实时多处理器系统中基于能量节约的动态调度算法

当前处理器由于较高的能量消耗。导致处理器热量散发的提高及系统可靠性的降低，已经成为目前计算机领域较为关心的问题．然而目前一些有效降低能量消耗的技术大多针对单处理器系统，较少考虑多处理器系统．本文提出的调度算法针对多处理器系统，以最短任务优先调度为基础，结合其它有效技术，如共享空闲时间回收等，使得实时任务在其截止期内完成的同时能够有效地减低整个系统的能量消耗．针对独立任务集及具有依赖关系的任务集，本文提出两种算法：STFBA1及STFBA2（Shortest Task First—Based Algorithm）．与目前所知的有效算法相比，我们的算法具有更好的性能（调度长度及能量消耗）．     

Material flow analysis and integration of watersheds and drain systems: II. Integration and solution strategies with application to ammonium management in Bahr El-Baqar drain system

The objective of this paper is to develop a systematic methodology for mass integration in drain systems and watersheds. Mass integration is a holistic approach to the tracking, transformation, and allocation of species and streams. The watershed and drain system is first discretized into reaches. The MFA model developed in part I of this work (Simulation and Application to Ammonium Management in Bahr El-Baqar Drain System) is used to describe the environmental phenomena that affect the fate and transport of targeted species and the operators that characterize the system inputs and outputs as they relate to the surroundings. Next, we develop an integration framework which encompasses sources, sinks, and interception technologies to aid in the development for nitrogen-management strategies. The simulation model was transformed into a synthesis model by introducing optimization variables and including models for the potential management strategies. The problem of minimizing negative environmental impact subject to technical, social, economic, and regulatory constraints was posed as a nonlinear optimization program whose solution identified and synthesized the most effective solution strategies. These mathematical models and management strategies were coded into a computer-aided tool using LINGO programming platform. The program can be readily modified to address a variety of cases. Tradeoffs and sensitivity analysis were established using the devised model. The devised framework was applied to an Egyptian drain system (Bahr El-Baqar) along with the outfall to Lake Manzala. The results of the case study provide solution strategies for nitrogen management along with their technical, economic, and environmental implications.     

Reliability analysis of nuclear containment without metallic liners against jet aircraft crash

The present study presents a methodology for detailed reliability analysis of nuclear containment without metallic liners against aircraft crash. For this purpose, a nonlinear limit state function has been derived using violation of tolerable crack width as failure criterion. This criterion has been considered as failure criterion because radioactive radiations may come out if size of crack becomes more than the tolerable crack width. The derived limit state uses the response of containment that has been obtained from a detailed dynamic analysis of nuclear containment under an impact of a large size Boeing jet aircraft. Using this response in conjunction with limit state function, the reliabilities and probabilities of failures are obtained at a number of vulnerable locations employing an efficient first-order reliability method (FORM). These values of reliability and probability of failure at various vulnerable locations are then used for the estimation of conditional and annual reliabilities of nuclear containment as a function of its location from the airport. To study the influence of the various random variables on containment reliability the sensitivity analysis has been performed. Some parametric studies have also been included to obtain the results of field and academic interest.     

Microstructural and electrical properties of varistors prepared from coated ZnO nanopowders

This paper describes a solution-based technique for fabrication of varistor grade composite nanopowders. The method consists of coating major varistor dopants on the surface of the ZnO nanoparticles. As a result, a homogenous mixture of dopants and ZnO nanoparticles will be achieved. TEM results indicated that a composite layer of dopants with the average particle size of 9 nm on the surface of ZnO nanoparticles has been successfully prepared. Sintering of the coated powders was performed in temperatures as low as 850 °C and final specimens with average particle size of 900 nm and density of 98.5% were achieved. In comparison to conventional mixing, varistors prepared from coated nanopowders exhibited superior electrical properties and microstructure homogeneity. The improvement of electrical properties can be attributed to small grain size, homogenous distribution of dopants and elimination of large Bi-Pockets. In addition, the processing route of schottky barrier formation is quite different from what is generally considered as the method of barrier formation in ZnO grain boundaries.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Modeling of multicomponent diffusions and natural convection in unfractured and fractured media by discontinuous Galerkin and mixed methods

Computation of the distribution of species in hydrocarbon reservoirs from diffusions (thermal, molecular, and pressure) and natural convection is an important step in reservoir initialization. Current methods, which are mainly based on the conventional finite‐difference approach, may not be numerically efficient in fractured and other media with complex heterogeneities. In this work, the discontinuous Galerkin (DG) method combined with the mixed finite element (MFE) method is used for the calculation of compositional variation in fractured hydrocarbon reservoirs. The use of unstructured gridding allows efficient computations for fractured media when the cross flow equilibrium concept is invoked. The DG method has less numerical dispersion than the upwind finite‐difference methods. The MFE method ensures continuity of fluxes at the interface of the grid elements. We also use the local DG (LDG) method instead of the MFE to calculate the diffusion fluxes. Results from several numerical examples are presented to demonstrate the efficiency, robustness, and accuracy of the model. Various features of convection and diffusion in homogeneous, layered, and fractured media are also discussed.     

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

N6-methyladenine (6mA) has been recognized as a key epigenetic alteration that affects a variety of biological activities. Precise prediction of 6mA modification sites is essential for understanding the logical consistency of biological activity. There are various experimental methods for identifying 6mA modification sites, but in silico prediction has emerged as a potential option due to the very high cost and labor-intensive nature of experimental procedures. Taking this into consideration, developing an efficient and accurate model for identifying N6-methyladenine is one of the top objectives in the field of bioinformatics. Therefore, we have created an in silico model for the classification of 6mA modifications in plant genomes. ENet-6mA uses three encoding methods, including one-hot, nucleotide chemical properties (NCP), and electron–ion interaction potential (EIIP), which are concatenated and fed as input to ElasticNet for feature reduction, and then the optimized features are given directly to the neural network to get classified. We used a benchmark dataset of rice for five-fold cross-validation testing and three other datasets from plant genomes for cross-species testing purposes. The results show that the model can predict the N6-methyladenine sites very well, even cross-species. Additionally, we separated the datasets into different ratios and calculated the performance using the area under the precision–recall curve (AUPRC), achieving 0.81, 0.79, and 0.50 with 1:10 (positive:negative) samples for F. vesca, R. chinensis, and A. thaliana, respectively.     

Biometric authentication based on PCG and ECG signals: present status and future directions

Due to the great advances in biomedical digital signal processing, new biometric traits have showed noticeable improvements in authentication systems. Recently, the ElectroCardioGram (ECG) and the PhonoCardioGraph (PCG) have been proposed as novel biometrics. This paper aims to review the previous studies related to the usage of the ECG and PCG signals in human recognition. In addition, we discuss briefly the most important techniques and methodologies used by researchers in the preprocessing, feature extraction and classification of the ECG and PCG signals. At the end, we introduce some future considerations that can be applied in this topic such as: the fusion between different techniques previously used, use both ECG and PCG signals in a multimodal biometric authentication system and building a prototype system for real-time authentication.     

Eigenvalue approach for an unbounded medium with a spherical cavity based upon two-temperature generalized thermoelastic theory

The thermoelastic interaction in an unbounded medium with a spherical cavity is studied using two-temperature generalized thermoelasticity theory. The medium is assumed to be initially quiescent. The inner surface of the cavity is taken traction free and subjected to a thermal shock. By the Laplace transformation, the basic equations are expressed in the form of a vector-matrix differential equation, which is solved by an eigenvalue approach. Some comparison have been shown in figures to estimate the effect of the two-temperature parameter.     

Preparation, characterization and application of advanced isoconversional kinetics to epoxy/1,4-Bis(3-aminopropoxy) butane/MWCNT nanocomposite

A novel nanocomposite consisting bisphenol A diglycidyl ether/1,4-Bis(3-aminopropoxy) butane (1,4-APB)/multiwall carbon nanotube (MWCNT) was synthesized and characterized. Kinetics of the reaction was described by applying differential scanning calorimetry (DSC) data to isoconversional methods of Flynn-Wall-Ozawa (FWO), advanced isoconversional method of Vyazovkin, and non-linear integral isoconversional algorithm (NLN). It was found that at the presence of MWCNT the thermal decomposition temperature increased by rising the curing temperature and time. Data from dynamic mechanical thermal analysis (DMTA) showed that the glass transition temperature of the cured nanocomposite is 7 °C higher than that value found for the system without carbon nanotube. Scanning electron microscopy (SEM) was used to observe the fracture surface morphology and results indicated evidence of the interfacial interaction improvement and adhesion strength due to good dispersion of MWCNT.