ON THE USE OF BUFFER INVENTORIES TO MINIMIZE COSTS WITH AN UNRELIABLE MACHINE

In this paper, we discuss the problem of quality control with an unreliable machine which produces defects at a rate of Λ₀, per unit when in-control and a rate of Lambda; ₁, when out-of-control (where Λ₁ Λ ₀). Every h time periods, we sample n units, count the number of defects, and (using a process based on a Shewart c-chart) test the hypothesis that the machine is in control by comparing the total number of defects to an upper control limit (UCL). More important, we introduce the concept that a buffer inventory which immediately follows the unreliable machine may reduce expected total costs. This buffer serves to delay the movement of items from the unreliable machine to the next stage of the production process. In this way, we can isolate and repair most defective items before they are embedded in a product downstream or sold to customers where repair is more costly. To search for the optimal control policy, we find bounds for n, h, and UCL; given values for these variables, we show how the optimal buffer size can be determined directly. Numerical results illustrate the magnitude of potential savings.     

Alternating active-phase algorithm for multimaterial topology optimization problems: a 115-line MATLAB implementation

Structural and Multidisciplinary Optimization - A new algorithm for the solution of multimaterial topology optimization problems is introduced in the present study. The presented method is based on...     

Applying gravitational search algorithm in the QoS-based Web service selection problem

With the growing use of service-oriented architecture for designing next generation software systems,the service composition problem and its execution complexity have become even more important in resp...     

单翼迷宫式滴灌带抗热氧老化性能的影响研究

单翼迷宫式滴灌带产品新国家标准中增加了氧化诱导时间（OIT）,这对产品的抗热氧老化性能提出了要求。本文从滴灌带配方出发,讨论了产品原料与其OIT的料效关系,结果表明,防老化母料的加入比例对滴灌带的OIT影响最大,增加其比例可增大滴灌带的OIT;高密度聚乙烯（PE?HD）的加入比例对滴灌带的OIT影响大于线性低密度聚乙烯（PE?LLD）和低密度聚乙烯（PE?LD）的加入比例,增加PE?HD比例、减小PE?LLD和PE?LD比例可增大滴灌带的OIT;PE?LD的加入比例对滴灌带OIT的影响最小,在配方设计时应综合使用比例合理的PE?HD、PE?LLD、PE?LD以满足滴灌带良好的加工和其他性能要求;防老化母料中稳定剂比例不应随成本而减少,稳定剂比例增大时,滴灌带的OIT增大;通过对滴灌带的熔体质量流动速率（MFR）、炭黑含量及OIT指标测试,得出滴灌带MFR和炭黑含量与其OIT无明显的相互变化关系。     

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.     

A Hybrid Reconfigurability Structure and Improved Gain Characteristics for a Novel 5G Monopole Antenna for Future Mobile Communication

Wireless Personal Communications - Mobile edge computation (MEC) is a potential technology to reduce the energy consumption and task execution delay for tackling computation-intensive tasks on...