As a member of the lead-halide perovskite family, inorganic CsPbBr3 exhibits excellent optical and electrical properties with higher stability to environment. However, former efforts obtain large-size single crystals satisfactory quality using low temperature solution methods reached limited results. In this work, we have studied growth antisolvent vapor-assisted crystallization (AVC) method. By adjusting mole ratio PbBr2 CsBr, phase diagram final products is acquired. Five regions are...

A major concern with co-administration of different drugs is the high risk interference between their mechanisms action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to organism. Although several computational methods have been proposed for identifying potential DDIs, there still room improvement. Existing are not explicitly based on knowledge that DDIs fundamentally caused by chemical substructure instead whole drugs' structures. Furthermore, most...

Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of requires much money time. Computational approaches have exhibited their abilities predict potential on a large scale by utilizing pre-market properties (e.g. chemical structure). Nevertheless, none them can two comprehensive types DDIs, including enhancive degressive which increases decreases...

Drug-drug interactions (DDIs) are with adverse effects on the body, manifested when two or more incompatible drugs taken together. They can be caused by chemical compositions of involved. We introduce gated message passing neural network (GMPNN), a which learns substructures different sizes and shapes from molecular graph representations for DDI prediction between pair drugs. In GMPNN, edges considered as gates control flow passing, therefore delimiting in learnable way. The final drug is...

In complex networks, it is significant how to rank the nodes according their importance. Most of existing methods ranking key (e.g. degree-based, betweenness-based) only consider one factor but not integration whole network in evaluating importance nodes, so those each have a limited application range. this paper, multi-attribute decision-making method identify networks proposed. our method, node regarded as solution, and evaluation criterion solution's attribute. After that, we calculate...

Because drug-drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approaches provide a much cheaper screening for potential on large scale manner. Nevertheless, most them only predict whether not one interacts with another, but neglect their enhancive (positive) and depressive (negative)...

Abstract Computational prediction of multiple-type drug–drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore to study which local structures drugs cause DDIs, and their interpretability is still weak. In this paper, by supposing that the interactions between two given are caused chemical (substructures) DDI types determined linkages different substructure sets, we design a novel...

A novel low-profile monopole antenna fabricated on flexible substrate based a pent angle-loop radiator is presented and investigated in this letter. The proposed designed to operate at GPS L2/Bluetooth/WiMAX/wireless local-area network frequency bands. To maintain the small size of element, an improved angle loop adopted higher order modes are also utilized. Also, pair symmetrical V-shaped parasitic radiators modified rectangular ground plane coplanar-waveguide feeding structure used improve...

Abstract It is tough to detect unexpected drug–drug interactions (DDIs) in poly-drug treatments because of high costs and clinical limitations. Computational approaches, such as deep learning-based are promising screen potential DDIs among numerous drug pairs. Nevertheless, existing approaches neglect the asymmetric roles two drugs interaction. Such an asymmetry crucial since it determines priority co-prescription. This paper designs a directed graph attention network (DGAT-DDI) predict...

The association rule algorithm in data mining is used to study the factors that may affect students’ performance, make suggestions for teaching work, and provide decision-making basis teachers administrators, which has practical significance. There are many potential applications facial expression recognition technology. For example, process, technology helps understand students judge reactions certain things. Based on current research status of emotion algorithms, this paper improves...

Abstract Motivation During lead compound optimization, it is crucial to identify pathways where a drug-like metabolized. Recently, machine learning-based methods have achieved inspiring progress predict potential metabolic for compounds. However, they neglect the knowledge that are dependent on each other. Moreover, inadequate elucidate why compounds participate in specific pathways. Results To address these issues, we propose novel Multi-Label Graph Learning framework of Metabolic Pathway...

Abstract Drug–drug interactions (DDI) may lead to adverse reactions in human body and accurate prediction of DDI can mitigate the medical risk. Currently, most computer-aided methods construct models based on drug-associated features or network, ignoring potential information contained drug-related biological entities such as targets genes. Besides, existing network-based could not make effective predictions for drugs without any known records. To address above limitations, we propose an...

Prediction of drug-drug interactions (DDIs) can reveal potential adverse pharmacological reactions between drugs in co-medication. Various methods have been proposed to address this issue. Most them focus on the traditional link prediction drugs, however, they ignore cold-start scenario, which requires known having approved DDIs and new no DDI. Moreover, they're restricted infer whether occur, but are not able deduce diverse DDI types, important clinics.In paper, we propose a cold start...

There is an irreversibility in the decline of Li-ion batteries, and performance individual cells battery pack will gradually as number times on-board charged discharged increases This situation can significantly affect daily use electric vehicles, for example by shortening driving range, addition, deterioration probability vehicle breakdowns. Very little work has been done on prediction lithium degradation long-mileage states, accurate future reduce EV failure, making very important. In this...

It is a critical step in lead optimization to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. Classical single-task learning (STL) has effectively predicted individual ADMET endpoints with abundant labels. Conversely, multi-task (MTL) can predict multiple fewer labels, but ensuring task synergy highlighting key molecular substructures remain challenges. To tackle these issues, this work elaborates graph framework for...