To the editor:A wide range of affective disorders affects people of all ages globally and contributes significantly to the global disease burden.1 In China,a nationwide survey found a 3.21% prevalence of affective dis...To the editor:A wide range of affective disorders affects people of all ages globally and contributes significantly to the global disease burden.1 In China,a nationwide survey found a 3.21% prevalence of affective disorders in children and adolescents,with major depressive disorder(MDD)at 2.00%and bipolar disorder at 0.86%.展开更多
Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resource...Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely MATD3-CO, for iterative optimization. Specifically, we first decouple SOM into two sub-problems, i.e., joint sub-channel allocation and task offloading sub-problem, and computation resource allocation sub-problem. Then, we propose MATD3 to optimize the sub-channel allocation and task offloading ratio, and employ the convex optimization to allocate the computation resource with a closed-form expression derived by the Karush-Kuhn-Tucker (KKT) conditions. The solution is obtained by iteratively solving these two sub-problems. The experimental results indicate that the MATD3-CO scheme, when compared to the benchmark schemes, significantly decreases system overhead with respect to both delay and energy consumption.展开更多
随着医院信息系统(Hospital Information System,HIS)的规模化发展,医疗数据呈现高维化、非线性增长趋势。传统数据挖掘方法在特征冗余处理和模型参数优化上存在局限性,导致挖掘精度不足,为提高HIS挖掘的精度,提出一种基于递归特征消除...随着医院信息系统(Hospital Information System,HIS)的规模化发展,医疗数据呈现高维化、非线性增长趋势。传统数据挖掘方法在特征冗余处理和模型参数优化上存在局限性,导致挖掘精度不足,为提高HIS挖掘的精度,提出一种基于递归特征消除-模拟退火-支持向量机(Recursive Feature Elimination-Simulated Annealing-Support Vector Machine,RFE-SA-SVM)的医疗数据挖掘算法。首先,针对医疗数据的高维特性,运用RFE对医疗数据进行特征量筛选,降低医疗数据的特征维度;然后,运用模拟SA算法优化SVM模型参数,建立基于筛选出的特征向量SA-SVM医疗数据挖掘模型。研究结果表明:与SA-SVM、和RFE-SVM相比,RFE-SA-SVM模型性能更优,并且灵敏度达0.902,说明RFE-SA-SVM算法能够有效提高医疗数据挖掘的准确率。展开更多
基金the Tianjin Health Research Project(Grant No.TJWJ2023MS038)Tianjin Education Commission Research Project(Grant No.2023KJ044)S&T Program of Hebei(SG2021189)。
文摘To the editor:A wide range of affective disorders affects people of all ages globally and contributes significantly to the global disease burden.1 In China,a nationwide survey found a 3.21% prevalence of affective disorders in children and adolescents,with major depressive disorder(MDD)at 2.00%and bipolar disorder at 0.86%.
基金supported by the National Natural Science Foundation of China under Grants 92267108,62173322 and 61821005the Science and Technology Program of Liaoning Province under Grants 2023JH3/10200004 and 2022JH25/10100005.
文摘Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely MATD3-CO, for iterative optimization. Specifically, we first decouple SOM into two sub-problems, i.e., joint sub-channel allocation and task offloading sub-problem, and computation resource allocation sub-problem. Then, we propose MATD3 to optimize the sub-channel allocation and task offloading ratio, and employ the convex optimization to allocate the computation resource with a closed-form expression derived by the Karush-Kuhn-Tucker (KKT) conditions. The solution is obtained by iteratively solving these two sub-problems. The experimental results indicate that the MATD3-CO scheme, when compared to the benchmark schemes, significantly decreases system overhead with respect to both delay and energy consumption.
