Search

Background: Frequently, recurrence of genital herpes imposes significant physical and psychological burdens on patients, and existing treatments are often ineffective in preventing recurrence. Previous studies have indicated that photodynamic therapy (PDT) showed promising outcomes in the treatment of labial herpes simplex virus (HSV) infections; however, its efficacy for genital herpes remained unclear. Method: The study screened patients with genital herpes at Daping Hospital from July 2020 to May 2024. Patients were divided into two groups based on whether they received PDT treatment. Over a one-year follow-up period, patients' healing time and recurrence rates were compared between the two groups. The cumulative incidence of recurrence and restricted mean survival time (RMST) were used to assess outcomes. Propensity score matching (PSM) was employed to minimize bias. Result: A total of 41 patients enrolled in our study, with 8 (19.5 %) received PDT. The non-PDT group exhibited a slower skin lesion healing time, averaging at 7.2 days compared to 5.4 days in the PDT group. A significant difference was observed in the cumulative incidence of recurrence between the PDT and non-PDT groups (37.5 % versus 71.4 %) after PSM. The RMST in the PDT group was 9.94 days, compared to 5.13 days in the non-PDT group before PSM, and 4.14 after PSM. Conclusion: Our study demonstrated that the PDT effectively reduced lesion recovery time and delayed recurrences of genital herpes. We recommend considering PDT as a potential treatment option for patients with recurrent genital herpes.

Abstract Background Additional resection for invasive cancer at perihilar cholangiocarcinoma (pCCA) resection margins has become a consensus. However, controversy still exists regarding whether additional resection is necessary for residual biliary intraepithelial neoplasia (BilIN). Method Consecutive patients with pCCA from two hospitals were enrolled. The incidence and pattern of resection margin BilIN were summarized. Prognosis between patients with negative margins (R0) and BilIN margins were analyzed. Cox regression with a forest plot was used to identify independent risk factors associated with overall survival (OS) and recurrence-free survival (RFS). Subgroup analysis was performed based on BilIN features and tumor characteristics. Results 306 pCCA patients receiving curative resection were included. 255 had R0 margins and 51 had BilIN margins. There was no significant difference in OS (P = 0.264) or RFS (P = 0.149) between the two group. Specifically, 19 patients with BilIN at distal bile ducts and 32 at proximal bile ducts. 42 patients showed low-grade BilIN, and 9 showed high-grade. Further analysis revealed no significant difference in long-term survival between different locations (P = 0.354), or between different grades (P = 0.772). Portal vein invasion, poor differentiation and lymph node metastasis were considered independent risk factors for OS and RFS, while BilIN was not. Subgroup analysis showed no significant difference in long-term survival between the lymph node metastasis subgroup, or between the portal vein invasion subgroup. Conclusion For pCCA patients underwent curative resection, residual BilIN at resection margin is acceptable. Additional resection is not necessary for such patients to achieve absolute R0 margin.

In response to the difficulty of existing attack detection methods in dealing with advanced persistent threat (APT) with longer durations, complex and covert attack methods, a model for APT attack detection based on attention mechanisms and provenance graphs was proposed.Firstly, provenance graphs that described system behavior based on system audit logs were constructed.Then, an optimization algorithm was designed to reduce the scale of provenance graphs without sacrificing key semantics.Afterward, a deep neural network (DNN) was utilized to convert the original attack sequence into a semantically enhanced feature vector sequence.Finally, an APT attack detection model named DAGCN was designed.An attention mechanism was applied to the traceback graph sequence.By allocating different weights to different positions in the input sequence and performing weight calculations, sequence feature information of sustained attacks could be extracted over a longer period of time, which effectively identified malicious nodes and reconstructs the attack process.The proposed model outperforms existing models in terms of recognition accuracy and other metrics.Experimental results on public APT attack datasets show that, compared with existing APT attack detection models, the accuracy of the proposed model in APT attack detection reaches 93.18%.

To solve the problem that advanced persistent threat (APT) in the Internet of things (IoT) environment had the characteristics of strong concealment, long duration, and fast update iterations, it was difficult for traditional passive detection models to quickly search, a hypergraph Transformer threat-hunting network (HTTN) was proposed.The HTTN model had the function of quickly locating and discovering APT attack traces in IoT systems with long time spans and complicated information concealment.The input cyber threat intelligence (CTI) log graph and IoT system kernel audit log graph were encoded into hypergraphs by the model, and the global information and node features of the log graph were calculated through the hypergraph neural network (HGNN) layer, and then they were extracted for hyperedge position features by the Transformer encoder, and finally the similarity score was calculated by the hyperedge, thus the threat-hunting of APT was realized in the network environment of the Internet of things system.It is shown by the experimental results in the simulation environment of the Internet of things that the mean square error is reduced by about 20% compared to mainstream graph matching neural networks, the Spearman level correlation coefficient is improved by about 0.8%, and improved precision@10 is improved by about 1.2% by the proposed HTTN model.

BackgroundRecent epidemiological studies and animal experiments have highlighted the significant role of oxidative stress in the development of osteoporosis (OP). The provision of antioxidants is widely considered a fundamental strategy to combat free radical-induced stress, inhibit oxidative damage, and potentially reverse the adverse effects of oxidative stress on bone health. However, there is no consensus in the scientific literature regarding the practical effectiveness of antioxidants in OP prevention and treatment. Some studies have not shown a clear connection between antioxidant supplementation and decreased OP risk. Therefore, it is essential to clarify the potential causal relationship between antioxidants and the development of OP.MethodsThe study utilized the inverse variance weighted (IVW) approach as the primary analytical method in the Mendelian Randomization (MR) framework to investigate the causal effects of five exogenous and six endogenous antioxidants on the risk of OP. To thoroughly assess potential pleiotropic effects and heterogeneity among the data analyzed, the MR-Egger intercept test was employed, and Cochran’s Q statistic was calculated.ResultsIn the evaluation of exogenous antioxidants, single-directional two-sample MR analyses did not reveal any statistically significant relationship between these agents and the risk of OP. Regarding endogenous antioxidants, bidirectional two-sample MR analyses were conducted, which generally indicated that most genetically regulated endogenous antioxidants had no significant association with the onset risk of OP. A significant causal relationship was found between OP and serum albumin levels (β: −0.0552, 95%CI: −0.0879 to −0.0225, p< 0.0011 after Bonferroni adjustment, power = 100%).ConclusionThe research uncovers OP as a possible determinant contributing to a decrement in serum albumin levels, and further suggests a potentially intimate relationship between the downward trajectory of serum albumin concentrations and the advancement of the OP disease process.

Supernumerary robotic limbs (SRLs) are a novel type of wearable robot used as the third limb to assist the wearer to perform operations that are difficult or impossible for human hands. Although SRLs can compensate for and enhance human physiological abilities, the unpredictable disturbances caused by human movements significantly affect the coordinating control between the robot and wearer. In this study, a general modeling of supernumerary robotic arms (SRAs) on an omnidirectional floating base is presented. Using position and orientation feedback from sensors at the base and tip, three control methods based on different sensor feedback are proposed to improve tracking accuracy. Experiments on point and trajectory tracking are conducted on the SRAs. In the results (point and circular trajectory‐tracking errors with the manipulator as floating base: 1.18 ± 0.56 mm [mean ± standard deviation(SD) error] and 1.42 ± 0.43 mm, point trajectory‐tracking errors with the human shoulder as floating base: 1.37 ± 0.58 mm, and the performance in perforation positioning operation experiment), it is demonstrated that the proposed controllers enable the SRAs to achieve high‐precision tracking and good adaptability to different user movements and frequencies. Also, in the results, future studies on dynamic high‐precision manipulation of SRLs are motivated.

As a renewable and sustainable source for energy, environment, and biomedical applications, microalgae and microalgal biodiesel have attracted great attention. However, their applications are confined due to the cost-efficiency of microalgal mass production. One-step strategy and continuous culturing systems could be solutions. However, current studies for optimization throughout microalgae-based biofuel production pipelines are generally derived from the batch culture process. Better tools are needed to study algal growth kinetics in continuous systems.A microfluidic chemostatic bioreactor was presented here, providing low-bioadhesive cultivations for algae in a cooperative environment of gas, nutrition, and temperature (GNT) involved with high throughput. The chip was used to mimic the continuous culture environment of bioreactors. It allowed simultaneously studying of 8 × 8 different chemostatic conditions on algal growth and oil production in parallel on a 7 × 7 cm2 footprint. On-chip experiments of batch and continuous cultures of Chlorella. sp. were performed to study growth and lipid accumulation under different nitrogen concentrations. The results demonstrated that microalgal cultures can be regulated to grow and accumulate lipids concurrently, thus enhancing lipid productivity in one step. The developed on-chip culturing condition screening, which was more suitable for continuous bioreactor, was achieved at a half shorter time, 64-times higher throughput, and less reagent consumption. It could be used to establish chemostat cultures in continuous bioreactors which can dramatically accelerate the development of renewable and sustainable algal for CO2 fixation and biosynthesis and related systems for advanced sustainable energy, food, pharmacy, and agriculture with enormous social and ecological benefits.

Supernumerary robotic limbs (SRLs) are a new type of wearable robot that add artificial limbs to the human body to perform collaborative tasks. In contrast with exoskeletons, SRLs are kinematically independent of human limbs, allowing the wearer to overcome the limitations of human physiological ability, such as realizing the expansion of space in the human body, rather than enhancing existing limbs. In this study, a lightweight and compact hexagonal reconfigurable lower‐limb SRL system is proposed to assist human locomotion in daily activities, including walking, crouching, and stair climbing. To adapt to multiple scenarios, the hexagonal mechanism can be adjusted to different configurations including convex hexagonal configuration, pentagonal configuration, and concave hexagonal configuration. To achieve more optimized performance of different configurations, the optimization for identifying the optimal dimensions of each link was carried out. Subsequently, the detailed design methodology and specifics are presented. Finally, the load and wearing performance experiments were evaluated. The experiment results demonstrated that the tested maximum load in different configurations exceeded 90% of the simulated value and the entire equipment has a good wearing adaptability. This study may inspire the design of other lower‐limb SRLs and provide efficient solutions for stable support assistance in various scenarios.

Multiple myeloma (MM) is characterized by the accumulation of malignant plasma cells preferentially in the bone marrow. Currently, emerging chemotherapy drugs with improved biosafety profiles, such as immunomodulatory agents and protease inhibitors, have been used in clinics to treat MM in both initial therapy or maintenance therapy post autologous hematopoietic stem cell transplantation (ASCT). We previously discovered that caffeic acid phenethyl ester (CAPE), a water-insoluble natural compound, inhibited the growth of MM cells by inducing oxidative stress. As part of our continuous effort to pursue a less toxic yet more effective therapeutic approach for MM, the objective of this study is to investigate the potential of CAPE for in vivo applications by using magnetic resonance imaging (MRI)-capable superparamagnetic iron oxide nanoparticles (IONP) as carriers. Cyclo (Arg-Gly-Asp-D-Phe-Cys) (RGD) is conjugated to IONP (RGD-IONP/CAPE) to target the overexpressed αvβ3 integrin on MM cells for receptor-mediated internalization and intracellular delivery of CAPE. A stable loading of CAPE on IONP can be achieved with a loading efficiency of 48.7% ± 3.3% (wt%). The drug-release studies indicate RGD-IONP/CAPE is stable at physiological (pH 7.4) and basic pH (pH 9.5) and subject to release of CAPE at acidic pH (pH 5.5) mimicking the tumor and lysosomal condition. RGD-IONP/CAPE causes cytotoxicity specific to human MM RPMI8226, U266, and NCI-H929 cells, but not to normal peripheral blood mononuclear cells (PBMCs), with IC50s of 7.97 ± 1.39, 16.75 ± 1.62, and 24.38 ± 1.71 μM after 72-h treatment, respectively. Apoptosis assays indicate RGD-IONP/CAPE induces apoptosis of RPMI8226 cells through a caspase-9 mediated intrinsic pathway, the same as applying CAPE alone. The apoptogenic effect of RGD-IONP/CAPE was also confirmed on the RPMI8226 cells co-cultured with human bone marrow stromal cells HS-5 in a Transwell model to mimic the MM microenvironment in the bone marrow. In conclusion, we demonstrate that water-insoluble CAPE can be loaded to RGD-IONP to greatly improve the biocompatibility and significantly inhibit the growth of MM cells in vitro through the induction of apoptosis. This study paves the way for investigating the MRI-trackable delivery of CAPE for MM treatment in animal models in the future.

It is essential to determine the occurrence of emerging organic contaminants (EOCs), such as pharmaceuticals and personal care products (PPCPs), in the ambient environment to address growing public concerns. However, such analysis is quite challenging due to the low trace level of such contaminants in water, which therefore requires several litres of water samples. In this study, a large volume solid phase extraction (LV-SPE) device was developed and evaluated for its performance in monitoring PPCPs as an example. Relatively good recoveries and reproducibility were obtained under specific operating conditions: a water sample volume of no more than 20 L, a flow rate not exceeding 120 mL/min, and a methanol elution volume of at least 30 mL. In addition, the results from the onsite enrichment approach using LV-SPE were compared with those from the conventional approach using a standard SPE device in the laboratory for real groundwater samples. Among the eight selected PPCPs (nalidixic acid, carbamazepine, bezafibrate, clofibric acid, sulfadiazine, sulfamethoxypyridazine, sulfamethazine and sulfamonomethoxine), LV-SPE approach detected more target compounds. While the detected concentrations were generally comparable, slightly higher concentrations were observed for carbamazepine, clofibric acid, sulfadiazine and sulfamethazine using the LV-SPE method. The developed LV-SPE device provides an alternative approach for trace analysis of PPCPs and may also be applicable for other emerging organic contaminants.

Background The Cangshan National Nature Reserve of Dali City was adopted as the research object to clarify the vertical distribution characteristics of soil organic carbon (SOC) and vegetation types at different elevations in western Yunnan. Methods The contents of SOC, light fraction organic carbon (LFOC), heavy fraction organic carbon (HFOC), and water-soluble organic carbon (WSOC) in the 0–30 cm soil layer at different elevations (2,400, 2,600, 2,800, 3,000, 3,200, 3,400, and 3,600 m) were determined, and the above-ground vegetation types at different elevations were investigated. Results Results showed that the SOC content was the highest in 0–20 cm surface soil and gradually decreased with the deepening of the soil layer. It increased then decreased with the increase in elevation, and it peaked at 3,000 m. The LFOC content was between 1.28 and 7.3515 g kg−1. It exhibited a decreasing trend and little change in profile distribution. The HFOC content ranged between 12.9727 and 23.3708 g kg−1; it increased then decreased with the increase in profile depth. The WSOC content was between 235.5783 and 392.3925 mg kg−1, and the response sensitivity to elevation change was weak. With the increase in elevation, WSOC/SOC and LFOC/SOC showed a similar trend, whereas HFOC presented an opposite trend. This observation indicates that the active organic carbon content at 3,600 m was lower than that at 2,400 m, and the middle elevation was conducive to the storage of active organic carbon. Meanwhile, the physical and chemical properties of soil affected the distribution of organic carbon to a certain extent. The vegetation type survey showed that the above-ground dominant species within 2,400–2,800 m were Pinus yunnanensis and Pinus armandii. Many evergreen and mixed coniferous broadleaf forests were distributed from 3,000 m to 3,200 m. Species of Abies delavayi were mainly distributed from 3,400 m to 3,600 m. This research serves as a reference for the study of forest soil carbon stability in high-elevation areas and plays an important role in formulating reasonable land use management policies, protecting forest soil, reducing organic carbon loss, and investigating the carbon sequestration stability of forest ecosystems.

Polycystic ovary syndrome (PCOS) is a set of endocrine disorder syndrome characterized by ovulation disorder. Increased insulin resistance (IR) and compensatory hyperinsulinemia play a vital role in the pathogenesis of PCOS. Therefore, insulin sensitizing agents have been studied in the treatment of PCOS. Berberine (BBR) has been proved to alleviate IR in patients with PCOS, but the mechanism remained unclear. This study was aimed to verify the regulatory mechanism of BBR on PCOS-IR rats. Firstly, we established a female rat PCOS-IR model induced by dehydroepiandrosterone (DHEA) and found that estrus cycle was disrupted in the PCOS-IR group, serum fasting insulin (FINS) level and the homeostasis model assessment of insulin resistance (HOMA-IR) index were significantly higher than normal control group. BBR treatment could recover estrous cycle, reduce abnormal serum hormone levels like luteotropic hormone (LH) and testosterone (T). Most importantly, BBR could concentration-dependently reduce serum FINS level in PCOS-IR rat model. Meanwhile, BBR may improve the abnormal lipid metabolism levels in PCOS-IR group by decreasing low density lipoprotein (LDL), total cholesterol (TC) and triglyceride (TG). Histological results showed that BBR can also protect normal histological structures of ovaries in PCOS-IR rats. Our results indicated that BBR plays a protective role in PCOS-IR, increasing insulin sensitivity, improving hyperandrogens and recovering abnormal blood lipids. Therefore, Our research provides novel insights for therapeutic treatment of BBR in patients with glucolipid metabolic disturbances.

Network attacks pose a significant challenge for smart grid networks, mainly due to the existence of several multi-directional communication devices coupling consumers to the grid. One of the network attacks that can affect the smart grid is the distributed denial of service (DDoS), where numerous compromised communication devices/nodes of the grid flood the smart grid network with false data and requests, leading to disruptions in smart meters, data servers, and the state estimator, ultimately effecting the services for end-users. Machine learning-based strategies show distinctive benefits in resolving the challenge of securing the network from DDoS attacks. Regardless, a notable hindrance in deploying machine learning-based techniques is the requirement of model retraining whenever new attack classes arise. Practically, disrupting the normal operations of smart grid is really discouraged. To handle this challenge effectively and detect DDoS attacks without major disruptions, we propose the deployment of reconstructive deep learning techniques. A primary benefit of our proposed technique is the minimum disruption during the introduction of a new attack class, even after complete deployment. We trained several deep and shallow reconstructive models to get representations for each attack type separately, and we performed attack detection by class-specific reconstruction error-based classification. Our technique experienced rigid evaluation via multiple experiments using two well-acknowledged standard databases exclusively for DDoS attacks, including their subsets. Later, we performed a comparative estimation of our outcomes against six methods prevalent within the same domain. Our outcomes reveal that our technique attained higher accuracy, and notably eliminates the requirement of a complete model retraining in the event of the introduction of new attack classes. This method will not only boost the security of smart grid networks but also ensure the stability and reliability of normal operations, protecting the critical infrastructure from ever-evolving network attacks. As smart grid is advancing rapidly, our approach proposes a robust and adaptive way to overcome the continuous challenges posed by network attacks.

To overcome the problem that the real-time optimization of the quality of service (QoS) in software-defined security scenarios was hindered by the mismatch between security protection measures and business scenarios, which led to difficulties in application and performance degradation., a novel algorithm based on deep reinforcement learning for optimizing QoS in software defined security middle platforms (SDSmp) in real-time was proposed.Firstly, the fragmented security requirements and infrastructure were integrated into the SDSmp cloud model.Then by leveraging the power of deep reinforcement learning and cloud computing technology, the real-time matching and dynamic adaptation capabilities of the security middle platform were enhanced.Finally, a real-time scheduling strategy for security middle platform resources that meet QoS goals was generated.Experimental results demonstrate that compared to existing real-time methods, the proposed algorithm not only ensures load balancing but also improves job success rate by 18.7% for high QoS and reduces the average response time by 34.2%, and it is highly robust and better suited for real-time environments than existing methods.

In view of the problem that the functions of the current privacy protection data aggregation scheme were insufficient to meet the increasingly rich application requirements, a multi-function supported privacy protection data aggregation (MFPDA) scheme for V2G network was proposed.By using cryptographic algorithms such as BGN, BLS, and Shamir’s secret sharing, as well as fog computing and consortium blockchain technology, multiple security functions like fault tolerance, resistance to internal attacks, batch signature verification, no need for trusted third parties, and multiple aggregation functions were integrated into one privacy protection data aggregation scheme.Security analysis shows that the proposed scheme can protect data aggregation’s security, privacy and reliability.The performance evaluation shows that the introduction of fog computing can significantly reduce the computing overhead of the control center, and the reduction rate can be as high as 66.6%; the improvement of the consortium blockchain can effectively reduce the communication and storage overhead of the system, and the reduction rate can reach 16.7% and 24.9% respectively.

IntroductionIn cancer treatment, every minute counts. Due to the unpredictable behavior of cancer cells caused by continuous mutations, each cancer patient has a unique situation and may or may not respond to a specific drug or treatment. The process of finding an effective therapy can be time-consuming, but cancer patients do not have the luxury of time for trial and error. Therefore, a novel technology to fast generate a patient relevant organoid for the therapies selecting is urgently needed.MethodsUtilizing the new organoid technology by specially dissolving the mesenchyme in tumor tissues acquired from cancer patients, we realized the work of creating patient-specific organoids (PSO) within one day.ResultsPSO properties reflect those of its respective original in vivo tumor tissue and can be utilized to perform various in vitro drug sensitivity tests to identify the most effective clinical treatment for patients. Additionally, PSO can aid in assessing the efficacy of immune cell therapies.DiscussionOrganoid technology has advanced significantly in recent years. However, current cancer organoid methods involve creating 3D tumor tissue from 2D cancer cells or cell clusters, primarily for cancer research purposes aimed at investigating related molecular and cellular mechanisms of tumor development. These methods are research-driven, not tailored towards clinical applications, and cannot provide personalized information for individual patients. PSO filled the gap of clinic-driven and time-saving method for the personalized therapies selecting to the cancer patients.

Although reconfigurable architecture is an inevitable choice for dealing with high-intensive applications, the opacity of hardware structure programming and the fuzziness or imprecision of many dependencies in applications make it difficult to develop applications and take full advantage of reconfigurable architecture. In order to address these challenges, this paper proposes an automatic compilation framework based on a scalable instruction group library, using convolutional neural network (CNN) applications as an example. The proposed framework first identifies functions such as data extraction, data distribution, data summary, and data processing, and divides the reconfigurable processors into multiple logical types. Next, the calculation modes are extracted by analyzing the CNN calculation process. Based on these calculation modes, efficient assembly instruction groups are designed, constituting the scalable instruction group library. Using this library, efficient mapping for CNN applications written in high-level languages can be easily realized on reconfigurable array processors. The experimental results demonstrate that the proposed method reduces the difficulty of programming and achieves better speedup performance compared with OpenMP and LLVM parallel compilation models. Moreover, the scalability of the instruction group library provides helpful guidance for the reconfigurable implementation of domain-oriented applications.

Coke production is an important source of environmental polycyclic aromatic compounds (PACs), including parent polycyclic aromatic hydrocarbons (PAHs) and their derivatives. The focus near coking plants has primarily been on parent-PAH contamination, with less attention given to highly toxic derivatives. In this study, soil samples were collected from both within and outside of a coking plant. The concentrations of parent-PAHs and their derivatives, including methylated-PAHs, oxygenated-PAHs, and nitrated-PAHs, were examined. Spatial interpolation was employed to determine their spatial distribution patterns. Methods for identifying potential sources and conducting incremental lifetime cancer risk analysis were used. This could achieve a comprehensive understanding of the status of PAC pollution and the associated health risks caused by coke production. The concentrations of total PACs inside the plant ranged from 7.4 to 115.8 mg/kg, higher than those outside (in the range of 0.2 to 65.7 mg/kg). The spatial distribution of parent-PAH concentration and their derivatives consistently decreased with increasing distance from the plant. A significant positive correlation (p < 0.05) among parent-PAHs and their derivatives was observed, indicating relatively consistent sources. Based on diagnostic ratios, the potential emission sources of soil PACs could be attributed to coal combustion and vehicle emissions, while principal component analysis–multiple linear regression further indicated that primary emissions and secondary formation jointly influenced the PAC content, accounting for 60.4% and 39.6%, respectively. The exposure risk of soil PACs was dominated by 16 priority control PAHs; the non-priority PAHs’ contribution to the exposure risk was only 6.4%.

Viral hepatitis is a major worldwide public health issue, affecting hundreds of millions of people and causing substantial morbidity and mortality. The majority of the worldwide burden of viral hepatitis is caused by five biologically unrelated hepatotropic viruses: hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Metabolomics is an emerging technology that uses qualitative and quantitative analysis of easily accessible samples to provide information of the metabolic levels of biological systems and changes in metabolic and related regulatory pathways. Alterations in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and amino acid metabolism. These changes in metabolites and metabolic pathways are associated with the pathogenesis and medication mechanism of viral hepatitis and related diseases. Additionally, differential metabolites can be utilized as biomarkers for diagnosis, prognosis, and therapeutic responses. In this review, we present a thorough overview of developments in metabolomics for viral hepatitis.

Abstract Recent advances in society have resulted in the emergence of both hyperlipidemia and obesity as life-threatening conditions in people with implications for various types of diseases, such as cardiovascular diseases and cancer. This is further complicated by a global rise in the aging population, especially menopausal women, who mostly suffer from overweight and bone loss simultaneously. Interestingly, clinical observations in these women suggest that osteoarthritis may be linked to a higher body mass index (BMI), which has led many to believe that there may be some degree of bone dysfunction associated with conditions such as obesity. It is also common practice in many outpatient settings to encourage patients to control their BMI and lose weight in an attempt to mitigate mechanical stress and thus reduce bone pain and joint dysfunction. Together, studies show that bone is not only a mechanical organ but also a critical component of metabolism, and various endocrine functions, such as calcium metabolism. Numerous studies have demonstrated a relationship between metabolic dysfunction in bone and abnormal lipid metabolism. Previous studies have also regarded obesity as a metabolic disorder. However, the relationship between lipid metabolism and bone metabolism has not been fully elucidated. In this narrative review, the data describing the close relationship between bone and lipid metabolism was summarized and the impact on both the normal physiology and pathophysiology of these tissues was discussed at both the molecular and cellular levels.

DJ-1 is significantly elevated in various malignancies. However, the clinical significance of DJ-1 in hormone receptor (HR)-positive (HR+) breast cancer remains unclear. We evaluated DJ-1 expression in different databases and validated in vitro assay by RT-PCR and western blot among HR+ breast cancer. The correlations between DJ-1 level and tumor-immune were calculated. Mutational landscape, enriched signaling pathways, and drug sensitivity analyses were also assessed between DJ-1 high and low-expression groups. DJ-1 was upregulated in HR+ breast cancer, and high DJ-1 expression was significantly linked with poor prognosis. DJ-1 was correlated with the expression and function of different immune cells. The low DJ-1 group showed sensitivity to paclitaxel and docetaxel, while the high-expression group showed sensitivity to doxorubicin. CTLA4 and PD-L1 were more sensitive in high-DJ-1 group. It is involved in a range of pathways and might behave as a novel biomarker of prognostic value for the immune environment and drug sensitivity in HR+ breast cancer.

BackgroundPolycystic ovary syndrome (PCOS) not only increases fertility challenges for women of reproductive age, but also leads to increased complications during pregnancy and even affects the birth weight of newborns. Also, hyperandrogenemia is associated with lower pregnancy rates and lower live birth rates and may even play a role in preterm delivery and pre-eclampsia in patients with PCOS. However, it is still controversial whether PCOS patients are treated with androgen-lowering therapy before pregnancy.ObjectiveTo assess the effect of anti-androgen therapy prior to ovulation induction on maternal and infant pregnancy outcomes in patients with PCOS.MethodsProspective cohort study.ResultsA total of 296 patients with PCOS were enrolled in the study. The prevalence of adverse pregnancy outcomes, and neonatal complications was lower in DRSP(with drospirenone ethinyl estradiol tablets (II) pretreatment) group than in NO-DRSP(without drospirenone ethinyl estradiol tablets (II) pretreatment) groups (DRSP vs. NO-DRSP: adverse pregnancy outcomes, 12.16% vs. 27.03%, P=0.001; neonatal complications, 17.16% vs. 36.67%, P0.05).ConclusionOur findings suggest that preconception androgen-lowering therapy in patients with PCOS improves pregnancy outcomes and reduces neonatal complications.

Abstract Background As a nutritional index, preoperative serum prealbumin highly correlates with surgical complications. However, the correlation between preoperative prealbumin and postoperative complications remains unclear in liver transplantation (LT). Methods A total of 191 patients who underwent LT between 2015 and 2019 were included in the retrospective analysis. According to a cut-off value calculated from a receiver operating characteristic (ROC) curve, the patients were divided into normal and low preoperative prealbumin groups. Univariable and multivariable logistic regression analyses were used to identify independent risk factors for postoperative complications. In addition, patients were divided into subgroups by Model for End-stage Liver Disease (MELD) score, and the association between preoperative prealbumin and postoperative complications was also assessed in each group. Results A total of 111 (58.1%) patients were included in the low prealbumin group based on a cut-off value of 120 mg/L. The area under the ROC curve (AUC) was 0.754 (95% confidence interval [CI] 0.678–0.832). Low prealbumin (95% CI 1.51–12.8, P = 0.007) was identified as a predictor for postoperative complications based on multivariable regression. In the low and normal prealbumin groups, the prevalence rates of postoperative complications were 27.5% and 8.0% (P = 0.003) in the MELD score ≤ 15 subgroup and 53.3% and 20.0% (P = 0.197) in the MELD score > 15 subgroup, respectively. Conclusions Preoperative prealbumin was associated with postoperative complications in LT, and preoperative nutritional support benefitted postoperative recovery, especially for patients with low MELD scores.

In the field of image enhancement, using deep learning methods to enhance low-light images is currently mainstream. However, the methods often have complex network structures of a large number of parameters, and their training often uses paired data-sets, which are difficult to obtain in actual practice. To solve these problems, this paper proposes a simple generative adversarial network and Retinex model, dubbed RetinexGAN, that is completely trained using unpaired data-sets. It contains a decomposition network and two discriminator networks. To reduce the parameters of the network, only two convolution layers are used in the decomposition network. We show more challenging testing data where some parts of the image are underexposed and others are normal light. Both quantitative and visual results show that RetinexGAN is largely superior to state-of-the-art methods.

The intelligence of energy storage devices has led to a sharp increase in the amount of detection data generated. Data sharing among distributed energy storage networks can realize collaborative control and comprehensive analysis, which effectively improves the clustering and intelligence. However, data security problems have become the main obstacle for energy storage devices to share data for joint modeling and analysis. The security issues caused by information leakage far outweigh property losses. In this article, we first proposed a blockchain-based machine learning scheme for secure data sharing in distributed energy storage networks. Then, we formulated the data sharing problem into a machine-learning problem by incorporating secure federated learning. Innovative verification methods and consensus mechanisms were used to encourage participants to act honestly, and to use well-designed incentive mechanisms to ensure the sustainable and stable operation of the system. We implemented the scheme of SFedChain and experimented on real datasets with different settings. The numerical results show that SFedChain is promising.

Against the background of increasing installed capacity of wind power in the power generation system, high-precision ultra-short-term wind power prediction is significant for safe and reliable operation of the power generation system. We present a method for ultra-short-term wind power prediction based on a copula function, bivariate empirical mode decomposition (BEMD) algorithm and gated recurrent unit (GRU) neural network. First we use the copula function to analyze the nonlinear correlation between wind power and external factors to extract the key factors influencing wind power generation. Then the joint data composed of the key factors and wind power are decomposed into a series of stationary subsequence data by a BEMD algorithm which can decompose the bivariate data jointly. Finally, the prediction model based on a GRU network uses the decomposed data as the input to predict the power output in the next four hours. The experimental results show that the proposed method can effectively improve the accuracy of ultra-short-term wind power prediction.

In order to solve the problem of harmonic waves caused by battery energy storage (BES) and distributed generation (DG) inverters in an active distribution network, an intelligent optimal dispatching method based on a modified flower pollination algorithm (MFPA) is proposed. Firstly, the active distribution network dispatching model considering the power quality (PQ) problem caused by BES and DG is proposed. In this model, the objective function considers the additional network loss caused by a harmonic wave, as well as the constraints of the harmonic wave and voltage unbalance. Then, the MFPA is an improvement of a flower pollination algorithm (FPA). Because the MFPA has the characteristics of higher solution accuracy and better convergence than the FPA and it is not easy to fall into local optimal, the MFPA is used to solve the proposed model. Finally, simulation experiments are carried out on IEEE 37 bus and IEEE 123 bus systems, respectively. The experimental results show that this method can achieve satisfactory power quality while optimizing the total active power loss of the branch. The comparative experimental results show that the developed algorithm has better convergence than the FPA.

The smart grid concept is predicated upon the pervasive With the construction and development of distribution automation, distributed power supply needs to be comprehensively considered in reactive power optimization as a supplement to reactive power. The traditional reactive power optimization of a distribution network cannot meet the requirements of an active distribution network (ADN), so the Improved Grey Wolf Optimizer (IGWO) is proposed to solve the reactive power optimization problem of the ADN, which can improve the convergence speed of the conventional GWO by changing the level of exploration and development. In addition, a weighted distance strategy is employed in the proposed IGWO to overcome the shortcomings of the conventional GWO. Aiming at the problem that reactive power optimization of an ADN is non-linear and non-convex optimization, a convex model of reactive power optimization of the ADN is proposed, and tested on IEEE33 nodes and IEEE69 nodes, which verifies the effectiveness of the proposed model. Finally, the experimental results verify that the proposed IGWO runs faster and converges more accurately than the GWO.

Introduction Surgical exploration is widely performed in hilar cholangiocarcinoma (HCCA), but the intraoperative resectability rate is only 60%-80%. Exploration substantially increases pain and mental stress, and the costs and length of hospital stay are considerably increased. Identifying preoperative risk factors associated with unresectability could decrease unnecessary exploration. Materials and methods In total, 440 HCCA patients from multiple centers were enrolled. Those receiving surgical exploration were divided into the resected and unresected groups. Morphological variables including Bismuth classification, lymph node metastasis and vessel invasion were obtained from radiological exams. Logistic regression for the training cohort was used to identify risk factors for unresectability, and a nomogram was constructed to calculate the unresectability rate. A calibration curve assessed the power of the nomogram. Results Among 311 patients receiving surgical exploration, 45 (14.7%) were unresectable by intraoperative judgment. Compared with the resected group, unresected patients had similar costs (p = 0.359) and lengths of hospital stay (p = 0.439). Multivariable logistic regression of the training cohort (235 patients) revealed that CA125, Bismuth-Corlette type IV, lymph node metastasis and hepatic artery invasion were risk factors for unresectability. Liver atrophy (p = 0.374) and portal vein invasion (p = 0.114) were not risk factors. The nomogram was constructed based on the risk factors. The concordance index (C-index) values of the calibration curve for predicting the unresectability rate of the training and validation (76 patients) cohorts were 0.900 (95% CI, 0.835–0.966) and 0.829 (95% CI, 0.546–0.902), respectively. Conclusion Analysis of preoperative factors could reveal intraoperative unresectability and reduce futile surgical explorations, ultimately benefiting HCCA patients.

In today’s business environment, reducing costs is crucial due to the variety of Internet of Things (IoT) devices and security infrastructure. However, applying security measures to complex business scenarios can lead to performance degradation, making it a challenging task. To overcome this problem, we propose a novel algorithm based on deep reinforcement learning (DRL) for optimizing cost in multi-party computation software-defined security middle platforms (MPC-SDSmp) in real-time. To accomplish this, we first integrate fragmented security requirements and infrastructure into the MPC-SDSmp cloud model with privacy protection capabilities to reduce deployment costs. By leveraging the power of DRL and cloud computing technology, we enhance the real-time matching and dynamic adaptation capabilities of the security middle platform (Smp). This enables us to generate a real-time scheduling strategy for Smp resources that meet low-cost goals to reduce operating costs. Our experimental results demonstrate that the proposed method not only reduces the costs by 13.6% but also ensures load balancing, improves the quality-of-service (QoS) satisfaction by 18.7%, and reduces the average response time by 34.2%. Moreover, our solution is highly robust and better suited for real-time environments compared to the existing methods.

The creatine transporter (CrT) maintains brain creatine (Cr) levels, but the effects of its deficiency on energetics adaptation under stress remain unclear. There are also no effective treatments for CrT deficiency, the second most common cause of X-linked intellectual disabilities. Herein, we examined the consequences of CrT deficiency in brain energetics and stress-adaptation responses plus the effects of intranasal Cr supplementation. We found that CrT-deficient (CrT–/y) mice harbored dendritic spine and synaptic dysgenesis. Nurtured newborn CrT–/y mice maintained baseline brain ATP levels, with a trend toward signaling imbalance between the p-AMPK/autophagy and mTOR pathways. Starvation elevated the signaling imbalance and reduced brain ATP levels in P3 CrT–/y mice. Similarly, CrT–/y neurons and P10 CrT–/y mice showed an imbalance between autophagy and mTOR signaling pathways and greater susceptibility to cerebral hypoxia-ischemia and ischemic insults. Notably, intranasal administration of Cr after cerebral ischemia increased the brain Cr/N-acetylaspartate ratio, partially averted the signaling imbalance, and reduced infarct size more potently than intraperitoneal Cr injection. These findings suggest important functions for CrT and Cr in preserving the homeostasis of brain energetics in stress conditions. Moreover, intranasal Cr supplementation may be an effective treatment for congenital CrT deficiency and acute brain injury.

Abstract In order to remove mid-spatial frequency errors on aspheric and freeform surfaces, we have developed an aspheric smoothing tool which, unusually, is rigid. This has been proved feasible in the special case where the abrasive grit size exceeds the aspheric misfit, providing a cushion. Firstly, experimental parameters were derived from simulation of Influence Functions regarding misfit between the tool and the surface, which leads to dynamic Influence functions. Then the experimental part was polished into an aspheric surface from generated spherical surface. Thirdly, the choice of tool’s shape parameters was completed with optimisation of conic constant and tilt angle. The tool was machined into aspheric shape with a single-point cutter. Finally, experiments were carried out to compare this tool with a standard spherical smoothing tool. The results showed that this aspheric smoothing tool can removal mid-spatial errors effectively on aspheric surfaces.

A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.

False data injection (FDI) attack causes a disadvantage to the safety of a power system. In the past, the adversary launching FDI attack mostly needed the complete network topology information of the power system and a large amount of measurements. However, the reality is that the adversary has limited resources and can hardly manipulate massive measurements without being detected. This paper proposes a new method to form attack vector in the condition of incomplete system information. The proposed method first maps the restricted data into a new Jacobian matrix by a kernel-independent component analysis, then constructs the undetectable attack model, and finally, designs a less costly attack vector to accomplish the FDI attack. The experimental results on different IEEE buses with Matpower tool illustrate the validity of the proposed method. Additionally, the proposed method shows a relatively high success rate in the cases of different degrees of incomplete information and various quantities of manipulated data in different power systems.

The rise of the smart grid, which applies the Internet and smart metering technologies to the power system, results in massive, heterogeneous power data and a large number of attacks on the smart grid and causes great threats to the confidentiality and integrity of the power data. At the same time, with the development of quantum information, it is possible to use quantum cryptography to protect data transmission. Aiming at the problem of data security transmission in smart grid, this paper proposes a lightweight transmission scheme for power data security. In the scheme, we combine quantum cryptography with ”one-time pad” mechanism, use a quantum random number generator which compensates the defects of the traditional random number generator and quantum key distribution protocol which ensure the security of key distribution, and design a new lightweight stream cipher encryption algorithm. Through theoretical analysis and experiments, it is proved that the proposed transmission scheme can ensure the security of power data transmission.

With the development of image processing technology, pencil drawing has been widely used in video games and mobile phone applications. However, the existing pencil drawing algorithms require a large amount of time to convert a real picture into a pencil drawing; hence, it is difficult to apply them to real-time systems. This paper proposes a parallel fast pencil drawing generation algorithm based on the graphics processing unit (GPU) to accelerate the real-time rendering process of sketch painting. The parallelism of the pencil drawing generation algorithm is identified via a theoretical analysis at first. Then, sub-algorithms of the sequential algorithm are designed in parallel using the compute unified device architecture (CUDA) programming model and executed via thread-level parallel techniques. Furthermore, an optimal cache pattern of data that reduce the access time of the most frequently used data is structured using shared memory and constant memory. Finally, task-level parallelism is achieved by the CUDA stream technology, which overlaps independent sub-tasks for further acceleration. On the CUDA platform, the experimental results demonstrate that the proposed parallel algorithm can achieve a significant increase in speedup. The proposed algorithm achieves a performance improvement of 448.59 times compared with the sequential algorithm, on 2560×1920-resolution images, and maintains a high degree of similarity with the real pencil paintings. Hence, the proposed algorithm is suitable for real-time pencil drawing rendering and has promising application prospects in non-photorealistic rendering.

In order to realize peer-to-peer (P2P) transactions between electric vehicles (EVs) in vehicle-to-grid (V2G) networks, we propose an EV power trading model based on blockchain and smart contract. Firstly, based on the blockchain and smart contract technology, a decentralized power trading model is proposed to realize the information equivalence and transparent openness of power trading. Then, considering the randomness and uncertainty of EV charging and discharging, the EV trading parties use the reverse auction mechanism based on dynamic pricing strategy to complete the transaction matching, which can not only improve the profit of the less competitive power seller, but also it can reduce the cost of the electricity purchaser. Finally, in order to verify the feasibility of our proposed scheme, V2G's EV power trading smart contract was designed, and the smart contract was released to Ethereum and simulated experiments were carried out. The effectiveness of the proposed scheme is verified by simulation experiments and comparison with traditional power trading schemes.

Bidirectional communication infrastructure of smart systems, such as smart grids, are vulnerable to network attacks like distributed denial of services (DDoS) and can be a major concern in the present competitive market. In DDoS attack, multiple compromised nodes in a communication network flood connection requests, bogus data packets or incoming messages to targets like database servers, resulting in denial of services for legitimate users. Recently, machine learning based techniques have been explored by researchers to secure the network from DDoS attacks. Under different attack scenarios on a system, measurements can be observed either in an online manner or batch mode and can be used to build predictive learning systems. In this work, we propose an efficient DDoS attack detection technique based on multilevel auto-encoder based feature learning. We learn multiple levels of shallow and deep auto-encoders in an unsupervised manner which are then used to encode the training and test data for feature generation. A final unified detection model is then learned by combining the multilevel features using and efficient multiple kernel learning (MKL) algorithm. We perform experiments on two benchmark DDoS attack databases and their subsets and compare the results with six recent methods. Results show that the proposed method outperforms the compared methods in terms of prediction accuracy.

How to generate the path planning of mobile robots quickly is a problem in the field of robotics. The Q-learning(QL) algorithm has recently become increasingly used in the field of mobile robot path planning. However, its selection policy is blind in most cases in the early search process, which slows down the convergence of optimal solutions, especially in a complex environment. Therefore, in this paper, we propose a continuous local search Q-Learning (CLSQL) algorithm to solve these problems and ensure the quality of the planned path. First, the global environment is gradually divided into independent local environments. Then, the intermediate points are searched in each local environment with prior knowledge. After that, the search between each intermediate point is realized to reach the destination point. At last, by comparing other RL-based algorithms, the proposed method improves the convergence speed and computation time while ensuring the optimal path.

Environmental pollution due to resistance genes from livestock manure has become a serious issue that needs to be resolved. However, little studies focused on the removal of resistance genes in simultaneous processing of livestock feces and urine. This study investigated the fate of antibiotic resistance genes (ARGs), metal resistance genes (MRGs), and class 1 integron-integrase gene (intI1) during thermophilic fermentation of swine manure in an ectopic fermentation system (EFS), which has been regarded as a novel system for efficiently treating both feces and urine. The abundances of MRGs and tetracycline resistance genes were 34.44–97.71% lower in the EFS. The supplementation of heavy metals significantly increased the abundance of intI1, with the enhancement effect of copper being more prominent than that of zinc. The highest abundances of resistance genes and intI1 were observed at high Cu levels (A2), indicating that Cu can increase the spreading of resistance genes through integrons. Network analysis revealed the co-occurrence of ARGs, MRGs, and intI1, and these genes potentially shared the same host bacteria. Redundancy analysis showed that the bacterial community explained most of the variations in ARGs, and environmental factors had influences on ARGs abundances by modulating the bacterial community composition. The decreased Sphingomonas, Comamonas, Acinetobacter, Lactobacillus, Bartonella, Rhizobium, and Bacteroides were mainly responsible for the reduced resistance genes. These results demonstrate that EFS can reduce resistance genes in simultaneous processing of livestock feces and urine.

In the V2G network, the release and sharing of real-time data are of great value for data mining. However, publishing these data directly to service providers may reveal the privacy of users. Therefore, it is necessary that the data release model with a privacy protection mechanism protects user privacy in the case of data utility. In this paper, we propose a privacy protection mechanism based on differential privacy to protect the release of data in V2G networks. To improve the utility of the data, we define a variable sliding window, which can dynamically and adaptively adjust the size according to the data. Besides, to allocate the privacy budget reasonably in the variable window, we consider the sampling interval and the proportion of the window. Through experimental analysis on real data sets, and comparison with two representative w event privacy protection methods, we prove that the method in this paper is superior to the existing schemes and improves the utility of the data.

The prognosis of hepatocellular carcinoma (HCC) is closely associated with the occurrence of distant metastases, which is likely due to circulating tumor cells (CTCs). However, the low number of CTCs is the main obstacle limiting research of the mechanism of CTC metastasis. Here, We evaluated the role of ubiquitin-specific protease 1 (USP1) in promoting CTC survival during blood-borne metastases. We observed that USP1 was frequently upregulated in CTCs and correlated with metastasis and a reduced overall survival rate of patients. Additionally, genetic knockout of USP1 the survival rate of CTCs. Further analyses showed that USP1 mediates oncogenic activity by deubiquitinating and stabilizing transducin β-like 1 X-linked receptor 1 (TBLR1), which plays essential roles in regulating Wnt signaling. These results demonstrated that USP1 may act as an essential factor in promoting the survival of CTCs and suggest that inhibition of USP1 is a potential strategy for HCC treatment.

Background: As the sixth most common cancer of worldwide, head and neck cancers (HNC) are springing from oral cavity, pharynx and larynx and there is no strong biomarker for prognosis. Rates of 5 years survival with HNC remain relatively low in decades with improvement of treatments. Evidence that single nucleotide polymorphisms (SNPs) play a part in cancer prognosis is growing.Methods: We conducted an exome-wide association study among 261 patients with head and neck squamous cell carcinoma (HNSCC) and then validated in The Cancer Genome Atlas (TCGA) database for survival by using the Cox proportional hazards regression models and Kaplan–Meier analyses.Results: After combining the result of the two stages, 4 SNPs were significantly associated with HNSCC survival (rs16879870 at 6q14.3: adjusted HR = 2.02, 95%CI = 1.50–2.73, P = 3.88 × 10−6; rs2641256 at 17p13.2: adjusted HR = 0.67, 95%CI = 0.56–0.80, P = 7.51 × 10−6; rs2761591 at 11p13: adjusted HR = 2.07, 95%CI = 1.50–2.87, P = 1.16 × 10−5; and rs854936 at 22q11.21: adjusted HR = 1.92, 95%CI = 1.43–2.57, P = 1.27 × 10−5). Besides, we constructed a receiver operating characteristic (ROC) model to estimate predictive effect of the novel SNPs combined with clinical stage in HNSCC prognosis (AUC = 0.715). We also found the genotype of rs16879870 and rs854936 was significantly associated with the expression of gene GJB7 (P = 0.013) and RTN4R (P = 0.047) in cancer tissues of TCGA, respectively.Conclusion: Our findings suggested that the SNPs (rs16879870, rs2641256, rs2761591, rs854936) might play a crucial role in prognosis of HNSCC.

The false data injection attack can tamper with the measurement information collected by the Supervisory Control and Data Acquisition, threat the security of state estimation. Therefore, the analysing methods and detection methods of false data injection attacks have important theoretical and practical significance for ensuring the safe operation of smart grids. This study uses the invertible automatic encoder to reduce the dimension of the original data and uses the long-short-term memory to detect false data injection attacks. This method overcomes the shortcomings of shallow algorithm and traditional machine learning algorithm for power big data training and avoiding the problems of gradient explosion and gradient disappearing during training. Finally, the authors perform a large number of experiments in the IEEE 118-node test system and the 300-node test system and verify the effectiveness of the proposed method.

Open source software has been widely used in various industries due to its openness and flexibility, but it also brings potential security problems. Therefore, security analysis is required before using open source software. The current mainstream open source software vulnerability analysis technology is based on source code, and there are problems such as false positives, false negatives and restatements. In order to solve the problems, based on the further study of behavior feature extraction and vulnerability detection technology, a method of using dynamic behavior features to detect open source software vulnerabilities is proposed. Firstly, the relationship between open source software vulnerability and API call sequence is studied. Then, the behavioral risk vulnerability database of open source software is proposed as a support for vulnerability detection. In addition, the CNN-IndRNN classification model is constructed by improving the Independently Recurrent Neural Net-work (IndRNN) algorithm and applies to open source software security vulnerability detection. The experimental results verify the effectiveness of the proposed open source software security vulnerability detection method based on dynamic behavior features.

In recent years, various types of power theft incidents have occurred frequently, and the training of the power-stealing detection model is susceptible to the influence of the imbalanced data set and the data noise, which leads to errors in power-stealing detection. Therefore, a power-stealing detection model is proposed, which is based on Improved Conditional Generation Adversarial Network (CWGAN), Stacked Convolution Noise Reduction Autoencoder (SCDAE) and Lightweight Gradient Boosting Decision Machine (LightGBM). The model performs Generation- Adversarial operations on the original unbalanced power consumption data to achieve the balance of electricity data, and avoids the interference of the imbalanced data set on classifier training. In addition, the convolution method is used to stack the noise reduction auto-encoder to achieve dimension reduction of power consumption data, extract data features and reduce the impact of random noise. Finally, LightGBM is used for power theft detection. The experiments show that CWGAN can effectively balance the distribution of power consumption data. Comparing the detection indicators of the power-stealing model with various advanced power-stealing models on the same data set, it is finally proved that the proposed model is superior to other models in the detection of power stealing.

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

With the rise in biometric-based identity authentication, facial recognition software has already stimulated interesting research. However, facial recognition has also been subjected to criticism due to security concerns. The main attack methods include photo, video, and three-dimensional model attacks. In this paper, we propose a multifeature fusion scheme that combines dynamic and static joint analysis to detect fake face attacks. Since the texture differences between the real and the fake faces can be easily detected, LBP (local binary patter) texture operators and optical flow algorithms are often merged. Basic LBP methods are also modified by considering the nearest neighbour binary computing method instead of the fixed centre pixel method; the traditional optical flow algorithm is also modified by applying the multifusion feature superposition method, which reduces the noise of the image. In the pyramid model, image processing is performed in each layer by using block calculations that form multiple block images. The features of the image are obtained via two fused algorithms (MOLF), which are then trained and tested separately by an SVM classifier. Experimental results show that this method can improve detection accuracy while also reducing computational complexity. In this paper, we use the CASIA, PRINT-ATTACK, and REPLAY-ATTACK database to compare the various LBP algorithms that incorporate optical flow and fusion algorithms.