Microorganisms play an indispensable role in the fermentation of cigar tobacco such as improvement of flavor,aroma and quality.However,little is known about additional microorganism impact on shift of the color of cig...Microorganisms play an indispensable role in the fermentation of cigar tobacco such as improvement of flavor,aroma and quality.However,little is known about additional microorganism impact on shift of the color of cigar wrappers.Here,three bacterial strains that could alter the wrapper’s color were isolated and screened from cigar tobacco leaves(CTLs)at different stages of fermentation.The three strains were identified to be Sphingomonas parapaucimobilis DS1,Kosakonia cowanii DS101,and Comamonas thiooxydans DS309 based on the genome sequences.The overall color difference(ΔE)of CTLs was significantly deepened with the addition of those three strains under upscaled stack fermentation,respectively(p≤0.05),showing the same fundamental quality of cigar wrapper.The ascorbic acid oxidase activity was significantly decreased and cigarillo polyphenol oxidase activity was significantly increased added with the three bacteria compared to the control(p≤0.05).Moreover,the exogenous strains substantially altered the community structure of the cigar wrapper.In particular,the abundance of dominant Staphylococcus nepalensis significantly increased under addition of strain DS101 and DS309 except for strain DS1 as dominant species.In addition,non-targeted metabolomics analyses showed that the exogenous microbial addition treatments demonstrated substantial variations in a variety of metabolites,including a reduced expression in the pathways related to the manufacture of polyphenols and unsaturated fatty acids(p≤0.05).In conclusion,addition of bacteria DS1,DS101,and DS309 enhanced oxidation of polyphenols to brown quinones and oxidation of unsaturated fatty acids,resulting in membrane lipid peroxidation and then deepened the color of cigar wrappers.It provides a novel strategy to improve the color of cigar wrappers by utilizing additional bacteria in cigar fermentation.展开更多
基于IP(intellectual property)核的系统级芯片的测试已成为SoC(system on chip)发展中的瓶颈,提出了一种采用BBO(biogeography based optimization)算法的Wrapper扫描链设计方法,使得Wrapper扫描链均衡化,从而达到IP核测试时间最小化...基于IP(intellectual property)核的系统级芯片的测试已成为SoC(system on chip)发展中的瓶颈,提出了一种采用BBO(biogeography based optimization)算法的Wrapper扫描链设计方法,使得Wrapper扫描链均衡化,从而达到IP核测试时间最小化的目的。本算法基于群体智能,通过实施迁徙操作和变异操作,实现Wrapper扫描链均衡化设计。本文以ITC'02 Test bench-marks中的典型IP核为实验对象,实验结果表明本算法相比BFD(best fit decrease)等算法,能够进一步缩短Wrapper扫描链,从而缩短IP核测试时间。展开更多
Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may preven...Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。展开更多
测试问题已成为SoC发展过程中的瓶颈,提出一种新的Wrapper扫描链平衡算法以期缩短IP核测试时间。算法首先计算Wrapper扫描链长度平均值,再结合特定的余量值,计算得到一个取值区间,记该区间为平均值余量;然后将IP核的内部扫描链按其长度...测试问题已成为SoC发展过程中的瓶颈,提出一种新的Wrapper扫描链平衡算法以期缩短IP核测试时间。算法首先计算Wrapper扫描链长度平均值,再结合特定的余量值,计算得到一个取值区间,记该区间为平均值余量;然后将IP核的内部扫描链按其长度降序排列,每次均将最长的内部扫描链添加到某条Wrapper扫描链上,直到该Wrapper扫描链长度在平均值余量所指定的区间内为止。以ITC'02 SoC Test Benchmarks内的所有测试集为对象完成的实验证明本算法能极其有效的通过扫描链平衡设计缩短IP核测试时间。展开更多
One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrom...One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.展开更多
An asynchronous wrapper with novel handshake circuits for data communication in globally asynchronous locally synchronous (GALS) systems is proposed. The handshake circuits include two communication ports and a loca...An asynchronous wrapper with novel handshake circuits for data communication in globally asynchronous locally synchronous (GALS) systems is proposed. The handshake circuits include two communication ports and a local clock generator. Two approaches for the implementation of communication ports are presented, one with pure standard cells and the others with Mttller-C elements. The detailed design methodology for GALS systems is given and the circuits are validated with VHDL and circuits simulation in standard CMOS technology.展开更多
提出用于均衡Wrapper扫描链的交换优化算法以及用于测试调度的局部最优算法,这两种算法依据测试总线空闲率(IBPTB)指标,可从IP层和系统顶层对系统芯片(SOC)测试时间实现联合优化,进而使SOC的测试时间大大降低.为了验证两种算法及其联合...提出用于均衡Wrapper扫描链的交换优化算法以及用于测试调度的局部最优算法,这两种算法依据测试总线空闲率(IBPTB)指标,可从IP层和系统顶层对系统芯片(SOC)测试时间实现联合优化,进而使SOC的测试时间大大降低.为了验证两种算法及其联合优化性能的有效性和可靠性,对基于ITC’02国际SOC基准电路进行了相关的验证试验.针对p93791基准电路中core6 IP核,交换优化算法能得到比经典BFD(best fit decreasing)算法更均衡的Wrapper扫描链,在最佳情况下最长Wrapper扫描链长度减少2.6%;针对d695基准电路,局部最优算法根据IP核的IBPTB指标,可使相应SOC的测试时间在最优时比经典整数线性规划(ILP)算法减少12.7%.展开更多
序贯三支决策是近年来发展起来的一种新兴粒计算模型,由于其在处理代价敏感问题上的明显优势,已被广泛的应用于诸多领域.为了降低传统静态分类器的分类成本,本文将序贯三支决策的思想引入分类过程中,利用"三分而治"的动态分...序贯三支决策是近年来发展起来的一种新兴粒计算模型,由于其在处理代价敏感问题上的明显优势,已被广泛的应用于诸多领域.为了降低传统静态分类器的分类成本,本文将序贯三支决策的思想引入分类过程中,利用"三分而治"的动态分类策略和多粒度的静态分类器对样本进行差异化处理,进一步考虑粒化过程中虑冗余属性和属性添加顺序对分类结果的影响,通过引入Wrapper特征选择框架对属性进行选择和排序,提出了Wrapper特征选择下的序贯三支分类方法(Wrapper with Sequential three-way classifier,WS3WC).最后,以两种经典分类器逻辑回归(LOG)和支持向量机(SVM)为例,对WS3WC进行实验验证.实验结果表明,WS3WC不但保持了良好的分类质量,而且能够大幅降低分类成本.展开更多
基金supported by the Foundation of Enshi Branch,Hubei Tobacco Company(2023ES3CGJSXJ2B005).
摘要Microorganisms play an indispensable role in the fermentation of cigar tobacco such as improvement of flavor,aroma and quality.However,little is known about additional microorganism impact on shift of the color of cigar wrappers.Here,three bacterial strains that could alter the wrapper’s color were isolated and screened from cigar tobacco leaves(CTLs)at different stages of fermentation.The three strains were identified to be Sphingomonas parapaucimobilis DS1,Kosakonia cowanii DS101,and Comamonas thiooxydans DS309 based on the genome sequences.The overall color difference(ΔE)of CTLs was significantly deepened with the addition of those three strains under upscaled stack fermentation,respectively(p≤0.05),showing the same fundamental quality of cigar wrapper.The ascorbic acid oxidase activity was significantly decreased and cigarillo polyphenol oxidase activity was significantly increased added with the three bacteria compared to the control(p≤0.05).Moreover,the exogenous strains substantially altered the community structure of the cigar wrapper.In particular,the abundance of dominant Staphylococcus nepalensis significantly increased under addition of strain DS101 and DS309 except for strain DS1 as dominant species.In addition,non-targeted metabolomics analyses showed that the exogenous microbial addition treatments demonstrated substantial variations in a variety of metabolites,including a reduced expression in the pathways related to the manufacture of polyphenols and unsaturated fatty acids(p≤0.05).In conclusion,addition of bacteria DS1,DS101,and DS309 enhanced oxidation of polyphenols to brown quinones and oxidation of unsaturated fatty acids,resulting in membrane lipid peroxidation and then deepened the color of cigar wrappers.It provides a novel strategy to improve the color of cigar wrappers by utilizing additional bacteria in cigar fermentation.
摘要基于IP(intellectual property)核的系统级芯片的测试已成为SoC(system on chip)发展中的瓶颈,提出了一种采用BBO(biogeography based optimization)算法的Wrapper扫描链设计方法,使得Wrapper扫描链均衡化,从而达到IP核测试时间最小化的目的。本算法基于群体智能,通过实施迁徙操作和变异操作,实现Wrapper扫描链均衡化设计。本文以ITC'02 Test bench-marks中的典型IP核为实验对象,实验结果表明本算法相比BFD(best fit decrease)等算法,能够进一步缩短Wrapper扫描链,从而缩短IP核测试时间。
摘要Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。
摘要测试问题已成为SoC发展过程中的瓶颈,提出一种新的Wrapper扫描链平衡算法以期缩短IP核测试时间。算法首先计算Wrapper扫描链长度平均值,再结合特定的余量值,计算得到一个取值区间,记该区间为平均值余量;然后将IP核的内部扫描链按其长度降序排列,每次均将最长的内部扫描链添加到某条Wrapper扫描链上,直到该Wrapper扫描链长度在平均值余量所指定的区间内为止。以ITC'02 SoC Test Benchmarks内的所有测试集为对象完成的实验证明本算法能极其有效的通过扫描链平衡设计缩短IP核测试时间。
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through Project Number WE-44-0033.
摘要One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.
摘要An asynchronous wrapper with novel handshake circuits for data communication in globally asynchronous locally synchronous (GALS) systems is proposed. The handshake circuits include two communication ports and a local clock generator. Two approaches for the implementation of communication ports are presented, one with pure standard cells and the others with Mttller-C elements. The detailed design methodology for GALS systems is given and the circuits are validated with VHDL and circuits simulation in standard CMOS technology.
摘要提出用于均衡Wrapper扫描链的交换优化算法以及用于测试调度的局部最优算法,这两种算法依据测试总线空闲率(IBPTB)指标,可从IP层和系统顶层对系统芯片(SOC)测试时间实现联合优化,进而使SOC的测试时间大大降低.为了验证两种算法及其联合优化性能的有效性和可靠性,对基于ITC’02国际SOC基准电路进行了相关的验证试验.针对p93791基准电路中core6 IP核,交换优化算法能得到比经典BFD(best fit decreasing)算法更均衡的Wrapper扫描链,在最佳情况下最长Wrapper扫描链长度减少2.6%;针对d695基准电路,局部最优算法根据IP核的IBPTB指标,可使相应SOC的测试时间在最优时比经典整数线性规划(ILP)算法减少12.7%.
摘要序贯三支决策是近年来发展起来的一种新兴粒计算模型,由于其在处理代价敏感问题上的明显优势,已被广泛的应用于诸多领域.为了降低传统静态分类器的分类成本,本文将序贯三支决策的思想引入分类过程中,利用"三分而治"的动态分类策略和多粒度的静态分类器对样本进行差异化处理,进一步考虑粒化过程中虑冗余属性和属性添加顺序对分类结果的影响,通过引入Wrapper特征选择框架对属性进行选择和排序,提出了Wrapper特征选择下的序贯三支分类方法(Wrapper with Sequential three-way classifier,WS3WC).最后,以两种经典分类器逻辑回归(LOG)和支持向量机(SVM)为例,对WS3WC进行实验验证.实验结果表明,WS3WC不但保持了良好的分类质量,而且能够大幅降低分类成本.