank for each vertex instead of each set, resulting in a fine-grained and efficient pruning process. The experimental results conducted on six real-world datasets demonstrate the performance of our algorithms in efficiently addressing the single-source and top-k query problem.

The global COVID-19 pandemic has presented a significant, ongoing challenge since its emergence in late 2019. Today, the Omicron strain, which is less lethal but more contagious than the original outbreak strain, continues to pose substantial health risks. In this background, the management of psoriatic comorbidities has become even more complex, particularly for patients with underlying inflammatory, metabolic, or cardiovascular diseases. This review aims to summarize current research on comorbid COVID-19 and psoriasis, and provide insights into the development of evidence-based management strategies. By providing appropriate patient instruction, implementing protective measures, and re-evaluating medication prescriptions based on each patient’s unique situation, healthcare professionals can effectively address the challenges faced by patients with comorbid psoriasis in the COVID-19 era.

Polymers with low ceiling temperatures (Tc) are highly desirable as they can depolymerize under mild conditions, but they typically suffer from demanding synthetic conditions and poor stability. Here, the authors envision that this challenge can be addressed by developing high-Tc polymers that can be converted into low-Tc polymers on demand.

Abstract Background To explore the effects of nutrition support team (NST) intervention on elderly patients with gastric cancer (GC). Methods The elderly GC patients (tumor stage I/II/III), admitted to our department from January 2015 to September 2021, were retrospectively analyzed and divided into NST group and traditional nutrition (TN) group according to nutritional management methods. The immune, inflammatory, nutrition-related indices, postoperative recovery and long-term prognosis of two groups were analyzed. Results A total of 258 elderly GC patients were included (NST group, n = 125; TN group, n = 133). After propensity score matching (PSM) in ratio of 1:1, 73 pairs of patients were matched. There were statistically significant differences in CD3 and CD4 level postoperative one month and IgG level postoperative one week between NST group and TN group (P 0.05). There were significant differences in body mass index (BMI) between the two groups postoperative one month (P 0.05). Conclusions Compared with TN management, NST intervention might be benefit to the immune function recovery and nutritional status, but there was no evidence that NST could improve the prognosis of elderly GC patients.

The influence maximization (IM) problem asks for a group of seed users in a social network under a given propagation model, so that the information spread is maximized through these users.Existing algorithms have two main problems.Firstly, these algorithms were difficult to be applied in large-scale social networks due to limited expected influence and high time complexity.Secondly, these algorithms were limited to specific propagation models and could only solve the IM problem under a single type of social network.When they were used in different types of networks, the effect was poor.In this regard, an efficient algorithm (MTIM) based on two classic propagation models and reverse influence sampling (RIS) was proposed.To verify the effectiveness of MTIM, experiments were conducted to compare MTIM with greedy algorithms such as IMM, TIM and PMC, and heuristic algorithms such as OneHop and Degree Discount on four real social networks.The results show that MTIM can return a ( 1− 1 e −ε ) approximate solution, effectively expand the expected influence and significantly improve the efficiency.

IntroductionCyanogenic glycosides (CNglcs) are bioactive plant products involving in plant defense against herbivores by virtue of their abilities to release toxic hydrogen cyanide (HCN). Aspergillus niger has been shown to be effective in producing β-glucosidase, which could degrade CNglcs. However, whether A. niger could remove CNglcs under ensiling conditions is still unknown.MethodsIn this study, we first investigated the HCN contents in ratooning sorghums for two years, then the sorghums were ensiled with or without the addition of A. niger.ResultsTwo years’ investigation indicated that the contents of HCN in fresh ratooning sorghum were larger than 801 mg/kg FW (fresh weight), which could not be reduced by silage fermentation under safety threshold (200 mg/kg FW). A. niger could produce β-glucosidase over a range of pH and temperature, which degraded the CNglcs and removed the hydrogen cyanide (HCN) at early days of ratooning sorghum fermentation. The addition of A. niger (2.56 × 107 CFU/ml) altered the microbial community, increased bacterial diversity, improved the nutritive qualities, and reduced the HCN contents in ensiled ratooning sorghum lower than 100 mg/kg FW after 60 days of fermentation. Overall, the addition of 150 ml A. niger + 50 ml sterile water per 3 kg silage could efficiently remove CNglcs from ratooning sorghum silage.ConclusionIn conclusion, A. niger could produce β-glucosidase which degraded the CNglcs during the early days of fermentation, benefiting the ensiling process and improving the utilization of ratooning sorghum.

Abstract Background To evaluate the immune function of gastric cancer patients after single-incision laparoscopic distal gastrectomy (SIDG) or multiport laparoscopic distal gastrectomy (MLDG) guided by enhanced recovery after surgery (ERAS). Methods A retrospective cohort study was performed on 120 patients who underwent laparoscopic distal gastrectomy for gastric cancer. The patients were divided into two groups according to operation method: group A (MLDG) and group B (SIDG), both guided by ERAS concept. The indicators reflecting immune function and inflammation, such as CD3+, CD4+, CD8+ and NK cell count, CD4+/CD8+ cell ratios, IgA, IgM and IgG levels, C-reactive protein (CRP), total lymphocyte count (TLC) and neutrophil-to-lymphocyte ratio (NLR) were tested 3 days and 7 days after surgery. Results The skin incision length of patients in group B was significantly shorter than that in group A, but the operation time was significantly longer in group B than that in group A (P 0.05). On the 7th day after surgery, the immune indexes of both groups recovered somewhat, approaching the preoperative level (P > 0.05). Inflammation indexes increased 3 days after surgery and decreased 7 days after surgery in both groups, among them the CRP level in group A was higher than that in group B (P

In order to deal with the fluctuation and intermittency of photovoltaic (PV) cells, the battery energy storage system (BESS) as a supplementary power source has been widely concerned. In BESS, the unknown parameters of the battery can affect its output, and its structure determines these parameters. Therefore, it is essential to establish the battery model and extract the parameters accurately, and the existing methods cannot effectively solve this problem. This study proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOLADE) to deal with the issue. In DOLADE, the number of elite particles and particles with poor performance is expanded, the population’s search area is increased, and the population’s exploration capability is improved. The particles’ search area is dynamically changed to ensure the population has a good exploitation capability. The dynamic opposite learning (DOL) strategy increases the population’s diversity and improves the probability of obtaining the global optimum with a considerable convergence rate. The various discharging experiments are performed, the battery model parameters are identified, and the results are compared with the existing well-established algorithms. The comprehensive results indicate that DOLADE has excellent performance and could deal with similar problems.

Given a directed graph, a k-hop reachability query, u→?kv, is used to check for the existence of a directed path from u to v that has a length of at most k. Addressing k-hop reachability queries is a fundamental task in graph theory and has been extensively investigated. However, existing algorithms can be inefficient when answering queries because they require costly graph traversal operations. To improve query performance, we propose an approach based on a vertex cover. We construct an index that covers all reachability information using a small set of vertices from the input graph. This allows us to answer k-hop reachability queries without performing graph traversal. We propose a linear-time algorithm to quickly compute a vertex cover, S, which we use to develop a novel labeling scheme and two algorithms for efficient query answering. The experimental results demonstrate that our approach significantly outperforms the existing approaches in terms of query response time.

Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation applications. Manual labeling-based clustering is both time-consuming and less accurate. Currently, popular methods for extracting features from data use deep learning techniques, such as a Convolutional Neural Network (CNN). These methods can generate high-dimensional feature vectors, which are effective for image clustering. However, high dimensions can lead to the curse of dimensionality, which makes subsequent clustering difficult. The fashion images-oriented deep clustering method (FIDC) is proposed in this paper. This method uses CNN to generate a 4096-dimensional feature vector for each fashion image through migration learning, then performs dimensionality reduction through a deep-stacked auto-encoder model, and finally performs clustering on these low-dimensional vectors. High-dimensional vectors can represent images, and dimensionality reduction avoids the curse of dimensionality during clustering tasks. A particular point in the method is the joint learning and optimization of the dimensionality reduction process and the clustering task. The optimization process is performed using two algorithms: back-propagation and stochastic gradient descent. The experimental findings show that the proposed method, called FIDC, has achieved state-of-the-art performance.

Given a directed graph and a node $v$ , the transitive closure (TC) of $v$ is the set of nodes that $v$ can reach in the graph. TC size is very important in many applications but the cost of TC size computation is high in both time and space, which makes computing accurate TC size not applicable to the scenario where we need to know the TC size quickly for large graphs. Considering that existing approaches are either inefficient or inaccurate, we propose an algorithm, namely buTCS, to efficiently make more accurate estimation. Our approach works in linear time and space. The basic idea is to compute a node’s TC size based on that of its out-neighbors and perform a top-down verification. We further propose two optimizations to improve the estimation accuracy. The experimental results on 13 real datasets show that buTCS achieves better estimation on TC size efficiently.

A maximal clique ( ${MC}$ ) is a complete subgraph satisfying that no other cliques can take it as their proper subgraph. Given an uncertain graph, the $top\text{-}K\,\,{MC}\text{s}$ enumeration problem studies how to return $k {MC}\text{s}$ with the highest rank value. Existing algorithms rank ${MC}\text{s}$ according to their probabilities, thus usually return ${MC}\text{s}$ with higher probabilities but less number of vertices, and fail to return large ${MC}\text{s}$ that convey more useful information. Considering this problem, this paper studies the problem of enumerating $top\text{-}K\,\,{MC}\text{s}$ . Our approach returns $k {MC}\text{s}$ with the most number of vertices satisfying that their probabilities $\geq \alpha $ , where each ${MC}$ is called an $\alpha $ - ${MC}$ , and computing $k$ largest $\alpha $ - ${MC}\text{s}$ is called as $(k,\alpha)$ - ${MC}\text{s}$ . We propose an efficient $(k,\alpha)$ - ${MC}\text{s}$ enumeration algorithm, $Top\text{-}KMC$ , which works in three steps, including partition, enumeration and verification. Here, partition means that we compute the set $\mathcal {M}$ of all ${MC}\text{s}$ without considering the probability information, as if the graph is partitioned into a set of subgraphs. Enumeration means that we compute $\alpha $ - ${MC}\text{s}$ from each ${MC}$ of $\mathcal {M}$ . As each such subgraph is an ${MC}$ , the cost of computing common neighbors for finding $\alpha $ - ${MC}\text{s}$ can be reduced. Verification means that we need to verify whether an $\alpha $ - ${MC}$ is a subgraph of another $\alpha $ - ${MC}$ . If not, it is an $\alpha $ - ${MC}$ ; otherwise, it is a useless $\alpha $ - ${MC}$ and should be removed. We further propose an optimized algorithm $Top\text{-}KMC+$ to reduce both time and space by merging the above three steps into a whole step. The experimental results on real datasets show that both $Top\text{-}KMC$ and $Top\text{-}KMC+$ can return $k$ largest $\alpha $ - ${MC}\text{s}$ efficiently.

Given a directed acyclic graph (DAG) G, G's transitive reduction (TR) Gtr is the unique DAG satisfying that Gtr has the minimum number of edges and has the same transitive closure (TC) as G. TR computation has been extensively studied during the past decades and was used in many applications, where the main problem is how to compute TR efficiently for large graphs. However, existing approaches have either large space complexity or higher time complexity, which makes them cannot compute TR efficiently on large dense graphs. We propose a novel approach for TR computation, which takes every single edge as the basic processing unit, and utilizes existing reachability algorithms to test whether it is redundant or not. In this way, we avoid the costly graph traversal operation of existing approaches. We identify the performance bottleneck and propose a set of heuristics to sort edges, such that to reduce the average processing cost of each edge. We show by experimental results that our approach works much better than all the existing approaches, and can be faster than the state-of-the-art approach by more than two orders of magnitude on large dense graphs.

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of cancer. There is a comparable number of lncRNAs to protein‐coding genes, but the expression patterns, functions, and molecular mechanisms of most lncRNAs in colorectal cancer (CRC) remain unclear. In this study, we report the identification of a novel lncRNA, named long noncoding RNA regulating IL‐6 transcription (LNRRIL6), which is upregulated in CRC tissues and cell lines. Increased LNRRIL6 expression is associated with aggressive clinicopathological characteristics and poor prognosis of CRC patients. Functional experiments showed that enhanced expression of LNRRIL6 promotes CRC cell proliferation and survival in vitro and CRC tumor growth in vivo. Conversely, depletion of LNRRIL6 inhibits CRC cell proliferation and survival in vitro and CRC tumor growth in vivo. Mechanistically, we revealed that LNRRIL6 physically binds to the IL‐6 promoter, thereby increasing IL‐6 transcription, inducing IL‐6 autocrine signaling, and activating the IL‐6/STAT3 pathway. The expression of IL‐6 is positively associated with that of LNRRIL6 in CRC tissues. Blocking the IL‐6/STAT3 pathway using the FDA‐approved IL‐6‐receptor antagonist antibody, tocilizumab, abolished the oncogenic role of LNRRIL6 in CRC. Taken together, these findings identify a novel lncRNA, LNRRIL6, that promotes CRC cell survival through activation of the IL‐6/STAT3 pathway and suggest that LNRRIL6 may be a potential prognostic biomarker and therapeutic target for CRC.

With the development of society, the demand for energy keeps increasing. Solar energy has received widespread concern for its renewable and environmentally friendly advantages. As one of the most efficient solar energy devices, the output power of photovoltaic (PV) cells is easily affected by the external environment. In order to solve the problem of the maximum power output of PV cells, this paper proposed a maximum power point tracking (MPPT) method. Based on the online particle swarm optimization (PSO) variable step length algorithm, the pulse width modulation (PWM) control module parameters are set according to the parameters of the PV cells’ output voltage. By dynamically adjusting the output voltage step of the PV cells online, the output of the PV cells is stabilized near the maximum power point (MPP). The simulation results concluded that the method and model could accurately adjust the output voltage according to the external environment changes in real time and reduce the voltage fluctuation at the MPP, providing a new idea to solve the problem of MPPT of PV cells.

Topotactic transformation was one of the most frequently related processes in the synthesis process of cobalt oxide nanomaterials. Microstructural evolution studies of this process inform and enable the process of controlling the shape of the final products. One such topotactic transformation was investigated here, the transition from Co(OH)2 hexagonal nano platelets to Co3O4 octahedra by a hydrothermal method in an ammonia solution. Evolution of phase and microstructure was studied during different stages of the reaction to reveal the criteria which prompt this transformation. A phase transformation from Co(OH)2 to CoOOH and hence to Co3O4 was observed. High resolution transmission electron microscopy and image simulation were utilised to disclose the role of defects in the topotactic transformation. A high density of defects including edge dislocations and displacements was found on the surface of dried Co(OH)2 nano platelets. The coexistence of CoOOH domains embedded in the Co(OH)2 surface indicates the absence of the hydrogen bonds of the starting materials. The breaking of hydrogen bonds leads to bonding variation and the further creation of defects, finally prompting the transformation from Co(OH)2 to CoOOH. The breaking of hexagonally arranged hydrogen bonds creates seeds for the formation of Co3O4. DOI: http://dx.doi.org/10.5755/j01.ms.25.3.22005

Given a directed acyclic graph (DAG), a $k$ -hop reachability query ${u}\xrightarrow {?k}{v}$ is used to answer whether there exists a path from $u$ to $v$ with length $\leq k$ . Answering $k$ -hop reachability queries is a fundamental graph operation and has been extensively studied during the past years. Considering that existing approaches still suffer from inefficiency in practice when processing large graphs, we propose a novel labeling scheme, namely HT, to accelerate $k$ -hop reachability queries answering. HT uses a constrained 2hop distance label to maintain the length of shortest paths between a set of hop nodes and other nodes, and for the remaining reachability information, HT uses a novel topological level to accelerate graph traversal. Further, we propose to enhance HT by two optimization techniques. The experimental results show that compared with the state-of-the-art approaches, HT works best for most graphs when answering $k$ -hop reachability queries with small index size and reasonable index construction time.

Methyleugenol (Mlg), a natural ingredient of many herbs and used as a flavoring substance in dietary products, inhibits inflammation and oxidative stress. The aim of the study is to explore the antioxidative potential of Mlg against tert-butyl hydroperoxide (t-BHP)-triggered oxidative injury and the involvement of antioxidative mechanisms. Our findings indicated that Mlg exposure significantly alleviated t-BHP-stimulated cytotoxicity, suppressed reactive oxygen species (ROS) generation, and increased superoxide dismutase (SOD) and glutathione (GSH) levels, which were related to the induction of the glutamate-cysteine ligase catalytic/modifier (GCLC/GCLM) subunit, heme oxygenase-1 (HO-1), and NAD (P) H: quinone oxidoreductase (NQO1) largely dependent upon upregulating the nuclear factor-erythroid 2-related factor 2 (Nrf2) induction, inhibiting the Keap1 protein expression, and heightening the antioxidant response element (ARE) activity. Additionally, Mlg exposure obviously induced AMP-activated protein kinase (AMPK), glycogen synthase kinase 3β (GSK3β) and extracellular signal-regulated kinase (ERK) phosphorylation, but AMPK and ERK inhibitors treatment exhibited effectively reduced levels of Mlg-enhanced Nrf2 nuclear translocation, respectively. Furthermore, Mlg exposure significantly lessened t-BHP-induced cytotoxicity and ROS production which were evidently abolished by treatment with AMPK and ERK inhibitors and Nrf2 siRNA. Accordingly, Mlg might exhibit a protective role against t-BHP-triggered cytotoxicity via the activation of the AMPK/GSK3β- and ERK-Nrf2 signaling pathways. Keywords: Methyleugenol, Oxidative stress, Reactive oxygen species, Nrf2

The NF-E2 p45-related factor 2 (Nrf2), a transcription factor that regulates the cellular adaptive response to oxidative stress, is a target for limiting tissue damage from exposure to environmental toxins, including arsenic. In the current study, we determine whether Pterostilbene (Pts), as a potent activator of Nrf2, has a protective effect on arsenic-induced cytotoxicity and apoptosis in human keratinocytes. Human keratinocytes (HaCaT) or mouse epidermal cells (JB6) were pretreated with Pts for 24 h prior to arsenic treatment. Harvested cells were analyzed by MTT, DCFH-DA, commercial kits, Flow cytometry assay and western blot analysis. Our results demonstrated that Pts effectively regulated the viability in HaCaT and JB6 cells, decreased the reactive oxygen species (ROS) generation and lipid peroxidation (MDA), and improved the NaAsO2-induced depletion of superoxide dismutase (SOD). Moreover, Pts treatment further dramatically inhibited NaAsO2-induced apoptosis, specifically the mitochondrial mediation of apoptosis, which coincided with the effective recovery of NaAsO2-induced mitochondrial membrane potential (ΔΨm) depolarization and cytochrome c release from the mitochondria. Furthermore, arsenic-induced decrease of anti-apoptotic factor Bcl-2 and Bcl-xl, and increase of pro-apoptotic factor Bax and Bad, as well as survival signal related factor caspase 3 activation were reversed by Pts treatment. Further mechanistic studies confirmed that Pts increased antioxidant enzyme expression in a dose-dependent manner, which was related to Nrf2 nuclear translocation. In addition, the effects of Pts on NaAsO2-induced cell viability were largely weakened when Nrf2 was knocked down. Together, our results provide evidence for the use of Pts to activate the Nrf2 pathway to alleviate arsenic-induced dermal damage.

In this study, the authors study a deep learning model that can convert vision into tactile information, so that different texture images can be fed back to the tactile signal close to the real tactile sensation after training and learning. This study focuses on the classification of different image visual information and its corresponding tactile feedback output mode. A training model of ensembled generative adversarial networks is proposed, which has the characteristics of simple training and stable efficiency of the result. At the same time, compared with the previous methods of judging the tactile output, in addition to subjective human perception, this study also provides an objective and quantitative evaluation system to verify the performance of the model. The experimental results show that the learning model can transform the visual information of the image into the tactile information, which is close to the real tactile sensation, and also verify the scientificity of the tactile evaluation method.

This paper studies the orbital pursuit-evasion problem with imperfect information, including measurement noise and input delay. The presence of imperfect information will degrade the players’ control performance and lead to mission failure. To solve this problem, a compensation control strategy for the players is proposed. The compensation control strategy consists of two parts: the guaranteed cost strategy and the time delay compensation method. First, a near-optimal feedback strategy called guaranteed cost strategy with perfect information is proposed based on a Lyapunov-like function and matrix analysis theory. Second, a time delay compensation method based on an uncertainty set is proposed to compensate for delayed information. The compensation control strategy is derived by combining the time delay compensation method with the guaranteed cost strategy. While applying this strategy to the game, the input of the strategy is generated by processing the measured data with the state estimation algorithm based on the unscented Kalman filter (UKF). The simulation results show that the proposed strategy can handle the orbital pursuit-evasion problem with imperfect information effectively.

The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.

Acetaminophen (APAP) overdose-induced fatal hepatotoxicity is majorly characterized by overwhelmingly increased oxidative stress while enhanced nuclear factor-erythroid 2-related factor 2 (Nrf2) is involved in prevention of hepatotoxicity. Although Licochalcone A (Lico A) upregulates Nrf2 signaling pathway against oxidative stress-triggered cell injury, whether it could protect from APAP-induced hepatotoxicity by directly inducing Nrf2 activation is still poorly elucidated. This study aims to explore the protective effect of Lico A against APAP-induced hepatotoxicity and its underlying molecular mechanisms. Our findings indicated that Lico A effectively decreased tert-butyl hydroperoxide (t-BHP)- and APAP-stimulated cell apoptosis, mitochondrial dysfunction and reactive oxygen species generation and increased various anti-oxidative enzymes expression, which is largely dependent on upregulating Nrf2 nuclear translocation, reducing the Keap1 protein expression, and strengthening the antioxidant response element promoter activity. Meanwhile, Lico A dramatically protected against APAP-induced acute liver failure by lessening the lethality; alleviating histopathological liver changes; decreasing the alanine transaminase and aspartate aminotransferase levels, malondialdehyde formation, myeloperoxidase level and superoxide dismutase depletion, and increasing the GSH-to-GSSG ratio. Furthermore, Lico A not only significantly modulated apoptosis-related protein by increasing Bcl-2 expression, and decreasing Bax and caspase-3 cleavage expression, but also efficiently alleviated mitochondrial dysfunction by reducing c-jun N-terminal kinase phosphorylation and translocation, inhibiting Bax mitochondrial translocation, apoptosis-inducing factor and cytochrome c release. However, Lico A-inhibited APAP-induced the lethality, histopathological changes, hepatic apoptosis, and mitochondrial dysfunction in WT mice were evidently abrogated in Nrf2-/- mice. These investigations firstly implicated that Lico A has protective potential against APAP-induced hepatotoxicity which may be strongly associated with the Nrf2-mediated defense mechanisms.

This paper studies the orbital pursuit-evasion-defense problem with the continuous low thrust propulsion. A control strategy for the pursuer is proposed based on the fuzzy comprehensive evaluation and the differential game. First, the system is described by the Lawden’s equations, and simplified by introducing the relative state variables and the zero effort miss (ZEM) variables. Then, the objective function of the pursuer is designed based on the fuzzy comprehensive evaluation, and the analytical necessary conditions for the optimal control strategy are presented. Finally, a hybrid method combining the multi-objective genetic algorithm and the multiple shooting method is proposed to obtain the solution of the orbital pursuit-evasion-defense problem. The simulation results show that the proposed control strategy can handle the orbital pursuit-evasion-defense problem effectively.

A chaotic map-based mutual authentication scheme with strong anonymity is proposed in this paper, in which the real identity of the user is encrypted with a shared key between the user and the trusted server. Only the trusted server can determine the real identity of a user during the authentication, and any other entities including other users of the system get nothing about the user’s real identity. In addition, the shared key of encryption can be easily computed by the user and trusted server using the Chebyshev map without additional burdensome key management. Once the partnered two users are authenticated by the trusted server, they can easily proceed with the agreement of the session key. Formal security analysis demonstrates that the proposed scheme is secure under the random oracle model.

In this paper, a novel velocimeter based on laser self-mixing Doppler technology has been developed for speed measurement. The laser employed in our experiment is a distributed feedback (DFB) fiber laser, which is an all-fiber structure using only one Fiber Bragg Grating to realize optical feedback and wavelength selection. Self-mixing interference for optical velocity sensing is experimentally investigated in this novel system, and the experimental results show that the Doppler frequency is linearly proportional to the velocity of a moving target, which agrees with the theoretical analysis commendably. In our experimental system, the velocity measurement can be achieved in the range of 3.58 mm/s–2216 mm/s with a relative error under one percent, demonstrating that our novel all-fiber configuration velocimeter can implement wide-range velocity measurements with high accuracy.

The global COVID-19 pandemic has presented a significant, ongoing challenge since its emergence in late 2019. Today, the Omicron strain, which is less lethal but more contagious than the original outbreak strain, continues to pose substantial health risks. In this background, the management of psoriatic comorbidities has become even more complex, particularly for patients with underlying inflammatory, metabolic, or cardiovascular diseases. This review aims to summarize current research on comorbid COVID-19 and psoriasis, and provide insights into the development of evidence-based management strategies. By providing appropriate patient instruction, implementing protective measures, and re-evaluating medication prescriptions based on each patient's unique situation, healthcare professionals can effectively address the challenges faced by patients with comorbid psoriasis in the COVID-19 era. [ABSTRACT FROM AUTHOR]