Identification of Important FPGA Modules Based on Complex Network

The globalization of hardware designs and supply chains,as well as the integration of third-party intellectual property(IP)cores,has led to an increased focus from malicious attackers on computing hardware.However,existing defense or detection approaches often require additional circuitry to perform security verification,and are thus constrained by time and resource limitations.Considering the scale of actual engineering tasks and tight project schedules,it is usually difficult to implement designs for all modules in field programmable gate array(FPGA)circuits.Some studies have pointed out that the failure of key modules tends to cause greater damage to the network.Therefore,under limited conditions,priority protection designs need to be made on key modules to improve protection efficiency.We have conducted research on FPGA designs including single FPGA systems and multi-FPGA systems,to identify key modules in FPGA systems.For the single FPGA designs,considering the topological structure,network characteristics,and directionality of FPGA designs,we propose a node importance evaluationmethod based on the technique for order preference by similarity to an ideal solution(TOPSIS)method.Then,for the multi-FPGA designs,considering the influence of nodes in intra-layer and inter-layers,they are constructed into the interdependent network,and we propose a method based on connection strength to identify the important modules.Finally,we conduct empirical research using actual FPGA designs as examples.The results indicate that compared to other traditional indexes,node importance indexes proposed for different designs can better characterize the importance of nodes.

Beyond statistical significance:Embracing minimal clinically important difference for better patient care

The minimal clinically important difference(MCID)represents a pivotal metric in bridging the gap between statistical significance and clinical relevance,addressing the direct impact of medical interventions from the patient's perspective.This comprehensive review analyzes the evolution,applications,and challenges of MCID across medical specialties,emphasizing its necessity in ensuring that clinical outcomes not only demonstrate statistical significance but also offer genuine clinical utility that aligns with patient expectations and needs.We discuss the evolution of MCID since its inception in the 1980s,its current applications across various medical specialties,and the methodologies used in its calculation,highlighting both anchor-based and distribution-based approaches.Furthermore,the paper delves into the challenges associated with the application of MCID,such as methodological variability and the interpretation difficulties that arise in clinical settings.Recommendations for the future include standardizing MCID calculation methods,enhancing patient involvement in setting MCID thresholds,and extending research to incorporate diverse global perspectives.These steps are critical to refining the role of MCID in patient-centered healthcare,addressing existing gaps in methodology and interpretation,and ensuring that medical interventions lead to significant,patient-perceived improvements.

An Analysis of Innovative Contributions to the Important Discourse on the Development of New Quality Productivity

The discourse on developing high-quality productivity marks a significant theoretical innovation,which is conducive to the modernization and sinicization of Marxism.It refines our understanding of“new quality productivity,”defining it as an advanced form driven by innovation,embodying“high technology,efficiency,and quality,”with the aim of comprehensively enhancing productivity.It elucidates the necessity of cultivating such forces,asserting that they are crucial for achieving high-quality development,securing a leading position in global technology,and fulfilling the aspirations for a better life.Moreover,it outlines a new implementation route,emphasizing strategies such as fostering technological autonomy,nurturing emerging industries,integrating education and talent in technology,adopting a“build through challenges”approach,adjusting solutions locally,and providing categorized guidance,all of which are based on the ongoing comprehensive reforms.

Important edge identification in complex networks based on local and global features

Identifying important nodes and edges in complex networks has always been a popular research topic in network science and also has important implications for the protection of real-world complex systems.Finding the critical structures in a system allows us to protect the system from attacks or failures with minimal cost.To date,the problem of identifying critical nodes in networks has been widely studied by many scholars,and the theory is becoming increasingly mature.However,there is relatively little research related to edges.In fact,critical edges play an important role in maintaining the basic functions of the network and keeping the integrity of the structure.Sometimes protecting critical edges is less costly and more flexible in operation than just focusing on nodes.Considering the integrity of the network topology and the propagation dynamics on it,this paper proposes a centrality measure based on the number of high-order structural overlaps in the first and second-order neighborhoods of edges.The effectiveness of the metric is verified by the infection-susceptibility(SI)model,the robustness index R,and the number of connected branchesθ.A comparison is made with three currently popular edge importance metrics from two synthetic and four real networks.The simulation results show that the method outperforms existing methods in identifying critical edges that have a significant impact on both network connectivity and propagation dynamics.At the same time,the near-linear time complexity can be applied to large-scale networks.

The minimal clinically important difference for gait speed in significant unilateral vestibular hypofunction after vestibular rehabilitation

Gait speed is a valid measure of both physical function and vestibular health.Vestibular rehabilitation is useful to improve gait speed for patients with vestibular hypofunction,yet there is little data to indicate how changes in gait speed reflect changes in patient-reported health outcomes.We determined the minimal clinically important difference in the gait speed of patients with unilateral vestibular hypofunction,mostly due to deafferentation surgery,as anchored to the Dizziness Handicap Index and the Activities Balance Confidence scale,validated using regression analysis,change difference,receiveroperator characteristic curve,and average change methods.After six weeks of vestibular rehabilitation,a change in gait speed from 0.20 to 0.34 m/s with 95%confidence was required for the patients to perceive a significant reduction in perception of dizziness and improved balance confidence.

中国古代槟榔文化的接受与传播

作为舶来品的槟榔,在饮食、医药、赋税等方面扮演过重要的角色,具有丰厚的历史文化。梳理文献发现,两汉三国时期,槟榔视作“奇草异木”记载在各种《异物志》中,开始被接受;南北朝时期,槟榔是贵族士宦的“奢侈品”,唐代受佛教平等思想影响日渐向大众传播,宋代逐渐成为百姓家的“寻常物”。槟榔之所以能被接受与传播,并在海南用于婚俗,是因其本身的物质文化之外,还与其因贵异而用于重要的人际礼仪、文人墨客极力推崇渲染和赋予了槟榔君子品格等精神文化有着莫大的关系。

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.

Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

基于Null Importance和Stacking模型的知识追踪研究

为应对基于游戏的学习平台在知识追踪应用方面的不足,本研究利用Field Day Lab提供的教育游戏用户日志进行深入分析。采用方差法和Null Importance方法对数据集进行降维处理,并结合K折交叉验证与LightGBM算法,建立了一个高效的预测模型。此外,通过集成Logistic模型,构建起Stacking模型。研究表明,该模型在验证集上的Macro-F1值显著提升至0.699,同时也在测试集上显示出优异的泛化能力。本研究为教育游戏领域的知识追踪提供了创新方法,并为游戏开发与教育实践提供了宝贵参考,支持教育游戏的开发者为学生创造更有效的学习体验。

Prognostic prediction models for postoperative patients with stageⅠtoⅢcolorectal cancer based on machine learning

BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting survival.In addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.AIM To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢCRC.METHODS More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap validation.Important variables were screened with multivariable regression methods.Model performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model decision-making.Nomograms were constructed for practical model application.RESULTS Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was effective.The highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free survival.Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets.The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors.The nomograms were created.CONCLUSION We constructed a comprehensive,high-accuracy,important variable-based ML architecture for predicting the 3-category survival times.This architecture could serve as a vital reference for managing CRC patients.

Development of endemicity and conservation importance indices for tropical forests and the floral diversity assessment of Mt.Natoo in Benguet,Philippines

The scientific community faces the challenge of measuring progress toward biodiversity targets and indices have been traditionally used.However,recent inventories in secondary tropical mountain forests using traditional biodiversity indices have yielded results that are indistinct with primary ones.This shows the need to develop complementary indices that goes beyond species count but integrates the distribution and conservation status of the species.This study developed endemicity and conservation importance index for tropical forest that incorporated the distribution and conservation status of the species.These indices were applied to Mt.Natoo,a remnant primary mossy forest in Buguias,Benguet,Philippines,that resulted to endemicity index of 81.07 and conservation importance index of 42.90.Comparing these with secondary forest sites with comparable Shannon-Wiener,Simpson,Evenness and Margalef’s indices,our endemicity and conservation indices clearly differentiates primary forest(our study site)with higher values from secondary forests with much lower values.Thus,we are proposing these indices for a direct but scientifically-informed identification of specific sites for conservation and protection in tropical forests.Additionally,our study documented a total of 168 vascular plant species(79 endemic and 12 locally threatened species)in Mt.Nato-o.Majority are of tropical elements for both generic and species levels with some temperate elements that could be attributed to the site's high elevation and semi-temperate climate.These are important baseline information for conservation plans and monitoring of tropical mossy forests.

Study on Determination Methods of Important Landscapes along the Mainstream of Yellow River

According to the response relationship between landscape and water resource,the selection principle of important landscapes was determined from the perspectives of nature and history in this study,and determination methods or steps of important landscapes along the mainstream of Yellow River were also established to select important landscapes that should be protected.

High rate of clinically relevant improvement following anatomical total shoulder arthroplasty for glenohumeral osteoarthritis

BACKGROUND The minimal clinically important difference(MCID)is defined as the smallest meaningful change in a health domain that a patient would identify as important.Thus,an improvement that exceeds the MCID can be used to define a successful treatment for the individual patient.AIM To quantify the rate of clinical improvement following anatomical total shoulder arthroplasty for glenohumeral osteoarthritis.METHODS Patients were treated with the Global Unite total shoulder platform arthroplasty between March 2017 and February 2019 at Herlev and Gentofte Hospital,Denmark.The patients were evaluated preoperatively and 3 months,6 months,12 months,and 24 months postoperatively using the Western Ontario Osteoarthritis of the Shoulder index(WOOS),Oxford Shoulder Score(OSS)and Constant-Murley Score(CMS).The rate of clinically relevant improvement was defined as the proportion of patients who had an improvement 24 months postoperatively that exceeded the MCID.Based on previous literature,MCID for WOOS,OSS,and CMS were defined as 12.3,4.3,and 12.8 respectively.RESULTS Forty-nine patients with a Global Unite total shoulder platform arthroplasty were included for the final analysis.Mean age at the time of surgery was 66 years(range 49.0-79.0,SD:8.3)and 65%were women.One patient was revised within the two years follow-up.The mean improvement from the preoperative assessment to the two-year follow-up was 46.1 points[95%confidence interval(95%CI):39.7-53.3,P<0.005]for WOOS,18.2 points(95%CI:15.5-21.0,P<0.005)for OSS and 37.8 points(95%CI:31.5-44.0,P<0.005)for CMS.Two years postoperatively,41 patients(87%)had an improvement in WOOS that exceeded the MCID,45 patients(94%)had an improvement in OSS that exceeded the MCID,and 42 patients(88%)had an improvement in CMS that exceeded the MCID.CONCLUSION Based on three shoulder-specific outcome measures we find that approximately 90%of patients has a clinically relevant improvement.This is a clear message when informing patients about their prognosis.

Treatment of a patient with severe cerebral malaria during the COVID-19 pandemic in China:A case report

BACKGROUND On June 30,2021,China received certification from the World Health Organization for malaria elimination.However,this certification does not signify the absence of malaria within China.Due to the increasing frequency of international exchanges and collaborations,the threat of imported malaria persists in China.Consequently,the prevention and control of imported malaria have become a primary focus for our country to maintain its malaria elimination status.CASE SUMMARY Herein,we present a case report of a 53-year-old Chinese man who worked in Africa for nearly two years.He was diagnosed with malaria in the Democratic Republic of the Congo between November 19 and November 23,2022.After receiving effective treatment with oral antimalarial drugs,his condition improved,allowing him to return to China.He was later admitted to our hospital on January 12,2023,during the coronavirus disease 2019 pandemic in Huangshi,China.Through a thorough evaluation of the patient's symptoms,clinical signs,imaging and laboratory test results,and epidemiological data,he was rapidly diagnosed with severe cerebral malaria.The patient underwent successful treatment through a series of intensive care unit interventions.CONCLUSION The successful treatment of this imported case of severe cerebral malaria provides a valuable reference for managing patients with similar malaria infections and has significant clinical implications.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

Proposal of Equivalent Porosity Indicator for Foam Aluminum Based on GRNN

To gain a more comprehensive understanding and evaluate foam aluminum's performance,researchers have introduced various characterization indicators.However,the current understanding of the significance of these indicators in analyzing foam aluminum's performance is limited.This study employs the Generalized Regression Neural Network(GRNN)method to establish a model that links foam aluminum's microstructure characterization data with its mechanical properties.Through the GRNN model,researchers extracted four of the most crucial features and their corresponding weight values from the 13 pore characteristics of foam aluminum.Subsequently,a new characterization formula,called“Wang equivalent porosity”(WEP),was developed by using residual weights assigned to the feature weights,and four parameter coefficients were obtained.This formula aims to represent the relationship between foam aluminum's microstructural features and its mechanical performance.Furthermore,the researchers conducted model verification using compression data from 11 sets of foam aluminum.The validation results showed that among these 11 foam aluminum datasets,the Gibson-Ashby formula yielded anomalous results in two cases,whereas WEP exhibited exceptional stability without any anomalies.In comparison to the Gibson-Ashby formula,WEP demonstrated an 18.18%improvement in evaluation accuracy.

CCPIT

CCPIT Spokesperson Comments on EU's Adoption by Vote of Draft Definitive Findings of Anti-subsidy Investigation into China-made BEV Imports On October 4,representatives of European Union (EU) member states voted to adopt the draft definitive findings of an antisubsidy investigation into imports of battery electric vehicles (BEVs)submitted by the European Commission,which proposes to impose definitive countervailing duties on BE Vs originating in China.The spokesperson for the China Council for the Promotion of International Trade (CCPIT)commented on this issue.

基于灰色-改进二元语义的导弹装备维修保障效能评估

针对导弹装备维修保障效能系统复杂庞大,指标间相互关联影响、数据信息获取困难且易丢失扭曲的现状,提出一种基于灰色—改进二元语义的方法对维修保障效能进行评估。该方法利用改进G1法和改进CRITIC(criteria importance though intercrieria correlation)法分别得出主客观权重,依据最小信息鉴别法获取综合权重,利用灰色聚类分析法将定量数据转化为二元语义形式,运用改进二元语义将定性语言评估信息进行转化并对完整系统进行评估。获取的各指标权重更加科学合理,避免了信息的丢失扭曲,评估结果更加准确。该模型评估结果与实际情况基本相符,能真实科学反映导弹装备维修保障效能水平并可针对性查找薄弱环节,可为导弹装备维修保障系统的管理和完善提供依据。

What Is the Convenience of Treating a Wetland as an Agricultural Area?

The Gelsari and Lentini marshes, in east-central Sicily, are wetland under heavy anthropogenic pressure. This area is regularly subjected to water withdrawals and agriculture is practiced. Periodic bird census activities had been conducted in this area between 2016 and 2022 with collected data being supplemented with the data from a previous survey conducted between 2010 and 2016. During the censuses, numerous photographs were collected, which were later used for counts. In total, 121 surveys were carried out, with visits occurring approximately 20 times per year, covering all seasons. The data collected, both from bibliography and unpublished observations, were organised in table. 120 species and subspecies have been reported, 41 of which are in Annex I of the Birds Directive. Between 2021 and 2022, a water column measurement was taken in a central area of the Lentini marsh and compared with rainfall data in the area. It was found that water removal, occurring in both marshes in different modes, produced different effects on habitat conservation. Of the two marshes, Lentini is the one in which the existing favorable conditions in its most depressed areas, located below sea level, have allowed for the adequate conservation of the wetland. This environmental improvement having occurred with respect to the previous period has restored to the marsh its historically recognized possibility of not drying up completely during the summer period, allowing permanence of numerous birds. The obtained results suggest total elimination of agriculture and discontinuation of water withdrawals in the Lentini marsh as well as regulation of withdrawals in the Gelsari marsh.