Multiple Abiotic Stresses Applied Simultaneously Elicit Distinct Responses in Two Contrasting Rice Cultivars

Rice crops are often subject to multiple abiotic stresses simultaneously in both natural and cultivated environments, resulting in yield reductions beyond those expected from single stress. We report physiological changes after a 4 day exposure to combined drought, salt and extreme temperature treatments, following a 2 day salinity pre-treatment in two rice genotypes—Nipponbare (a paddy rice) and IAC1131 (an upland landrace). Stomata closed after two days of combined stresses, causing intercellular CO2 concentrations and assimilation rates to diminish rapidly. Abscisic acid (ABA) levels increased at least five-fold but did not differ significantly between the genotypes. Tandem Mass Tag isotopic labelling quantitative proteomics revealed 6215 reproducibly identified proteins in mature leaves across the two genotypes and three time points (0, 2 and 4 days of stress). Of these, 987 were differentially expressed due to stress (cf. control plants), including 41 proteins that changed significantly in abundance in all stressed plants. Heat shock proteins, late embryogenesis abundant proteins and photosynthesis-related proteins were consistently responsive to stress in both Nipponbare and IAC1131. Remarkably, even after 2 days of stress there were almost six times fewer proteins differentially expressed in IAC1131 than Nipponbare. This contrast in the translational response to multiple stresses is consistent with the known tolerance of IAC1131 to dryland conditions.     

Investigation and Simulation of a Two-Channel Drop Filter with Tunable Double Optical Resonators

In this paper, a photonic crystal (PhC) two-channel drop filter based on two 2×2 & 2×3 ring resonators is proposed. This structure is made of Germanium rods in an air background at a two-dimensional (2D) square lattice. Refractive index is chosen in a way in which that device can be easily fabricated. The photonic crystal two-channel drop filter is composed using a horizontal waveguide and two ring resonators, which are placed symmetrically about the horizontal axis. These ring resonators operate as energy coupling and capture the electromagnetic energy propagated in bus waveguide at their resonance frequencies. The filter characteristics are calculated using 2D finite-difference time-domain (FDTD) and plane wave expansion (PWE) methods. We show a two-channel drop filter with two resonators, based on studied basic structures and achieving optimal modes for channel drop filters with one resonator. We have done this through choosing the proper radii for all rods of lattice, setting radii of coupling rods, lattice constant, and studying basic structures having different refractive indexes. Finally, we show 84 % and 100 % dropping efficiencies can be achieved at D and C ports in the communication window and 100 % in direct port. The size of this device is 14.56 μm (length)×11.96 μm (width). This small size makes it possible to use the device in multiplexer applications in future communication systems and in all-optical integrated circuits.     

Game theory approach for detecting vulnerable data centers in cloud computing network

Nowadays, cloud computing has many benefits to accessibility, scalability, and cost‐effectiveness, leading to network security risks and vulnerabilities. Cloud computing is gaining in popularity with the advances and growth of its systems. Therefore, the security of this system and the identification of vulnerable data centers are more complicated than the past. Definitely discovering vulnerable data centers that are vulnerable to attacks can help to strengthen these data centers and provide a safer and more secure network structure. This paper examines the vulnerability of malware data centers in the infrastructure and cloud computing network structure. Based on the analysis of the cloud computing system in the field of game theory, we introduce a developed model for identifying vulnerable data centers in cloud computing. The developed model in this paper is based on the game theory as a mathematical tool. According to the game theory, we introduce a measure of the degree of vulnerability of data centers in the cloud computing network.     

Stable haptic rendering in interactive virtual control laboratory

Stable control of haptic interfaces is one of the most important challenges in haptic simulations, because any instability of a haptic interface can cause it to get far from the realistic sense. In this paper, the control strategies employed for a stable haptic rendering in an interactive virtual control laboratory are presented. In this interactive virtual laboratory, there are different scenarios to teach the control concepts, in which a haptic interface is used in the two cases of force control and position control. In this regard, two control strategies are employed to avoid instability. An energy-compensating controller is utilized to remove energy leakage. Besides, a fuzzy impedance control is used along with the energy-compensating controller for the position control scenarios. The results obtained indicate the proposed approaches practically guarantee the stability of the haptic interface for an educational application in practice.     

The evaluation of autothermal methane reforming for hydrogen production over Ni/CeO2 catalysts

Nanocrystalline Ni/CeO₂ catalysts with various loadings of Ni (10, 15, 20, and 25%) were synthesised by a facile solvent deficient precipitation method for methane autothermal reforming process. The characterisation techniques such as XRD, BET, TPH, H₂-TPR were carried out on fresh and spent samples to investigate the catalytic properties of the Ni/CeO₂. On the basis of characterisation results, the 20% Ni/CeO₂ performs the best activity among the catalysts with different Ni contents. The optimal reaction conditions for autothermal methane reforming has been investigated by evaluating the effect of reaction parameters including the reactivity temperature, the gas hourly space velocity (GHSV) and H₂O/CH₄ (S/C) and O₂/CH₄ (O/C) molar ratios. The stability of 20 wt% Ni/CeO₂ catalyst at 700 °C is examined for 20 h on-stream reaction. It reveals that the methane conversion starts a graduate decrease trend from the second 10 h, which is found to be because of the sintering of Ni nanoparticles by TPH and BET analysis.     

A new machine learning method consisting of GA-LR and ANN for attack detection

Advances in computer networks led to the generation of much data that computer networks must be capable of transmitting. The security of this volume of data is a major challenge for companies. Intrusion detection systems is one of the solutions that researchers introduced for this challenge. This research aims to introduce a new machine learning model for intrusion detection. The proposed model includes two stages of feature selection and attack identification. The feature selection stage uses genetic algorithm and logistic regression algorithm to find a correlated subset of features. In the attack detection phase, the ANN algorithm is used. ANN is trained by particle optimization (PSO) and gravitational search (GS) algorithms. To evaluate the proposed model, two sets of NSL-KDD and KDD Cup'99 are used and results are compared with ANN based on gradient descent (GD-ANN) and decision tree, ANN based on genetic algorithm (GA-ANN) methods, ANN based on GSPSO (GSPSO-ANN), ANN based on PSO (PSO-ANN) and ANN based on GS (GS-ANN) indicate the superiority of the proposed method.

     

Machine Learning Algorithms for Predicting Electricity Consumption of Buildings

Inter-satellite optical wireless communication (IsOWC) is a developing free-space optical technology used to communicate among satellites in space. At the same time, SAC-OCDMA (spectral amplitude coding optical code division multiple access) is an encouraging research area in the domain of optical communication because of its high bandwidth, speed, huge capacity, and ability to carry bursty and asynchronous information transmission. The present paper is concerned with the hybrid IsOWC non-coherent SAC-OCDMA system based on PM-ZCC (Permutation Matrix Zero Cross-Correlation) code for long-range high data rate transmission. The advanced modulation format (CSRZ) and direct detection (DD) techniques have been used to design the proposed system. The system is designed for five stations (each carrying 10 Gb/s). The system's performance is investigated for pointing error (with and without) over a space distance up to 12000 km in terms of Q factor, eye diagrams, BER and SNR. Moreover, the performance of a single IsOWC link has also been compared with multiple IsOWC links for a distance of 6500 km at 10 Gb/s data rate. The results show that system performance improves by using CSRZ format and multiple ISL links.

     

A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease

This paper presents a new method based on fuzzy cognitive map (FCM) and possibilistic fuzzy c-means (PFCM) clustering algorithm for categorizing celiac disease (CD). CD is a complex disorder whose development is affected by genetics (HLA alleles) and gluten ingestion. The celiac patients who are not treated are at a high risk of cancer, malignant lymphoma, and small bowel neoplasia. Therefore, CD diagnosis and grading are of paramount importance. The proposed FCM models human thinking for the purpose of classifying patients suffering from CD. We used the latest grading method where three grades A, B1, and B2 are used. To improve FCM efficiency and classification capability, a nonlinear Hebbian learning algorithm is applied for adjusting the FCM weights. To this end, 89 cases are studied. Three experts extracted seven main determinant characteristics of CD which were considered as FCM concepts. The mutual effects of these concepts on one another and on the final concept were expressed in the form of fuzzy rules and linguistic variables. Using the center of gravity defuzzifier, we obtained the numerical values of these weights and obtained the total weight matrix. Ultimately, combining the FCM model with PFCM algorithm, we obtained the grades A, B1, and B2 accuracies as 88, 90, and 91%, respectively. The main advantage of the proposed FCM is the good transparency and interpretability in the decision-making procedure, which make it a suitable tool for daily usage in the clinical practice.

     

A framework for designing cognitive trajectory controllers using genetically evolved interval type-2 fuzzy cognitive maps

In this paper, we provide a complete framework for the design of genetically evolved cognitive tracking controller based on interval type-2 (IT2) fuzzy cognitive map (FCM). We construct the cognitive controller based on a nonlinear controller by transforming its representation into a FCM. This representation gives the opportunity to prove the stability of the cognitive controller in the framework of nonlinear control theory. Moreover, with the deployment of IT2-fuzzy sets which are known to be capable to handle high level of uncertainty, the proposed cognitive controller has the ability to deal with uncertainty that are encountered in real-time world applications. To accomplish the design of the cognitive controller, we present a systematic approach based on genetic algorithm to optimize its parameters and learn fuzzy rules by extracting them from model space (e.g., a set of rules). Within the paper, all steps in constructing and designing the IT2-FCM-based cognitive controller are presented. We first show the performance improvements of the proposed IT2-FCM-based tracking controller with extensive and comparative simulation results and then with experimental results that were collected on real-world mobile robot. The results clearly show the superiority of proposed cognitive control systems when compared to its conventional and fuzzy controller counterparts. We believe that the proposed genetically evolved design approach of the IT2-FCM-based cognitive controller will provide a bridge between the well-developed cognitive sciences and control theory.     

Modified bundled-data as a new protocol for NoC asynchronous links

Asynchronous interconnection paradigm in NoCs has attracted many system designers in the recent years, through its different possible implementation strategies. In this paper, we present a new insight on how to categorize asynchronous protocols and explore a suitable protocol for the NoC asynchronous links. The new categorization criterion is based on the type of dependency between data transferring and handshaking tasks in a protocol. Furthermore, a new protocol called modified bundled-data (MBD) is introduced. MBD is a bundled-data-like protocol with two pairs of two-phase dual-rail encoded parity lines on the lateral sides of data lines, besides one two-phase acknowledgement line. The new protocol is evaluated by comparing its simulation results with those of bundled-data (BD) and dual-rail (DR) protocols on a 32-bit flit NoC asynchronous link. For this purpose, a new comprehensive interconnect model has been developed. The simulation results show that the new protocol's features such as power consumption, throughput, and latency are comparable with BD protocol's, while its signal integrity features are close to DR's.