On the influence of cobamides on organohalide respiration and mercury methylation

Corrinoids are metallic cofactors that serve as prosthetic groups for numerous reactions catalyzed by various microorganisms, ranging from carbon and nitrogen cycling to toxic waste remediation. Importantly, the number of organisms that require corrinoids far outweigh that which can produce corrinoid de novo. This imbalance in supply and demand reveals interdependencies at the molecular scale amongst various organisms, where a corrinoid auxotroph may need to coexist with a corrinoid prototroph. A further determinant of corrinoid function lies in the lower base, and essential component of functional corrinoids. In the case of Dehalococcoides mccartyi, studies have shown that differences in lower base structure impact the activity of corrinoid-binding reductive dehalogenase enzymes. This body of work examines a gene from Geobacter lovleyi implicated in the synthesis of the 5-methoxybenzimidazole lower base, and how this lower base may play a role in regulating corrinoid-dependent reactions including reductive dechlorination in Dehalococcoides mccartyi and mercury methylation

The Estimation of Nodal Power Supply Reliability through the Network Connectivity by Complex Network Method

The paper studies the reliability of the power system from the perspective of node loads. The reliability of the whole system can be estimated by evaluating the power supply reliability of each node. A measure, connectivity observed at load node (Ci), is proposed. Ci is calculated through a recursion equation by evaluating the generation capacity that can be transferred from the further neighbor to the nearest neighbor of load node i. IEEE-30 bus system is taken as a test system. We calculated the index of 7 load nodes at 2 different load levels with different N-1 failures. The test results show that the variation of the index and that of the percentage load shedding at selected load nodes show good consistency

Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation

Facial sketch synthesis (FSS) aims to generate a vivid sketch portrait from a given facial photo. Existing FSS methods merely rely on 2D representations of facial semantic or appearance. However, professional human artists usually use outlines or shadings to covey 3D geometry. Thus facial 3D geometry (e.g. depth map) is extremely important for FSS. Besides, different artists may use diverse drawing techniques and create multiple styles of sketches; but the style is globally consistent in a sketch. Inspired by such observations, in this paper, we propose a novel Human-Inspired Dynamic Adaptation (HIDA) method. Specially, we propose to dynamically modulate neuron activations based on a joint consideration of both facial 3D geometry and 2D appearance, as well as globally consistent style control. Besides, we use deformable convolutions at coarse-scales to align deep features, for generating abstract and distinct outlines. Experiments show that HIDA can generate high-quality sketches in multiple styles, and significantly outperforms previous methods, over a large range of challenging faces. Besides, HIDA allows precise style control of the synthesized sketch, and generalizes well to natural scenes and other artistic styles. Our code and results have been released online at: https://github.com/AiArt-HDU/HIDA.Comment: To appear on ICCV'2

Stability enhancement method and experiment of orchard vehicle control

Study on orchard working vehicle rollover and tipping prediction is important to maintain vehicle stability control in complicated operation conditions of orchard. Existing rollover and tipping prediction models for vehicles can not directly apply to orchard working vehicle, which structure and loading are changing under operation. So it is necessary to move ahead study on orchard vehicle bodywork posture prediction and rollover and tipping prediction by theoretical analysis, mathematical modelling, real vehicle test and other methods. In this paper, firstly, we establish orchard working vehicle dynamic model, analyses variation of key parameters during vehicle instability state, and look for characteristic parameters of vehicle instability. Secondly, active safety control algorithm which based on posture detection of vehicle body is researched. Finally, control model is verified and optimized by scaled test

Facial Attribute Capsules for Noise Face Super Resolution

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202

Knowledge Distillation Meets Label Noise Learning: Ambiguity-Guided Mutual Label Refinery

Knowledge distillation (KD), which aims at transferring the knowledge from a complex network (a teacher) to a simpler and smaller network (a student), has received considerable attention in recent years. Typically, most existing KD methods work on well-labeled data. Unfortunately, real-world data often inevitably involve noisy labels, thus leading to performance deterioration of these methods. In this article, we study a little-explored but important issue, i.e., KD with noisy labels. To this end, we propose a novel KD method, called ambiguity-guided mutual label refinery KD (AML-KD), to train the student model in the presence of noisy labels. Specifically, based on the pretrained teacher model, a two-stage label refinery framework is innovatively introduced to refine labels gradually. In the first stage, we perform label propagation (LP) with small-loss selection guided by the teacher model, improving the learning capability of the student model. In the second stage, we perform mutual LP between the teacher and student models in a mutual-benefit way. During the label refinery, an ambiguity-aware weight estimation (AWE) module is developed to address the problem of ambiguous samples, avoiding overfitting these samples. One distinct advantage of AML-KD is that it is capable of learning a high-accuracy and low-cost student model with label noise. The experimental results on synthetic and real-world noisy datasets show the effectiveness of our AML-KD against state-of-the-art KD methods and label noise learning (LNL) methods. Code is available at https://github.com/Runqing-forMost/ AML-KD

Comparison of housing facility management between mainland China and Taiwan region

Recently, mainland China has experienced the fastest urbanization in the world; however, the development of structural regulations regarding facility management (FM) services for housing is relatively recessive. As a result, disputes and conflicts in facility management of the private housing sector have become a serious problem in urban communities, affecting social sustainable development of the building industry. Comparatively, the private housing FM system in the urban areas in the Taiwan region was developed much earlier; thus, it is more advanced and mature than that in mainland China. This paper intends to compare the FM sectors between the two regions to provide suggestions for improving the service quality of the FM system in mainland China.Natural Science Foundation of Shandong (Grant No. ZR2013GQ014) and Independent Innovation Foundation of Shandong University (Grant No. IFW12108; IFW12065).http://ascelibrary.org/journal/jpcfevhb2016Graduate School of Technology Management (GSTM

A strategy of using temporary space-holders to increase the capacity for Li-S batteries

The porous structure within the cathode material influences the capacity and operating performance of Li-S batteries. Here, we prepared special carbon material using sodium chloride as template/temporary space-holders to design mixture of micro-/meso-/macroporous structure with large surface area through a facile water bath and freeze-drying process. Such modification led to a high sulfur content of 73.2 with remarkable initial capacity of 1378 mAh g-1 at 0.1 A g‒1, and maintains 48.6 after 100 cycles. Moreover, the Li-S battery displayed superior rate capability of 807 mAh g–1 at 1.0 A g–1. This abundant micro-/meso-/macropores can improve the content of sulfur, relieve the loss of sulfur and exhibit superior cycling performance and rate capability