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.     

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.     

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.     

Error analysis of task performance with laptop in vibration environment

The present study investigates influences of vibration directions, vibration magnitudes, object sizes, object distances and angles of approach on producing errors while performing pointing and clicking activity on a laptop monitor similar to passengers working on moving trains. Attempts made outside the boundary of an object while performing the activity is registered as an error. The number of errors produced under different exposure conditions is computed. Higher vibration magnitudes, smaller size of the objects, and diagonal angular movements are found to create more errors and affected accuracy of the activity and hence showed difficulties to perform the activity.     

NLPCA as a diagnostic tool for control valve stiction

A significant number of control loops in process plants perform poorly due to control valve stiction. Developing a method to detect valve stiction in the early phase is imperative to avoid major disruptions to the plant operations. Nonlinear principal component analysis (NLPCA), widely known for its capability in unravelling nonlinear correlations in process data, is extended in this paper to diagnose control valve stiction problems. The present work is based on distinguishing the difference between the shapes of the signals caused by stiction and other sources, and utilizes the operating data of controlled variable-controller output (pv–op). The structure of pv–op data used in this work is of sufficiently low dimension such that the NLPCA’s output allows the usage of simple mathematical tests in quantifying the nonlinear behavior of the loop. It is shown that if the underlying structure of pv–op data is linear, the NLPCA output generally approximates to a straight line with a regression coefficient (R²) greater than 0.8, otherwise there is a possibility of presence of nonlinearity or non-Gaussianity. The presence of stiction is then detected via a new and simple NLPCA curvature index, I_NC. Results from simulated and real industrial case studies show that NLPCA is a very promising tool for detecting valve stiction.     

Near-Infrared Imaging of Steroid Hormone Activities Using Bright BRET Templates

Bioluminescence (BL) is an excellent optical readout for bioassays and molecular imaging. Herein, we accomplished new near infrared bioluminescence resonance energy transfer (NIR-BRET) templates for monitoring molecular events in cells with higher sensitivity. We first identified the best resonance energy donor for the NIR-BRET templates through the characterization of many coelenterazine (CTZ)–marine luciferase combinations. As a result, we found that NLuc–DBlueC and ALuc47–nCTZ combinations showed luminescence in the blue emission wavelength with excellent BL intensity and stability, for example, the NLuc–DBlueC and ALuc47–nCTZ combinations were 17-fold and 22-fold brighter than their second highest combinations, respectively, and were stably bright in living mammalian cells for at least 10 min. To harness the excellent BL properties to the NIR-BRET systems, NLuc and ALuc47 were genetically fused to fluorescent proteins (FPs), allowing large “blue-to-red” shifts, such as LSSmChe, LSSmKate2, and LSSmNep (where LSS means Large Stokes Shift). The excellent LSSmNep–NLuc combination showed approximately 170 nm large resonance energy shift from blue to red. The established templates were further utilized in the development of new NIR-BRET systems for imaging steroid hormone activities by sandwiching the ligand-binding domain of a nuclear receptor (NR-LBD) between the luciferase and the FP of the template. The NIR-BRET systems showed a specific luminescence signal upon exposure to steroid hormones, such as androgen, estrogen, and cortisol. The present NIR-BRET templates are important additions for utilizing their advantageous imaging of various molecular events with high efficiency and brightness in physiological samples.     

Morphological, structural, and functional properties of maranta (Maranta arundinacea L) starch

Starch isolated from maranta (Maranta arundinacea) tuber and studied for its various physicochemical characteristics. The amylose content of the starch was 24.8%. SEM showed that the granules were small indented and spherical. Maranta starch granule size has a range of 2.92–6.42 μm, (mean of 4.84 μm), length/degree of 1.20, and roundness of 0.73. Maranta starch has a gelatinization temperature of 74.8°C, peak viscosity of 498 BU, and cold paste viscosity of 669 BU. It also possessed higher freeze-thaw stability. Dynamic rheological properties of maranta starch, measured using parallel plate geometry showed increased storage modulus (G’) values, while loss modulus (G″) values were decreased with increasing frequency values (0–100 Hz). The low gelatinization temperature and high freeze thaw stability of starch indicates its potential for application as a thickener in food industries.