Carbon and Nitrogen Based Nanosheets as Fluorescent Probes with Tunable Emission

2D carbon and nitrogen based semiconductors (CN) have attracted widespread attention for their possible use as low‐cost and environmentally friendly materials for various applications. However, their limited solution‐dispersibility and the difficulty in preparing exfoliated sheets with tunable photophysical properties restrain their exploitation in imaging‐related applications. Here, the synthesis of carbon and nitrogen organic scaffolds with highly tunable optical properties, excellent dispersion in water and DMSO, and good bioimaging properties is reported. Tailored photophysical and chemical properties are acquired by the synthesis of new starting monomers containing different substituent chemical groups with varying electronic properties. Upon monomer condensation at moderate temperature, 350 °C, the starting chemical groups are fully preserved in the final CN. The low condensation temperature and the effective molecular‐level modification of the CN scaffold lead to well‐dispersed photoluminescent CN thin sheets with a wide range of emission wavelengths. The good bioimaging properties and the tunable fluorescence properties are exemplified by in situ visualization of giant unilamellar vesicles in a buffered aqueous solution as a model system. This approach opens the possibility for the design of tailor‐made CN materials with tunable photophysical and chemical properties toward their exploitation in various fields, such as photocatalysis, bioimaging, and sensing.     

Carbon‐Based Alloy‐Type Composite Anode Materials toward Sodium‐Ion Batteries

In the scenario of renewable clean energy gradually replacing fossil energy, grid‐scale energy storage systems are urgently necessary, where Na‐ion batteries (SIBs) could supply crucial support, due to abundant Na raw materials and a similar electrochemical mechanism to Li‐ion batteries. The limited energy density is one of the major challenges hindering the commercialization of SIBs. Alloy‐type anodes with high theoretical capacities provide good opportunities to address this issue. However, these anodes suffer from the large volume expansion and inferior conductivity, which induce rapid capacity fading, poor rate properties, and safety issues. Carbon‐based alloy‐type composites (CAC) have been extensively applied in the effective construction of anodes that improved electrochemical performance, as the carbon component could alleviate the volume change and increase the conductivity. Here, state‐of‐the‐art CAC anode materials applied in SIBs are summarized, including their design principle, characterization, and electrochemical performance. The corresponding alloying mechanism along with its advantages and disadvantages is briefly presented. The crucial roles and working mechanism of the carbon matrix in CAC anodes are discussed in depth. Lastly, the existing challenges and the perspectives are proposed. Such an understanding critically paves the way for tailoring and designing suitable alloy‐type anodes toward practical applications.     

SMArDT modeling for automotive software testing

Efficient testing is a crucial prerequisite to engineer reliable automotive software successfully. However, manually deriving test cases from ambiguous textual requirements is costly and error-prone. Model-based software engineering captures requirements in structured, comprehensible, and formal models, which enables early consistency checking and verification. Moreover, these models serve as an indispensable basis for automated test case derivation. To facilitate automated test case derivation for automotive software engineering, we conducted a survey with testing experts of the BMW Group and conceived a method to extend the BMW Group's specification method for requirements, design, and test methodology by model-based test case derivation. Our method is realized for a variant of systems modeling language activity diagrams tailored toward testing automotive software and a model transformation to derive executable test cases. Hereby, we can address many of the surveyed practitioners' challenges and ultimately facilitate quality assurance for automotive software.     

Cache Memory Design for Single Bit Architecture with Different SenseAmplifiers

Most modern microprocessors have one or two levels of on-chip caches to make things run faster, but this is not always the case. Most of the time, these caches are made of static random access memory cells. They take up a lot of space on the chip and use a lot of electricity. A lot of the time, low power is more important than several aspects. This is true for phones and tablets. Cache memory design for single bit architecture consists of six transistors static random access memory cell, a circuit of write driver, and sense amplifiers (such as voltage differential sense amplifier, current differential sense amplifier, charge transfer differential sense amplifier, voltage latch sense amplifier, and current latch sense amplifier, all of which are compared on different resistance values in terms of a number of transistors, delay in sensing and consumption of power. The conclusion arises that single bit six transistor static random access memory cell voltage differential sense amplifier architecture consumes 11.34 μW of power which shows that power is reduced up to 83%, 77.75% reduction in the case of the current differential sense amplifier, 39.62% in case of charge transfer differential sense amplifier and 50% in case of voltage latch sense amplifier when compared to existing latch sense amplifier architecture. Furthermore, power reduction techniques are applied over different blocks of cache memory architecture to optimize energy. The single-bit six transistors static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique consumes 8.078 μW of power, i.e., reduce 28% more power that makes single bit six transistor static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique more energy efficient.     

Simulated evolution and simulated annealing algorithms for solving multi-objective open shortest path first weight setting problem

Optimal utilization of resources in present-day communication networks is a challenging task. Routing plays an important role in achieving optimal resource utilization. The open shortest path first (OSPF) routing protocol is widely used for routing packets from a source node to a destination node. This protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest path between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. This paper formulates the OSPF weight setting problem as a multi-objective optimization problem, with maximum utilization, number of congested links, and number of unused links as the optimization objectives. Since the objectives are conflicting in nature, an efficient approach is needed to balance the trade-off between these objectives. Fuzzy logic has been shown to efficiently solve multi-objective optimization problems. A fuzzy cost function for the OSPF weight setting problem is developed in this paper based on the Unified And-OR (UAO) operator. Two iterative heuristics, namely, simulated annealing (SA) and simulated evolution (SimE) have been implemented to solve the multi-objective OSPF weight setting problem using a fuzzy cost function. Results are compared with that found using other cost functions proposed in the literature (Sqalli et al. in Network Operations and Management Symposium, NOMS, 2006). Results suggest that, overall, the fuzzy cost function performs better than existing cost functions, with respect to both SA and SimE. Furthermore, SimE shows superior performance compared to SA. In addition, a comparison of SimE with NSGA-II shows that, overall, SimE demonstrates slightly better performance in terms of quality of solutions.     

Top-k outlier detection from uncertain data

Uncertain data are common due to the increasing usage of sensors, radio frequency identification(RFID), GPS and similar devices for data collection. The causes of uncertainty include limitations of measurements, inclusion of noise, inconsistent supply voltage and delay or loss of data in transfer. In order to manage, query or mine such data, data uncertainty needs to be considered. Hence,this paper studies the problem of top-k distance-based outlier detection from uncertain data objects. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. The naive approach of distance-based outlier detection makes use of nested loop. This approach is very costly due to the expensive distance function between two uncertain objects. Therefore,a populated-cells list(PC-list) approach of outlier detection is proposed. Using the PC-list, the proposed top-k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top-k outliers. Two approximate top-k outlier detection algorithms are presented to further increase the efficiency of the top-k outlier detection algorithm.An extensive empirical study on synthetic and real datasets is also presented to prove the accuracy, efficiency and scalability of the proposed algorithms.     

Carbon nanotube‐based thermoplastic polyurethane‐poly(methyl methacrylate) nanocomposites for pressure sensing applications

We have synthesized unique flexible pressure‐sensitive nanocomposites by means of a solution mixing method, by adding multiwalled carbon nanotubes (MWCNTs) into a thermoplastic urethane (TPU) matrix along with poly(methyl methacrylate) (PMMA) microbeads of various sizes. The influence of the various PMMA bead sizes on the pressure sensing properties of the nanocomposites was studied over a range of pressures. The PMMA microbeads were used to achieve an early percolation threshold at low loadings of MWCNTs. We used scanning electron microscopy to study the nanocomposites' morphology, and conducted differential scanning calorimetry analyses to investigate their thermal properties. The nanocomposites' electrical and thermal conductivities were also measured under various applied pressures. The nanocomposites displayed repeatable electrical responses under various applied pressures, demonstrating their suitability for use as pressure sensing materials. The proposed material is an ideal candidate for use in the preparation of pressure‐sensitive devices. POLYM. ENG. SCI. 56:1031–1036, 2016. © 2016 Society of Plastics Engineers     

高负荷轴流压气机内以氦-氙混合气为工作介质的评估（英文）

本文评价了分子量为15g/mole的氦-氙双项混合气体作为流动工质在核电厂轴流压气机的应用。由于纯氦具有普遍的传输特性,在很多情况下很难被压缩,因此是一种最佳的冷却剂。同时,纯氦工质在高温气冷堆(HTGR)的应用引起了涡轮机械的尺寸更大、质量越来越大、成本更高及叶轮机械的动力问题。为此,本文设计了一台高负荷氦氙压气机,并对其性能进行了分析。结果显示,只有18%的氦气压气机级需要在高负荷氦气-氙气压气机中将气体压缩至所需压力。通过设计分析,工作在闭式布雷顿循环(CBC)中高温气冷堆(HTGR)压气机,可由16级减少到2级。因此,在高温气冷堆电厂的涡轮压压气中,氦氙比纯氦更有优势。     

Average Convergence for Directed & Undirected Graphs in Distributed Systems

Consensus control of multi-agent systems is an innovative paradigm for the development of intelligent distributed systems. This has fascinated numerous scientific groups for their promising applications as they have the freedom to achieve their local and global goals and make their own decisions. Network communication topologies based on graph and matrix theory are widely used in a various real-time applications ranging from software agents to robotics. Therefore, while sustaining the significance of both directed and undirected graphs, this research emphases on the demonstration of a distributed average consensus algorithm. It uses the harmonic mean in the domain of multi-agent systems with directed and undirected graphs under static topologies based on a control input scheme. The proposed agreement protocol focuses on achieving a constant consensus on directional and undirected graphs using the exchange of information between neighbors to update their status values and to be able to calculate the total number of agents that contribute to the communication network at the same time. The proposed method is implemented for the identical networks that are considered under the directional and non-directional communication links. Two different scenarios are simulated and it is concluded that the undirected approach has an advantage over directed graph communication in terms of processing time and the total number of iterations required to achieve convergence. The same network parameters are introduced for both orientations of the communication graphs. In addition, the results of the simulation and the calculation of various matrices are provided at the end to validate the effectiveness of the proposed algorithm to achieve consensus.     

Reliability improvement in distribution systems employing an integrated voltage sag mitigation method using binary gravitational search algorithm

A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy, namely, distribution network reconfiguration （DNR） followed by DSTATCOM placement. Initially, an optimal DNR is applied to reduce the propagated voltage sags during the test period. The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network. The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM. Reliability assessment is performed using the well-known indices. The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.