A Social Network Analysis of Consumers’ Perceived Brand Positions in the Running Shoes Market

Despite the slightly downturn in the footwear market due to weak economic performance in the US, the sales of running shoes gains steadily to $2.46 billion in 2011(Running USA, 2012) . However, increased intensity of competition in this section leads to more homogeneous products. Products targeting the same needs or competing on the same attributes decrease the profitability of the market as well as of each player (Porter, 1979). Therefore, branding strategies aiming at establishing a unique brand position in the market is crucial for all the brands in the running shoes market

Exploring College Students’ Shopping Motivation for Secondhand Clothing

Trade and use of secondhand clothing has been a norm of the western society since antiquity. Today, popularity of vintage fashion, desire for uniqueness and conspicuously low prices of branded luxury used clothes has given rise to a range of consignment stores, boutiques, and high-street concessions that resell previously worn garments (Hansen 2010). Although trade of secondhand clothing is on increase, there is lack of research done on motives that drive consumers to purchase secondhand clothing

Numerical simulation and manifold learning for the vibration of molten steel draining from a ladle

To ensure the purity of molten steel and maintain the continuity of casting, the slag detection utilizing vibration signals has been widely applied in the continuous casting. Due to the non-stationary and non-linear flow behavior of molten steel, it is hard to construct a reliable criterion to identify the slag entrapment from the vibration signals. In this paper, a numerical simulation model is built to reveal the flow process of molten steel draining from a ladle. By the analysis of the volume fraction, path line and velocity field, the flow state at the moment of slag outflowing is captured. According to the simulated results, a method based on the manifold learning is proposed to deal with the vibration signals. Firstly, the non-stationary vibration signals are decomposed into sub-bands by the continuous wavelet transform and the energy of the signal component at each wavelet scale is calculated to constitute the high dimensional feature space. Then, a manifold learning algorithm called local target space alignment (LTSA) is employed to extract the non-linear principal manifold of the feature space. Finally, the abnormal spectral energy distribution caused by slag entrapment is indicated by the one-dimensional principal manifold. The proposed method is evaluated by the vibration acceleration signals acquired from a steel ladle of 60 tons. Results show that the slag entrapment is exactly and timely identified

Automatic artifacts removal from epileptic EEG using a hybrid algorithm

Electroencephalogram (EEG) examination plays a very important role in the diagnosis of disorders related to epilepsy in clinic. However, epileptic EEG is often contaminated with lots of artifacts such as electrocardiogram (ECG), electromyogram (EMG) and electrooculogram (EOG). These artifacts confuse EEG interpretation, while rejecting EEG segments containing artifacts probably results in a substantial data loss and it is very time-consuming. The purpose of this study is to develop a novel algorithm for removing artifacts from epileptic EEG automatically. The collected multi-channel EEG data are decomposed into statistically independent components with Independent Component Analysis (ICA). Then temporal and spectral features of each independent component, including Hurst exponent, skewness, kurtosis, largest Lyapunov exponent and frequency-band energy extracted with wavelet packet decomposition, are calculated to quantify the characteristics of different artifact components. These features are imported into trained support vector machine to determine whether the independent components represent EEG activity or artifactual signals. Finally artifact-free EEGs are obtained by reconstructing the signal with artifact-free components. The method is evaluated with EEG recordings acquired from 15 epilepsy patients. Compared with previous work, the proposed method can remove artifacts such as baseline drift, ECG, EMG, EOG, and power frequency interference automatically and efficiently, while retaining important features for epilepsy diagnosis such as interictal spikes and ictal segments

Stability and migration of slab-derived carbonate-rich melts above the transition zone

We present a theoretical model of the stability and migration of carbonate-rich melts to test whether they can explain seismic low-velocity layers (LVLs) observed above stalled slabs in several convergent tectonic settings. The LVLs, located atop the mantle transition zone, contain small (similar to 1 vol%) amounts of partial melt, possibly derived from melting of subducted carbonate-bearing oceanic crust. Petrological and geochemical evidence from inclusions in superdeep diamonds supports the existence of slab-derived carbonate melt, which may potentially explain the origin of the observed melt in the LVL. However, the presumptive reducing nature of the ambient mantle can be an impediment to the stability of carbonated melt. To reconcile this apparent contradiction, we test the stability and migration rates of carbonate-rich melts atop a stalled slab as a function of melt percolation, redox freezing, amount of carbon supplied by subduction, and the metallic Fe concentration in the mantle. Our results demonstrate that carbonaterich melts in the LVL can potentially survive redox freezing over long geological time scales. We also show that the amount of subducted carbon exerts a stronger influence on the stability of carbonate melt than does the mantle redox condition. Concentration dependent melt density leads to rapid melt propagation through channels while a constant melt density causes melt to migrate as a planar front. Our calculations suggest that the LVLs can sequester significant fractions of carbon transported to the mantle by subduction. (C) 2019 Elsevier B.V. All rights reserved

Fault diagnosis of mechanical drives under non-stationary conditions based on manifold learning of kernel mapping

For the detection of mechanical faults under the operating conditions of varying speeds and loads (such as wind turbines, excavators or helicopters, etc.), a new method for extracting the low-dimensional embedding of vibration data sets of mechanical drives under variable operation conditions is proposed. The hypothesis is that the space spanned by a set of vibration signals can be captured in a varying condition, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set. The embedding manifold generated by vibration signals can be constructed from the feature set of parameters. Taking the variable operation condition into consideration, the kernel mapping is also introduced to improve the identification of submanifolds in terms of the projection distance. With the kernel mapping, the manifold coordinates can accurately capture the differences of the varying operation conditions. Experimental vibration signals obtained from normal and chipped tooth fault of gearbox in varying operation conditions are analyzed in this study. Results show that the proposed method is superior in identifying fault patterns and effective for gearbox condition monitoring

Evidence of Volatile-Induced Melting in the Northeast Asian Upper Mantle

International audienc

Health status of the population in Naqu, Tibet and its latent class analysis: a cross-sectional survey

BackgroundThrough a survey and analysis of the population’s present state of health, it is possible to give data support for improving the health status of inhabitants in Naqu, Tibet. Additionally, it is possible to provide specific recommendations for the development of medical and healthcare facilities in Tibet.MethodsThe health scores of the participants were based on their responses to the four main sections of the questionnaire: dietary habits, living habits, health knowledge, and clinical disease history, and the variability of health status among groups with different characteristics was analyzed based on the scores. The four major sections were used to create classes of participants using latent class analysis (LCA). Using logistic regression, the factors influencing the classification of latent classes of health status were investigated.ResultsA total of 995 residents from 10 counties in Naqu were selected as the study subjects. And their demographic characteristics were described. The mean health score of residents after standardization was 81.59 ± 4.68. With the exception of gender, health scores differed between groups by age, education level, different occupations, marital status, and monthly income. The health status in Naqu, Tibet, was divided into two groups (entropy = 0.29, BLRT = 0.001, LMRT = 0.001) defined as the “good health group” and the “general health group.” A monthly income of more than ¥5000 adverse to good health in Naqu, Tibet.DiscussionSingle, well-educated young adults in Naqu, Tibet, have outstanding health. The vast majority of people in Tibet’s Naqu region were in good health. Furthermore, the population’s latent health status was divided into two classes, each with good dietary and living habits choices, low health knowledge, and a history of several clinical diseases. Univariate and multivariate logistic regression analysis showed that monthly income more than ¥5000 was an independent risk factor for poor health status

Mendelian randomization supports genetic liability to hospitalization for COVID-19 as a risk factor of pre-eclampsia

BackgroundPre-eclampsia and eclampsia are among the major threats to pregnant women and fetuses, but they can be mitigated by prevention and early screening. Existing observational research presents conflicting evidence regarding the causal effects of coronavirus disease 2019 (COVID-19) on pre-eclampsia risk. Through Mendelian randomization (MR), this study aims to investigate the causal effect of three COVID-19 severity phenotypes on the risk of pre-eclampsia and eclampsia to provide more rigorous evidence.MethodsTwo-sample MR was utilized to examine causal effects. Summary-level data from genome-wide association studies (GWAS) of individuals of European ancestry were acquired from the GWAS catalog and FinnGen databases. Single-nucleotide polymorphisms associated with COVID-19 traits at p < 5 × −8 were obtained and pruned for linkage disequilibrium to generate instrumental variables for COVID-19. Inverse variance weighted estimates were used as the primary MR results, with weighted median and MR-Egger as auxiliary analyses. The robustness of the MR findings was also evaluated through sensitivity analyses. Bonferroni correction was applied to primary results, with a p < 0.0083 considered significant evidence and a p within 0.083–0.05 considered suggestive evidence.ResultsCritical ill COVID-19 [defined as hospitalization for COVID-19 with either a death outcome or respiratory support, OR (95% CI): 1.17 (1.03–1.33), p = 0.020] and hospitalized COVID-19 [defined as hospitalization for COVID-19, OR (95% CI): 1.10 (1.01–1.19), p = 0.026] demonstrated suggestive causal effects on pre-eclampsia, while general severe acute respiratory syndrome coronavirus 2 infection did not exhibit a significant causal effect on pre-eclampsia. None of the three COVID-19 severity phenotypes exhibited a significant causal effect on eclampsia.ConclusionsOur investigation demonstrates a suggestive causal effect of genetic susceptibility to critical ill COVID-19 and hospitalized COVID-19 on pre-eclampsia. The COVID-19 severity exhibited a suggestive positive dose–response relationship with the risk of pre-eclampsia. Augmented attention should be paid to pregnant women hospitalized for COVID-19, especially those needing respiratory support

Increased uterine androgen receptor protein abundance results in implantation and mitochondrial defects in pregnant rats with hyperandrogenism and insulin resistance.

Funder: University of GothenburgIn this study, we show that during normal rat pregnancy, there is a gestational stage-dependent decrease in androgen receptor (AR) abundance in the gravid uterus and that this is correlated with the differential expression of endometrial receptivity and decidualization genes during early and mid-gestation. In contrast, exposure to 5α-dihydrotestosterone (DHT) and insulin (INS) or DHT alone significantly increased AR protein levels in the uterus in association with the aberrant expression of endometrial receptivity and decidualization genes, as well as disrupted implantation. Next, we assessed the functional relevance of the androgen-AR axis in the uterus for reproductive outcomes by treating normal pregnant rats and pregnant rats exposed to DHT and INS with the anti-androgen flutamide. We found that AR blockage using flutamide largely attenuated the DHT and INS-induced maternal endocrine, metabolic, and fertility impairments in pregnant rats in association with suppressed induction of uterine AR protein abundance and androgen-regulated response protein and normalized expression of several endometrial receptivity and decidualization genes. Further, blockade of AR normalized the expression of the mitochondrial biogenesis marker Nrf1 and the mitochondrial functional proteins Complexes I and II, VDAC, and PHB1. However, flutamide treatment did not rescue the compromised mitochondrial structure resulting from co-exposure to DHT and INS. These results demonstrate that functional AR protein is an important factor for gravid uterine function. Impairments in the uterine androgen-AR axis are accompanied by decreased endometrial receptivity, decidualization, and mitochondrial dysfunction, which might contribute to abnormal implantation in pregnant PCOS patients with compromised pregnancy outcomes and subfertility. KEY MESSAGES: The proper regulation of uterine androgen receptor (AR) contributes to a normal pregnancy process, whereas the aberrant regulation of uterine AR might be linked to polycystic ovary syndrome (PCOS)-induced pregnancy-related complications. In the current study, we found that during normal rat pregnancy there is a stage-dependent decrease in AR abundance in the gravid uterus and that this is correlated with the differential expression of the endometrial receptivity and decidualization genes Spp1, Prl, Igfbp1, and Hbegf. Pregnant rats exposed to 5α-dihydrotestosterone (DHT) and insulin (INS) or to DHT alone show elevated uterine AR protein abundance and implantation failure related to the aberrant expression of genes involved in endometrial receptivity and decidualization in early to mid-gestation. Treatment with the anti-androgen flutamide, starting from pre-implantation, effectively prevents DHT + INS-induced defects in endometrial receptivity and decidualization gene expression, restores uterine mitochondrial homeostasis, and increases the pregnancy rate and the numbers of viable fetuses. This study adds to our understanding of the mechanisms underlying poor pregnancy outcomes in PCOS patients and the possible therapeutic use of anti-androgens, including flutamide, after spontaneous conception