Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions

Diesel engines are fuel efficient which benefits the reduction of CO₂ released to the atmosphere compared with gasoline engines, but still result in negative environmental impact related to their emissions. As new degrees of freedom are created, due to advances in technology, the complicated processes of emission formation are difficult to assess. This paper studies the feasibility of using artificial neural networks (ANNs) in combination with genetic algorithms (GAs) to optimize the diesel engine settings. The objective of the optimization was to find settings that complied with the increasingly stringent emission regulations while also maintaining, or even reducing the fuel consumption. A large database of stationary engine tests, covering a wide range of experimental conditions was used for this analysis. The ANNs were used as a simulation tool, receiving as inputs the engine operating parameters, and producing as outputs the resulting emission levels and fuel consumption. The ANN outputs were then used to evaluate the objective function of the optimization process, which was performed with a GA approach. The combination of ANN and GA for the optimization of two different engine operating conditions was analyzed and important reductions in emissions and fuel consumption were reached, while also keeping the computational times low     

Facile Preparation of Multifunctional WS2/WOx Nanodots for Chelator‐Free 89Zr‐Labeling and In Vivo PET Imaging

While position emission tomography (PET) is an important molecular imaging technique for both preclinical research and clinical disease diagnosis/prognosis, chelator‐free radiolabeling has emerged as a promising alternative approach to label biomolecules or nanoprobes in a facile way. Herein, starting from bottom‐up synthesized WS₂ nanoflakes, this study fabricates a unique type of WS₂/WO_x nanodots, which can function as inherent hard oxygen donor for stable radiolabeling with Zirconium‐89 isotope (⁸⁹Zr). Upon simply mixing, ⁸⁹Zr can be anchored on the surface of polyethylene glycol (PEG) modified WS₂/WO_x (WS₂/WO_x‐PEG) nanodots via a chelator‐free method with surprisingly high labeling yield and great stability. A higher degree of oxidation in the WS₂/WO_x‐PEG sample (WS₂/WO_x (0.4)) produces more electron pairs, which would be beneficial for chelator‐free labeling of ⁸⁹Zr with higher yields, suggesting the importance of surface chemistry and particle composition to the efficiency of chelator‐free radiolabeling. Such ⁸⁹Zr‐WS₂/WO_x (0.4)‐PEG nanodots are found to be an excellent PET contrast agent for in vivo imaging of tumors upon intravenous administration, or mapping of draining lymph nodes after local injection.     

State-based predictions with self-correction on Enterprise Desktop Grid environments

The abundant computing resources in current organizations provide new opportunities for executing parallel scientific applications and using resources. The Enterprise Desktop Grid Computing (EDGC) paradigm addresses the potential for harvesting the idle computing resources of an organization’s desktop PCs to support the execution of the company’s large-scale applications. In these environments, the accuracy of response-time predictions is essential for effective metascheduling that maximizes resource usage without harming the performance of the parallel and local applications. However, this accuracy is a major challenge due to the heterogeneity and non-dedicated nature of EDGC resources. In this paper, two new prediction techniques are presented based on the state of resources. A thorough analysis by linear regression demonstrated that the proposed techniques capture the real behavior of the parallel applications better than other common techniques in the literature. Moreover, it is possible to reduce deviations with a proper modeling of prediction errors, and thus, a Self-adjustable Correction method (SAC) for detecting and correcting the prediction deviations was proposed with the ability to adapt to the changes in load conditions. An extensive evaluation in a real environment was conducted to validate the SAC method. The results show that the use of SAC increases the accuracy of response-time predictions by 35%. The cost of predictions with self-correction and its accuracy in a real environment was analyzed using a combination of the proposed techniques. The results demonstrate that the cost of predictions is negligible and the combined use of the prediction techniques is preferable.     

Feasibility of neural network approach in spectral mixture analysis of reflectance spectra

In the present work, we perform spectral mixture analysis using Chi‐square minimization (χ² minimization) procedure and test the feasibility of applying an inverse technique, neural network (NN) approach, for the spectral unmixing. The training of NN is carried out using the Levenberg–Marquardt algorithm (LM) with the initial weights for training being chosen randomly. The experiments are performed in the laboratory by mixing young, matured and dead leaves of a sequoia tree in various proportions and reflectance spectra of these mixtures are recorded. The proportions are chosen to model a few near‐real situations like different kinds of vegetation in a forest (by mixing young leaves and matured leaves) and trees damaged in a forest fire or affected by certain virus (by mixing matured and dead leaves) and a combination of all these (by mixing young, matured and dead leaves). The spectral mixture analysis employing χ² minimization and the inverse procedure utilizing NN with two hidden layers yielded consistent results in accordance with the proportion of each kind of leaf.     

Self-adaptive systems: A survey of current approaches,research challenges and applications

Self-adaptive software is capable of evaluating and changing its own behavior, whenever the evaluation shows that the software is not accomplishing what it was intended to do, or when better functionality or performance may be possible. The topic of system adaptivity has been widely studied since the mid-60s and, over the past decade, several application areas and technologies relating to self-adaptivity have assumed greater importance. In all these initiatives, software has become the common element that introduces self-adaptability. Thus, the investigation of systematic software engineering approaches is necessary, in order to develop self-adaptive systems that may ideally be applied across multiple domains. The main goal of this study is to review recent progress on self-adaptivity from the standpoint of computer sciences and cybernetics, based on the analysis of state-of-the-art approaches reported in the literature. This review provides an over-arching, integrated view of computer science and software engineering foundations. Moreover, various methods and techniques currently applied in the design of self-adaptive systems are analyzed, as well as some European research initiatives and projects. Finally, the main bottlenecks for the effective application of self-adaptive technology, as well as a set of key research issues on this topic, are precisely identified, in order to overcome current constraints on the effective application of self-adaptivity in its emerging areas of application.     

Search engines as a security threat

Because they implement vulnerable security policies, search engines are excellent tools for helping hackers to attack machines anonymously, search for easy targets or gather confidential data. Securing all channels against hackers trying to penetrate a vulnerable system isn't possible, but there is no reason for search engines to be wide-open channels that continue to help hackers find and penetrate weak systems. Because it is so easy to use a search engine to cloak an attack, search-engine-based hacker abuse has become a real threat that poses serious risks. However, not all blame should fall on those who operate the search engines. Search engines aren't responsible for the huge numbers of poorly configured and insecure machines all over the Internet even if the search engines do aid in identifying them, but the search engines must take some blame if they continue to provide easy ways to locate weak and penetrable machines     

Hot metal pretreatment by powder injection of lime‐based reagents

A mathematical model earlier proposed has been improved to predict the kinetics of multicomponent reactions in the hot metal pretreatment through the injection of reactive fluxes. It is assumed that there are two reaction zones along the flux injection operation: a transitory reaction between the rising particles and the bulk metal, and the permanent reaction between the metal and the top slag. A criterion to estimate the fraction of solids which will react with molten iron in a three‐phase jet (gas‐solid‐liquid) was considered; this fraction of solids carries out the transitory reaction. The model also takes into account the thermodynamic changes produced in the metal and slag due to the chemical reactions. Calculated results of the model are in good agreement with experimental results for the desulfurization of hot metal through the injection of CaO‐SiO₂‐CaF₂‐FeO‐Na₂O reagents at 1400 ‐ 1450 °C. Two kinds of hot metal were tested, one with a low carbon mass content of 3 % and the other with a high carbon mass content of 4.5 %.     

建立精密的直流浮动电流源

尽管有源滤波器的理论家和设计者都对GIC（通用阻抗变换器）非常熟悉，但一般模拟人员对它并不了解。一个单端口有源电路一般包括低成本运放、电阻和电容，GIC将容抗转换为感抗，因而可以替代滤波器中以RLC传输函数描述的电感。另外，GIC输入阻抗方程的灵活性允许进行虚拟阻抗的设计，这在实际元器件中是不存在的，例如依赖频率的电阻。GIC是30年前推出的，已广泛应用于交流电路和有源滤波电路。