Acute Complication Prediction and Diagnosis Model CLSTM-BPR:A Fusion Method of Time Series Deep Learning and Bayesian Personalized Ranking

Acute complication prediction model is of great importance for the overall reduction of premature death in chronic diseases.The CLSTM-BPR proposed in this paper aims to improve the accuracy,interpretability,and generalizability of the existing disease prediction models.Firstly,through its complex neural network structure,CLSTM-BPR considers both disease commonality and patient characteristics in the prediction process.Secondly,by splicing the time series prediction algorithm and classifier,the judgment basis is given along with the prediction results.Finally,this model introduces the pairwise algorithm Bayesian Personalized Ranking(BPR)into the medical field for the first time,and achieves a good result in the diagnosis of six acute complications.Experiments on the Medical Information Mart for Intensive Care IV(MIMIC-IV)dataset show that the average Mean Absolute Error(MAE)of biomarker value prediction of the CLSTM-BPR model is 0.26,and the average accuracy(ACC)of the CLSTM-BPR model for acute complication diagnosis is 92.5%.Comparison experiments and ablation experiments further demonstrate the reliability of CLSTM-BPR in the prediction of acute complication,which is an advancement of current disease prediction tools.

基于信息化平台联合BPR理论在手术患者周转流程中的应用

目的:探讨基于信息化平台联合业务流程再造(BPR)理论在手术患者周转流程中的应用效果。方法:选取2021年1月1日~6月30日收治的165例手术患者作为对照组,实施传统院内制订的手术患者周转流程路径管理模式;选取2022年1月1日~6月30日收治的165例手术患者作为观察组,实施基于信息化平台联合BPR理论的手术患者周转流程管理模式。比较两组手术室利用率、手术接台时间、患者满意度及医生满意度。结果:观察组手术室利用率、患者满意度及医生满意度均高于对照组(P<0.01,P<0.05);观察组手术接台时间短于对照组(P<0.01,P<0.05)。结论:通过基于信息化平台结合BPR理论进行分析和优化手术患者周转流程,可提高手术患者周转效率,减少医疗资源的浪费,提升医疗服务质量。

电针治结合涤痰汤加减治疗精神分裂症的效果及对PANSS、BPRS的影响

研究和分析电针结合涤痰汤加减治疗精神分裂症的效果及对PANSS、BPRS的影响。方法 选择80例精神分裂症患者作为研究对象,均于2022年1月-2023年12月在我院接受治疗,根据来院就诊顺序进行分组,前40例设为对照组,后40例设为观察组。对照组行常规西药治疗,观察组加用电针结合涤痰汤加减治疗。观察和对比两组治疗前后阳性和阴性综合征量表(PANSS)、简明精神病评定量表(BPRS)评分变化。结果 经30 d的治疗,两组一般精神病症状等各维度PANSS评分降低,且观察组更低(P<0.05)。经30 d的治疗,两组BPRS评分降低,且观察组更低(P<0.05)。结论:加用电针结合涤痰汤加减治疗,可进一步降低精神分裂症患者的PANSS、BPRS评分。

Ranking academic institutions based on the productivity,impact,and quality of institutional scholars

Purpose:The quantitative rankings of over 55,000 institutions and their institutional programs are based on the individual rankings of approximately 30 million scholars determined by their productivity,impact,and quality.Design/methodology/approach:The institutional ranking process developed here considers all institutions in all countries and regions,thereby including those that are established,as well as those that are emerging in scholarly prowess.Rankings of individual scholars worldwide are first generated using the recently introduced,fully indexed ScholarGPS database.The rankings of individual scholars are extended here to determine the lifetime and last-five-year Top 20 rankings of academic institutions over all Fields of scholarly endeavor,in 14 individual Fields,in 177 Disciplines,and in approximately 350,000 unique Specialties.Rankings associated with five specific Fields(Medicine,Engineering&Computer Science,Life Sciences,Physical Sciences&Mathematics,and Social Sciences),and in two Disciplines(Chemistry,and Electrical&Computer Engineering)are presented as examples,and changes in the rankings over time are discussed.Findings:For the Fields considered here,the Top 20 institutional rankings in Medicine have undergone the least change(lifetime versus last five years),while the rankings in Engineering&Computer Science have exhibited significant change.The evolution of institutional rankings over time is largely attributed to the recent emergence of Chinese academic institutions,although this emergence is shown to be highly Field-and Discipline-dependent.Practical implementations:Existing rankings of academic institutions have:(i)often been restricted to pre-selected institutions,clouding the potential discovery of scholarly activity in emerging institutions and countries;(ii)considered only broad areas of research,limiting the ability of university leadership to act on the assessments in a concrete manner,or in contrast;(iii)have considered only a narrow area of research for comparison,diminishing the broader applicability and impact of the assessment.In general,existing institutional rankings depend on which institutions are included in the ranking process,which areas of research are considered,the breadth(or granularity)of the research areas of interest,and the methodologies used to define and quantify research performance.In contrast,the methods presented here can provide important data over a broad range of granularity to allow responsible individuals to gauge the performance of any institution from the Overall(all Fields)level,to the level of the Specialty.The methods may also assist identification of the root causes of shifts in institution rankings,and how these shifts vary across hundreds of thousands of Fields,Disciplines,and Specialties of scholarly endeavor.Originality/value:This study provides the first ranking of all academic institutions worldwide over Fields,Disciplines,and Specialties based on a unique methodology that quantifies the productivity,impact,and quality of individual scholars.

Requirements ranking based on crowd-sourcing high-end product USs

Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.

基于BPR的计量业务过程时间模型研究

为解决计量技术机构周期巡检和日常送检设备业务量不平衡的问题,本文基于业务流程再造(Business Process Reengineering)管理思想,综合运用排队论和IDEFO过程控制图模型,根据各分流程之间的关联关系建立过程时间模型,并通过案例分析,计算计量业务各分流程及全流程时间,为计量检定计划的制定提供依据。

BPR管理理念在医学高校管理中的流程优化

高等教育发展水平是一个国家发展水平和发展潜力的重要标志,要实现中华民族的伟大复兴,高等教育发挥着重要的作用,当前我国高等教育的主要任务就是要加快建设世界一流专业和一流学科,高校的管理直接影响了其工作建设效率。文章根据自身工作岗位的管理实践经验,基于BPR管理理念对医学高校管理的流程优化进行了进一步探索,基于BPR管理理念可进一步提升整体工作效率,达到效率最大化。

DP-BPR:Destination prediction based on Bayesian personalized ranking

Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving experience.However,the relevant research is mainly based on driving trajectory of vehicles to predict the destinations,which is challenging to achieve the early destination prediction.To this end,we propose a model of early destination prediction,DP-BPR,to predict the destinations by users’travel time and locations.There are three challenges to accomplish the model:1)the extremely sparse historical data make it challenge to predict destinations directly from raw historical data;2)the destinations are related to not only departure points but also departure time so that both of them should be taken into consideration in prediction;3)how to learn destination preferences from historical data.To deal with these challenges,we map sparse high-dimensional data to a dense low-dimensional space through embedding learning using deep neural networks.We learn the embeddings not only for users but also for locations and time under the supervision of historical data,and then use Bayesian personalized ranking(BPR)to learn to rank destinations.Experimental results on the Zebra dataset show the effectiveness of DP-BPR.

基于LBPR-NET的融合时空特征的兴趣点推荐模型

传统的兴趣点推荐算法都是通过用户签到信息建模用户与兴趣点之间的偏好直接进行兴趣点的推荐及优化,针对传统方法存在高维稀疏、噪声、计算复杂度高和无法建模用户兴趣的动态变化等问题,提出基于LBPR-NET的融合时空特征的兴趣点推荐模型.首先引入时间特征建模用户兴趣的动态变化,通过观察用户不同时刻在兴趣点类别之间的转移关系,并采用List-BPR方法进行兴趣点类别建模,根据ListNet方法优化兴趣点类别排序列表,克服了高维稀疏问题,降低计算复杂度;其次,在类别内部通过启发式算法筛选出兴趣点的候选集,降低了噪声.实验结果表明,该模型能有效提高推荐质量和准确率.

Traffic Incident Management Performance Measures: Ranking Agencies on Roadway Clearance Time

This study develops a procedure to rank agencies based on their incident responses using roadway clearance times for crashes. This analysis is not intended to grade agencies but to assist in identifying agencies requiring more training or resources for incident management. Previous NCHRP reports discussed usage of different factors including incident severity, roadway characteristics, number of lanes involved and time of incident separately for estimating the performance. However, it does not tell us how to incorporate all the factors at the same time. Thus, this study aims to account for multiple factors to ensure fair comparisons. This study used 149,174 crashes from Iowa that occurred from 2018 to 2021. A Tobit regression model was used to find the effect of different variables on roadway clearance time. Variables that cannot be controlled directly by agencies such as crash severity, roadway type, weather conditions, lighting conditions, etc., were included in the analysis as it helps to reduce bias in the ranking procedure. Then clearance time of each crash is normalized into a base condition using the regression coefficients. The normalization makes the process more efficient as the effect of uncontrollable factors has already been mitigated. Finally, the agencies were ranked by their average normalized roadway clearance time. This ranking process allows agencies to track their performance of previous crashes, can be used in identifying low performing agencies that could use additional resources and training, and can be used to identify high performing agencies to recognize for their efforts and performance.

Modified Computational Ranking Model for Cloud Trust Factor Using Fuzzy Logic

Through the use of the internet and cloud computing,users may access their data as well as the programmes they have installed.It is now more challenging than ever before to choose which cloud service providers to take advantage of.When it comes to the dependability of the cloud infrastructure service,those who supply cloud services,as well as those who seek cloud services,have an equal responsibility to exercise utmost care.Because of this,further caution is required to ensure that the appropriate values are reached in light of the ever-increasing need for correct decision-making.The purpose of this study is to provide an updated computational ranking approach for decision-making in an environment with many criteria by using fuzzy logic in the context of a public cloud scenario.This improved computational ranking system is also sometimes referred to as the improvised VlseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)method.It gives users access to a trustworthy assortment of cloud services that fit their needs.The activity that is part of the suggested technique has been broken down into nine discrete parts for your convenience.To verify these stages,a numerical example has been evaluated for each of the six different scenarios,and the outcomes have been simulated.

Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous repercussions.An Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks.Although extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still challenging.Therefore,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm rate.Initially,to improve training data quality,various preprocessing techniques are utilized.The Adaptive Synthetic oversampling technique generates synthetic samples for minority classes.In the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHAP)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its rank.Then by simple plurality voting,the most optimal features are retrieved.Then the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the performance.The experiments were carried out on the original imbalanced data and balanced data.The outcomes demonstrate that the balanced data scenario knocked out the imbalanced data.Finally,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD),Canadian Institute for Cybersecurity(CIC-IDS 2017),and UNSW-NB15,datasets respectively.In addition,the suggested technique has been examined and contrasted with the state of art methods on three datasets.

Advanced DAG-Based Ranking(ADR)Protocol for Blockchain Scalability

In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention.However,current blockchain systems face the problems of limited throughput,poor scalability,and high latency.Due to the failure of consensus algorithms in managing nodes’identities,blockchain technology is considered inappropriate for many applications,e.g.,in IoT environments,because of poor scalability.This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking(ADR)protocol to improve blockchain scalability and throughput.The ADR protocol uses the directed acyclic graph ledger,where nodes are placed according to their ranking positions in the graph.It allows honest nodes to use theDirect Acyclic Graph(DAG)topology to write blocks and verify transactions instead of a chain of blocks.By using a three-step strategy,this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability.The first step involves the safe entry of nodes into the system by verifying their private and public keys.The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions.In the third step,a ranking algorithm is developed to separate the nodes created by attackers.After eliminating attacker nodes,the nodes are ranked according to their performance in the system,and true nodes are arranged in blocks in topological order.As a result,the ADR protocol is suitable for applications in the Internet of Things(IoT).We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes.Based on the simulation results,this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications(IOTA)and ByteBall.

Ranking of Web Pages in a Personalized Search

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.

The Effect of Coding Method on Cause-of-Death Rankings

Background: Cause-of-death rankings are often used for planning or evaluating health policy measures. In the European Union, some countries produce cause-of-death statistics by a manual coding of death certificates, while other countries use an automated coding system. The outcome of these two different methods in terms of the selected underlying cause of death for statistics may vary considerably. Therefore, this study explores the effect of coding method on the ranking of countries by major causes of death. Method: Age and sex standardized rates were extracted for 33 European (related) countries from the cause-of-death registry of the European Statistical Office (Eurostat). Wilcoxon’s rank sum test was applied to the ranking of countries by major causes of death. Results: Statistically significant differences due to coding method were identified for dementia, stroke and pneumonia. These differences could be explained by a different selection of dementia or pneumonia as underlying cause of death and by a different certification practice for stroke. Conclusion: Coding method should be taken into account when constructing or interpreting rankings of countries by cause of death.

New Ranking of Generalized Quadrilateral Shape Fuzzy Number Using Centroid Technique

The output of the fuzzy set is reduced by one for the defuzzification procedure.It is employed to provide a comprehensible outcome from a fuzzy inference process.This page provides further information about the defuzzifica-tion approach for quadrilateral fuzzy numbers,which may be used to convert them into discrete values.Defuzzification demonstrates how useful fuzzy ranking systems can be.Our major purpose is to develop a new ranking method for gen-eralized quadrilateral fuzzy numbers.The primary objective of the research is to provide a novel approach to the accurate evaluation of various kinds of fuzzy inte-gers.Fuzzy ranking properties are examined.Using the counterexamples of Lee and Chen demonstrates the fallacy of the ranking technique.So,a new approach has been developed for dealing with fuzzy risk analysis,risk management,indus-trial engineering and optimization,medicine,and artificial intelligence problems:the generalized quadrilateral form fuzzy number utilizing centroid methodology.As you can see,the aforementioned scenarios are all amenable to the solution pro-vided by the generalized quadrilateral shape fuzzy number utilizing centroid methodology.It’s laid out in a straightforward manner that’s easy to grasp for everyone.The rating method is explained in detail,along with numerical exam-ples to illustrate it.Last but not least,stability evaluations clarify why the Gener-alized quadrilateral shape fuzzy number obtained by the centroid methodology outperforms other ranking methods.

Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research

Education quality has undoubtedly become an important local and international benchmark for education,and an institute’s ranking is assessed based on the quality of education,research projects,theses,and dissertations,which has always been controversial.Hence,this research paper is influenced by the institutes ranking all over the world.The data of institutes are obtained through Google Scholar(GS),as input to investigate the United Kingdom’s Research Excellence Framework(UK-REF)process.For this purpose,the current research used a Bespoke Program to evaluate the institutes’ranking based on their source.The bespoke program requires changes to improve the results by addressing these methodological issues:Firstly,Redundant profiles,which increased their citation and rank to produce false results.Secondly,the exclusion of theses and dissertation documents to retrieve the actual publications to count for citations.Thirdly,the elimination of falsely owned articles from scholars’profiles.To accomplish this task,the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new evaluation.The data extracted from GS is processed into structured data,and afterward,it is utilized to generate statistical computations of citations’analysis that contribute to the ranking based on their citations.The research promoted the predictive approach of correlational research.Furthermore,experimental evaluation reported encouraging results in comparison to the previous modi-fication made by the proposed taxonomy.This paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm.

Multi-Criteria Decision-Making for Power Grid Construction Project Investment Ranking Based on the Prospect Theory Improved by Rewarding Good and Punishing Bad Linear Transformation

Using the improved prospect theory with the linear transformations of rewarding good and punishing bad(RGPBIT),a new investment ranking model for power grid construction projects(PGCPs)is proposed.Given the uncertainty of each index value under the market environment,fuzzy numbers are used to describe qualitative indicators and interval numbers are used to describe quantitative ones.Taking into account decision-maker’s subjective risk attitudes,a multi-criteria decision-making(MCDM)method based on improved prospect theory is proposed.First,the[−1,1]RGPBIT operator is proposed to normalize the original data,to obtain the best andworst schemes of PGCPs.Furthermore,the correlation coefficients between interval/fuzzy numbers and the best/worst schemes are defined and introduced to the prospect theory to improve its value function and loss function,and the positive and negative prospect value matrices of the project are obtained.Then,the optimization model with the maximum comprehensive prospect value is constructed,the optimal attribute weight is determined,and the PGCPs are ranked accordingly.Taking four PGCPs of the IEEERTS-79 node system as examples,an illustration of the feasibility and effectiveness of the proposed method is provided.

全氟辛酸对成骨细胞骨形成和破骨细胞骨吸收作用研究

考察全氟辛酸(PFOA)对成骨细胞骨形成和破骨细胞骨吸收的作用和机制,为探索PFOA暴露引起骨丢失提供科学依据。采用原代新生大鼠颅盖骨分离成骨细胞,考察1,10和100μmol/L PFOA对成骨细胞增殖,碱性磷酸酶(ALP)活性和骨结节形成的影响;采用巨噬细胞集落刺激因子(M-CSF)及核因子κB受体激活因子配体(RANKL)诱导大鼠骨髓单核细胞分化破骨细胞模型,考察1,10和100μmol/L PFOA对破骨细胞活力,抗酒石酸酸性磷酸酶(TRAP)活性和骨吸收陷窝形成指标的影响;采用Western blot考察成骨细胞护骨素(OPG)和RANKL蛋白表达。PFOA在1和10μmol/L时促进成骨细胞活力,但对ALP活性和骨结节形成无影响。100μmol/L PFOA可显著抑制细胞增殖,ALP活性和骨结节形成,同时抑制OPG/RANKL蛋白表达的比例。PFOA对破骨细胞活力无影响,10和100μmol/L PFOA可显著抑制促进TRAP活性和骨吸收陷窝形成面积。PFOA抑制成骨细胞骨形成作用可能通过下调Wnt通路,通过抑制OPG/RANKL/RANK通路,促进破骨细胞骨吸收作用。

Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions

In the conventional technique,in the evaluation of the severity index,clustering and loading suffer from more iteration leading to more com-putational delay.Hence this research article identifies,a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects.The polynomial load modelling or ZIP(constant impedances(Z),Constant Current(I)and Constant active power(P))is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security.The process of finding the severity of the line using a Hybrid Line Stability Ranking Index(HLSRI)is used for assisting the concepts of machine learning with J48 algorithm,infers the superior affected lines by adopting the IEEE standards in concern to be compensated in maintaining the power system stability.The simulation is performed in the WEKA environment and deals with the supervisor learning in order based on severity to ensure the safety of power system.The Unified Power Flow Controller(UPFC),facts devices for the purpose of compensating the losses by maintaining the voltage characteristics.The finite element analysis findings are compared with the existing procedures and numerical equations for authentications.