STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

Foodborne doping and supervision in sports

Cases of foodborne doping are frequently reported in sports events and can cause severe consequences for athletes.The foodborne doping can be divided into natural endogenous and artifi cially added foods according to the sources,including anabolic agents,stimulants,diuretics,β-blockers,β2 agonists and others.In order to control foodborne doping,chromatographic technique,immunoassay,nuclear magnetic resonance,biosensor technology,pyrolytic spectroscopy,comprehensive analysis and electrochemical analysis have usually used as analytical and inspection strategies.Meanwhile,the legislation of anti-doping,the improvement of testing standard and technology,and the prevention and control of food safety,as well as the improvement of risk perception of athletes are highly necessary for achieving the effective risk control and supervision of foodborne doping,which will be benefi cial for athletes,doctors and administrators to avoid the risks of foodborne doping test and reduce foodborne doping risks for the health of athletes.

Research on the intelligent supervision and operation platform of railway real estate

Railway real estate is the fundamental element of railway transportation production and operation.Effective management and rational utilization of railway real estate is essential for railway asset operation.Based on the investigation of the requirements of railway real estate management and operation,combined with Beidou positioning,GIS(Geographic Information System),multi-source data fusion and other cutting-edge technologies,this paper puts forward the multi-dimensional dynamic statistical method of real estate information,the identification method of railway land occupation and the comprehensive evaluation method of real estate development and utilization potential,and build the railway real estate supervision and operation platform,design the function of the platform,so as to provide intelligent solutions for the railway real estate operation.

One-step Eco-friendly Fabrication of Antibacterial Polyester Via On-line Amination Reaction by Melt Coextrusion

The work is dedicated to develop a one-step eco-friendly method to prepare antibacterial polyethylene terephthalate(PET).We report a one-step eco-friendly method to manufacture antibacterial PET via on-line amination reaction by melt coextrusion.Beside evenly mixing of poly(hexamethylene guanidine)(PHMG)and PET in the melt coextrusion procedure,the amination reaction also occurred between PHMG and PET under high temperature(230-270℃).The antibacterial ability of composite PET showed obvious PHMG concentration dependence,and antibacterial activity reached more than 99%when PHMG content was 2.5 wt%.Moreover,LIVE/DEAD fluorescence test further confirmed that the composite PET could kill bacteria quickly and efiectively(within 30 min);while negligible cytotoxicity was observed to HSF and HUVEC cells.Onestep eco-friendly fabrication of composite antibacterial PET was accomplished by on-line melt coextrusion.The composite antibacterial PET has potential use in multiple fields to combat with pathogenic including textiles,packaging materials,decoration materials and biomedical devices,etc.

Routine Supportive Supervision and Management of Medicines and Other Health Products and Technologies in Vihiga County, Kenya

Health Products and Technologies (HPTs) are pivotal for an efficient health system. Availability and accessibility to affordable health products are critical indicators towards achieving universal health coverage. Routine supportive supervision, performance monitoring, recognition of efforts and client feedback are vital activities toward health supply chain system strengthening. This is a descriptive paper that describes a model of integrated commodity supportive supervision, and mentorship and its impact on various outcomes of health commodity management. Data were abstracted from the standardized scored checklists used during integrated commodity supportive supervision and supply chain audit in public health facilities in Vihiga County. Scores for the period 2020 to 2022 were analyzed on the eight key areas of interest. The analysis was done using Statistical Package for Social Sciences (SPSS version 26). Results are interpreted at 95% Confidence interval. This paper also shares findings from both quantitative and qualitative data from client exit and facility managers’ interviews. Six complete rounds of supervisions, three clients and service providers’ interviews, and three annual award events have been conducted. We observed trends across six data collections points and compared the results at first point or baseline (January-June 2020) to the results at the last point or end line (April-June 2022). Findings show significant improvements on the eight parameters in terms of mean scores as follows: resolution of issues from previous visits by 35.06% (46.75% - 81.81%);storage of HPTs by 17.41% (68.72% - 86.13%);inventory management by 28.16% (42.67% - 70.83%);availability and use of commodity data management information systems (MIS) tools by 22.39% (74.40% - 96.79%);verification of commodity data by 25.61% (65.56% - 91.17%);availability of guidelines and job aids for commodity management by 46.28% (36.65% - 82.93%). There was an improvement on the mean score on accountability by 20.22% (58.58% - 83.51%). The composite (final) score improved by 28.33% (56.19% - 84.52%). There was progressive narrowing of the standard deviations on all the indicators across the study period. This demonstrates that there is standardization of practices and positive competition among all the public health facilities. There were significant improvements on all the eight indicators. Routine integrated commodity supportive supervision has proven to be an effective high impact intervention in improving management of health products and technologies in Vihiga County, Kenya.

Model-constrained and data-driven double-supervision acoustic impedance inversion

Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geophysical inversion problem is essentially an ill-posedness problem,which means that there are many solutions corresponding to the same seismic data.Therefore,regularization schemes,which can provide stable and unique inversion results to some extent,have been introduced into the objective function as constrain terms.Among them,given a low-frequency initial impedance model is the most commonly used regularization method,which can provide a smooth and stable solution.However,this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data,which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions.Therefore,we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network,which regards seismic data as time-series rather than image-like patches.Compared with the model-based inversion method,the data-driven approach provides higher resolution inversion results,which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components.However,judging from the inversion results for characterization the spatial distribution of thin-layer sands,the accuracy of high-frequency components is difficult to guarantee.Therefore,we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes.First,constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network,which provides the predicted impedance with higher resolution.Then,convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results,which makes the synthetic seismic data obtained from the inversion result consistent with the input data.Finally,we test the proposed scheme based on the synthetic and field seismic data.Compared to model-based and purely data-driven impedance inversion methods,the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity.The inversion results accurately characterize the spatial distribution relationship of thin sands.The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data.Moreover,tests on the oil field data indicate the practicality and adaptability of the proposed method.

Human-pose estimation based on weak supervision

Background In computer vision,simultaneously estimating human pose,shape,and clothing is a practical issue in real life,but remains a challenging task owing to the variety of clothing,complexity of de-formation,shortage of large-scale datasets,and difficulty in estimating clothing style.Methods We propose a multistage weakly supervised method that makes full use of data with less labeled information for learning to estimate human body shape,pose,and clothing deformation.In the first stage,the SMPL human-body model parameters were regressed using the multi-view 2D key points of the human body.Using multi-view information as weakly supervised information can avoid the deep ambiguity problem of a single view,obtain a more accurate human posture,and access supervisory information easily.In the second stage,clothing is represented by a PCA-based model that uses two-dimensional key points of clothing as supervised information to regress the parameters.In the third stage,we predefine an embedding graph for each type of clothing to describe the deformation.Then,the mask information of the clothing is used to further adjust the deformation of the clothing.To facilitate training,we constructed a multi-view synthetic dataset that included BCNet and SURREAL.Results The Experiments show that the accuracy of our method reaches the same level as that of SOTA methods using strong supervision information while only using weakly supervised information.Because this study uses only weakly supervised information,which is much easier to obtain,it has the advantage of utilizing existing data as training data.Experiments on the DeepFashion2 dataset show that our method can make full use of the existing weak supervision information for fine-tuning on a dataset with little supervision information,compared with the strong supervision information that cannot be trained or adjusted owing to the lack of exact annotation information.Conclusions Our weak supervision method can accurately estimate human body size,pose,and several common types of clothing and overcome the issues of the current shortage of clothing data.

Strategic interaction between institutional investors and supervision department:a theoretical analysis of low‑price collusion in SBIC

We introduce evolutionary game method to analyze low-price collusion in inquiry market of Sci-Tech Innovation Board of China(SIBC)from the perspective of strategic interaction between large institutional investors(LIIs),small and medium-sized institutional investors(SMIIs),and supervision department(SD).The results show that supervision behaviors of SD,and quotation behaviors of institutional investors,are subject to supervision conditions.Under the condition that benefits of tough supervision are lower a lot than minimum benefits of light supervision(light supervision condition),SD will choose light supervision and institutional investors will turn to illegal quotation in response.Finally,a steady-state equilibrium with low-price collusion will form in SIBC’s inquiry market even with a large supervision penalty for illegal quotation.On the contrary,under the condition that benefits of tough supervision are higher a lot than maximum benefits of light supervision(tough supervision condition)and with a large penalty for illegal quotation,SD and institutional investors will choose tough supervision and legal quotation.Further numerical simulations under light supervision condition show that:(1)High-price culling rule will become a booster for low-price collusion and accelerate SMIIs’evolutionary process to imitative quotation.(2)Blindly increasing penalties for illegal quotation or reducing the culling rate is not an appropriate approach to solve the problem of low-price collusion since it cannot shift supervision condition from light into tough and make SD supervise toughly.(3)Institutional investors’choices of quotation strategies are more volatile and highly susceptible to supervision behaviors of SD when facing exogenous uncertainty.Therefore,the keys to solving the problem of low-price collusion are shifting supervision condition from light into tough through increasing incremental benefits of tough supervision,and providing institutional investors with a stable and predictable supervision policy.In conclusion,the creation of a fair inquiry market doesn’t only depend on restraint and punishment to institutional investors,but also requires the establishment of supervision mechanism those are compatible with market-based inquiry.

On the Expansion and Realization of the Right to Environmental Information in Environmental Protection Supervision

As an innovation in the environmental governance system that breaks the traditional hierarchical structure,environmental protection supervision has not only played a significant role in protecting tangible environmental rights but also expanded the basic scope of the right to environmental information—part of procedural environmental rights.In the supervision of environmental protection,the objects of the right to environmental information and the subjects of the obligation to provide environmental information have been both expanded,with the focus shifting from government information to Party information and from administrative organs to Party organs.This vividly demonstrates the Communist Party of China’s concrete efforts to protect human rights in the field of the endeavor to build an ecological civilization.At present,the realization of the right to environmental information in environmental protection supervision still faces problems such as insufficient standards and norms,disordered practice and operation,and lack of liability guarantee.In this context,based on renewing relevant subjects’cognition of the right to know in environmental protection supervision,we should further improve and specify the rule for disclosing information about environmental protection supervision,rationally distribute the obligations for information disclosure in environmental protection supervision,and clarify the accountability rules for violating relevant requirements for information disclosure,so as to promote the overall development of the environmental protection supervision system while guaranteeing the realization of the right to environmental information.

Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations

Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

Supervision of Public Opinion under the Background of Social Co-governance-Take the Changsheng Vaccine Incident as an Example

Objective To provide reference for the news media to give play to the role of public opinion supervision in time based on the background of drug safety and social co-governance.Methods The method of case analysis was used to make a retrospective study on the Changsheng vaccine incident in 2018.Then the role of mainstream media,pharmaceutical media,and self-media in the supervision of public opinion was investigated.Results and Conclusion Both mainstream and pharmaceutical media played an excellent role in supervising the Changchun Changsheng vaccine incident.However,the content published by some pharmaceutical media was hard to understand by ordinary people.Besides,the role of self-media in public opinion supervision was polarized.Some self-media closely kept pace with mainstream media in public opinion supervision.Other self-media unilaterally pursued the click rate,publishing false information to guide wrong public opinion.The news media should optimize the supervision efficiency of drug safety.On the one hand,pharmaceutical media should pay attention to the fact that readers may not understand the difficult terms because they are not professional.On the other hand,self-media practitioners should improve their professional quality so that they will not publish some fake news to mislead public opinion.

Edge-Federated Self-Supervised Communication Optimization Framework Based on Sparsification and Quantization Compression

The federated self-supervised framework is a distributed machine learning method that combines federated learning and self-supervised learning, which can effectively solve the problem of traditional federated learning being difficult to process large-scale unlabeled data. The existing federated self-supervision framework has problems with low communication efficiency and high communication delay between clients and central servers. Therefore, we added edge servers to the federated self-supervision framework to reduce the pressure on the central server caused by frequent communication between both ends. A communication compression scheme using gradient quantization and sparsification was proposed to optimize the communication of the entire framework, and the algorithm of the sparse communication compression module was improved. Experiments have proved that the learning rate changes of the improved sparse communication compression module are smoother and more stable. Our communication compression scheme effectively reduced the overall communication overhead.

Decentralized Semi-Supervised Learning for Stochastic Configuration Networks Based on the Mean Teacher Method

The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.

Distant Supervision方法中对齐数据的聚类去噪

基于聚类和寻找表达语义关系的句子模式,提出一种基于聚类去噪的distant supervision方法,解决了传统distant supervision方法在知识库和文本集对齐阶段会引入噪声数据的问题,得到了一种改进的distant supervision方法.实验结果表明,该方法可有效提高关系抽取任务的准确率.

On-line血液透析滤过对移植肾功能延迟恢复的影响

目的 研究On -line血液透析滤过 (HDF)在移植肾功能延迟恢复患者中的应用。方法 我院2 0 0 2年 1月至 2 0 0 3年 7月肾移植术后发生移植肾功能延迟恢复患者 38例 ,2 9例采用Freseniuson -lineHDF透析机 ,F6 0透析器 ,透析滤过时间 3～ 4h ,血流量在 180～ 2 5 0ml/min ,置换液采取前稀释法 ,9例行HD治疗 ,采用GambroAK2 0 0透析机 ,F7透析器。结果 38例移植肾功能延迟恢复患者 ,2 9例用联机血液透析滤过 ,平均肾功能恢复时间为 ( 11.2± 5 .6 )天 ,治疗期间收缩压变化在 110～ 170mmHg,透析期间低血压发生率为 2 .6 % ,9例HD治疗者平均肾功能恢复时间为 ( 2 2 .3± 15 .7)天 ,透析期间低血压发生率为 13.2 %。结论 与HD比较 ,On -lineHDF对血压影响较小 ,可缩短移植肾功能恢复的时间。

平行机的实时到达on-line算法下界的改进

本文考虑了平行机实时到达的在线问题．模型中，工件是陆续到达的，工件的个数、到达时间是事先未知的，而且只有当工件到达，才知其加工时间，目标是使所有工件都加工完的时间达到最小．Ｃｈｅｎ与Ｖｅｓｔｊｅｎｓ（１９９６年）证明了该在线问题不存在性能比小于１．３４７３的ｏｎ－ｌｉｎｅ算法．本文将此界改进为（５－５）／２．