Two-step pyrolytic engineering to form porous nitrogen-rich carbons with a 3D network structure for Zn-air battery oxygen reduction electrocatalysis

It is of great urgency to design inexpensive and high-performance oxygen reduction reaction (ORR) electrocatalysts derived from biowastes as substitutes for Pt-based materials in electrochemical energy-conversion devices. Here we propose a strategy to synthesize three-dimensional (3D) porous nitrogen-doped network carbons to catalyze the ORR from two-step pyrolysis engineering of biowaste scale combined with the use of a ZnCl₂ activator and a FeCl₂ promotor. Electrochemical tests show that the synthesized network carbons have exhibited comparable ORR catalytic activity with a half-wave potential (~0.85 V vs. RHE) and outstanding cyclical stability in comparison to the Pt/C catalyst. Beyond that, a high electron transfer number (~3.8) and a low peroxide yield (<7.6%) can be obtained, indicating a four-electron reaction pathway. The maximum power density is ~68 mW cm^?2, but continuous discharge curves (at a constant potential of ~1.30 V) for 12 h are not obviously declined in Zn-air battery tests using synthesized network carbons as the cathodic catalyst. The formation of 3D porous structures with high BET surface area can effectively expose the surface catalytic sites and promote mass transportation to boost the ORR activity. This work may open a new idea to prepare porous carbon-based catalysts for some important reactions in new energy devices.     

A new method for distributing power usage across a sensor network

We present a method for more uniformly distributing the energy burden across a wireless ground-based sensor network communicating with an overhead unmanned aerial vehicle (UAV). A subset of sensor nodes, termed a transmit cluster, receives and aggregates data gathered by the entire network, and forms a distributed antenna array, concentrating the radiated transmission into a narrow beam aimed towards the UAV. Because these duties are power-intensive, the role of transmit cluster must be shifted to different nodes as time progresses. We present an algorithm to reassign the transmit cluster, specifying the time that should elapse between reassignments and the number of hops that should be placed between successive transmit clusters in order to achieve three competing goals: first, we wish to better and more broadly spread the energy load across the sensor network while, second, minimizing the energy expended in moving the transmit cluster, all the while, third, reducing to the extent practicable the time to bring the UAV and the sensor network’s beam into alignment. Additionally, we present a method for reconfiguring the communication burden between the ground-based sensor network and the UAV. We describe and analyze two alternative strategies to bring the UAV and the sensor network’s beam into alignment, while minimizing the energy expended by the sensor network. The performance of the two strategies is compared in terms of probability of beam-UAV alignment as a function of time, and the expected time to alignment. We examine the performance tradeoff between the choice of strategy and parameters of the sensor network that affect power conservation.     

Calculations of optical properties in p-doped nitrides quaternary alloys multiple quantum wells

We present theoretical photoluminescence (PL) and absorption spectra of p-doped InGaN/AlInGaN and AlInGaN/InGaN multiple quantum wells (MQWs). The calculations were performed within the k.p method by means of solving a full eight-band Kane Hamiltonian together with the Poisson equation in a plane wave representation, including exchange–correlation effects within the local density approximation. Strain effects due to the lattice mismatch and an internal electric field are also taken into account. We show that by changing the In and Al composition we can reach short and long emission wavelengths. The trends in the calculated Stokes shift, due to many-body effects within the quasi-two-dimensional hole gas (2DHG), are analyzed as a function of the acceptor doping concentration. Since the studies of optical properties of quantum wells based on nitrides quaternary alloys are at an early stage, the results reported here will provide guidelines for the interpretation of forthcoming experiments.     

Stereocomplex Crystallization Induced Significant Improvement in Transparency and Stiffness–Toughness Performance of Core-Shell Rubber Nanoparticles Toughened Poly(l-lactide) Blends

Toughening modification of poly(l -lactide) (PLLA) with rubber particles is often realized at the cost of transparency, mechanical strength, and modulus because high rubber loadings are generally required for toughening. In this work, a promising strategy to simultaneously improve the transparency and stiffness–toughness performance of poly(butyl acrylate)-poly(methyl methacrylate) (BAMMA) core-shell rubber nanoparticles toughened PLLA blends by utilizing the stereocomplex (SC) crystallization between PLLA and poly(d -lactide) (PDLA) is devised. The results reveal that the construction of SC crystallites in PLLA matrix via melt-mixing PLLA/BAMMA blends with PDLA can prevent BAMMA nanoparticles from aggregation and promote them to form network-like structure at lower contents. As a result, not only higher toughening efficiency with less rubber contents but also superior transparency is achieved in the PLLA/PDLA/BAMMA blends as compared with the PLLA/BAMMA ones where large aggregated BAMMA clusters are formed. Moreover, the outstanding reinforcement of SC crystallites network for PLLA can impart an enhanced tensile strength and modulus to PLLA/PDLA/BAMMA blends, thus improving the stiffness–toughness performance of PLLA/PDLA/BAMMA blends to a higher degree. This work demonstrates that SC crystallization is a promising solution to solve the contradiction between transparency and mechanical properties and then obtain superior comprehensive performances in rubber toughened PLLA blends.     

Context-aware cognitive design assistant: Implementation and study of design rules recommendations

Design rules are an essential interface to facilitate the information exchange between designers and experts. Despite many innovations in Knowledge-Based Engineering and Knowledge Management, unstructured design rules documents are still widely used in the manufacturing industry. Due to the complexity of the design process, these documents often contain hundreds of design rules, applicable in varying design contexts. Searching for the right rules according to a design context is demanding in time and cognitive resources. In this paper, we propose a Context-Aware Cognitive Design Assistant (CACDA) to capture the design context and perform tasks such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Contrary to existing works in model quality testing, the CACDA uses a proactive approach of design rules application and helps designers to provide error-free designs on first attempt. In this paper, we present the design rules recommendation system of the CACDA, its capabilities and its implementation. Then, to measure the impact of design rules recommendations on the design process, we compare our approach with the use of traditional design rules documents. Results show that the use of the CACDA’s design rules recommendations lower the perceived difficulty of design rules retrieval from 75 to 43.5 on a scale of 100. On average, participants that used the demonstrator successfully applied 8.6 design rules on the 25 applicable design rules of our set. Participants that used unstructured documentation correctly applied 4.3 design rules. The global cognitive weight of the design activity as well as the design rules retrieval performances appear to be unchanged. These results demonstrate the usability of the demonstrator and show a positive impact on the design process and on the quality of CAD models. Future works will focus on the overcome of the main limitations of our current experiments, with a panel of professional designers, a lager design rules set and the implementation of several lacking features of the CACDA into the demonstrator.     

基于径向基函数的多目标回归特征构建算法

多目标回归（MTR）是一种针对单个样本同时具有多个连续型输出的回归问题。现有的多目标回归算法都基于同一个特征空间学习回归模型,而忽略了各输出目标本身的特殊性质。针对这一问题,提出基于径向基函数的多目标回归特征构建算法。首先,将各目标的输出作为额外的特征对各输出目标进行聚类,根据聚类中心在原始特征空间构成了目标特定特征空间的基;然后,通过径向基函数将原始特征空间映射到目标特定特征空间,构造目标特定的特征,并基于这些目标特定特征构建各输出目标的基回归模型;最后,用基回归模型的输出组成隐藏空间,采用低秩学习算法在其中发掘和利用输出目标之间的关联。在18个多目标回归数据集上进行实验,并把所提算法与层叠单目标回归（SST）、回归器链集成（ERC）、多层、多目标回归（MMR）等经典的多目标回归算法进行对比,结果表明所提算法在14个数据集上都取得了最好的性能,并且在18个数据集上的平均性能排序居第一位。可见所提算法构建的目标特定特征能够提高各输出目标的预测准确性,并结合低秩学习得到输出目标间的关联性以从整体上提升多目标回归的预测性能。     

海绵城市生物滞留设施关键技术研究进展

海绵城市生物滞留设施是适用于分散式雨水处理与利用的代表性生态技术,但目前仍旧存在一些问题制约其运行效率及寿命。综述了海绵城市建设中生物滞留设施关键技术及国内外研究进展,指出高效净化能力填料的研制、污染物迁移转化模型的建立及设计参数的优化、有机微污染物累积风险评价及修复技术的构建等将成为海绵城市生物滞留设施未来的研究热点。     

Prediction of urban stormwater quality using artificial neural networks

There are a vast number of complex, interrelated processes influencing urban stormwater quality. However, the lack of measured fundamental variables prevents the construction of process-based models. Furthermore, hybrid models such as the buildup-washoff models are generally crude simplifications of reality. This has created the need for statistical models, capable of making use of the readily accessible data. In this paper, artificial neural networks (ANN) were used to predict stormwater quality at urbanized catchments located throughout the United States. Five constituents were analysed: chemical oxygen demand (COD), lead (Pb), suspended solids (SS), total Kjeldhal nitrogen (TKN) and total phosphorus (TP). Multiple linear regression equations were initially constructed upon logarithmically transformed data. Input variables were primarily selected using a stepwise regression approach, combined with process knowledge. Variables found significant in the regression models were then used to construct ANN models. Other important network parameters such as learning rate, momentum and the number of hidden nodes were optimized using a trial and error approach. The final ANN models were then compared with the multiple linear regression models. In summary, ANN models were generally less accurate than the regression models and more time consuming to construct. This infers that ANN models are not more applicable than regression models when predicting urban stormwater quality.     

Robust and efficient direction identification for groupwise additive multiple-index models and its applications

This paper concerns robust and efficient direction identification for a groupwise additive multiple-index model, in which each additive function has a single-index structure. Interestingly, without involving non-parametric approach, we show that the directions of all the index parameter vectors can be recovered by a simple linear composite quantile regression (CQR). As a specific application, a iterative-free CQR estimation procedure for the partially linear single-index model is proposed. Furthermore, it can also be used to develop a penalized CQR procedure for variable selection in the high-dimensional settings. The new method has superiority in robustness and efficiency by inheriting the advantage of the CQR approach. Simulation results and real-data analysis also confirm our method.     

基于UML的实验室预约管理系统设计

针对高等院校开放性实验管理上的不足,利用SSH框架、UML与SQL Server 2005等软件构建了基于UML的实验预约管理系统。利用JAVA语言与UML技术,实现了实验预约、审核、查询和管理等功能。该系统使实验室的开放管理科学化,仪器设备使用效能最大化。