Multiagent based differential evolution approach to optimal power flow

This paper proposes a new differential evolution approach named as multiagent based differential evolution (MADE) based on multiagent systems, for solving optimal power flow problem with non-smooth and non-convex generator fuel cost curves. This method integrates multiagent systems (MAS) and differential evolution (DE) algorithm. An agent in MADE represents an individual to DE and a candidate solution to the optimization problem. All agents live in a lattice like environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, each agent competes and cooperates with its neighbors and it can also use knowledge. Making use of these agent-agent interaction and DE mechanism, MADE realizes the purpose of minimizing the value of objective function. MADE applied to optimal power flow is evaluated on 6 bus system and IEEE 30 bus system with different generator characteristics. Simulation results show that the proposed method converges to better solutions much faster than earlier reported approaches.     

An attention-based deep learning model for credibility assessment of online health information

With the surge of searching and reading online health-based articles, maintaining the quality and credibility of online health-based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health-related articles' credibility. The paper proposed an Attention-based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed “Pubhealth,” which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1-score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short-term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN-CNN. The experimental results showed that the proposed model produced state-of-the-art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1-score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles.     

Particle swarm optimization based K-means clustering approach for security assessment in power systems

Security assessment is a major concern in planning and operation studies of a power system. Conventional method of security evaluation performed by simulation involves long computer time and generates voluminous results. This paper presents a K-means clustering approach for classifying power system states as secure/insecure under a given operating condition and contingency. This paper demonstrates how the traditional K-means clustering algorithm can be profitably modified to be used as a classifier algorithm. The proposed algorithm combines particle swarm optimization (PSO) with the traditional K-means algorithm to satisfy the requirements of a classifier. The proposed PSO based K-means clustering technique is implemented in IEEE 30 Bus, 57 Bus, 118 Bus and 300 Bus standard test systems for static security and transient security evaluation. The simulation results of the proposed algorithm are compared with unsupervised K-means clustering, which uses different methods for cluster center initialization.     

Golden-Free Hardware Trojan Detection with High Sensitivity Under Process Noise

Malicious modification of integrated circuits in untrusted design house or foundry has emerged as a major security threat. Such modifications, popularly referred to as Hardware Trojans, are difficult to detect during manufacturing test. Sequential hardware Trojans, usually triggered by a sequence of rare events, represent a common and deadly form of Trojans that can be extremely hard to detect using logic testing approaches. Side-channel analysis has emerged as an effective approach for detection of hardware Trojans. However, existing side-channel approaches suffer from increasing process variations, which largely reduce the detection sensitivity and sets a lower limit of the sizes of Trojans detectable. In this paper, we present TeSR, a Temporal Self-Referencing approach that compares the current signature of a chip at two different time windows to isolate the Trojan effect. Since it uses a chip as a reference to itself, the method completely eliminates the effect of process noise and other design marginalities (e.g. capacitive coupling), thus providing high detection sensitivity for Trojans of varying size. Furthermore, unlike most of the existing approaches, TeSR does not require a golden reference chip instance, which may impose a major limitation. Associated test generation, test application, and signature comparison approaches aimed at maximizing Trojan detection sensitivity are also presented. Simulation results for three complex sequential designs and three representative sequential Trojan circuits demonstrate the effectiveness of the approach under large inter- and intra-die process variations. The approach is also validated with current measurement results from several Xilinx Virtex-II FPGA chips.     

Structural and Thermal Validations of Y<Subscript>3</Subscript>Ba<Subscript>5</Subscript>Cu<Subscript>8</Subscript>O<Subscript>18</Subscript> Composites Synthesized via Citrate Sol-Gel Spontaneous Combustion Method

The fabrication of single-phase superconductors enabled promising advancements in a wide range of technological applications. Numerous parameters including processing temperature, thermal cycle, chemical composition, doping, and atmosphere will be carefully addressed and optimized. This paper investigates phase stability and compound formation of Y₃Ba₅Cu₈O_y (Y-358) preform powders which were sintered at different temperatures. The precursor powder synthesized via chemical route and employing spontaneous combustion technique, respectively. The presence of Y-358 and YBa₂Cu₃O_7-δ (Y-123) phases in composites sintered at different temperatures with varied phase fractions were further confirmed through Rietveld refinement. The formation of Y-123 phase is dominant in all samples and composite was sintered at 900 °C, which was exhibited higher as compared to Y-358 phase content. The decomposition of different phases present in the composites and reaction temperatures were investigated with simultaneous differential thermal and thermogravimetry analysis.     

Milk trace elements in lactating cows environmentally exposed to higher level of lead and cadmium around different industrial units

The present investigation was carried out to assess the trace mineral profile of milk from lactating cows reared around different industrial units and to examine the effect of blood and milk concentration of lead and cadmium on copper, cobalt, zinc and iron levels in milk. Respective blood and milk samples were collected from a total of 201 apparently healthy lactating cows above 3 years of age including 52 cows reared in areas supposed to be free from pollution. The highest milk lead (0.85 ± 0.11 µg/ml) and cadmium (0.23 ± 0.02 µg/ml) levels were recorded in lactating cows reared around lead–zinc smelter and steel manufacturing plant, respectively. Significantly (P < 0.05) higher concentration of milk copper, cobalt, zinc and iron compared to control animals was recorded in cows around closed lead cum operational zinc smelter. Analysis of correlation between lead and other trace elements in milk from lactating cows with the blood lead level > 0.20 µg/ml (n = 79) revealed a significant negative correlations between milk iron and milk lead (r = − 0.273, P = 0.015). However, such trend was not recorded with blood lead level < 0.20 µg/ml (n = 122). The milk cobalt concentration was significantly correlated (r = 0.365, P < 0.001) with cadmium level in milk and the highest milk cadmium (> 0.10 to 0.39 µg/ml) group had significantly (P < 0.05) increased milk cobalt. It is concluded that increased blood and milk lead or cadmium level as a result of natural exposure of lactating cows to these environmental toxicants significantly influences trace minerals composition of milk and such alterations affect the milk quality and nutritional values.     

Electrical bistability in zinc oxide nanoparticle-polymer composites

The article reports observed electrical bistability in thin films of ZnO nanoparticles embedded in an insulating polymer matrix. From the current-voltage and impedance characteristics, they studied transport mechanisms involved in the two conducting-states. The electrical bistability in such films has been associated with a memory phenomenon. The bistability, which is reversible in nature, led to read-only and random-access memory applications in the devices based on such nanoparticles.     

Flame stability studies in a hydrogen-air premixed flame annular microcombustor

Flame stability in an annular heat recirculating microcombustor burning stoichiometric hydrogen-air mixture was explored by means of a rigorous thermal analysis. The analysis is based on computational fluid dynamics model of reacting fluid flow accounting for interactions in flow, species, and conjugate thermal field in fluid and solid. Consideration of thermal diffusion effects in the model was necessary for realistic predictions in all the cases. Flame stability under different inlet velocity and wall thermal conductivities was studied. Results showed that a stable flame could stabilize in this combustor in the velocity range of 3-35 m/s. However, the upper stability limit widened for lower wall thermal conductivity. Low velocity flashback and high velocity blowout bounded the stability region with respect to inlet velocity for lower thermal conductivity wall material. Lower flame stability limit was influenced by thermal design of the microcombustor that prevented flame extinction and ability of flame to stabilize at the heated wall even at higher inlet velocity controlled the upper flame stability limit. Flame established well within the combustor for the lowest wall thermal conductivity without blowout and approached flashback for the highest conductivity when wall thermal conductivity was varied at constant inlet velocity. Relative importance of axial and radial wall heat conduction in flame stabilization was explored at the extremes of operating conditions. Both the components played equally important roles in flame stabilization by influencing heat recirculation and losses within the microcombustor. A suitable combination of structural materials could provide a stable flame with high surface temperatures in a lightweight system.     

Investigation on jojoba biodiesel and producer gas in dual-fuel mode

This paper exhibits the emission characteristics of a diesel engine fueled with jojoba oil methyl ester and its blends (10, 20, and 30%) utilizing the groundnut shell producer gas. Emission parameters of jojoba biodiesel were tested in dual-fuel mode at constant gas flow rate of 22.72 kg/h. Various oil characterizations like kinematic viscosity, specific gravity, flash and fire point, oxidation stability, calorific value, cetane number, sulfur content, and so on and emission parameters such as carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), nitrogen oxide (NO_x), and smoke emissions are were taken into account. From the experimental values it can be resolved that there is substantial advancement in both oil characterization and emission parameters for minor blends of jojoba oil methyl esters in comparison to those of neat diesel.