基于综合用电煤耗的电力高质量发展评价指标研究及应用

煤电平均供电煤耗作为评价能源系统效率的指标已在行业内广泛应用。但该项指标无法很好地反映一个国家、地区的用电结构与用电能耗水平。为此，参照行业内普遍采用的火电厂发电煤耗和供电煤耗指标，定义“综合用电煤耗”，即用户每用1 kW?h电“消耗”的标准煤量（单位:g/(kW?h)）。在此基础上，提出“综合发电煤耗”与“综合供电煤耗”的概念，形成完整的指标评价体系。对近年典型省份指标值的研究表明，该指标体系能够很好地反映供给侧国家、省区或企业的发电结构与电能生产能耗水平。这一指标体系能够为正确理解和计算用电、输电与电能替代项目的化石能源消耗量及其排放量提供依据。     

超分子作用阻尼材料的动态力学模型修正

为提高飞机起落架载荷实测精度，以支柱式起落架结构为研究对象，探讨处理其载荷标定数据的工程方法。首先，对实际受载情况和单向加载工况标定数据的分析，挖掘出三向载荷、缓冲支柱压缩行程与应变码值间的数学关系，将预测的数学关系代入到多向工况的标定数据中，验证了它们的准确性;其次，根据两种标定数据回归方法，提出了对应使用的支柱式起落架载荷-应变标定方程的数学模型;最后，将两种标定方程代回到标定数据中，计算起落架三向载荷，所有反算载荷的误差均在可控范围内，表明标定方程满足精度要求。将标定方程代入实测数据中，实测曲线符合变化规律。该处理方法的应用能有力提高支柱式起落架的起飞-着陆载荷实测和载荷谱编制的准确度。     

A Dual Inhibitor of DYRK1A and GSK3β for β-Cell Proliferation: Aminopyrazine Derivative GNF4877

Loss of β-cell mass and function can lead to insufficient insulin levels and ultimately to hyperglycemia and diabetes mellitus. The mainstream treatment approach involves regulation of insulin levels; however, approaches intended to increase β-cell mass are less developed. Promoting β-cell proliferation with low-molecular-weight inhibitors of dual-specificity tyrosine-regulated kinase 1A (DYRK1A) offers the potential to treat diabetes with oral therapies by restoring β-cell mass, insulin content and glycemic control. GNF4877, a potent dual inhibitor of DYRK1A and glycogen synthase kinase 3β (GSK3β) was previously reported to induce primary human β-cell proliferation in vitro and in vivo. Herein, we describe the lead optimization that lead to the identification of GNF4877 from an aminopyrazine hit identified in a phenotypic high-throughput screening campaign measuring β-cell proliferation.     

水泥厂堆取料机轨道梁设计优化及安装

正0前言近年来苏州中材建设有限公司承建了多条海外5 000 t/d水泥熟料生产线EPC总承包项目。原燃料堆场多采用圆型堆场或长型堆场,例如400 m长型辅助原料堆场有约1 600 m轨道梁、4 200多支螺栓,直径Φ90 m圆型堆场有约282 m轨道梁、1150支螺栓,合计共有约5 350多支螺栓需要安装,燃料为煤的生产线,工作量更是庞大。堆取料机轨道行程较长,对固定轨道螺栓的安     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.