目的 分析X-pert联合核苷酸结合寡聚化结构域蛋白2(NOD2)、自噬相关蛋白16样蛋白1(ATG16L1)在活动性肺结核患者疾病转归评估中的应用价值。方法 前瞻性选取2023年4月至2024年4月池州市人民医院收治的110例活动性肺结核患者为研究对象。...目的 分析X-pert联合核苷酸结合寡聚化结构域蛋白2(NOD2)、自噬相关蛋白16样蛋白1(ATG16L1)在活动性肺结核患者疾病转归评估中的应用价值。方法 前瞻性选取2023年4月至2024年4月池州市人民医院收治的110例活动性肺结核患者为研究对象。所有患者均行抗结核治疗、疾病转归评估,将转归患者60例作为转归组,未转归患者50例作为未转归组。收集两组患者的临床资料(年龄、体重指数、性别、吸烟史、贫血、累及肺野数、肺部空洞病变、利福平耐药)。治疗前行X-pert、NOD2、ATG16L1检测,比较两组X-pert阳性率及NOD2、ATG16L1表达水平。采用多因素Logistics回归分析分析活动性肺结核患者疾病转归的影响因素,采用受试者操作特征(ROC)曲线分析X-pert、NOD2、ATG16L1对活动性肺结核患者疾病转归的预测价值。结果 两组体重指数、吸烟史、贫血、累及肺野数、利福平耐药比较,差异均无统计学意义(P>0.05);与转归组相比,未转归组患者年龄较大[(56.15±19.34)vs.(63.18±12.84)岁],男性(71.67 vs. 90.00)%、肺部空洞病变(11.67 vs. 32.00)%比例较高,差异均有统计学意义(P<0.05)。与转归组相比,未转归组患者X-pert阳性率(75.00 vs. 90.00)%、NOD2[(164.31±15.55)vs.(199.29±24.63)ng/L]、ATG16L1[(8.95±1.1.74)vs.(12.15±2.26)ng/L]表达水平均较高,差异均有统计学意义(P<0.05)。多因素Logistics回归分析结果显示,年龄、性别、肺部空洞病变、X-pert、NOD2、ATG16L1为活动性肺结核患者疾病转归的危险因素(P<0.05)。与X-pert、NOD2、ATG16L1单项诊断相比,X-pert、NOD2、ATG16L1联合检测对活动性肺结核患者疾病转归的预测价值较高(P<0.05)。结论 疾病未转归活动性肺结核患者X-pert阳性率、NOD2、ATG16L1表达水平均高于转归患者,X-pert、NOD2、ATG16L1为活动性肺结核患者疾病转归的危险因素,X-pert联合NOD2、ATG16L1对活动性肺结核患者疾病转归的预测价值较高,为活动性肺结核患者疾病转归评估提供了有效依据。展开更多
The idea of linear Diophantine fuzzy set(LDFS)theory with its control parameters is a strong model for machine learning and optimization under uncertainty.The activity times in the critical path method(CPM)representat...The idea of linear Diophantine fuzzy set(LDFS)theory with its control parameters is a strong model for machine learning and optimization under uncertainty.The activity times in the critical path method(CPM)representation procedures approach are initially static,but in the Project Evaluation and Review Technique(PERT)approach,they are probabilistic.This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy(LDF)environment.The LDF expected task time,LDF variance,LDF critical path,and LDF total expected time for determining the project network are all computed using LDF numbers as the time of each activity in the project network.The primary premise of the LDF-PERT approach is to address ambiguities in project network activity timesmore simply than other approaches such as conventional PERT,Fuzzy PERT,and so on.The LDF-PERT is an efficient approach to analyzing symmetries in fuzzy control systems to seek an optimal decision.We also present a new approach for locating LDF-CPM in a project network with uncertain and erroneous activity timings.When the available resources and activity times are imprecise and unpredictable,this strategy can help decision-makers make better judgments in a project.A comparison analysis of the proposed technique with the existing techniques has also been discussed.The suggested techniques are demonstrated with two suitable numerical examples.展开更多
As an essential category of public event management and control,sentiment analysis of online public opinion text plays a vital role in public opinion early warning,network rumor management,and netizens’person-ality p...As an essential category of public event management and control,sentiment analysis of online public opinion text plays a vital role in public opinion early warning,network rumor management,and netizens’person-ality portraits under massive public opinion data.The traditional sentiment analysis model is not sensitive to the location information of words,it is difficult to solve the problem of polysemy,and the learning representation ability of long and short sentences is very different,which leads to the low accuracy of sentiment classification.This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model of the disordered language model,bidirectional long-term and short-term memory network and attention mechanism.The model first uses the PERT model pre-trained from the lexical location information of a large amount of corpus to process the text data and obtain the dynamic feature representation of the text.Then the semantic features are input into BiLSTM to learn context sequence information and enhance the model’s ability to represent long sequences.Finally,the attention mechanism is used to focus on the words that contribute more to the overall emotional tendency to make up for the lack of short text representation ability of the traditional model,and then the classification results are output through the fully connected network.The experimental results show that the classification accuracy of the model on NLPCC14 and weibo_senti_100k public data sets reach 88.56%and 97.05%,respectively,and the accuracy reaches 95.95%on the data set MDC22 composed of Meituan,Dianping and Ctrip comment.It proves that the model has a good effect on sentiment analysis of online public opinion texts on different platforms.The experimental results on different datasets verify the model’s effectiveness in applying sentiment analysis of texts.At the same time,the model has a strong generalization ability and can achieve good results for sentiment analysis of datasets in different fields.展开更多
PERT网络计划技术及Monte Carlo Simulation(MCS)求解PERT网络计划方法在工程项目的进度计划与控制中虽已广泛应用,但仍存在着不足。本文基于PERT网络分布的假设,阐述了常规三时估计方法的不足,并提出了相应解决对策,并采用限定概率三...PERT网络计划技术及Monte Carlo Simulation(MCS)求解PERT网络计划方法在工程项目的进度计划与控制中虽已广泛应用,但仍存在着不足。本文基于PERT网络分布的假设,阐述了常规三时估计方法的不足,并提出了相应解决对策,并采用限定概率三时估计法及拟合方差最小模型,分别对常规三时估计方法及常规!分布函数参数确定方法进行了改进。实例证实,限定概率三时估计法能统一网络活动时间估计标准,提高估计精度;拟合方差最小模型能提高分布函数确定精度。展开更多
文摘目的 分析X-pert联合核苷酸结合寡聚化结构域蛋白2(NOD2)、自噬相关蛋白16样蛋白1(ATG16L1)在活动性肺结核患者疾病转归评估中的应用价值。方法 前瞻性选取2023年4月至2024年4月池州市人民医院收治的110例活动性肺结核患者为研究对象。所有患者均行抗结核治疗、疾病转归评估,将转归患者60例作为转归组,未转归患者50例作为未转归组。收集两组患者的临床资料(年龄、体重指数、性别、吸烟史、贫血、累及肺野数、肺部空洞病变、利福平耐药)。治疗前行X-pert、NOD2、ATG16L1检测,比较两组X-pert阳性率及NOD2、ATG16L1表达水平。采用多因素Logistics回归分析分析活动性肺结核患者疾病转归的影响因素,采用受试者操作特征(ROC)曲线分析X-pert、NOD2、ATG16L1对活动性肺结核患者疾病转归的预测价值。结果 两组体重指数、吸烟史、贫血、累及肺野数、利福平耐药比较,差异均无统计学意义(P>0.05);与转归组相比,未转归组患者年龄较大[(56.15±19.34)vs.(63.18±12.84)岁],男性(71.67 vs. 90.00)%、肺部空洞病变(11.67 vs. 32.00)%比例较高,差异均有统计学意义(P<0.05)。与转归组相比,未转归组患者X-pert阳性率(75.00 vs. 90.00)%、NOD2[(164.31±15.55)vs.(199.29±24.63)ng/L]、ATG16L1[(8.95±1.1.74)vs.(12.15±2.26)ng/L]表达水平均较高,差异均有统计学意义(P<0.05)。多因素Logistics回归分析结果显示,年龄、性别、肺部空洞病变、X-pert、NOD2、ATG16L1为活动性肺结核患者疾病转归的危险因素(P<0.05)。与X-pert、NOD2、ATG16L1单项诊断相比,X-pert、NOD2、ATG16L1联合检测对活动性肺结核患者疾病转归的预测价值较高(P<0.05)。结论 疾病未转归活动性肺结核患者X-pert阳性率、NOD2、ATG16L1表达水平均高于转归患者,X-pert、NOD2、ATG16L1为活动性肺结核患者疾病转归的危险因素,X-pert联合NOD2、ATG16L1对活动性肺结核患者疾病转归的预测价值较高,为活动性肺结核患者疾病转归评估提供了有效依据。
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.GRANT3862].
文摘The idea of linear Diophantine fuzzy set(LDFS)theory with its control parameters is a strong model for machine learning and optimization under uncertainty.The activity times in the critical path method(CPM)representation procedures approach are initially static,but in the Project Evaluation and Review Technique(PERT)approach,they are probabilistic.This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy(LDF)environment.The LDF expected task time,LDF variance,LDF critical path,and LDF total expected time for determining the project network are all computed using LDF numbers as the time of each activity in the project network.The primary premise of the LDF-PERT approach is to address ambiguities in project network activity timesmore simply than other approaches such as conventional PERT,Fuzzy PERT,and so on.The LDF-PERT is an efficient approach to analyzing symmetries in fuzzy control systems to seek an optimal decision.We also present a new approach for locating LDF-CPM in a project network with uncertain and erroneous activity timings.When the available resources and activity times are imprecise and unpredictable,this strategy can help decision-makers make better judgments in a project.A comparison analysis of the proposed technique with the existing techniques has also been discussed.The suggested techniques are demonstrated with two suitable numerical examples.
基金supported by the Chongqing Natural Science Foundation of China (Grant No.CSTB2022NSCQ-MSX1417)the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No.KJZD-K202200513)Chongqing Normal University Fund (Grant No.22XLB003).
文摘As an essential category of public event management and control,sentiment analysis of online public opinion text plays a vital role in public opinion early warning,network rumor management,and netizens’person-ality portraits under massive public opinion data.The traditional sentiment analysis model is not sensitive to the location information of words,it is difficult to solve the problem of polysemy,and the learning representation ability of long and short sentences is very different,which leads to the low accuracy of sentiment classification.This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model of the disordered language model,bidirectional long-term and short-term memory network and attention mechanism.The model first uses the PERT model pre-trained from the lexical location information of a large amount of corpus to process the text data and obtain the dynamic feature representation of the text.Then the semantic features are input into BiLSTM to learn context sequence information and enhance the model’s ability to represent long sequences.Finally,the attention mechanism is used to focus on the words that contribute more to the overall emotional tendency to make up for the lack of short text representation ability of the traditional model,and then the classification results are output through the fully connected network.The experimental results show that the classification accuracy of the model on NLPCC14 and weibo_senti_100k public data sets reach 88.56%and 97.05%,respectively,and the accuracy reaches 95.95%on the data set MDC22 composed of Meituan,Dianping and Ctrip comment.It proves that the model has a good effect on sentiment analysis of online public opinion texts on different platforms.The experimental results on different datasets verify the model’s effectiveness in applying sentiment analysis of texts.At the same time,the model has a strong generalization ability and can achieve good results for sentiment analysis of datasets in different fields.
文摘PERT网络计划技术及Monte Carlo Simulation(MCS)求解PERT网络计划方法在工程项目的进度计划与控制中虽已广泛应用,但仍存在着不足。本文基于PERT网络分布的假设,阐述了常规三时估计方法的不足,并提出了相应解决对策,并采用限定概率三时估计法及拟合方差最小模型,分别对常规三时估计方法及常规!分布函数参数确定方法进行了改进。实例证实,限定概率三时估计法能统一网络活动时间估计标准,提高估计精度;拟合方差最小模型能提高分布函数确定精度。
