Logistics Sourcing Strategies in Supply Chain Design

A company's logistics sourcing strategy determines whether it structures and organizeslogistics within the company or company group or integrates logistics upstream and downstreamin the supply chain. First, three different types of logistics sourcing strategies in supply chaindesign are described and the theoretical background for the development of these strategies,including both transaction cost theory and network theory, is analyzed. Two special casesabout logistics sourcing strategy decision-making in China's electric household appliance (EHA)industry are discussed, based on the above theoretical analysis. Then, the factors that drive theselection of a company's logistics sourcing strategy are analyzed. These factors include marketfactors, external logistics service provision factors and a company's internal factors. Chinesefeatures in logistics sourcing strategies are summarized based on the above case studies. Finally,some management insights are discussed

Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning. Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework. In this paper, we explore the clustering-based MLC problem. Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy. In this study, we consider feature dependence and feature interaction simultaneously, and we propose a multilabel feature selection algorithm as a preprocessing stage before MLC. Typically, existing cluster-based MLC frameworks employ a hard cluster method. In practice, the instances of multilabel datasets are distinguished in a single cluster by such frameworks; however, the overlapping nature of multilabel instances is such that, in real-life applications, instances may not belong to only a single class. Therefore, we propose a MLC model that combines feature selection with an overlapping clustering algorithm. Experimental results demonstrate that various clustering algorithms show different performance for MLC, and the proposed overlapping clustering-based MLC model may be more suitable

Universal scaling of strange particle pTp_{\rm T} spectra in pp collisions

As a complementary study to that performed on the transverse momentum ($p_{\rm T}$) spectra of charged pions, kaons and protons in proton-proton (pp) collisions at LHC energies 0.9, 2.76 and 7 TeV, we present a scaling behaviour in the $p_{\rm T}$ spectra of strange particles ($K_{S}^{0}$, $\rm \Lambda$, $\rm \Xi$ and $\phi$) at these three energies. This scaling behaviour is exhibited when the spectra are expressed in a suitable scaling variable $z=p_{\rm T}/K$, where the scaling parameter $K$ is determined by the quality factor method and increases with the center of mass energy ($\sqrt{s}$). The rates at which $K$ increases with $\mathrm{ln}\sqrt{s}$ for these strange particles are found to be identical within errors. In the framework of the colour string percolation model, we argue that these strange particles are produced through the decay of clusters that are formed by the colour strings overlapping. We observe that the strange mesons and baryons are produced from clusters with different size distributions, while the strange mesons (baryons) $K_{S}^{0}$ and $\phi$ ($\rm \Lambda$ and $\rm \Xi$) originate from clusters with the same size distributions. The cluster's size distributions for strange mesons are more dispersed than those for strange baryons. The scaling behaviour of the $p_{\rm T}$ spectra for these strange particles can be explained by the colour string percolation model in a quantitative way.Comment: 8 pages, 10 figures, accepted by EPJ

A simplified model for acoustic focalization in environments with seabed uncertainties

Introduction: Parameter mismatch poses a challenge to source localization in cases involving environments with seabed uncertainties. By including environmental parameters in the search space, focalization can be used to estimate the location of the source using environmental information that is limited a priori. Methods: To reduce the number of parameters, a simplified seabed model is proposed here for such focalization. Only two geoacoustic parameters—the amplitude F and phase cF of reflection—are used to describe the seabed. Focalization is generally tested using genetic algorithms for the colored noise case (COLNOISE) benchmark problem. Results: The proposed simplified model can obtain the location of the source more easily than a layered model. Due to its advantage in terms of parameter sensitivity and inter-coupling, the simplified model can ensure the robustness of the results of inversion. The proposed method was tested on a broadband signal in the Asian Seas International Acoustics Experiment (ASIAEX2001), where both the location and the geoacoustic parameters were easily inverted. Discussion: The simplified model provides a sufficiently high acoustic resolution for focalization, and its reduction of the geoacoustic parameters helpes solve the problem of inversion

New model-data fit indices for item response theory (IRT): an evaluation and application

I reviewed the recently developed limited-information model fit statistics by Maydeu-Olivares and colleagues (e.g., Maydeu-Olivares & Joe, 2005; Maydeu-Olivares & Liu, 2012; Liu & Maydeu-Olivares, 2014) and conducted a simulation study to explore the properties of these new statistics under conditions often seen in practice. The results showed that the overall and piecewise fit statistics were to some extent sensitive to misfit caused by multidimensionality, although the limited-information fit statistics tended to flag more item pairs as misfit than the heuristic fit statistics. I also applied the fit statistics to three AP® exams, one personality inventory, and a rating scale used in organizational settings. Although a unidimensional IRT model was expected to fit the Physics B Exam better than the English Literature Exam, the average piecewise fit statistics showed no such difference. The fit statistics also suggested that a more advanced IRT model should be fitted to the self-rated personality inventory. Finally, the fit statistics seemed to be effective in detecting misfit caused by data skewness

LncRNA ZFAS1 contributes to osteosarcoma progression via miR-520b and miR-520e-mediated inhibition of RHOC signaling

Objectives: We examined the expression of Lnc-ZFAS1 in osteosarcoma and comprehensively evaluated its effects on osteosarcoma in vitro and vivo. Moreover, we revealed the regulatory mechanism between Lnc-ZFAS1 and miR-520b/miR-520e-mediated RHOC and provided a novel clue for ameliorating osteosarcoma. Method: The expression of Long non-coding RNA Zinc Finger Antisense 1 (LncRNA ZFAS1) osteosarcoma tissues and normal tissues in the TCGA database was analyzed. Then, LncRNA ZFAS1 expression was further verified in clinical samples and osteosarcoma cell lines (U2OS and KHOS), as well as the human osteoblast cell line hFOB1.19 by qRT-PCR. Thereafter, LncRNA ZFAS1 was overexpressed or silenced to explore its effects on cell proliferation, apoptosis, migration, invasion, and Epithelial-Mesenchymal Transition (EMT). The fundamental mechanism through which Lnc-ZFAS1 affects osteosarcoma progression was further investigated and verified. Results: We found that LncRNA ZFAS1 was upregulated in osteosarcoma, and Lnc-ZFAS1 overexpression facilitated osteosarcoma cells proliferation, migration, invasion and EMT, while Lnc-ZFAS1 silence exerted reverse influence. Mechanistically, Lnc-ZFAS1 functionally acted as a sponger of microRNA-520b (miR-520b) and microRNA-520e (miR-520e) to up-regulate Ras Homologue C (RHOC). In addition, depleted Lnc-ZFAS1 restrained osteosarcoma cells proliferation, migration, and invasion, which could be rescued by RHOC overexpression. Lnc-ZFAS1 was upregulated in osteosarcoma and Lnc-ZFAS1 could exert promoted impact upon osteosarcoma cells proliferation, migration, invasion, and EMT in vitro. Conclusions: Lnc-ZFAS1 acted sponger of miR-520b and miR-520e to promote RHOC, indicating that Lnc-ZFAS1/miR-520b/RHOC and Lnc-ZFAS1/miR-520e/RHOC axes might serve as potential therapeutic strategies against osteosarcoma