Boron-doped activated carbon from the stems of Prosopis juliflora as an effective electrode material in symmetric supercapacitors

For a supercapacitor electrode, carbon-based materials have received great attention for their high surface area and stability. In this work, sustainable and cost-effective synthesis of boron-doped activated biomass-derived carbon from the stems of Prosopis juliflora has been reported for supercapacitor applications. The activation by KOH creates pores and boron induces p-type doping in the carbon matrix. The material gave a higher specific capacitance of 307.14 F/g at a current density of 0.5 A/g. The symmetric supercapacitor device delivered 156.29 F/g of specific capacitance with 98.1% of energy efficiency. The observed energy and power densities were 7.81 Wh/Kg and 150 W/Kg, respectively. The device was further studied with stability test for 1000 charge/discharge cycles and showed 98.6% of capacitance retention and 97.9% of coulombic efficiency.

     

Drawn arc aluminum stud welding for automotive applications

Federal regulations have been enacted to significantly reduce atmospheric pollution caused by motor vehicles. This forced the automotive manufacturers to improve fuel efficiency of cars and light trucks by using lightweight materials such as aluminum. The focus of the current study is to develop welding procedures using the drawn arc process for 5754-0 and 6061-T6 aluminum alloys. The mechanical and macrostructural characteristics of the welded joints were evaluated using tensile tests, torque tests, and optical microscopy. Preliminary study indicates that these alloys can be welded with a minimal amount of porosity and good mechanical properties.     

On the improvement of detection characteristics of trans-stilbene crystals by improving crystal perfection

Organic molecular scintillating crystals are noted for their good timing and particle discrimination process. Trans-stilbene is one such candidate noted for its good particle detection characteristics for the past five decades. Progressive strengthening of detection characteristics of trans-stilbene has been attempted by improving crystal perfection. A series of timing resolution studies have been carried out for the Bridgman grown trans-stilbene crystals under different experimental conditions. The results were compared with the previously reported values. Pulse shape discrimination process has been carried out for ²⁴¹Am and ²⁵²Cf sources and good discrimination has been obtained for gamma-alpha and gamma-neutron sources from the grown organic phosphor crystal. Electronic Publication     

Studies on nucleation kinetics of InxGa1−xN/GaN heterostructures

Nucleation kinetics during the growth of In_xGa_1−xN on a GaN substrate have been studied. The behavior of nonequilibrium between the In_xGa_1−xN and the GaN substrate has been analyzed, and hence, the expression derived for the stress-induced supercooling/superheating has been numerically evaluated. The maximum amount of stress-induced supercooling is found to be 1.017 K at x=0.12. These values are incorporated in the classical heterogeneous nucleation theory. Using the regular solution model, the interfacial tension between the nucleus and substrate and, hence, the interfacial tension between nucleus and mother phase and thermodynamical potential of the compounds have been calculated. The amount of driving force available for the nucleation has been determined for different compositions and degrees of supercooling. It has been shown that the value of the interaction parameter of InN-GaN plays a dominant role in nucleation and growth kinetics of In_xGa_1−xN on a GaN substrate. These values have been used to evaluate the nucleation parameters. It is shown that the nucleation barrier for the formation of a In_xGa_1−xN nucleus on a GaN substrate is minimum in the range of x=0.12 to x=0.17, and it has been qualitatively proved that good quality In_xGa_1−xN on GaN can be grown only in the range 0<x≤0.2.     

Modeling electric field transfer of excitation at cell junctions

The electric field (EF) model was first developed on a "breadboard" using physical electric components (e.g., resistors, capacitors, batteries) and was then modeled mathematically by a series of differential equations and matrix equations and simulated on a large computer (CDC-6400). The results obtained by the two methods agreed very closely. However, these two methods of analysis are quite cumbersome. Therefore, in order to simplify the EF simulation, we wanted to model it on the PSpice program. In this article we discuss how we succeeded in demonstrating transmission of excitation from cell to cell in cardiac muscle and smooth muscle based on EF transmission at the cell junctions.     

Hydrogen production from used lubricating oils

Used lubricating oils (lube oils) are generated throughout the year and collected in central locations in many communities. Disposing lube oil in an improper manner contaminates environment to a great degree. Used lube oil can be valuable as a re-refined lubricant or as an energy source. Lube oil is a complex mixture of aliphatic and polycyclic hydrocarbons formulated to withstand high service temperatures in internal combustion engines. Both synthetic and mineral oils contain a high concentration of hydrogen (about 13–14 wt%). At the Florida Solar Energy Center, we have developed a process that converts lube oils to hydrogen and other valuable low molecular weight hydrocarbons. The lube oil reformation experiments were carried out using several commercially available dehydrogenation catalysts at a range of reactor temperatures and pressures, residence times and steam to carbon ratios. In this paper, the data obtained to date and the results are presented and discussed.     

HamleDT: Harmonized multi-language dependency treebank

We present HamleDT—a HArmonized Multi-LanguagE Dependency Treebank. HamleDT is a compilation of existing dependency treebanks (or dependency conversions of other treebanks), transformed so that they all conform to the same annotation style. In the present article, we provide a thorough investigation and discussion of a number of phenomena that are comparable across languages, though their annotation in treebanks often differs. We claim that transformation procedures can be designed to automatically identify most such phenomena and convert them to a unified annotation style. This unification is beneficial both to comparative corpus linguistics and to machine learning of syntactic parsing.     

Improved 2D mass-spring-damper model with unstructured triangular meshes

In this paper, we investigate both the visual realism and the physical accuracy of the 2D mass-spring-damper (MSD) model with general unstructured triangular meshes for the simulation of rigid cloth. For visual realism, the model should, at a minimum, bend smoothly under pure bending load conditions. For physical accuracy, it should bend approximately the same amount and shape as dictated by continuum mechanics. By matching the 2D MSD model with an elastic plate, we obtain a series of constraints on the parameters of the model. We find that for a 2D unstructured MSD model, it is necessary to apply preloads on the springs for accurate modeling of bending resistance. By simultaneously applying the constraints for both visual realism and physical accuracy, we can optimize the parameters of the model to enhance its fidelity. The simulation shows that the deformation of the optimized MSD model with preload is very close to the result obtained by the finite element method (FEM) under either point load condition or pressure load condition. With a much smaller computational burden compared with FEM, the optimized MSD model is especially suitable for real time haptic applications.     

Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.