BMS Derivatives C7-Linked to β-Cyclodextrin and Hyperbranched Polyglycerol Retain Activity against R5-HIV-1NLAD8 Isolates and Can Be Deemed Potential Microbicides

Amides from indole-3-glyoxylic acid and 4-benzoyl-2-methylpiperazine, which are related to entry inhibitors developed by Bristol-Myers Squibb (BMS), have been synthesized with aliphatic chains located at the C7 position of the indole ring. These spacers contain an azido group suitable for the well-known Cu(I)-catalyzed (3+2)-cycloaddition or an activated triple bond for the nucleophilic addition of thiols under physiological conditions. Reaction with polyols (β-cyclodextrin and hyperbranched polyglycerol) decorated with complementary click partners has afforded polyol-BMS-like conjugates that are not cytotoxic (TZM.bl cells) and retain the activity against R5-HIV-1_NLAD8 isolates. Thus, potential vaginal microbicides based on entry inhibitors, which can be called of 4^th generation, are reported here for the first time.     

一类分数阶微积分的中值定理及应用

Riemann-Liouville分数阶微积分算子是一类带有一个函数的分数阶微积分算子的特殊情形，以Riemann-Liouville分数阶微积分算子的积分中值定理和微分中值定理为基础，我们得到了一类带有一个函数的分数阶微积分算子的积分中值定理和微分中值定理，并给出其在计算方面的一些应用。     

磨矿动力学研究现状及应用

磨矿动力学是描述被磨物料的磨碎速率与磨矿时间之间关系规律的一种数学模型，对分析物料在磨矿过程中的粒级及能量变化具有重要作用。为充分发挥磨矿动力学在磨矿过程中的作用，论文在分析国内外研究现状的基础上，系统介绍了两种典型的磨矿动力学模型：m阶磨矿动力学模型和磨矿总体平衡动力学模型，分析了模型中各参数的含义；以磨矿总体平衡动力学模型为重点，分析了破碎速率函数和破碎分布函数的求解方式，包括零阶产出率法、奥-勒理论简算法、卡普尔G-H算法以及经验公式法等；从物料性质、磨矿介质及配比、磨矿方式及参数、化学添加剂等几个方面分析了影响磨矿动力学模型的因素；指出了磨矿动力学模型在矿物加工工程领域的应用现状并对其未来的研究方向提出展望。研究表明磨矿动力学在矿物加工领域具有广泛而重要的应用，为进一步改善磨矿工艺提供了理论依据。     

Stable power supply of an independent power source for a remote island using a Hybrid Energy Storage System composed of electric and hydrogen energy storage systems

We propose a self-sustaining power supply system consisting of a “Hybrid Energy Storage System (HESS)” and renewable energy sources to ensure a stable supply of high-quality power in remote islands. The configuration of the self-sustaining power supply system that can utilize renewable energy sources effectively on remote islands where the installation area is limited is investigated. It is found that it is important to select renewable energy sources whose output power curve is close to the load curve to improve the efficiency of the system. The operation methods that can increase the cost-effectiveness of the self-sustaining power supply system are also investigated. It is clarified that it is important for increasing the cost effectiveness of the self-sustaining power supply system to operate the HESS with a smaller capacity of its components by setting upper limits on the output power of the renewable energy sources and cutting the infrequent generated power.     

Protection and confinement effect of carbon on Co/CoxOy nano-catalyst for efficient NaBH4 hydrolysis

The hydrolysis of sodium borohydride (NaBH₄) over catalysts is a promising method to produce hydrogen. Although Co-based catalysts exhibit high activity for NaBH₄ hydrolysis, they are still far from satisfying practical applications, especially their poor durability in alkaline media. Herein, a carbon shell structure was designed and synthesized to improve the stability of the mixture of Co⁰ and Co_xO_y nanofilms (Co/Co_xO_y@C) during NaBH₄ hydrolysis via a facile polymerization-pyrolysis strategy with Co/Co_xO_y nanofilms as the precursor. As a result, the Co/Co_xO_y@C catalyst can achieve a remarkable H₂ generation rate of 4348.6 mL min^?1 g_Co^?1 with a low activation energy of 43.6 kJ mol^?1, which is superior to most previously reported catalysts. Moreover, the catalyst shows high stability with an H₂ generation-specific rate of 79% after five cycles. The excellent performance of carbon substrate can well prevent the agglomeration of Co-based nanoparticle and improve the corrosion resistance of the active Co to BO₂^? and OH^?. This work would widen the road for the preparation of nanoconfined catalysts, which has prospective application potentials for H₂ production from NaBH₄ hydrolysis.     

Entropy analysis of unsteady MHD three-dimensional flow of Williamson nanofluid over a convectively heated stretching sheet

This article addresses an investigation of the entropy analysis of Williamson nanofluid flow in the presence of gyrotactic microorganisms by considering variable viscosity and thermal conductivity over a convectively heated bidirectionally stretchable surface. Heat and mass transfer phenomena have been incorporated by taking into account the thermal radiation, heat source or sink, viscous dissipation, Brownian motion, and thermophoretic effects. The representing equations are nonlinear coupled partial differential equations and these equations are shaped into a set of ordinary differential equations via a suitable similarity transformation. The arising set of ordinary differential equations was then worked out by adopting a well-known scheme, namely the shooting method along with the Runge-Kutta-Felberge integration technique. The effects of flow and heat transfer controlling parameters on the solution variables are depicted and analyzed through the graphical presentation. The survey finds that magnifying viscosity parameter, Weissenberg number representing the non-Newtonian Williamson parameter cause to retard the velocity field in both the directions and thermal conductivity parameter causes to reduce fluid temperature. The study also recognizes that enhancing magnetic parameters and thermal conductivity parameters slow down the heat transfer rate. The entropy production of the system is estimated through the Bejan number. It is noticeable that the Bejan number is eminently dependent on the heat generation parameter, thermal radiation parameter, viscosity parameter, thermal conductivity parameter, and Biot number. The skillful accomplishment of the present heat and mass transfer system is achieved through the exteriorized choice of the pertinent parameters.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.