An Universal Image Attractiveness Ranking Framework

We propose a new framework to rank image attractiveness using a novel pairwise deep network trained with a large set of side-by-side multi-labeled image pairs from a web image index. The judges only provide relative ranking between two images without the need to directly assign an absolute score, or rate any predefined image attribute, thus making the rating more intuitive and accurate. We investigate a deep attractiveness rank net (DARN), a combination of deep convolutional neural network and rank net, to directly learn an attractiveness score mean and variance for each image and the underlying criteria the judges use to label each pair. The extension of this model (DARN-V2) is able to adapt to individual judge's personal preference. We also show the attractiveness of search results are significantly improved by using this attractiveness information in a real commercial search engine. We evaluate our model against other state-of-the-art models on our side-by-side web test data and another public aesthetic data set. With much less judgments (1M vs 50M), our model outperforms on side-by-side labeled data, and is comparable on data labeled by absolute score.Comment: Accepted by 2019 Winter Conference on Application of Computer Vision (WACV

Efficient Solar-to-Thermal Energy Conversion and Storage with High-Thermal-Conductivity and Form-Stabilized Phase Change Composite Based on Wood-Derived Scaffolds

Solar-to-thermal energy conversion is one of the most efficient ways to harvest solar energy. In this study, a novel phase change composite with porous carbon monolith derived from natural wood is fabricated to harvest solar irradiation and store it as thermal energy. Organic phase change material n-octadecane is physically adsorbed inside the porous structure of the carbonized wood, and a thin graphite coating encapsulates the exterior of the wood structure to further prevent n-octadecane leakage. The carbonized wood scaffold and the graphite coating not only stabilize the form of the n-octadecane during phase change, but also enhance its thermal conductivity by 143% while retaining 87% of its latent heat. Under 1-sun irradiation, the composite achieves an apparent 97% solar-to-thermal conversion efficiency

Large-scale synthesis of high moment FeCo nanoparticles using modified polyol synthesis

Binary alloys of Fe and Co have among the highest magnetizations of any transition metal alloy systems, but their affinity to form oxides act to reduce the magnetization of nanoparticles as their size is reduced below ∼30 nm. Here, we demonstrate the synthesis of single phase, size-controlled FeCo nanoparticles having magnetization greater than 200 emu/g via a non-aqueous method in which ethylene glycol served as solvent and reducing agent as well as surfactant. Experiments indicated pure-phase FeCo nanoparticles, having saturationmagnetization up to 221 emu/g for sizes of 20–30 nm, in single batch processes resulting in \u3e 2 g/batch. Post-synthesis oxidation of nanoparticles was investigated until very stable nanoparticles were realized with constant magnetization over time

Porous Wood Monoliths Decorated with Platinum Nano-Urchins as Catalysts for Underwater Micro-Vehicle Propulsion via H2O2 Decomposition

Porous carbon is becoming an important and promising high-surface area scaffold material for various energy-based applications including catalysis. Here we demonstrate the growth of urchin-like platinum nanoparticles (PtNPs) on carbon monoliths derived from basswood that work as catalysts for micro underwater vehicle (MUV) propulsion via H2O2 decomposition. The carbon monoliths were constructed of natural basswood that was carbonized in argon (Ar) and subjected to a subsequent CO2 activation process that rendered the material into a hardened 3D porous activated carbonized wood (ACW) with inner channel voids measuring 10-70 μm in diameter. The PtNP nanourchins (500 nm or less in total diameter, with individual nanospikes measuring 3-5 nm in diameter) form on the ACW via a facile electroless and template-free chemical deposition approach that utilized the reduction of chloroplatinic acid. The developed PtNP-ACW hybrid material exhibited higher catalytic efficiency as compared to previously reported platinum (Pt) catalysts with a low activation energy of 18.9 ± 2.5 kJ mol-1 for H2O2 decomposition. The catalyst also proved useful in an important energy application by its ability to rapidly decompose H2O2 fuel and generate O2 gas for propulsion of a 3D printed MUV prototype. The PtNP-ACW catalysts weighing only 0.14 g generated a propulsion thrust of 230 mN, which is sufficient to power MUVs. The natural wood derived carbon scaffolds significantly reduce the overall cost as compared to other carbon-based catalysts such as carbon nanotubes or graphene without reducing catalytic efficiency. Hence such catalysts act as a stepping stone for potential low cost and sustainable power for burst thrust operation of MUVs

The association of center volume with transplant outcomes in selected high-risk groups in kidney transplantation

BACKGROUND: In context of increasing complexity and risk of deceased kidney donors and transplant recipients, the impact of center volume (CV) on the outcomes of high-risk kidney transplants(KT) has not been well determined. METHODS: We examined the association of CV and outcomes among 285 U.S. transplant centers from 2000-2016. High-risk KT were defined as recipient age ≥ 70 years, body mass index (BMI) ≥ 35 kg/m RESULTS: Two hundred fifty thousand five hundred seventy-four KT were analyzed. Compared to high CV, recipients with BMI ≥ 35 kg/m CONCLUSIONS: Recipients of high-risk KT with BMI ≥ 35 kg/

Transient lysosomal activation is essential for p75 nerve growth factor receptor expression in myelinated Schwann cells during Wallerian degeneration

Myelinated Schwann cells in the peripheral nervous system express the p75 nerve growth factor receptor (p75NGFR) as a consequence of Schwann cell dedifferentiation during Wallerian degeneration. p75NGFR has been implicated in the remyelination of regenerating nerves. Although many studies have shown various mechanisms underlying Schwann cell dedifferentiation, the molecular mechanism contributing to the re-expression of p75NGFR in differentiated Schwann cells is largely unknown. In the present study, we found that lysosomes were transiently activated in Schwann cells after nerve injury and that the inhibition of lysosomal activation by chloroquine or lysosomal acidification inhibitors prevented p75NGFR expression at the mRNA transcriptional level in an ex vivo Wallerian degeneration model. Lysosomal acidification inhibitors suppressed demyelination, but not axonal degeneration, thereby suggesting that demyelination mediated by lysosomes may be an important signal for inducing p75NGFR expression. Tumor necrosis factor-α (TNF-α) has been suggested to be involved in regulating p75NGFR expression in Schwann cells. In this study, we found that removing TNF-α in vivo did not significantly suppress the induction of both lysosomes and p75NGFR. Thus, these findings suggest that lysosomal activation is tightly correlated with the induction of p75NGFR in demyelinating Schwann cells during Wallerian degeneration

Application of machine learning for risky sexual behavior interventions among factory workers in China

IntroductionAssessing the likelihood of engaging in high-risk sexual behavior can assist in delivering tailored educational interventions. The objective of this study was to identify the most effective algorithm and assess high-risk sexual behaviors within the last six months through the utilization of machine-learning models.MethodsThe survey conducted in the Longhua District CDC, Shenzhen, involved 2023 participants who were employees of 16 different factories. The data was collected through questionnaires administered between October 2019 and November 2019. We evaluated the model's overall predictive classification performance using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. All analyses were performed using the open-source Python version 3.9.12.ResultsAbout a quarter of the factory workers had engaged in risky sexual behavior in the past 6 months. Most of them were Han Chinese (84.53%), hukou in foreign provinces (85.12%), or rural areas (83.19%), with junior high school education (55.37%), personal monthly income between RMB3,000 (US$417.54) and RMB4,999 (US$695.76; 64.71%), and were workers (80.67%). The random forest model (RF) outperformed all other models in assessing risky sexual behavior in the past 6 months and provided acceptable performance (accuracy 78%; sensitivity 11%; specificity 98%; PPV 63%; ROC 84%).DiscussionMachine learning has aided in evaluating risky sexual behavior within the last six months. Our assessment models can be integrated into government or public health departments to guide sexual health promotion and follow-up services