Integration of electric vehicle load and charging infrastructure in distribution network

While road electrification offers economic and environmental advantages, the non-conventional load due to electric vehicles usage and charging patterns pose challenges to distribution systems. The strategic design of charging infrastructure is becoming an essential element to facilitate power system planning and decision-making process. This paper presents a probabilistic method to derive charging patterns and estimate the electric vehicle demand profiles under uncertainty and variability. We apply a Gaussian copula to capture correlations between the key multivariates. We investigate the optimal location and size of charging stations based on queueing theory and intercepted traffic flow model. We examine the impact of the charging demand occurred in residential and public area on distribution expansion investment and incremental operational cost. The feasibility of the approach is tested on an interconnected distribution grid and transportation system. The case studies show that a careful probabilistic analysis of the randomness intrinsic to the charging behavior is of great importance to define and implement an integrated power and transportation system design

Dual-view Curricular Optimal Transport for Cross-lingual Cross-modal Retrieval

Current research on cross-modal retrieval is mostly English-oriented, as the availability of a large number of English-oriented human-labeled vision-language corpora. In order to break the limit of non-English labeled data, cross-lingual cross-modal retrieval (CCR) has attracted increasing attention. Most CCR methods construct pseudo-parallel vision-language corpora via Machine Translation (MT) to achieve cross-lingual transfer. However, the translated sentences from MT are generally imperfect in describing the corresponding visual contents. Improperly assuming the pseudo-parallel data are correctly correlated will make the networks overfit to the noisy correspondence. Therefore, we propose Dual-view Curricular Optimal Transport (DCOT) to learn with noisy correspondence in CCR. In particular, we quantify the confidence of the sample pair correlation with optimal transport theory from both the cross-lingual and cross-modal views, and design dual-view curriculum learning to dynamically model the transportation costs according to the learning stage of the two views. Extensive experiments are conducted on two multilingual image-text datasets and one video-text dataset, and the results demonstrate the effectiveness and robustness of the proposed method. Besides, our proposed method also shows a good expansibility to cross-lingual image-text baselines and a decent generalization on out-of-domain data

Salvianolic Acid B Prevents Arsenic Trioxide-Induced Cardiotoxicity In Vivo and Enhances Its Anticancer Activity In Vitro

Clinical attempts to reduce the cardiotoxicity of arsenic trioxide (ATO) without compromising its anticancer activities remain to be an unresolved issue. In this study, we determined whether Sal B can protect against ATO-induced cardiac toxicity in vivo and increase the toxicity of ATO toward cancer cells. Combination treatment of Sal B and ATO was investigated using BALB/c mice and human hepatoma (HepG2) cells and human cervical cancer (HeLa) cells. The results showed that the combination treatment significantly improved the ATO-induced loss of cardiac function, attenuated damage of cardiomyocytic structure, and suppressed the ATO-induced release of cardiac enzymes into serum in BALB/c mouse models. The expression levels of Bcl-2 and p-Akt in the mice treated with ATO alone were reduced, whereas those in the mice given the combination treatment were similar to those in the control mice. Moreover, the combination treatment significantly enhanced the ATO-induced cytotoxicity and apoptosis of HepG2 cells and HeLa cells. Increases in apoptotic marker cleaved poly (ADP-ribose) polymerase and decreases in procaspase-3 expressions were observed through western blot. Taken together, these observations indicate that the combination treatment of Sal B and ATO is potentially applicable for treating cancer with reduced cardiotoxic side effects

2-Chloro­quinazolin-4(3H)-one

In the title compound, C8H5ClN2O, the quinazoline system is approximately planar with a maximum deviation from the least-squares plane of 0.034 (2) Å. In the crystal, classical N—H⋯O and weak non-classical C—H⋯N hydrogen bonds link the mol­ecules

4-(2,3-Dimeth­oxy­phen­yl)-1H-pyrrole-3-carbonitrile

The asymmetric unit of the title compound, C13H12N2O2, obtained in a search for analogs of the fungicide fludioxonil [systematic name: 4-(2,2-difluoro-1,3-benzodioxol-4-yl)-1H-pyrrole-3-carbonitrile], contains two independent mol­ecules, A and B. The benzene and pyrrole rings are inclined to each other at 38.5 (1) and 29.3 (1)° in mol­ecules A and B, respectively. In the crystal, bifurcated N—H⋯(O,O) hydrogen bonds link A mol­ecules into chains along [001], while B mol­ecules are linked into layers parallel to the bc plane via bifurcated N—H⋯(N,N) hydrogen bonds

4,4′-Bipyridinium bis(perchlorate)–4-aminobenzoic acid–4,4′-bipyridine–water (1/4/2/2)

In the structure of the title compound, C10H10N2 2+·2ClO4 −·4C7H7NO2·2C10H8N2·2H2O, the 4,4′-bipyridinium cation has a crystallographically imposed centre of symmetry. The cation is linked by N—H⋯N hydrogen bonds to adjacent 4,4′-bipyridine mol­ecules, which in turn inter­act via O—H⋯N hydrogen bonds with 4-amino­benzoic acid mol­ecules, forming chains running parallel to [30]. The chains are further connected into a three-dimensional network by N—H⋯O and O—H⋯O hydrogen-bonding inter­actions involving the perchlorate anion, the water mol­ecules and the 4-amino­benzoic acid mol­ecules. In addition, π–π stacking inter­actions with centroid–centroid distances ranging from 3.663 (6) to 3.695 (6) Å are present. The O atoms of the perchlorate anion are disordered over two sets of positions, with refined site occupancies of 0.724 (9) and 0.276 (9)

Poly[diaqua-μ-oxalato-μ-pyrazine-2-carbox­yl­ato-lanthanum(III)]

In the title complex, [La(C5H3N2O2)(C2O4)(H2O)2]n, the LaIII ion is coordinated by one N and three O atoms from two pyrazine-2-carboxylate ligands, by four O atoms from two oxalate ligands and by two O atoms of two water molecules, displaying a distorted bicapped square-anti­prismatic geometry. The carboxyl­ate groups of pyrazine-2-carboxyl­ate and oxalate ligands link the lanthanum metal centres, forming layers parallel to (10). The layers are further connected by inter­molecular O—H⋯O and N—H⋯O hydrogen-bonding inter­actions, forming a three-dimensional supra­molecular network

A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset

Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video understanding works only focus on the information that appeared within the video content, including the video frames, audio and text. However, introducing common sense knowledge from the external Knowledge Graph (KG) dataset is essential for video understanding when referring to the content which is less relevant to the video. Owing to the lack of video knowledge graph dataset, the work which integrates video understanding and KG is rare. In this paper, we propose a heterogeneous dataset that contains the multi-modal video entity and fruitful common sense relations. This dataset also provides multiple novel video inference tasks like the Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks. Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG. Comprehensive experiments indicate that combining video understanding embedding with factual knowledge benefits the content-based video retrieval performance. Moreover, it also helps the model generate better knowledge graph embedding which outperforms traditional KGE-based methods on VRT and VRV tasks with at least 42.36% and 17.73% improvement in HITS@10

Modulating electron density of vacancy site by single Au atom for effective CO2_{2} photoreduction

The surface electron density significantly affects the photocatalytic efficiency, especially the photocatalytic CO$_{2}$ reduction reaction, which involves multi-electron participation in the conversion process. Herein, we propose a conceptually different mechanism for surface electron density modulation based on the model of Au anchored CdS. We firstly manipulate the direction of electron transfer by regulating the vacancy types of CdS. When electrons accumulate on vacancies instead of single Au atoms, the adsorption types of CO$_{2}$ change from physical adsorption to chemical adsorption. More importantly, the surface electron density is manipulated by controlling the size of Au nanostructures. When Au nanoclusters downsize to single Au atoms, the strong hybridization of Au 5d and S 2p orbits accelerates the photo-electrons transfer onto the surface, resulting in more electrons available for CO$_{2}$ reduction. As a result, the product generation rate of Au$_{SA}$/Cd$_{1-x}$S manifests a remarkable at least 113-fold enhancement compared with pristine Cd$_{1-x}$S