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The study of molecular derivatives of biological dyes is of great importance for the green transformation of the printing, dyeing, and textile industries. In this study, B3LYP density functional methods are used to optimize the geometric configuration of the selected molecules and to explore the relationship between the structure and color of biological dye molecules and their color mechanism. This study focuses on the analysis of polyenes, quinones, indoles, and azide biological dyes in colors commonly used in the textile industry: blue, yellow, purple, red, green, and so on. Quantum chemical investigations show that the conjugate structure of a biological dye is directly related to its color and that the group or structure affecting the conjugate structure will in many cases cause a change in color. In addition, time-dependent density functional theory spectral calculations with the CAM-B3LYP functional for UV-Vis spectra show that in the visible band, the color of the remaining band after subtracting the absorption wavelengths was exactly the same as the color of the pigment. These results indicate that the color of the pigment is exactly complementary to the light absorption color of the material. Our study provides theoretical guidance for the design of molecular derivatives of biological dyes and is expected to promote the green transformation of the textile, printing, and dyeing industries to a certain extent. [ABSTRACT FROM AUTHOR]

Let 픤 be a finite-dimensional simple Lie algebra over ℂ. A Y(픤)-module is said to be weight if it is a weight 픤-module. We give a complete classification of simple weight modules for Y(픰픩2) which admits a one-dimensional weight space. We prove that there are four classes of such modules: finite, highest weight, lowest weight and dense modules. Different from the classical 픰픩2-representation theory, we show that there exists a class of Y(픰픩2) irreducible modules which have uniformly two-dimensional weight spaces. [ABSTRACT FROM AUTHOR]

Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival of the next n citations. In this article, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions. [ABSTRACT FROM AUTHOR]

Binary code similarity analysis is extremely useful, since it provides rich information about an unknown binary, such as revealing its functionality and identifying reused libraries. Robust binary similarity analysis is challenging, as heavy compiler optimizations can make semantically similar binaries have gigantic syntactic differences. Unfortunately, existing semantic-based methods still suffer from either incomplete coverage or low accuracy. In this article, we propose ARCTURUS, a new technique that can achieve high code coverage and high accuracy simultaneously by manipulating program execution under the guidance of code reachability. Our key insight is that the compiler must preserve program semantics (e.g., dependences between code fragments) during compilation; therefore, the code reachability, which implies the interdependence between code, is invariant across code transformations. Based on the above insight, our key idea is to leverage the stability of code reachability to manipulate the program execution such that deep code logic can also be covered in a consistent way. Experimental results show that ARCTURUS achieves an average precision of 87.8% with 100% block coverage, outperforming compared methods by 38.4%, on average. ARCTURUS takes only 0.15 second to process one function, on average, indicating that it is efficient for practical use. [ABSTRACT FROM AUTHOR]

Aerial base stations (AeBS), as crucial components of air-ground integrated networks, can serve as the edge nodes to provide flexible services to ground users. Optimizing the deployment of multiple AeBSs to maximize system energy efficiency is currently a prominent and actively researched topic in the AeBS-assisted edge-cloud computing network. In this paper, we deploy AeBSs using multi-agent deep reinforcement learning (MADRL). We describe the multi-AeBS deployment challenge as a decentralized partially observable Markov decision process (Dec-POMDP), taking into consideration the constrained observation range of AeBSs. The hypergraph convolution mix deep deterministic policy gradient (HCMIX-DDPG) algorithm is designed to maximize the system energy efficiency. The proposed algorithm uses the value decomposition framework to solve the lazy agent problem, and hypergraph convolutional (HGCN) network is introduced to strengthen the cooperative relationship between agents. Simulation results show that the suggested HCMIX-DDPG algorithm outperforms alternative baseline algorithms in the multi-AeBS deployment scenario. [ABSTRACT FROM AUTHOR]

The electronic, optical, and solid state properties of a series of monoradicals, anions and cations obtained from starting neutral diradicals have been studied. Diradicals based on s‐indacene and indenoacenes, with benzothiophenes fused and in different orientations, feature a varying degree of diradical character in the neutral state, which is here related with the properties of the radical redox forms. The analysis of their optical features in the polymethine monoradicals has been carried out in the framework of the molecular orbital and valence bond theories. Electronic UV‐Vis‐NIR absorption, X‐ray solid‐state diffraction and quantum chemical calculations have been carried out. Studies of the different positive‐/negative‐charged species, both residing in the same skeletal π‐conjugated backbone, are rare for organic molecules. The key factor for the dual stabilization is the presence of the starting diradical character that enables to indistinctively accommodate a pseudo‐hole and a pseudo‐electron defect with certainly small reorganization energies for ambipolar charge transport. [ABSTRACT FROM AUTHOR]

Aerosol effects on convective precipitation is critical for understanding human impacts on extreme weather and the hydrological cycle. However, even their signs and magnitude remain debatable. In particular, aerosol effects on vertical structure of precipitation have not been systematically examined yet. Combining 6‐year space‐borne and ground‐based observations over the North China Plain, we show a boomerang‐shape aerosol effect on the top height of convective precipitation, from invigoration to suppression. Further analyses reveal that the aerosols play distinct effects on precipitation rate at different layers. Particularly, near surface precipitation rate shows no significant responses to aerosol and precipitation‐top height due to strong evaporation. The competition of energy between released from condensation and freezing and absorbed by evaporation contributes to different responses of precipitation‐top height to aerosol and can explain the boomerang‐shape aerosol effect. Plain Language Summary: Aerosol particles in the atmosphere can alter precipitation efficiency and modulate the hydrological cycle, while their impacts on the cloud and precipitation vertical profiles remain poorly understood. Using 6‐year multi‐source observation data along with reanalysis meteorology, we find that aerosols exert distinct effects on precipitation rate at different layers. The observations show that aerosols enhance precipitation‐top height first and then suppress it under various dynamics and thermodynamics conditions, with a turning point at medium aerosol amount. In contrast, the response of near surface precipitation rate to aerosol perturbation is complex due to varying evaporation efficiency. These findings challenge the previous studies that suggested that the characteristics of cloud and precipitation at high altitude are closely correlated with precipitation rate near the surface. Key Points: Observations show a boomerang‐shape aerosol effect on the top height of convective precipitation from invigoration to suppressionAerosols impose distinct effects on precipitation rate at different layers, with no significant impact near surfaceEnergy change within conversion processes between hydrometeors and water vapor explains different responses of precipitation to aerosol [ABSTRACT FROM AUTHOR]

A visible light-induced carbene reactivity of acceptor diazoalkanes has been developed for the synthesis of difunctionalized ethers from cyclic ethers and various N/O/S nucleophiles. [ABSTRACT FROM AUTHOR]

In this study, we aimed to identify the influencing factors that drive highway construction companies to adopt smart construction technologies. Using expert interviews and expert scoring, we collected interview data from 25 experts in the field and we proposed the TOSE framework based on the TOE framework, identifying four dimensions and fourteen influencing factors. We analyzed the results using the Fuzzy DEMATEL-ISM method, and we then summarized the findings according to the evaluation criteria to determine the validity of the fourteen hypotheses and the extent to which they drive highway construction companies to adopt smart construction technologies. The findings of this paper are of high value to decision makers and participants in highway construction companies, as well as to other companies in the construction industry, in their decision to adopt smart construction technologies. [ABSTRACT FROM AUTHOR]

Summary: Fungal infections have emerged as a major global threat to human health because of the increasing incidence and mortality rates every year. The emergence of drug resistance and limited arsenal of antifungal agents further aggravates the current situation resulting in a growing challenge in medical mycology. Here, we identified that ponatinib, an FDA‐approved antitumour drug, significantly enhanced the activity of the azole fluconazole, the most widely used antifungal drug. Further detailed investigation of ponatinib revealed that its combination with fluconazole displayed broad‐spectrum synergistic interactions against a variety of human fungal pathogens such as Candida albicans, Saccharomyces cerevisiae and Cryptococcus neoformans. Mechanistic insights into the mode of action unravelled that ponatinib reduced the efflux of fluconazole via Pdr5 and suppressed the expression of the proton pump, Pma1. Taken together, our study identifies ponatinib as a novel antifungal that enhances drug activity of fluconazole against diverse fungal pathogens. [ABSTRACT FROM AUTHOR]