酪氨酸激酶抑制剂的三维定量构效关系研究

利用柔性原子受体模型(FLARM)方法对一系列的异黄酮和喹诺酮衍生物表皮生长因子受体酪氨酸激酶抑制剂进行了三维定量构效关系研究，得到了合理的构效关系模型.FLARM方法的计算结果还给出了虚拟的受体模型，该模型说明了抑制剂与受体之间可能的相互作用.由该虚拟受体模型得到的受体-配体相互作用与Novartis药效团模型比较类似.     

Unpaired 3d Electrons on Atomically Dispersed Cobalt Centres in Coordination Polymers Regulate both Oxygen Reduction Reaction (ORR) Activity and Selectivity for Use in Zinc–Air Batteries

Reversible oxygen conversion is important for various green energy technologies. Herein we synthesize a series of bimetallic coordination polymers by varying the Ni/Co ratio and using HITP (HITP=2,3,6,7,10,11-hexaiminotriphenylene) as the ligand, to interrogate the role of metal centres in modulating the activity of the oxygen reduction reaction (ORR). Co₃HITP₂ and Ni₃HITP₂ are compared. Unpaired 3d electrons in Co₃HITP₂ result in less coplanarity but more radical character. Thus, despite of a reduced crystallinity and conductivity, the best ORR activity, comparable to 20 % Pt/C, is obtained for Co₃HITP₂, showing the 3d orbital configuration of the metal centre promotes ORR. Experimental and DFT studies show a transition of ORR pathway from four-electron for Co₃HITP₂ to two-electron for Ni₃HITP₂. Rechargeable zinc–air batteries using Co₃HITP₂ as the air cathode catalyst demonstrate excellent energy efficiency and stability.     

Flexible thinking and met-befores: Impact on learning mathematics

In this paper we study the difficulties resulting from changes in meaning of the minus sign, from an operation on numbers, to a sign designating a negative number, to the additive inverse of an algebraic symbol on students in two-year colleges and universities. Analysis of the development of algebra reveals that these successive meanings that the student has met before often become problematic, leading to a fragile knowledge structure that lacks flexibility and leads to confusion and long-term disaffection. The problematic aspects that arise from changes in meaning of the minus sign are identified and the iconic function machine is utilized as a supportive strategy, along with formative assessment to encourage teachers and learners to seek more flexible and effective ways of making sense of increasingly sophisticated mathematics.     

A multi-stage stochastic programming approach in master production scheduling

Master Production Schedules (MPS) are widely used in industry, especially within Enterprise Resource Planning (ERP) software. The classical approach for generating MPS assumes infinite capacity, fixed processing times, and a single scenario for demand forecasts. In this paper, we question these assumptions and consider a problem with finite capacity, controllable processing times, and several demand scenarios instead of just one. We use a multi-stage stochastic programming approach in order to come up with the maximum expected profit given the demand scenarios. Controllable processing times enlarge the solution space so that the limited capacity of production resources are utilized more effectively. We propose an effective formulation that enables an extensive computational study. Our computational results clearly indicate that instead of relying on relatively simple heuristic methods, multi-stage stochastic programming can be used effectively to solve MPS problems, and that controllability increases the performance of multi-stage solutions.     

Adjustable robust counterpart of conic quadratic problems

This paper presents an approximate affinely adjustable robust counterpart for conic quadratic constraints. The theory is applied to obtain robust solutions to the problems of subway route design with implementation errors and a supply chain management with uncertain demands. Comparison of the adjustable solutions with the nominal and non-adjustable robust solutions shows that the adjustable (dynamic) robust solution maintains feasibility for all possible realizations, while being less conservative than the usual (static) robust counterpart solution.     

An effective architecture for learning and evolving flexible job-shop schedules

In recent years, the interaction between evolution and learning has received much attention from the research community. Some recent studies on machine learning have shown that it can significantly improve the efficiency of problem solving when using evolutionary algorithms. This paper proposes an architecture for learning and evolving of Flexible Job-Shop schedules called LEarnable Genetic Architecture (LEGA). LEGA provides an effective integration between evolution and learning within a random search process. Unlike the canonical evolution algorithm, where random elitist selection and mutational genetics are assumed; through LEGA, the knowledge extracted from previous generation by its schemata learning module is used to influence the diversity and quality of offsprings. In addition, the architecture specifies a population generator module that generates the initial population of schedules and also trains the schemata learning module. A large range of benchmark data taken from literature and some generated by ourselves are used to analyze the efficacy of LEGA. Experimental results indicate that an instantiation of LEGA called GENACE outperforms current approaches using canonical EAs in computational time and quality of schedules.     

A new model for classifying inputs and outputs and evaluating the performance of DMUs based on translog output distance function

In conventional data envelopment analysis it is assumed that the input versus output status of each chosen performance measures is known. In some conditions finding a statue of some variables from the point view of input or output is very difficult; these variables treat as both an input and output and are called flexible measures. This paper proposes a new model based on translog output distance function for classifying inputs and outputs and evaluating the performance of decision-making units by considering flexible measures. Monte Carlo simulation is applied to evaluate the presented model comparing with that of the recent model found in the literature. The result shows that the measure efficiencies of our model are statistically closer to true efficiencies and have higher rank correlation with true efficiencies. Also results obtained from simulated data show that there are high correlation between our model and that of the recent model.     

Glycol-assisted Cu-doped ZnS polyhedron-like structure as binder-free novel electrode materials

The facile, efficient, and straightforward preparation of electrode material for energy storage devices has drawn considerable interest for practical applications. In this study, we have synthesized the polyhedron Cu-doped ZnS (ZnS:Cu) structure on carbon cloth (CC) using a single-step glycol-assisted process. The highly interconnected polyhedron shaped ZnS:Cu functions as positive electrode material in an aqueous electrolyte for supercapacitor application. The ZnS:Cu polyhedron-like structures with higher electroactive sites and synergistic effect exhibited higher specific capacitance of 468 F g^?1 at 1 Ag^?1 and cycling stability of 890.5% after 5,000 cycles. The better electrochemical performance and higher cycling stability of ZnS:Cu can be dedicated to interconnected polyhedron-like structures, doping of Cu in ZnS, and binder-free electrode design. This underlines the potential of the Cu-doped ZnS-based supercapacitor for next-generation energy storage devices.     

Dual nanocatalysts co-decorated three-dimensional,laser-induced graphene hybrid nanomaterials integrated with a smartphone portable electrochemical system for point-of-care non-enzymatic glucose diagnosis

Non-enzymatic biosensors based on various nanomaterials with large surface-volume ratios and high catalytic efficiencies have been proposed to compensate for the non-stability and high cost of enzymatic biosensors. However, the construction of a stable, highly sensitive, flexible, three-dimensional (3D), microstructured, non-enzymatic biosensor integrated with a smartphone-based portable system has been challenging. Herein, highly conductive laser-induced graphene (LIG) array with a honeycomb-like 3D microstructure co-decorated with copper(I) oxide and gold nanocatalysts was developed via simple and green electro-deposition and chemical reduction approaches for a miniaturized electrochemical flexible non-enzymatic biosensor. SEM, XRD, Raman and XPS analyzations indicated that the Cu₂O and Au nanocatalysts co-decorated three-dimensional, laser-induced graphene hybrid nanomaterials were developed successfully. The signal of the biosensor was improved by more than 10 fold compared to the LIG alone due to the co-decorated with copper(I) oxide and gold nanocatalysts. The fabricated electrochemical biochip was integrated with a smartphone-based microstation for glucose monitoring, presenting a larger linear interval of 1–20 mM with an excellent sensitivity of 236 μA/mM/cm² and a relatively low detection limit of 0.31 μM. Noticeably, the biochip could measure blood sugar on curved surfaces and still deliver stable sensing signals after being bent back-and-forth 25 times. The novel biosensor is a potentially valuable flexible electronic device. The hybrid nanomaterials developed in this work may be applicable to other biosensing, catalytic, and energy devices (supercapacitors and batteries).