Deterministic Polymorphic Engineering of MoTe2 for Photonic and Optoelectronic Applications

Developing selective and coherent polymorphic crystals at the nanoscale offers a novel strategy for designing integrated architectures for photonic and optoelectronic applications such as metasurfaces, optical gratings, photodetectors, and image sensors. Here, a direct optical writing approach is demonstrated to deterministically create polymorphic 2D materials by locally inducing metallic 1T′-MoTe₂ on the semiconducting 2H-MoTe₂ host layer. In the polymorphic-engineered MoTe₂, 2H- and 1T′- crystalline phases exhibit strong optical contrast from near-infrared to telecom-band ranges (1–1.5 µm), due to the change in the band structure and increase in surface roughness. Sevenfold enhancement of third harmonic generation intensity is realized with conversion efficiency (susceptibility) of ≈1.7 × 10⁻⁷ (1.1 × 10⁻¹⁹ m² V⁻²) and ≈1.7 × 10⁻⁸ (0.3 × 10⁻¹⁹ m² V⁻²) for 1T′ and 2H-MoTe₂, respectively at telecom-band ultrafast pump laser. Lastly, based on polymorphic engineering on MoTe₂, a Schottky photodiode with a high photoresponsivity of 90 AW⁻¹ is demonstrated. This study proposes facile polymorphic engineered structures that will greatly benefit realizing integrated photonics and optoelectronic circuits.     

The Double Edge Sword Based Distributed Executor Service

Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.     

Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process

Computational Economics - This study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning...     

Low temperature annealing of metals with electrical wind force effects

Conventional annealing is a slow, high temperature process that involves heating atoms uniformly, i.e., in both defective and crystalline regions. This study explores an electrical alternative for energy efficiency,where moderate current density is used to generate electron wind force that produces the same outcome as the thermal annealing process. We demonstrate this on a zirconium alloy using in-situ electron back scattered diffraction(EBSD) inside a scanning electron microscope(SEM) and juxtaposing the results with that from thermal annealing. Contrary to common belief that resistive heating is the dominant factor, we show that 5 × 10~4 A/cm~2 current density can anneal the material in less than 15 min at only135?C. The resulting microstructure is essentially the same as that obtained with 600?C processing for360 min. We propose that unlike temperature, the electron wind force specifically targets the defective regions, which leads to unprecedented time and energy efficiency. This hypothesis was investigated with molecular dynamics simulation that implements mechanical equivalent of electron wind force to provide the atomistic insights on defect annihilation and grain growth.     

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction. For temporal sequence, this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short Term Memory (BiLSTM) to capture long-term dependencies. Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes. In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters. Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data. In contrast, the other uses optical flow images. Finally, the proposed ensemble approach using max hard voting outperforms state-of-the-art methods with 96.30% and 90.07% accuracies on the UCF101 and HMDB51 datasets.     

Correction on link relative based approach to CUSUM chart

Link relative-based approach was used in an article (see reference 1) to enhance the performance of the cumulative sum (CUSUM) control chart. This technique involves the use of firstly, the link relative variable to convert the process observations in a relative to the mean form and secondly, optimal constants to define a new variable which is used as the plotting statistic of the link relative CUSUM chart. In this article, it is proven through simulation study that the optimal constants with fixed values, as reported in the aforementioned article, give different results. Instead, if the regression technique is used, then the same results will be obtained.     

Temperature dependent H2S and Cl2 sensing selectivity of Cr2O3 thin films

The conductometric gas sensing characteristics of Cr₂O₃ thin films - prepared by electron-beam deposition of Cr films on quartz substrate followed by oxygen annealing - have been investigated for a host of gases (CH₄, CO, NO₂, Cl₂, NH₃ and H₂S) as a function of operating temperature (between 30 and 300 °C) and gas concentration (1-30 ppm). We demonstrate that these films are highly selective to H₂S at an operating temperature of 100 °C, while at 220 °C the films become selective to Cl₂. This result has been explained on the basis of depletion of chemisorbed oxygen from the surface of films due to temperature and/or interaction with Cl₂/H₂S, which is supported experimentally by carrying out the work function measurements using Kelvin probe method. The temperature dependent selectivity of Cr₂O₃ thin films provides a flexibility to use same film for the sensing of Cl₂ as well as H₂S.     

Power quality diagnosis using time frequency analysis and rule based techniques

Diagnosing a power quality disturbance means identifying the type and cause of the disturbance. Fast diagnosis of power quality disturbances is important so as to assist network operators in performing counter measures and implementing suitable power quality mitigation actions. In this study a novel method for performing power quality diagnosis is presented by using the S-transform and rule based classification techniques. The proposed power quality diagnosis method was evaluated for its functionality in detecting the type of short duration voltage disturbances and identifying the cause of the disturbances which may be due to permanent or non permanent faults. Based on the results, this new method has the potential to be used in the existing real time power quality monitoring system in Malaysia to expedite the diagnosis on the recorded voltage disturbances.     

Dynamic behaviour of a magnetically actuated floating liquid marble

This paper reports the dynamic behaviour of a magnetically actuated floating liquid marble by analysing the oscillation of the marble. A liquid marble is a liquid droplet coated with hydrophobic powder. Magnetite particles inside the marble make it magnetic. The marble floats on a carrier liquid that contains aqueous glycerol of various concentrations. A permanent magnet located under the carrier liquid drives the floating marble with the initial velocity. Stopping the magnet abruptly causes the marble to oscillate around its final position for a few seconds. The oscillation was recorded and analysed using customised image processing and evaluation software. The damped harmonic motion model was then applied to the data and tested. Subsequently, critical parameters of the system such as the initial displacement, friction correction factor, the apparent frequency and the spring constant were determined and discussed. The simple experimental set-up and convenient theoretical approach allow us to characterise the marble motion under the influence of a magnet with good accuracy.     

Synthesis of spherical Al2O3 and AlN powder from C@Al2O3 composite powder

A novel approach has been taken to produce (1) spherical Al₂O₃ particles by decarbonisation and (2) spherical AlN particles by nitridation and subsequent decarbonisation of C@Al₂O₃ composite particles. C@Al₂O₃ composite particles have been prepared by heterogeneous nucleation and crystallisation of Al(NO₃)₃ on surfactant encapsulated carbon nano particles followed by evaporative decomposition of the nitrate. Overpressure (0.4 MPa) of nitrogen and a temperature range (1723–1873 K) have been used for nitridation. Whiskers as well as spherical particles of AlN have been observed in the final product. The final product has been characterised by X-Ray Diffraction, Scanning Electron Microscope and Carbon–Hydrogen–Nitrogen content analysis by Elemental Analyser and the mechanism of the nitridation reaction has been analysed. The average size of the spherical AlN particles consisting of crystallites in nano-dimensions (30–50 nm) could be varied from 100 nm to 8 μm by changing the composition of the sol.