Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of 4H-SiC insulated-gate bipolar transistor turn-off performance for achieving low power loss

The dynamic characteristics of a 4H-SiC insulated-gate bipolar transistor (IGBT) at pulse switching is investigated by incorporating reported measurements of the interface defect density to device simulation. Different trap features such as energy states and trap time constants are investigated to determine the influence of traps on circuit performance. The capture cross-section parameter used in the simulation depicts the probability of traps to trap/detrap carriers which relates to the carrier trap time constant. It is demonstrated that trapped carriers from the on-state condition cause enhanced generation current during the off-state condition, which give rise to undesired leakage current in addition to the threshold voltage shift previously reported. The device power dissipation is increased by a factor of 100 due to the defects.
Insulating Ferromagnetic LaCoO 3-δ Films: A Phase Induced by Ordering of Oxygen Vacancies

The origin of ferromagnetism in strained epitaxial LaCoO3 films has been a long-standing mystery. Here, we combine atomically resolved Z-contrast imaging, electron-energy-loss spectroscopy, and densityfunctional calculations to demonstrate that, in epitaxial LaCoO3 films, oxygen-vacancy superstructures release strain, control the film’s electronic properties, and produce the observed ferromagnetism via the excess electrons in the Co d states. Although oxygen vacancies typically dope a material n-type, we find that ordered vacancies induce Peierls-like minigaps which, combined with strain relaxation, trigger a nonlinear rupture of the energy bands, resulting in insulating behavior.
An international perspective on ECHO and ECG interpretation skills: Insights from a KAP survey of medical residents in India

Echocardiography is a vital tool for the diagnosis and prognosis of arrhythmias, and it has become an indispensable component of diagnostic cardiology. Similarly, interpreting electrocardiograms (ECGs) is a crucial skill in cardiology, family medicine, internal medicine, and emergency medicine. Due to the complexity of the anatomy and technical aspects involved, as well as the diverse clinical scenarios and management implications, supervised experience is essential to avoid errors. The aim of this study is to analyze and enhance awareness of ECHO and ECG interpretation among Indian and international medical students.
Association Between the Attention Network Test, Neuropsychological Measures, and Disability in Post-Acute Traumatic Brain Injury

Cognitive impairment after traumatic brain injury (TBI) is persistent and disabling. Assessing cognitive function in a reliable and valid manner, using measures that are sensitive to the integrity of underlying neural substrates, is crucial in clinical research. The Attention Network Test (ANT) is one such assessment measure that has demonstrated associations with neural regions involved in attention; however, clinical utility of the ANT is limited because its relationship with neuropsychological measures of cognitive function (i.e., its construct validity) has not yet been established in TBI. We evaluated the association between the ANT and 1) a neuropsychological battery assessing executive function and memory and 2) global function assessed by the Glasgow Outcome Scale-Extended (GOSE). Forty-eight adults with complicated mild-severe TBI were evaluated ∼5 months post-injury. Using principal component analysis and multi-variate linear regression adjusted for age, gender, education, and cause of injury, we found that ANT reaction time and executive network scores predicted a principal component assessing processing speed and executive function. Conversely, the ANT did not predict a principal component assessing memory. The ANT was weakly associated with the GOSE. Among persons with TBI during the post-acute phase of recovery, the ANT has good construct validity as evidenced by its associations with neuropsychological measures of processing speed and executive function, but not memory. Given that ANT networks are known to relate to specific neuroanatomical regions, the ANT may be a useful outcome measure for evaluating novel therapeutics targeting attention and executive functions after TBI.
Inductively loaded and magnetically coupled small antenna with circular polarization

A magnetically coupled small antenna with circular polarization is presented. The antenna is composed of two orthogonal magnetic dipoles with inductively loaded hats, four capacitively loaded gaps, and a rotated driven semi-loop. Electronically, small size is implemented by employing a new inductively loaded technique. This technique consists of adding short circuited cylindrical covers to extremes of the structure, making the elements be inductively loaded, and as a result, increases the electronic length. The principle of implementing this antenna is presented in an equivalent circuit. An antenna prototype resonating at 1.3218 GHz with size of ( is proposed to verify this method. An effective volume reduction of about 17% is achieved by comparing with the newest published paper.
FPGA Emulation of Through-Silicon-Via (TSV) Dataflow Network for 3D Standard Chip Stacking System

Through-Silicon-Via (TSV) is expected to realize high-performance, low-power consumption, and lowcost 3D-LSI (Large Scale Integration) system. It is realized by integrating pre-manufactured chips with a 3D Standard Chip Stacking System (3D-SCSS) through a standard bus TSV connection. However, it is difficult to define a standard chip connection mechanism. This paper proposes an FPGA emulation of the TSV dataflow network for evaluating the performance of 3D-SCSS. To emulate 3D-SCSS, multiple-clock domains are assumed to overcome the problem of jitter in the global clock, which is a separated clock domain model. Simple dataflow experiments are done where processes are deployed to different chips and communicate among the chips in the 3D-SCSS. The evaluation shows that the emulation method is suitable to measure the latency performance of the proposed TSV dataflow network. (Keywords: 3D-LSI, TSV, FPGA, Emulation, Dataflow, 3D-SCSS)
Wideband Circularly Polarized Planar U-Shaped Antenna Array for Millimeter-Wave Applications

A wideband circularly polarized (CP) planar U-shaped antenna array is presented for millimeter-wave (mm-wave) applications. First, the antenna element consists of two tilted parasitic U-shaped patches fed by a slot etched on a substrate integrated waveguide (SIW). An improved simulated axial ratio (AR) bandwidth of 40% is achieved by stacking the slot and the patch element. Then, the right- and left-handed orthogonal unit vectors and resonant circuit technique are employed to explain the improved CP operating principle. Finally, an $8 \times 8 $ array is achieved based on the CP element, multiple SIW cavities, and a 16-way SIW feeding network using full-corporate scheme. To verify the design, a prototype is fabricated and measured, which demonstrates a wide impedance bandwidth of 40.4%, a wide AR bandwidth of 31%, a peak gain of 24.6 dBic and supporting dual-CP mode character. The proposed antenna array shows excellent performance and a novel structure, making it a promising candidate for mm-wave wireless systems.
Soft Inflatable Fingers: An Overview of Design, Prototyping and Sensorisation for Various Applications

Fabric-based soft actuators, grippers and manipulators are gaining in popularity due to their ability to handle large payloads while being lightweight, extremely compliant, low-cost and fully collapsible. Achieving full-pose sensing of fabric fingers without compromising on these advantageous properties, however, remains a challenge. This paper overviews work on soft fabric-based inflatable finger design, actuation and sensorisation carried out at the Centre for Advanced Robotics at Queen Mary (ARQ), University of London. Further experimental analysis has been performed to examine features such as bending control and eversion (growing from the tip) in fabric fingers for grasping applications. In addition, two types of grasp force have been measured for a bi-fingered gripper: envelope grasping and pinch grasping. Beyond force measurement, this paper advances a new concept for the sensorisation of fabric grippers using soft optical waveguide sensors and proposes shape estimation using image processing.
Empowering SMEs using New Forms of Digital Innovation: An Empirical Study on Fashion and Clothing Sector

The fashion and clothing sector is a highly competitive industry, where small and medium-sized enterprises (SMEs) face numerous challenges, such as limited resources, high market volatility, and the need to adapt to rapidly changing consumer preferences. However, the advent of new forms of digital innovation, such as social media platforms, e-commerce, and big data analytics, has created opportunities for SMEs to overcome these challenges and thrive in the market. The aim of this study is to explore the modern technological innovativeness through exploring three variables including social commerce constructs, brand image, and product features in e-commerce. Moreover, such study aims to propose a conceptual model that directed toward understanding how SMEs can utilized information and communication technologies in the fashion and clothing industry. This research is expected to enrich to the fashion and clothing literature by shedding the light on the importance of new ICT innovativeness in such area. The suggested model is depicted at the end of this work.
Optimization of reactor network design problem using Jumping Gene Adaptation of Differential Evolution

Detailed working principle of jumping gene adaptation of differential evolution (DE-JGa) is presented. The performance of the DE-JGa algorithm is compared with the performance of differential evolution (DE) and modified DE (MDE) by applying these algorithms on industrial problems. In this study Reactor network design (RND) problem is solved using DE, MDE, and DE-JGa algorithms: These industrial processes are highly nonlinear and complex with reference to optimal operating conditions with many equality and inequality constraints. Extensive computational comparisons have been made for all the chemical engineering problems considered. The results obtained in the present study show that DE-JGa algorithm outperforms the other algorithms (DE and MDE). Several comparisons are made among the algorithms with regard to the number of function evaluations (NFE)/CPU- time required to find the global optimum. The standard deviation and the variance values obtained using DE-JGa, DE and MDE algorithms also show that the DE-JGa algorithm gives consistent set of results for the majority of the test problems and the industrial real world problems.
Stochastic Semi-supervised Learning on Partially Labeled Imbalanced Data

In this paper, we describe the stochastic semi-supervised learning approach that we used in our submission to all six tasks in 2009-2010 Active Learning Challenge. The method is designed to tackle the binary classication problem under the condition that the number of labeled data points is extremely small and the two classes are highly imbalanced. It starts with only one positive seed given by the contest organizer. We randomly pick additional unlabeled data points and treat them as \negative" seeds based on the fact that the positive label is rare across all datasets. A classier is trained using the \labeled" data points and then is used to predict the unlabeled dataset. We take the nal result to be the average of n stochastic iterations. Supervised learning was used as a large number of labels were purchased. Our approach is shown to work well in 5 out of 6 datasets. The overall results ranked 3rd in the contest.
Graph Theoretic Approach for Decentralized Control Architecture of Cyber Physical Smart Grid

The need for power in the corporate, domestic, and industrial sectors is growing and driving the interest of researchers to hunt for emerging solutions for the future power grid. The smart grid is an electrical energy infrastructure that uses information and communication technologies. Moreover, the smart grid allows bi-directional power flow and an improved monitoring structure that is attack-resistant, reliable, and aware of predicting future uncertainty. Since the smart grid depends heavily on communication and cyber infrastructure, it is imperative to study the effects of cyber contingencies on the physical power system. This paper proposes a graph theory based modelling to perform cyber contingency analysis for a cyber physical smart grid. Both power and cyber networks are represented as two individual graphs in graph theory-based modeling. The effect of cyber contingency on the physical system is quantified using an optimal power flow algorithm. The control architecture considered in this paper is decentralized. The decentralized Optimal Power Flow (OPF) problem has been solved using the Alternating Direction Method of Multipliers (ADMM). The proposed algorithm is illustrated utilizing IEEE 39 bus system. The results show that the system cost is less, and power supplied to the customers is more in a decentralized control structure than in traditional centralized architecture.
Revisiting Financial Volatility in the Indonesian Islamic Stock Market: GARCH – MIDAS Approach

Introduction/main objectives: The aim of this research is to study the impact of macroeconomic variables on the Indonesian Islamic stock market’s volatility. Background issues: To predict the stock market’s volatility, daily or high-frequency data has been applied to the model’s explanatory variables with the same data frequency. However, when it comes to the macroeconomic variables as volatility drivers, the data is low-frequency, such as weekly, monthly, or quarterly. The current study uses a model which treats the data equally. Novelty: This study employs the mixed data sampling (MIDAS) model, which allows data from multiple frequencies to be included in the same model. This model can combine daily stock returns’ data with monthly or quarterly macro­economic data. Hence, this is the first paper to study the determinants of the volatility of Indonesia's Islamic stock index using GARCH-MIDAS. Research Methods: The Generalized Autoregressive Conditional Heteroscedasticity GARCH-MIDAS model captures the short-run from the long-term element of volatility; the findings show the asymmetry effect for the short-term element’s result. Finding/Results: Inflation does not influence long-term market volatility. Moreover, after the 2008 crisis, the study shows that inflation and short-term interest rates positively influenced market volatility. Conclusion: The positive effect of inflation suggests that stocks can function as inflation hedges for stock investors in the long run. Further, the positive impact of interest rates implies that Muslim investors use the conventional short-term interest rates as a benchmark for investment in Shari’ah-compliant instruments.
Autonomous and sustainable machine learning: pursuing new horizons of intelligent systems

: The paradigm of Artificial Intelligence and Machine Learning has resulted in an amazingly diverse plethora of models operating in various environments and quite often exhibiting numerous successes. There is a growing spectrum of challenging application areas of high criticality where one has to meet a number of fundamental requirements. Those manifest evidently when Machine Learning constructs have to function autonomously and any decisions being rendered entail far reaching implications. The carefully crafted learning process has to result with advanced models. Along with the developed models, they have to come hand-in-hand with credibility measures that are crucial to assess an extent to which the results generated by such measures are meaningful, trustworthy and credible. The credibility of the Machine Learning models becomes of paramount importance given the nature of application domains. Autonomous systems including autonomous vehicles, user identification (both using audio and video channels), financial systems (calling for sound mechanisms to quantify risk levels) require the ML system making classification or prediction decisions some level of self-awareness. Among others, this translates to forming sound answers to the following crucial questions emerging within the design process:
PEER EVALUATION'S IMPACT ON EFL LEARNERS’ LANGUAGE LEARNING, MORPHOLOGY UNDERSTANDING, AND VOCABULARY ACQUISITION

The primary goal of this study is to increase readers' understanding of assessment for language learning and development, particularly as it relates to morphology for students of EFL (English as a Foreign Language). We also consider noting the many assessment methods that might be employed to determine how well EFL students understand morphology. Second, we'll go over a variety of language-improvement strategies that we could use to help EFL students better understand English morphology. We'll look at a few benefits of peer evaluation for EFL learners' vocabulary and morphological comprehension in the third segment. This study will go over peer assessment's benefits, the process of language improvement in English morphology, and evaluation as a critical component of language learning.Keywords: morphology, assessment, affixation, compounding, conversion, etc.
Intelligent Reflecting Surface-Assisted Wireless Powered Heterogeneous Networks

In this paper, we introduce an intelligent reflecting surface (IRS)-assisted wireless powered heterogeneous network (WPHN) consisting of two heterogeneous groups of devices. Specifically, one group of devices, i.e., energy-harvesting devices (EHDs), are charged by external energy supplies, while the other group of devices, i.e., non-energy-harvesting devices (NEHDs), are powered by internal energy supplies. An IRS aims to participate in the wireless energy transfer (WET) in downlink and the wireless information transfer (WIT) in the uplink. A sum throughput maximization problem is formulated subject to the constraints of individual energy consumption, transmission time scheduling, and IRS phase shifts. To cope with the non-convexity of the problem, we first derive the optimal IRS phase shifts of the uplink WIT independently. Next, the semi-definite programming (SDP) relaxation is adopted to recast this non-convex problem into the convex one, which can be numerically solved. Then, a novel low-complexity scheme is developed to gain more insights and mitigate the computational complexity induced by the SDP relaxation. In particular, the dual problem and Karush-Kuhn-Tucker conditions are first utilized to obtain the optimal transmission time scheduling. Then, we propose a method based on Riemannian manifold optimization to compute the optimal IRS phase shifts of the downlink WET in closed-form. Finally, simulation results are presented to verify the optimality of our proposed scheme, and highlight the benefits induced by the IRS to coordinate these heterogeneous devices.
Image classification based on region of interest detection

For image classification tasks, the region containing object which plays a decisive role is indefinite in both position and scale. In this case, it does not seem quite appropriate to use the spatial pyramid matching (SPM) approach directly. In this paper, we describe an approach for handling this problem based on region of interest (ROI) detection. It verifies the feasibility of using a state-of-the-art object detection algorithm to separate foreground and background for image classification. It first makes use of an object detection algorithm to separate an image into object and scene regions, and then constructs spatial histogram features for them separately based on SPM. Moreover, the detection score is used to rescore. Our contributions include: i) verify the feasibility of using a state-of-the-art object detection algorithm to separate foreground and background used for image classification; ii) a simple method, called coarse object alignment matching, is proposed for constructing histogram using the foreground and background provided by object localization. Experimental results demonstrate an obvious superiority of our approach over the standard SPM method, and it also outperforms many state-of-the-art methods for several categories.
Divulging Magnitude of Perceptual Levels of Participants in National Dairy Mela (Fair) of India: A Farmers’ Perspective

A Likert-type perception scale was developed with 20 statements with Cronbach's alpha value of reliability being 0.85. The primary data were collected from 60 randomly selected farmers out of total 225 participants from Haryana, Punjab, Uttar Pradesh, and Delhi, states of India, during the 3 days event of Mela from February 25th to 27th, 2015. The findings indicate that the average monthly income per household of the participants was thrice than at all-India level average of monthly income per agricultural household. A total of 85% of the respondents had expressed their view that ‘the scorecard pro forma should be distributed among the farmers before the competition’ in order to bring more transparency in the judgment procedure. The results indicated that 43 (71.67%) respondents were having moderate to high perceptual level towards National Dairy Mela organised by ICAR-NDRI, India, which indicated respondents were roughly satisfied with the Mela. Only 20% were in favour of the statement ‘the decision of the Judges was biased and unjust’ but this doubt must be addressed on the priority basis. Variables such as herd size and milk production were highly associated with the perception level as the Pearson chi-square value for the test was highly significant. Apart from that annual family income, awards received by the participants, social participation, mass media exposure and extension contact parameters were also found to be associated with the perception level at 1% level of significance.