Surface modification of starch nanocrystals through ring‐opening polymerization of ε‐caprolactone and investigation of their microstructures

Bionanoparticles of starch obtained by submitting native potato starch granules to acid hydrolysis conditions. The resulted starch nanoparticles were used as core or macro initiator for polymerization of ε‐caprolactone (CL). Starch nanoparticle‐g‐polycaprolactone was synthesized through ring‐opening polymerization (ROP) of CL in the presence of Sn(Oct)₂ as initiator. The detailed microstructure of the resulted copolymer was characterized with NMR spectroscopy. Thermal characteristic of the copolymer was investigated using DSC and TGA. By introducing PCL, the range of melting temperature for starch was increased and degradation of copolymer occurred in a broader region. X‐ray diffraction and TEM micrographs confirmed that there was no alteration of starch crystalline structure and morphology of nanoparticles, respectively. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Evidence of short-range order in the disordered Cu-Ti alloys

Diffuse X-ray scattering measurements have been carried out on a series of Cu-Ti alloys, in order to determine the dependence of short-range order (SRO) parameters on the composition and annealing temperature. The diffuse scattering intensity (DSI) curves contain a maximum at the superstructural position 100 which provides evidence of SRO in this alloy. The degree of SRO has been estimated by calculating the Warren-Cowley parameter ₁ (WCP) and size-effect parameter ₁ from the DSI curves. The large negative values of WCP for all samples show the presence of heterogeneous SRO in these alloys. Comparison is also made between the values of experimental WCP and theoretical WCP calculated in the framework of electronic theory of short-range order and the high-temperature approximation of Clapp and Moss. A fairly good agreement between theory and experiment is observed.     

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.     

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.     

Design and Optimization of Full Comparator Based on Quantum‐Dot Cellular Automata

Quantum‐dot cellular automata (QCA) is one of the few alternative computing platforms that has the potential to be a promising technology because of higher speed, smaller size, and lower power consumption in comparison with CMOS technology. This letter proposes an optimized full comparator for implementation in QCA. The proposed design is compared with previous works in terms of complexity, area, and delay. In comparison with the best previous full comparator, our design has 64% and 85% improvement in cell count and area, respectively. Also, it is implemented with only one clock cycle. The obtained results show that our full comparator is more efficient in terms of cell count, complexity, area, and delay compared to the previous designs. Therefore, this structure can be simply used in designing QCA‐based circuits.     

Gaussian orthogonal matrix transform approach for PAPR reduction of optical OFDM signals

Optical orthogonal frequency division multiplexing (O-OFDM) has been widely adopted as a high-speed data transmission technique in visible light communication systems. This technique usually suffers from high peak-to-average-power ratio (PAPR). In this paper, a new PAPR reduction technique is proposed for O-OFDM signals. At the transmitter, a matrix transformation with the Gaussian elements is applied to the time-domain O-OFDM signal and at the receiver, the inverse matrix is used to recover the original signal. We show that the Gaussian orthogonal matrices can reconstruct the original signals without degrading the bit error rate (BER) performance. Gram-Schmidt technique is used to orthogonalize the Gaussian matrices. Computer simulations are conducted for 16-QAM baseband modulated symbols and about 3 dB PAPR reduction gain is achieved by the proposed approach compared with conventional O-OFDM.     

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.     

BSLPSDN: Base station location privacy via software-defined networking (SDN) in wireless sensor networks

Base station's location privacy in a wireless sensor network (WSN) is critical for information security and operational availability of the network. A key part of securing the base station from potential compromise is to secure the information about its physical location. This paper proposes a technique called base station location privacy via software-defined networking (SDN) in wireless sensor networks (BSLPSDN). The inspiration comes from the architecture of SDN, where the control plane is separated from the data plane, and where control plane decides the policy for the data plane. BSLPSDN uses three categories of nodes, namely, a main controller to instruct the overall operations, a dedicated node to buffer and forward data, and lastly, a common node to sense and forward the packet. We employ three kinds of nodes to collaborate and achieve stealth for the base station and thus protecting it against the traffic-analysis attacks. Different traits of the WSN including energy status and traffic density can actively be monitored by BSLPSDN, which positively affects the energy goals, expected life of the network, load on common nodes, and the possibility of creating diversion in the wake of an attack on the base station. We incorporated multiple experiments to analyze and evaluate the performance of our proposed algorithm. We use single controller with multiple sensor nodes and multiple controllers with multiple sensor nodes to show the level of anonymity of BS. Experiments show that providing BS anonymity via multiple controllers is the best method both in terms of energy and privacy.     

Synergistic effect of nanoparticles and Thalassospira sp. isolated from Bahregan area in biodegradation of oil sludge

Oil sludge is obtained from the contaminated site of Bahregan area in the Persian Gulf in Iran. Chemotaxis was used for the isolation of alkane-degrading bacteria from oil sludge and the alk genes were determined with specific primers. bac1 identified in Thalassospira was selected as a powerful strain for biodegradation of oil sludge. Biodegradation of oil sludge by bac1 in the presence of nanoparticles was investigated by GC-MS analysis. Synergistic effect of Fe₂O₃ and Fe₃O₄ nanoparticles on bacteria increases the biodegradation of oil sludge and produces cyclosiloxane compound which is used in the field of medicine; however, the Thalassospira reduced its toxicity in the environment.