Unsharp masking (USM) sharpening is a common operation to improve image visual clarity in image edition,However, it can cause serious harm if applied to scenarios involving the tampering of forensic evidence or public event photographs. In recent years, many methods for USM sharpening and anti-sharpening in digital image forensics have been proposed by researchers. So, a systematic review of the literature in this field is provided by this paper. The fundamental principles of the USM sharpening operation are first explained in this paper, followed by the categorization and summary of existing USM sharpening and anti-sharpening methods based on algorithm implementation principles. Additionally, commonly used datasets in related literature are introduced, and performance evaluations of existing methods are conducted for comparative purposes. Finally, the challenges faced by existing methods are analyzed and future research directions are discussed, mainly including the use of updated datasets, in-depth research on the game of forensics and anti-forensics, in-depth research on sharpening anti-forensics, the issue on the efficiency of forensics scheme execution, and the improvement of the reliability of anti-forensics.

Komagataella phaffii has been widely used in the pharmaceutical and biotechnology industries. In recent years, its potential as a research model organism has gained attention. Although baker's yeast is the most commonly used yeast model in research, it limits our understanding of the same organism. K. phaffii, which diverged from baker's yeast 250 million years ago, evolves at a slower rate and retains characteristics of ancient yeast ancestors, making it more similar to higher eukaryotic cells. K. phaffii can efficiently assimilate methanol as the sole carbon source, making it a valuable model organism for studying molecular cell biology of eukaryotes. This article reviews the research progress of using K. phaffii as a model organism, including methanol assimilation, peroxisome formation, mating and sporulation behavior, as well as protein secretion, lipid synthesis, and cell wall formation processes. By comparing the data of K. phaffii with other yeast species such as baker's yeast, this article highlights the great potential of K. phaffii in basic research, aiming to present a comprehensive and systematic review of the research progress on K. phaffii as a model organism.

Aerobic composting is one of the main technical methods to achieve the resource utilization of chicken manure. However, nitrogen loss is serious during chicken manure composting, which not only reduces the fertilizer efficiency, but also causes severe non-point source pollution. This paper summarizes the main causes of nitrogen loss during chicken manure composting, and reviews the effects of initial C/N, initial moisture content, turning frequency, and additive types on nitrogen loss during chicken manure composting. It is concluded that the optimal initial C/N, initial moisture content, and turning frequency for reducing nitrogen loss during chicken manure composting are 20-25, 60%-65%, and once/7d, respectively. Compound additives can reduce nitrogen loss during composting more than single additives. Finally, the future research prospects of chicken manure composting are proposed. This paper provides a theoretical basis for the study of nitrogen loss during chicken manure composting.

Bibliometric methods were used to sort out the current status, hotspots and development trends of research on dissolved organic matter(DOM) in inland waters by three-dimensional fluorescence spectroscopy at home and abroad. Using CiteSpace knowledge graph analysis tool and CNKI and WoS core collection as data sources, the annual publication volume, authors and countries, keyword clustering and highlighting were visualized and analyzed, and the data analysis methods and analytical teehniques of DOM and 3D fluorescence spectroscopy in inland water were reviewed. The results show that the annual publication volume of this field is on the rise and is mainly characterized by the rapid growth of foreign language literature, China is the country with the largest publication volume, the degree of international exchange and cooperation is high and a stable research group has been formed, and the research potential is sufficient. Domestic and international research on dissolved organic matter in inland waters using three-dimensional fluorescence spectroscopy is mainly focused on waters susceptible to human activities or serious eutrophication, including dissolved organic matter in eutrophic waters and sediments, colored dissolved organic matter and disinfection by-products, as well as dissolved organic matter-heavy metal interactions, global climate change response, bioavailability, etc. In addition to conventional data analysis methods, the comprehensive use of three-dimensional fluorescence spectroscopy combined with absorption spectroscopy, mass spectrometry and other techniques to analyze the composition and source characteristics of dissolved organic matter in inland waters at the molecular level is an important research hot spot and trend in this field in the future.

Accurately predicting the trajectory of vehicle is crucial to ensure the safety of autonomous vehicles. However, traditional methods have limited modeling and predictive capabilities when dealing with long sequence trajectories. To address this issue, a vehicle trajectory prediction model was proposed based on the Transformer network. The approach involves inputting the motion and interaction data of the vehicle into a driving intention prediction module to generate a probability intention vector. The trajectory prediction encoder is obtained after the Concatenate function is spliced with the trajectory information, and the trajectory features are fully extracted by using the multi-head attention mechanism. Through the decoder, a distribution of future vehicle trajectories is obtained. Validation on the NGSIM real vehicle trajectory dataset indicates that the accuracy of the driving intention prediction module can reach more than 85% under a prediction time of 2 seconds. Furthermore, the RMSE of the trajectory prediction model is reduced by more than 10% compared with the existing models under a prediction time of 4 seconds. The method provides technical support for accurately predicting the trajectory of autonomous vehicles.

The problem of state estimation and attack signal reconstruction for cyber-physical systems of the nonlinear descriptor system type under network attack is investigated. Firstly, the descriptor system is transformed into a general system, and the intermediate variable observer is designed by reconstructing the attack signal with the intermediate variable. Then, sufficient conditions for the existence of the observer gain matrix are obtained by linear matrix inequalities. Finally, the feasibility of the method is verified by numerical simulation. In the simulation analysis, the lane-keeping system of the intelligent vehicle is modeled, and the vehicle dynamic model and the vehicle keeping model are transformed into the form of a dynamic system. From the perspective of cyber-physical systems, the problem of vehicle safety state estimation is studied. When the system contains network attacks, the proposed observer is used to estimate the state of the lane-keeping system. The results show that the designed intermediate variable observer can accurately estimate the system state and attack signal, and the error systems tend to be stable within 4 s.

To address the impact of uncertainties in both the generation and demand sides of the power system on grid scheduling plans, a two-stage robust optimization model is proposed for integrated source-load uncertainty management. For the generation side, the uncertainties in wind and solar power outputs are considered. Probability density function models are established for wind and solar power, and the Latin hypercube sampling method is employed to generate scenarios and perform scenario reduction, resulting in power output intervals for wind and solar generation. For the demand side, the role of flexible loads in peak shaving and valley filling is considered, and an integrated demand response model based on smart communities is proposed. In the day-ahead stage, aiming to minimize the system operating cost and carbon trading cost while considering source-load uncertainties, a price-based demand response model is developed to determine the day-ahead scheduling plan. In the intra-day stage, based on the optimized results from the day-ahead stage, a two-stage robust optimization model is formulated with the objective of minimizing the operating cost and carbon trading cost of smart communities. The column-and-constraint generation (C&CG) algorithm is employed to convert the objective function, and the Karush-Kuhn-Tucker (KKT) conditions and Big-M constraint method are utilized to transform the max-min optimization problem into a mixed integer linear programming (MILP) model. The correctness of the proposed model and the effectiveness of the algorithm are verified through case studies.

The relationship between the symptoms of Brugada Syndrome (BrS) patients and their abnormal conduction velocity (CV) restitution and abnormal action potential duration (APD) restitution are investigated using cellular automaton in this paper. Firstly, based on the characteristics of the electrocardiogram signals of BrS patients, the cellular automaton model is dimensionalized, and CV recovery and APD recovery are considered in the model. The behavior of the spiral wave of electrocardiographic signal related to the tachycardia is studied by using this model. The results indicate that only when CV recovery occurs, tachycardia will be maintained and will not worsen; Under the combined influence of CV recovery and memoryless APD recovery, tachycardia may disappear or transform into ventricular fibrillation, with a probability of 54%, significantly higher than clinical data; Under the combined influence of CV recovery and memory based APD recovery, tachycardia may disappear, maintain, or transform into ventricular fibrillation, with a conversion rate of 35% to ventricular fibrillation, which is consistent with clinical data. Tracking and observing the conduction of electrocardiogram signals near the wave head, it is found that the development of symptoms in BrS patients is related to the electrical signal conduction block caused by CV or APD recovery. The more severe the conduction block, the more likely BrS patients are to develop from tachycardia to ventricular fibrillation. Memory based APD recovery can reduce the incidence of ventricular fibrillation due to its memory effect, which can reduce the oscillation amplitude of APD.

The small size and dense distribution of bleeding point lesions in the fundus image of diabetic retinopathy make it difficult for the existing algorithms to achieve accurate detection and localization of the lesions. A RCA-YOLO bleeding lesion detection algorithm is proposed. Based on YOLOv5s, the RCA-Net module is first used in the backbone network, so that the network can obtain the connection between each channel while retaining the location information of the target lesion, and enhance the feature extraction and localization ability of the network for the bleeding area. In the feature fusion stage, the lightweight feature pyramid network Tiny-BiFPN is adopted to reduce the number of network parameters and achieve high-efficiency multi-scale feature fusion. Finally, a small target feature enhancement module is proposed to improve the detection accuracy of the algorithm for small bleeding point lesions. The experimental results show that the improved RCA-YOLO algorithm can accurately detect and locate bleeding point lesions, and the average detection accuracy (mAP) can reach 79.3%, which is 9.5 percentage points higher than that of YOLOv5s algorithm, and its detection results are also better than mainstream algorithms such as Faster R-CNN, YOLOv6s, YOLOv7 and YOLOv8s.

The features extracted by the existing point cloud registration algorithms are not so rich, which makes it difficult to further improve the accuracy of the registration. To address this problem, a deep learning-based multi-scale feature fusion point cloud registration algorithm is proposed. EdgeConv is employed to extract multiple features of different scales through the algorithm at first, which can maintain the local geometric structure characteristics. Then Non-linear Polarized Self-attention is introduced to filter its output features, and thus the effectiveness of feature information is improved. And later the above multi-scale features are fused and EdgeConv is employed again to extract their features, thereby improving the expression ability of the features. In the rigid pose estimation stage, Lie algebra is used to process the rotational transformation to fully exploit the transformation information of the point cloud. According to the changes of the extracted point cloud features during the registration process, the weight values of the components of the loss function are dynamically adjusted to evaluate the prediction results of the model more accurately. Tested on the ModelNet40 dataset, when the sample types of the train and test sets are the same, the rotation error of the proposed algorithm is 1.826 7 and the displacement error is 0.001 0, and when the sample types of the train and test sets are not the same (experiments on generalization), the rotation error of the proposed algorithm is 2.979 4 and the displacement error is 0.001 0. The experimental results show that the registration accuracy of the proposed algorithm has improved compared with the current mainstream algorithms, and it exhibits good generalization performance.

In order to solve the problems of high communication complexity and low consensus efficiency of practical Byzantine fault-tolerant (PBFT) consensus mechanism in the consortium chain under the condition of increasing number of nodes in the blockchain network, a multi-primary-node Byzantine fault-tolerant consensus mechanism based on Raft IMRBFT is proposed. Firstly, the Maglev consensus hash algorithm is used to evenly group the nodes of the blockchain network, and the consensus process is divided into two parts: out-of-group consensus and intra-group consensus. The leader node is first selected in the group, and the node is divided into three levels through the credit mechanism: trusted node, ordinary node and untrusted node. Together with the voting mechanism, it reduces the probability of malicious nodes becoming leader nodes, and forms a committee with other group leader nodes, and the committee selects multiple primary nodes with the highest credit value through the external credit value mechanism to conduct PBFT consensus outside the group. On the basis of Raft consensus, intra-group consensus introduces supervision nodes and relay nodes to further improve security and consensus efficiency and reduce the evil behavior of malicious nodes. Finally, the experimental results show that the communication overhead of IMRBFT increases linearly, the traffic volume is 41.6% of PBFT, the throughput is 4.2 times that of PBFT, and the consensus delay is reduced by 76.4%. With the increase of nodes, the optimization is more obvious, which fully meets the requirements of small communication complexity, high throughput, short consensus delay, and high security and consensus efficiency of large-scale blockchain networks.

In response to the problems of excessive computational complexity and poor accuracy in traditional maximum likelihood direction of arrival (ML-DOA) estimation, improved monarch butterfly optimization algorithm is proposed and applied to ML-DOA estimation. By using elite reverse learning strategies to optimize the initial monarch butterfly population and obtaining individuals with better fitness values, the diversity of the monarch butterfly population can be improved; In order to improve the individual optimization method, the mutation operation and adaptive strategy inspired by differential evolution are used for expanding the search scope of the algorithm; At the same time, the Gaussian-Cauchy mutation operator is introduced to adjust the mutation step size, preventing the algorithm from trapping in local optima, and apply IMBO to ML-DOA. Experiments show that the proposed algorithm has better convergence performance, lower root-mean-square deviation and less computation under different number of sources, SNR and population, compared with the conventional DOA estimation algorithm.

In this paper, a stochastic delayed predator-prey model with Beddington-DeAngelis functional response is studied. Sufficient criteria for global existence, stochastically ultimately bounded and global asymptotic stability of the positive equilibrium are obtained. Numerical simulations are carried out to illustrate the analytical results.

The stability of random attractors of the stochastic reaction-diffusion equation with additive white noise is studied. First, with a general assumption on the nonlinear term, the solutions of the stochastic differential equation converge to the solutions of the deterministic equation in the initial space L²(R^N), and the upper semi-continuity of the random attractors is obtained. In particular, by using the nonlinear decomposition and the difference estimation, we technically obtain the convergence of solutions and the upper semi-continuity of random attractors in L^p(R^N)(p>2),where p is the growth index of the nonlinear function.

Under Type I hybrid censoring, the statistical analysis of step stress partially accelerated life tests for compound Rayleigh distribution competing failure products is studied.Based on the compound Rayleigh distribution competing failure products and tampered failure rate (TFR) model, the maximum likelihood estimation and asymptotic confidence interval of unknown parameters and acceleration factors are given by using the maximum likelihood theory and asymptotic approximation theory.The prior information of the unknown parameters and acceleration factors is selected, and the Bayesian estimation and the highest posterior probability density confidence interval (HPD) of the unknown parameters and acceleration factors are obtained by using the MH sampling algorithm. Finally, the two estimation methods are compared by Monte Carlo simulation. The results indicate that Bayesian estimation overall outperforms maximum likelihood estimation (MLE). At the same confidence level, the length of HPD based on Bayes estimation is superior to that of the asymptotic confidence interval based on MLE.

Phasmida have important research value because of their mimicry, limb regeneration, parthenogenesis, defense mechanism and other special behaviors. Through literature review, the checklist of Phasmida insects of Guangxi was sorted out, with a total of eighty-eight species and two subspecies in thirty-eight genera in six families, seven subfamilies, and the relevant literature records, distribution, type localities, type specimens and preservation places, sexual specimens and eggs of each species were provided. Compared with the previous catalogue, thirteen species and two subspecies have been added, and they were sorted and summarized according to the order of subfamily, genus and species taxonomic order, as well as the Latin alphabet. The catalogue provide reference for the taxonomy and conservation biology research of Phasmida insects of Guangxi.

Nr4a3 (nuclear receptor subfamily 4 group a member 3) belongs to the nuclear receptor subfamily and can regulate genes involved in proliferation, cell migration and apoptosis. Nr4a3 has been reported to be down-regulated in a variety of cancer cells. To explore the relationship between the expression of Nr4a3 in breast cancer tissues and the clinical prognosis of breast cancer patients, and its effect on cell proliferation and apoptosis, the expression of Nr4a3 in breast cancer tissues and its relationship with overall survival were analyzed using public databases. In 4T1 cell line, apoptosis and cell cycle were detected by flow cytometry, CCK-8 and colony formation were used to detect cell viability, cell scratch and invasion persisted to measure cell migration and invasion ability, the expression levels of cell cycle and apoptosis genes were detected by q-PCR. The results showed that the expression level of Nr4a3 was down-regulated in breast cancer tissues, which was positively correlated with the overall survival rate and recurrence-free survival rate of breast cancer patients. After Nr4a3 was knockdowned, cell proliferation was enhanced, cell apoptosis was reduced, and cell migration and invasion abilities were improved. This study reveals the potential role of Nr4a3 in breast cancer cell proliferation, apoptosis, migration and invasion, and may become a potential therapeutic target for breast cancer.

This study was aimed to investigate the effects of different levels fermented sweet potato residue on the growth performance, slaughter performance and meat quality of yellow-feathered broilers. Firstly, the apparent metabolic rates of nutrients in fermented sweet potato residue were determined by strong feeding metabolic test; then, 360 one-day-age yellow-feathered broilers were randomly divided into 4 groups (Ⅰ, II, III, IV), each with 6 replicates and 15 chickens per replicate. The broilers in the test group I were fed a basal diet as control group, during 1 to 21 days of age, the test group II, III and IV were fed with 10、 30 and 50 g·kg^-1 fermented sweet potato residue instead of corn in the basic diet, respectively; during 22 to 42 days of age and 43 to 70 days age were fed with 50、 80 and 100 g·kg^-1 fermented sweet potato residue instead of corn in the basic diet, respectively. The trial period was 70 days. The results showed that the apparent metabolic rate of nutrients in fermented sweet potato residue of yellow feather broilers was as follow: crude protein 63.67%, crude fat 65.72%, ash 70.85%, crude fiber 72.45% and gross energy 87.93%. Fermented sweet potato residue equivalent instead of corn had no significant effect on average daily gain, average daily feed intake and feed to gain ratio of yellow-feathered broilers (P>0.05). Compared with the control group, the slaughter rate, clearance rate, chest muscle rate, leg muscle rate and abdominal fat rate had no significant effect among the test groups (P>0.05). Group II, III, IV improved the pH values and a^* values of the pectoral muscles and leg muscles, reduced the drip loss, shear stress (P>0.05). The cooking loss of pectoral muscles and leg muscles reduced significantly (P<0.05). To sum up, the addition of 50 g·kg^-1 to 100 g·kg^-1 of fermented sweet potato residue equivalent instead of corn can improve meat quality of yellow-feathered broilers.

This study preliminarily investigated the potential mechanisms of the Qianggan capsule (QGC) in treating nonalcoholic fatty liver disease (NAFLD) through a combination of multi-target virtual screening, network pharmacology, and in vitro experiment verification. A multi-target virtual screening based on PPARα/γ, FXR and sEH was utilized to select the active ingredients in QGC with therapeutic properties for NAFLD. The systematic pharmacological approach has been used to predict active ingredient targets and disease-relevant targets, and ultimately to screen key targets for disease modulation. Molecular docking and molecular dynamics simulation analysis of key active ingredients and key targets were performed through Discovery Studio 2020 software and Gromacs software. Finally, an experimental verification of the efficacy of the key compounds in the mitigation of hepatocyte steatosis was carried out. The results indicated that 235 active ingredients of QGC and 320 ingredients-disease common targets were identified by multiple-target virtual screening and network pharmacology analysis, respectively. Molecular docking revealed that apigenin, isorhamnetin, rosmarinic acid, physcion, and luteolin showed strong binding capacity to PTPN1, PPARα, PPARγ, and AR, respectively. Molecular dynamics simulations further verified that compounds such as rosmarinic acid and physcion had good binding stability with key targets. In vitro testing showed that the five key active ingredients could improve the steatosis of HepG2 cells. Notably, rosmarinic acid, physcion, and isorhamnetin exhibited the highest potency among them. This study provides a theoretical basis for the development and application of QGC in the treatment of NAFLD through multi-level discussions.

The rapid development of large-scale transportation infrastructure exacerbates ecological risks, especially the construction of highways in ecologically fragile areas, which are prone to pose a major threat to biodiversity and ecosystems along the routes. There is still a lack of universal research on how to better optimize the selection of highways. To bridge this gap, by constructing an ecological sensitivity evaluation system for highways and using hierarchical analysis (AHP) and ArcGIS spatially weighted overlay analysis to guide the optimization of its selection in ecologically fragile areas, further fragmentation and loss of ecosystems can be effectively prevented. 1) Nine indexes: elevation (B1), slope (B2), aspect (B3), water source protection (B4), land use status (B5), Scenic resources protection (B6), vegetation coverage (B7), landscape fragmentation (B8), and geological hazard (B9) were evaluated, and the weight values were B7> B1> B4> B8> B5> B2> B9> B6>B3, indicating that vegetation cover contributed most to the ecological sensitivity of highways, while aspect played a relatively small role. 2)The ecological sensitivity score of Yuping-Zhenyuan highway was between 1.60 and 5.62, with 10.72% and 25.84% of the highly sensitive and moderately sensitive areas, respectively. 3) Comparing indicators of traditional road alignment (B line) with ecological sensitivity based alignment (K line), the ecological sensitivity of K line (4.34) was lower than B line (4.58), suitability of engineering construction and ecological restoration strategies for different road sections were proposed. This study provides a scientific basis for highway alignment selection and can be extended to planning and practice of green infrastructure in other fields.