High lithium storage of Co3O4 enabled by integrating hollow and porous carbon scaffolds

The Co₃O₄, as a potential anode of lithium-ion batteries, has gained considerable attention because of high theoretical capacity. However, the Co₃O₄ is suffering from serious structure deterioration and rapid capacity fading due to its bulky volume change during cyclic charge/discharge process. Herein, to stabilize the lithium storage performance of the Co₃O₄ nanoparticles, a characteristic carbon scaffold (HPC) integrating hollow and porous structures has been fabricated by a well-designed method for the first time. The ultrafine Co₃O₄ nanoparticles are cleverly anchored on the HPC (HPC@Co₃O₄) and hence achieve significantly improved electrochemical properties including high capacity, improved reaction kinetics and outstanding cycle stability, showing high capacity of 1084.7 mAh g^-1 after 200 cycles at 200 mA g^-1 as well as 681.4 mAh g^-1 after 300 cycles at 1000 mA g^-1. The HPC@Co₃O₄ therefore shows good promising for application in advanced lithium-ion battery anodes. The results of the systematically material and electrochemical characterizations indicate that the synergistic effects of ultrafine Co₃O₄ nanoparticles and well-designed HPC scaffolds are responsible for the outstand performance of the HPC@Co₃O₄ anode. Moreover, this work can enrich the understanding and development of stable and high-performance metal oxide-based lithium-ion battery anodes for advanced lithium storage.     

Real time power management strategy for fuel cell hybrid electric bus based on Lyapunov stability theorem

The fuel cell/battery durability and hybrid system stability are major considerations for the power management of fuel cell hybrid electric bus (FCHEB) operating on complicated driving conditions. In this paper, a real time nonlinear adaptive control (NAC) with stability analyze is formulated for power management of FCHEB. Firstly, the mathematical model of hybrid power system is analyzed, which is established for control-oriented design. Furthermore, the NAC-based strategy with quadratic Lyapunov function is set up to guarantee the stability of closed-loop power system, and the power split between fuel cell and battery is controlled with the durability consideration. Finally, two real-time power management strategies, state machine control (SMC) and fuzzy logic control (FLC), are implemented to evaluate the performance of NAC-based strategy, and the simulation results suggest that the guaranteed stability of NAC-based strategy can efficiently prolong fuel cell/battery lifespan and provide better fuel consumption economy for FCHEB.     

计算机网络办公自动化及安全策略探究

互联网通讯采取标准化模式主要以TCP/IP协议为载体,通讯的优越特性体现在同时具备便捷性与开放性,为办公提供很大的便利,但基于网络系统也会入侵病毒、也会给信息数据与办公体系安全性造成威胁,直接影响企业综合稳定发展。据此,为保障办公工作的顺利开展,本文对计算机网络办公自动化及安全策略进行详细分析。     

设计扶贫视角下西兰卡普的可持续设计策略研究

目的 探索湖北恩施地区西兰卡普的可持续设计策略。方法 分析隐身于贫困乡村的西兰卡普的文化价值和经济价值，对其进行设计转化的重要性与可行性分析；对湖北恩施地区西兰卡普进行实地考察与调研，梳理其可持续发展中所面临的传承主体和市场竞争方面的典型性困境与原由；以设计赋能传统手工艺作为设计扶贫的重要手段，基于西兰卡普所面临的困局系统性地提出其可持续性发展的设计策略。结论 在设计扶贫中，应构建多元主体参与、协同合作的再创新机制，注重强化造血能力，塑造地域名片，围绕当代消费需求，结合数字化技术进行展销模式、产品供给的升级和创新设计，并整合优势资源，实现产业的融合发展。总之，系统性地对西兰卡普进行设计赋能，是西兰卡普获得可持续性发展效力并助力当地贫困乡村经济发展的有效路径。     

某冰蓄冷项目运行策略分析

结合某办公建筑,对3种冰蓄冷运行策略进行对比分析。结果显示:3种运行策略日总移峰电量分别为28575 kW·h,28395 kW·h和28215 kW·h,日移高峰电量分别为16785 kW·h,21645 kW·h和17415 kW·h,单日节省电费分别为12464元,13082元和13509元;运行策略3的日总移峰电量最低,经济效益最高。指出冰蓄冷运行策略不能采用恒定制冷机组制冷量的运行方式,要综合考虑系统的负荷特征、融冰释冷量比例及制冷机组效率等因素。     

Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review

Against the background of smart manufacturing and Industry 4.0, how to achieve real-time scheduling has become a problem to be solved. In this regard, automatic design for shop scheduling based on hyper-heuristics has been widely studied, and a number of reviews and scheduling algorithms have been presented. Few studies, however, have specifically discussed the technical points involved in algorithm development. This study, therefore, constructs a general framework for automatic design for shop scheduling strategies based on hyper-heuristics, and various state-of-the-art technical points in the development process are summarized. First, we summarize the existing types of shop scheduling strategies and classify them using a new classification method. Second, we summarize an automatic design algorithm for shop scheduling. Then, we investigate surrogate-assisted methods that are popular in the current algorithm field. Finally, current problems and challenges are discussed, and potential directions for future research are proposed.     

Anticancer activity of flavonoids accompanied by redox state modulation and the potential for a chemotherapeutic strategy

Since researchers began studying the mechanism of flavonoids’ anticancer activity, little attention has been focused on the modulation of redox state in cells as a potential chemotherapeutic strategy. However, recent studies have begun identifying that the anticancer effect of flavonoids occurs both in their antioxidative activity which scavenges ROS and their prooxidative activity which generates ROS. Against this backdrop, this study attempts to achieve a comprehensive analysis of the individual and separate study findings regarding flavonoids’ modulation of redox state in cancer cells. It focuses on the mechanism behind the anticancer effect, and mostly on the modulation of redox potential by flavonoids such as quercetin, hesperetin, apigenin, genistein, epigallocatechin-3-gallate (EGCG), luteolin and kaempferol in both in vitro and animal models. In addition, the clinical applications of and bioavailability of flavonoids were reviewed to help build a treatment strategy based on flavonoids’ prooxidative potential.     

Distributed two‐stage state estimation with event‐triggered strategy for multirate sensor networks

This paper investigates the state estimation issue for a class of wireless sensor networks (WSNs) with the consideration of limited energy resources. First, a multirate estimation model is established, and then, a new event‐triggered two‐stage information fusion algorithm is developed based on the optimal fusion criterion weighted by matrices. Compared with the existing methods, the presented fusion algorithm can significantly reduce the communication cost in WSNs and save energy resources of sensors efficiently. Furthermore, by presetting a desired containment probability over the interval [0,1] with the developed event‐triggered mechanism, one can obtain a suitable compromise between the communication cost and the estimation accuracy. Finally, a numerical simulation for the WSN tracking system is given to demonstrate the effectiveness of the proposed method.     

A three-dimensional surface measurement system implemented with Gaussian process based adaptive sampling

Self-adaptive surface measurements that can reduce data redundancy and improve time efficiency are in high demand in many fields of science and technology. For this purpose, a system implemented with Gaussian process (GP) adaptive sampling is developed. The non-parametric GP model is applied to reconstruct the topography and guide the subsequent sampling position, which is determined from the inference uncertainty estimation. A criterion is proposed to terminate the GP adaptive measurement automatically without any prior model or data of the topography. Experiments on typical surfaces validate the intelligence, adaptability, and high accuracy of the GP method along with the stabilization of the automatic iteration termination. Compared with traditional raster sampling, data redundancy is reduced and the time efficiency is improved without sacrificing the surface reconstruction accuracy. The proposed method can be implemented in other systems with similar measurement principles, thus benefitting surface characterizations.     

Engineering Nanorobots for Tumor-Targeting Drug Delivery: From Dynamic Control to Stimuli-Responsive Strategy

Nanotechnology has been widely applied to the fabrication of drug delivery systems in the past decades. Recently, with the progress made in microfabrication approaches, nanorobots are steadily becoming a promising means for tumor-targeting drug delivery. In general, nanorobots can be divided into two categories: nanomotors and stimuli-responsive nanorobots. Nanomotors are nanoscale systems with the ability to convert surrounding energies into mechanical motion, whereas stimuli-responsive nanorobots are featured with activatable capacity in response to various endogenous and exogenous stimulations. In this minireview, the dynamic control of nanomotors and the rational design of stimuli-responsive nanorobots are overviewed, with particular emphasis on their contribution to tumor-targeting therapy. Moreover, challenges and perspectives associated with the future development of nanorobots are presented.