Increasingly fierce business competition requires companies to be able to meet customer desires on time, in the right amount and in the right quality. FMCG XXX Company is a skin care product ...manufacturing company that focuses on production activities. Therefore, the company transfers product distribution activities to customers (distributors) to Logistics Service Providers (LSP). FMCG XXX hold consumer’s satisfaction in the highest regard. Along with the increasing number of activities being diverted, FMCG XXX companies aim to evaluate LSP performance through appropriate indicators. The indicators evaluated in this study adopt dimensions in Physical Distribution Service Quality (PDSQ) which consist of three dimensions, namely timeliness, availability and condition. Based on the results of the study, 13 indicators were obtained that FMCG XXX Company used to evaluate LSP performance. In general, around 69% of LSP's actual performance has been able to meet the targets set by the company, where two indicators are able to exceed the target and seven indicators are in accordance with the target. However, the company still has to focus on four indicators that have negative deviations because the actual performance value is less than the target set by the company. Negative indicators come from the availability and condition dimensions. The company coordinates with LSP to design improvement projects in order to improve actual performance according to the company's targets.
The performance of traditional face recognition systems is sharply reduced when encountered with a low-resolution (LR) probe face image. To obtain much more detailed facial features, some face ...super-resolution (SR) methods have been proposed in the past decade. The basic idea of a face image SR is to generate a high-resolution (HR) face image from an LR one with the help of a set of training examples. It aims at transcending the limitations of optical imaging systems. In this paper, we regard face image SR as an image interpolation problem for domain-specific images. A missing intensity interpolation method based on smooth regression with a local structure prior (LSP), named SRLSP for short, is presented. In order to interpolate the missing intensities in a target HR image, we assume that face image patches at the same position share similar local structures, and use smooth regression to learn the relationship between LR pixels and missing HR pixels of one position patch. Performance comparison with the state-of-the-art SR algorithms on two public face databases and some real-world images shows the effectiveness of the proposed method for a face image SR in general. In addition, we conduct a face recognition experiment on the extended Yale-B face database based on the super-resolved HR faces. Experimental results clearly validate the advantages of our proposed SR method over the state-of-the-art SR methods in face recognition application.
A 316L alloy finds its application in diverse industries most profoundly used for marine application. Effect of corrosion becomes an inevitable negative attribute in the marine industry, ...particularly, the development of oxide layer formation restricted by the NaCl which creates a broken surface, developing into an inevitable failure of the surface and thereby the machinery. This study aims to develop a processing technique, namely the low pulse energy, laser shock peening process to slow down and inhibit the growth of the oxide layer. 316L alloys are primarily subjected to a low pulse laser shock peening process which is then treated in 3.5% of NaCl solution to analyze the corrosion behavior. The treated and untreated samples are subjected to various characterization techniques such as SEM to identify microstructural variation, XRD to evaluate the compressive residual stress induced, potentiostat galvanic device to understand the corrosion resistance of treated 316L. Promising results were observed through the polarization curve, wherein no 40% lower corrosion rate was obtained for the low pulse energy LSP of 400 mJ.
LSP(n), the largest small polygon with n vertices, is a polygon with a unit diameter that has a maximal of area A(n). It is known that for all odd values n≥3, LSP(n) is a regular n-polygon; however, ...this statement is not valid even for values of n. Finding the polygon LSP(n) and A(n) for even values n≥6 has been a long-standing challenge. In this work, we developed high-precision numerical solution estimates of A(n) for even values n≥4, using the Mathematica model development environment and the IPOPT local nonlinear optimization solver engine. First, we present a revised (tightened) LSP model that greatly assists in the efficient numerical solution of the model-class considered. This is followed by results for an illustrative sequence of even values of n, up to n≤1000. Most of the earlier research addressed special cases up to n≤20, while others obtained numerical optimization results for a range of values from 6≤n≤100. The results obtained were used to provide regression model-based estimates of the optimal area sequence {A(n)}, for even values n of interest, thereby essentially solving the LSP model-class numerically, with demonstrably high precision.
Although deficits in cognitive control and impaired creative associations have been displayed in participants with high trait anxiety, less is known about the impact of trait anxiety on externally ...driven creative discovery, in which the demands of cognitive control are situationally dependent. To address these issues, participants were asked to discover the usable tools to solve daily life problems from seemingly unrelated things, when they have or not have situational pressure for creation. The results showed that the negative impact of trait anxiety on the success rate of creative discovery appeared only under pressure conditions. Compared to no pressure conditions, greater conflict monitoring and resolution-related N2, N400 and LSP components were elicited in pressure conditions, which demonstrated greater demands of cognitive control in creative discovery. More importantly, trait anxiety was negatively correlated with the mean amplitude difference of N2 and N400 but not LSP between pressure and no pressure conditions, which suggested that, as the pressure for creation appeared, participants with high trait anxiety experienced greater conflicts but did not exert much more cognitive control to resolve conflicts and thus performed worse. The findings reveal the influences of trait anxiety on creative discovery from perspective of cognitive processing.
This reprint was established after the 9th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas ...covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.
To perform landslide susceptibility prediction (LSP), it is important to select appropriate mapping unit and landslide-related conditioning factors. The efficient and automatic multi-scale ...segmentation (MSS) method proposed by the authors promotes the application of slope units. However, LSP modeling based on these slope units has not been performed. Moreover, the heterogeneity of conditioning factors in slope units is neglected, leading to incomplete input variables of LSP modeling. In this study, the slope units extracted by the MSS method are used to construct LSP modeling, and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean, standard deviation and range. Thus, slope units-based machine learning models considering internal variations of conditioning factors (variant slope-machine learning) are proposed. The Chongyi County is selected as the case study and is divided into 53,055 slope units. Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations. Random forest (RF) and multi-layer perceptron (MLP) machine learning models are used to construct variant Slope-RF and Slope-MLP models. Meanwhile, the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors, and conventional grid units-based machine learning (Grid-RF and MLP) models are built for comparisons through the LSP performance assessments. Results show that the variant Slope-machine learning models have higher LSP performances than Slope-machine learning models; LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models. It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling, and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides. The research results have important reference significance for land use and landslide prevention.
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➢Slope units extracted by multi-scale segmentation method are appropriate for LSP;➢Heterogeneity of conditioning factors are reflected by mean, range and standard deviation values;➢Variant Slope-machine learning models have higher LSP accuracy than Slope-machine learning models;➢Slope-machine learning models have stronger engineering practicability than Grid-machine learning models.
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