CONFIDENCE IN LEARNING: INTER- AND INTRAORGANIZATIONAL LEARNING IN FOREIGN MARKET ENTRY DECISIONS

From an organizational learning perspective, we argue that the information signaled by the distribution attributes of foreign investors already operating in a location will influence the entry decisions of later arrivals by affecting their level of confidence in imitating. In the context of foreign investment decisions, the proportion of experienced firms in a location was shown to first increase a follower firm's confidence about imitating them, but then to decrease it, due to anticipated competition. The impact of learning from target organizations also varies with the experience of the learning organization. Data on the location choices of 7,478 manufacturing ventures in China by U.S. firms supported the hypotheses. The results provide a more integrated and nuanced understanding of learning in foreign direct investment. Copyright (c) 2014 John Wiley & Sons, Ltd

Automatic Defect Identification Method for Magnetic Particle Inspection of Bearing Rings Based on Visual Characteristics and High-Level Features

Fluorescent magnetic particle inspection (MPI) is a conventional non-destructive testing process for railway bearing rings that still needs to be completed manually. Due to the complexity of bearing ring surfaces in inspection, automatic detection for bearing rings based on image processing is difficult to apply. Therefore, we proposed a bearing ring defect identification method based on visual characteristics and high-level features. Inspired by the mechanism of human visual perception, defects can be identified from the complex background conveniently by human eyes. According to the linear structure characteristics and greyscale distribution characteristics of cracks in the acquired images, we introduce the centerline extraction and Gaussian similarity measure to reduce background noise and obtain the crack candidate regions. Then, an improved MobileNetV3 is used to extract high-level features of the candidate regions and determine whether they are defective, which uses a new attention module, Coordinate Attention (CA), to substitute the Squeeze-and-Excitation (SE) attention to improve the performance. The experimental results show that the detection accuracy rate of the proposed method is 96.5%. Compared with traditional methods, the proposed method can efficiently extract crack defects in a complex textured background and shows high-quality performance in recall and precision

A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma

Abstract Background Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions. Methods RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses. Results ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients. Conclusions The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients

Absolute and Relative Depth-Induced Network for RGB-D Salient Object Detection

Detecting salient objects in complicated scenarios is a challenging problem. Except for semantic features from the RGB image, spatial information from the depth image also provides sufficient cues about the object. Therefore, it is crucial to rationally integrate RGB and depth features for the RGB-D salient object detection task. Most existing RGB-D saliency detectors modulate RGB semantic features with absolution depth values. However, they ignore the appearance contrast and structure knowledge indicated by relative depth values between pixels. In this work, we propose a depth-induced network (DIN) for RGB-D salient object detection, to take full advantage of both absolute and relative depth information, and further, enforce the in-depth fusion of the RGB-D cross-modalities. Specifically, an absolute depth-induced module (ADIM) is proposed, to hierarchically integrate absolute depth values and RGB features, to allow the interaction between the appearance and structural information in the encoding stage. A relative depth-induced module (RDIM) is designed, to capture detailed saliency cues, by exploring contrastive and structural information from relative depth values in the decoding stage. By combining the ADIM and RDIM, we can accurately locate salient objects with clear boundaries, even from complex scenes. The proposed DIN is a lightweight network, and the model size is much smaller than that of state-of-the-art algorithms. Extensive experiments on six challenging benchmarks, show that our method outperforms most existing RGB-D salient object detection models

Multi-timescale Thermal Network Model of Power Devices Based on POD Algorithm

The heat transfer of power devices has the characteristics of multi-timescale. However, the traditional thermal network model is difficult to predict the temperature information of power devices at multi-timescale accurately. This paper proposes a multi-timescale thermal network model for power devices, with MOSFET as an example. First, we establish a finite element model for temperature calculation of power devices and then reduce the order of the finite element model based on the proper orthogonal decomposition (POD) algorithm. The reduced order model is converted into an equivalent circuit model by the node voltage method and integrated into the circuit simulation software. To demonstrate its general applicability, this paper also establishes thermal network models for both IGBT and SiC MOSFET devices, validates the multi-time scale thermal network models through ANSYS/Transient Thermal software and experiments. The temperature calculation results of the three power devices all indicate that the proposed multi-time scale thermal network model can calculate the temperature of power devices faster than ANSYS/Transient Thermal model, and has less than 5% error under test conditions. This paper is accompanied by a video demonstrating a comparison of the computational speed of the multi-timescale thermal network model with a finite element model. Finally, a case study of junction temperature calculation in a Buck converter is presented to illustrate the application method of the thermal network model.</p

Additional file 1 of A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma

Additional file 1. Fig. S1: Relationship between molecular subtypes and clinical characteristics, immune cell infiltration, immune score, immune checkpoint genes, and treatment sensitivities. A Distribution of the clinical characteristics in the 3 molecular clusters. B Degree of infiltration of the 10 types of immune cells in the 3 molecular clusters. C Immune scores in the 3 molecular clusters. D Difference in the expression of immune checkpoint genes in the 3 molecular clusters. E Immunotherapy response in the 3 molecular clusters. F Sensitivities of traditional chemotherapy in the 3 molecular clusters. Fig. S2: Differences in clinical features, immune cell infiltration, immune escape and chemosensitivity between the high- and low-risk groups. Association between the NK cell-related Risk Score and A clinical features, B immune cell infiltration, C immune escape, and D chemosensitivity in the TCGA database. Table S1: The primer sequences for qRT-PCR

Chromosomal Localization of Genes Conferring Desirable Agronomic Traits from Wheat-<i>Agropyron cristatum</i> Disomic Addition Line 5113

<div><p>Creation of wheat-alien disomic addition lines and localization of desirable genes on alien chromosomes are important for utilization of these genes in genetic improvement of common wheat. In this study, wheat-<i>Agropyron cristatum</i> derivative line 5113 was characterized by genomic <i>in situ</i> hybridization (GISH) and specific-locus amplified fragment sequencing (SLAF-seq), and was demonstrated to be a novel wheat-<i>A</i>. <i>cristatum</i> disomic 6P addition line. Compared with its parent Fukuhokomugi (Fukuho), 5113 displayed multiple elite agronomic traits, including higher uppermost internode/plant height ratio, larger flag leaf, longer spike length, elevated grain number per spike and spikelet number per spike, more kernel number in the middle spikelet, more fertile tiller number per plant, and enhanced resistance to powdery mildew and leaf rust. Genes conferring these elite traits were localized on the <i>A</i>. <i>cristatum</i> 6P chromosome by using SLAF-seq markers and biparental populations (F₁, BC₁F₁ and BC₁F₂ populations) produced from the crosses between Fukuho and 5113. Taken together, chromosomal localization of these desirable genes will facilitate transferring of high-yield and high-resistance genes from <i>A</i>. <i>cristatum</i> into common wheat, and serve as the foundation for the utilization of 5113 in wheat breeding.</p></div

Histograms showing the agronomic traits of 5113, Fukuho, 4844–12 and genetic populations produced from the crosses between 5113 and Fukuho.

<p>PH, Plant height; UIL, Uppermost internode length; FLL, Flag leaf length; FLW, Flag leaf width; SL, Spike length; GNPS, Grain number per spike; SNPS, Spikelet number per spike; KNMS, Kernel number in the middle spikelet; TGW, Thousand-grain weight; WGP, Whole growth period; FTN, Fertile tiller number per plant.</p

Morphological traits of 5113 and Fukuho.

<p>whole plants (a), flag leaves (b), spikes (c), spikelets with grains (d), disease responses to powdery mildew (e) and leaf rust (f).</p