Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of MOSFETs for Terahertz Detector Arrays

The terahertz (THz) region of the electromagnetic spectrum is one of the last remaining regions that has yet to be fully characterized. THz imaging is one of the foremost drivers of this technology gap and has the potential to push development in the near term to a similar capability level as infrared (IR). Properties of THz radiation are introduced, along with promising current applications. Interest in array based imaging of THz radiation (T-Rays) has gained traction lately, specifically using a CMOS process due to its ease of manufacturability and the use of MOSFETs as a detection mechanism. The thoery outlined explains that incident terahertz radiation on to the gate channel region of a properly configured MOSFET can be related to plasmonic response waves, which change the electron density and potential across the channel producing a photoinduced response. This work utilizes a test chip fabricated to investigate these effects. The 0.35 μm silicon CMOS MOSFETs tested contain varying structures, providing a range of detectors to analyze. Included are individual test MOSFETs for which various operating parameters and modes are studied and results presented. The focus on single transistor-antenna testing provides a path for discovering the most efficient combination for coupling 0.2 THz band energy. Specifically introduced, is a novel source region extension which is proven to improve MOSFET response. Sensitivity analysis and responsivity are described, in parallel with theoretical expectations of the plasmonic response in room temperature conditions. A maximum responsivity of 40 kV/W and corresponding NEP of 10 pW −1 √ Hz (±10% uncertainty) is demonstrated.
Investigation of Er3+-Doped Phosphate Glass for L+ Band Optical Amplification

In this work, Er3+-doped phosphate glass with good flatness of the emission spectrum of the L+ band was prepared by optimizing the content of BaO. The absorption and emission spectra were measured. Increase of BaO content is conducive to the broadening of the emission spectrum in the L+ band. The dependence of flatness with Raman intensity has been shown by linear fitting. The results show that B31 sample has best flatness of the emission spectrum in the L+ band. It is showed that the largest emission cross-section at 1630 nm is 0.0529 × 10-20 cm2 and higher than other Er3+-doped phosphate glass. Decay curves of the 4I13/2 level were measured and the longest fluorescent lifetime is 10.18 ms and longer than other glass except germinate glass. The broadening of the Er3+-doped phosphate glass spectrum of the L+ band has been achieved successfully, which provides a reference for the material of optical amplifiers.
Multi-Hop Question Generation Using Hierarchical Encoding-Decoding and Context Switch Mechanism

Neural auto-regressive sequence-to-sequence models have been dominant in text generation tasks, especially the question generation task. However, neural generation models suffer from the global and local semantic semantic drift problems. Hence, we propose the hierarchical encoding–decoding mechanism that aims at encoding rich structure information of the input passages and reducing the variance in the decoding phase. In the encoder, we hierarchically encode the input passages according to its structure at four granularity-levels: [word, chunk, sentence, document]-level. Second, we progressively select the context vector from the document-level representations to the word-level representations at each decoding time step. At each time-step in the decoding phase, we progressively select the context vector from the document-level representations to word-level. We also propose the context switch mechanism that enables the decoder to use the context vector from the last step when generating the current word at each time-step.It provides a means of improving the stability of the text generation process during the decoding phase when generating a set of consecutive words. Additionally, we inject syntactic parsing knowledge to enrich the word representations. Experimental results show that our proposed model substantially improves the performance and outperforms previous baselines according to both automatic and human evaluation. Besides, we implement a deep and comprehensive analysis of generated questions based on their types.
Indirect Measurements of the Composition of Ultrafine Particles in the Arctic Late‐Winter

We present indirect measurements of size‐resolved ultrafine particle composition conducted during the Ocean‐Atmosphere‐Sea Ice‐Snowpack (OASIS) Campaign in Utqiagvik, Alaska, during March 2009. This study focuses on measurements of size‐resolved particle hygroscopicity and volatility measured over two periods of the campaign. During a period that represents background conditions in this location, particle hygroscopic growth factors (HGF) at 90% relative humidity ranged from 1.45 to 1.51, which combined with volatility measurements suggest a mixture of ∼30% ammoniated sulfates and ∼70% oxidized organics. Two separate regional ultrafine particle growth events were also observed during this campaign. Event 1 coincided with elevated levels of H2SO4 and solar radiation. These particles were highly hygroscopic (HGF = 2.1 for 35 nm particles), but were almost fully volatilized at 160 °C. The air masses associated with both events originated over the Arctic Ocean. Event 1 was influenced by the upper marine boundary layer (200–350 m AGL), while Event 2 spent more time closer to the surface (50–150 m AGL) and over open ocean leads, suggesting marine influence in growth processes. Event 2 particles were slightly less hygroscopic (HGF = 1.94 for 35 nm and 1.67 for 15 nm particles), and similarly volatile. We hypothesize that particles formed during both events contained 60–70% hygroscopic salts by volume, with the balance for Event 1 being sulfates and oxidized organics for Event 2. These observations suggest that primary sea spray may be an important initiator of ultrafine particle formation events in the Arctic late‐winter, but a variety of processes may be responsible for condensational growth.
Hybrid Matrix Completion Model for improved Images Recovery and Recommendation Systems

Matrix completion methods have been widely applied in images recovery and recommendation systems. Most of them are only based on the low-rank characteristics of matrices to predict the missing entries. However, these methods lack consideration of local information. To further improve the performance of matrix completion. In this paper, we propose a novel model based on matrix decompositions and matrix local information. Specifically, we update a number of rank-one matrices, which circumvented the rank estimation in matrix decomposition. And a penalty function is designed to punish singular values without introducing additional parameters. The local information component extracts similar information by an adaptive filter via convolution operation which kernel is obtained by the minimum variance. Finally, we integrate matrix decomposition and local information components via different weights. We apply the proposed method to real-world image datasets and recommendation system datasets. The experimental results demonstrate the proposed model has a lower error and better robustness than several competing matrix completion methods.
Israel Facing COVID-19

This paper considers Israel’s response to the challenges raised by the Covid-19 pandemic with a specific focus on the invoked public policies and the related political, economic and legal concerns. I first provide some background information. Then, I outline the keys for the initial success in confronting the coronavirus pandemic. Three factors contributed to the initial Israeli success, namely: the government’s swift and effective reaction to the pandemic; the close cooperation and coordination between the organisations that were mobilised to counter the pandemic, and the effective implementation of governmental policies. However, mistakes were made during the second wave of the pandemic.
Preliminary Investigations into the Influence of Generator and Transformer Impedance on Power System Fault Behaviour

This investigation considers the influence of generator and unit transformer impedance upon fault behaviour of a power system containing either conventional or directly connected generators. The impact is quantified by considering the fault behaviour of a single machine infinite bus (SMIB) as transformer and generator impedance is varied. Changes in these parameters will affect the fault performance of the entire network. The major effect is a large reduction in the severity of generator terminals' faults and in the generator contribution to network fault currents. The impact of parameter variation upon fault performance was also considered using analytical techniques with the results obtained useful for design optimisation or sensitivity analysis.
STATISTICAL STUDY OF THE CLOSEST APPROACH OF AIRCRAFT TO GROUND-BASED EMITTERS RESULTS FOR SEATTLE AND COMPARISON WITH DENVER.

Abstract : A Technical Program was initiated by the Federal Aviation administration (FAA) William J. Hughes Technical Center to measure the distances that aircraft fly within high-intensity radiation emitters. This program was launched as the FAA and the High-Intensity Radiated Fields (HIRF) advisory committees were defining HIRF regulatory rulemaking requirements. This study was a follow-on of the work conducted at the Denver International Airport to determine the actual distances that aircraft flew within emitters. This study focused on a similar effort at Seattle International Airport. This program presented the data gathered from the Seattle Airport along with a detailed comparison of the data between the two airports. Any observable patterns and similarities between the two airports were recorded. Information on emitter location, frequency, power, etc., was obtained from the Government Master File. Aircraft flight information was obtained from the System Analysis Recordings (SAR) tapes at the Seattle En Route Center. This program was used in conjunction with a research effort which located all high-powered emitters in the U.S. and Europe and established the actual HIRF environment.
Ultrasonic Assessment of the Influence of Cold Rolling and Recrystallization Annealing on the Elastic Constants in a TWIP Steel

The evolution of the elastic constants, C33, C44 and C55, Poisson’s ratio and acoustic birefringence of a Fe-0.5 wt% C-21.5 wt% Mn twinning-induced plasticity (TWIP) steel with reduction by cold rolling and recrystallization annealing was assessed from measurements of the times of flight of ultrasonic waves propagating along the thickness of the rolled plates. As the reduction increased, changes in the elastic constants resulted in a steadily increasing orthotropy, which was clearly shown by Poisson’s ratio and acoustic birefringence. Although optical metallography and hardness measurements showed that partial or full recrystallization is attained after annealing at 600 °C and 700 °C, the ultrasonic measurements revealed that a high level of orthotropy remains.
Flexible Circuit Mechanisms for Context-Dependent Song Sequencing

Many sequenced behaviors, including locomotion, reaching, and vocalization, are patterned differently in different contexts, enabling animals to adjust to their current environments. However, how contextual information shapes neural activity to flexibly alter the patterning of actions is not yet understood. Prior work indicates such flexibility could be achieved via parallel motor circuits, with differing sensitivities to sensory context [1, 2, 3]; instead we demonstrate here how a single neural pathway operates in two different regimes dependent on recent sensory history. We leverage the Drosophila song production system [4] to investigate the neural mechanisms that support male song sequence generation in two contexts: near versus far from the female. While previous studies identified several song production neurons[5, 6, 7, 8], how these neurons are organized to mediate song patterning was unknown. We find that male flies sing ‘simple’ trains of only one syllable or mode far from the female but complex song sequences consisting of alternations between modes when near to her. We characterize the male song circuit from the brain to the ventral nerve cord (VNC), and find that the VNC song pre-motor circuit is shaped by two key computations: mutual inhibition and rebound excitability [9] between nodes driving the two modes of song. Weak sensory input to a direct brain-to-VNC excitatory pathway (via pC2 brain and pIP10 descending neurons) drives simple song far from the female. Strong sensory input to the same pathway enables complex song production via simultaneous recruitment of P1a neuron-mediated disinhibition of the VNC song pre-motor circuit. Thus, proximity to the female effectively unlocks motor circuit dynamics in the correct sensory context. We construct a compact circuit model to demonstrate that these few computations are sufficient to replicate natural context-dependent song dynamics. These results have broad implications for neural population-level models of context-dependent behavior [10] and highlight that canonical circuit motifs [11, 12, 13] can be combined in novel ways to enable circuit flexibility required for dynamic communication.
Prediction and control of sound propagation in turbofan engine bypass ducts

This thesis contains original research into the propagation of sound in acoustically lined ducts with flow. The motivation for this work is the requirement to predict the sound attenuation of acoustic liners in the bypass duct of modern turbofan aeroengines. The liners provide the most effective means with which to suppress the rear fan noise. It is therefore important to make the best possible use of the available lined area by optimising the liner configuration. A set of analytic and numerical methods for predicting the liner attenuation performance have been developed, which are suitable for use in intensive liner optimisation studies, or as preliminary design tools. Eigenvalue solvers have been developed to find modal solutions in rectangular ducts with uniform flow and annular ducts with sheared flow. The solvers are validated by replicating results from the scientific literature and the Finite Element method. The effect of mean core flow radial profile and boundary layers on the mode eigenfunctions and axial decay rates are considered. It is shown that solutions for thin boundary layer flows converge to those based on the commonly used slip flow boundary condition. It is demonstrated that realistic flow profiles should be used to assess acoustic mode propagation in bypass ducts. The flow profile can have strong effects upon low order modes and surface waves, and in fact at high frequencies, the profile can affect all the modes. Mode-matching schemes are developed to assess the power attenuation performance and modal scattering of finite length liners. The results of the schemes are used to show that refraction of sound by boundary layers increases attenuation at high frequency. Power attenuation is higher where the mean core flow gradient refracts sound towards the liner. It is found that asymmetric liners can provide improved attenuation, depending on the direction of the mean flow shear gradient. The optimisation of axially-segmented liners for single and multi-mode sources is demonstrated. It is found that potentially large improvements in the attenuation of tonal noise is possible, whilst benefits for broadband noise are more difficult to achieve.
Learning like a Minimalist—Learning from a Minimalist. Consumers as Disseminators for Sustainable Clothes Usage

Are minimalists pioneers for sustainable clothes usage? According to old and new media, minimalists use less resources and appear as sustainable consumers. So there is much to be learned from minimalists who turned from regular consumers into use-intensive consumers in a matter of months. Minimalists change their own everyday practices and in doing so, they gain knowledge that is later disseminated through blogs, videos, books or talks and seminars. As sustainability and clothing is a growing research field more studies are needed to not only understand mainstream consumers and their practices but also more sustainable niche consumption and the change from one to the other. This article focuses on the sartorial everyday practices of the niche group of self-described minimalists in Germany, Austria and Switzerland. Wardrobe interviews have been conducted in direct presence of all clothing in 46 households. The results of the study document the minimalist learnings through an ongoing process of reducing material objects together with an intensification of the relationship to the remaining items. The process is analysed in the three phases of sorting, using and gaining that each contain varied patterns of the practices of knowledge appropriation, trying out and establishing new behaviour. Minimalists, we conclude, are important disseminators for sustainable change because they broadcast the necessary change in everyday practices on a personal bottom-up level.
Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2 Fluorescence Microscopy Data

Live-cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.
Financial Constraints and Productivity Growth of Listed Non-financial Firms in Nigeria

The need to understand the channel of monetary transmission mechanism has motivated some studies on the impact of financial constraints on firm behavior in the recent time. Our study contributes to this debate by looking at an emerging market economy, Nigeria. Data for 76 listed non-financial firms from 1997 to 2007 were collected and analyzed with GMM econometric method to ascertain the impact of financial constraints on productivity growth of the firms. Our study documents two important findings. First, bank loans were found to impact positively on the productivity of firms which goes to confirm the existence of the bankruptcy hypothesis. Second, borrowing ratio was found to impact negatively on the productivity growth of the firms which goes to show that some profitable investment opportunities of the firms may have been adversely impacted. Hence, this study recommends that the conduct of tight monetary policy in Nigeria with regards to inflation targeting in particular, should not lose sight of its implication on productivity behavior of firms which is the source of long term sustainable growth and development with poverty reduction.
Finite Element Analysis of Tidal Turbine Blade Subjected to Impact Loads from Sea Animals

The present work investigates structural response of tidal stream turbine blades subjected to impact loads from sea animals. A full-scale tidal turbine blade model was developed using a finite element modelling software ABAQUS, while a simplified geometry of an adult killer whale (Orcinus orca) was assumed in simulating impact on the blade. The foil profiles along the turbine blade were based on the NACA 63-8XX series, while the geometric and material properties of the sea animal were calibrated with experimental results. The numerical model simulated the dynamic response of the blade, accounting for radial velocities of the blade corresponding to real life scenarios. Different magnitudes and trajectories of the velocity vector of the sea animal were simulated, in order to investigate their influence on the turbine blade’s plastic deformation. Furthermore, multiple impacts were analysed, in order to monitor the accumulation of plastic strain in the material of the blade. Finally, the potential application of stainless steel material in tidal stream turbine blades for impact resistance was evaluated, through comparison of numerical results obtained from models using stainless steel and mild carbon steel materials.
Parallel Implementation of the SHYFEM Model

Abstract. This paper presents the MPI-based parallelization of the three-dimensional hydrodynamic model SHYFEM (System of HydrodYnamic Finite Element Modules). The original sequential version of the code was parallelized in order to reduce the execution time of high-resolution configurations using state-of-the-art HPC systems. A distributed memory approach was used, based on the message passing interface (MPI). Optimized numerical libraries were used to partition the unstructured grid (with a focus on load balancing) and to solve the sparse linear system of equations in parallel in the case of semi-to-fully implicit time stepping. The parallel implementation of the model was validated by comparing the outputs with those obtained from the sequential version. The performance assessment demonstrates a good level of scalability with a realistic configuration used as benchmark.
Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks

Atrial fibrillation (AF) is a type of cardiac arrhythmia affecting millions of people every year. This disease increases the likelihood of strokes, heart failure, and even death. While dedicated medical-grade electrocardiogram (ECG) devices can enable gold-standard analysis, these devices are expensive and require clinical settings. Recent advances in the capabilities of general-purpose smartphones and wearable technology equipped with photoplethysmography (PPG) sensors increase diagnostic accessibility for most populations. This work aims to develop a single model that can generalize AF classification across the modalities of ECG and PPG with a unified knowledge representation. This is enabled by approximating the transformation of signals obtained from low-cost wearable PPG sensors in terms of Pulse Rate Variability (PRV) to temporal Heart Rate Variability (HRV) features extracted from medical-grade ECG. This paper proposes a one-dimensional deep convolutional neural network that uses HRV-derived features for classifying 30-s heart rhythms as normal sinus rhythm or atrial fibrillation from both ECG and PPG-based sensors. The model is trained with three MIT-BIH ECG databases and is assessed on a dataset of unseen PPG signals acquired from wrist-worn wearable devices through transfer learning. The model achieved the aggregate binary classification performance measures of accuracy: 95.50%, sensitivity: 94.50%, and specificity: 96.00% across a five-fold cross-validation strategy on the ECG datasets. It also achieved 95.10% accuracy, 94.60% sensitivity, 95.20% specificity on an unseen PPG dataset. The results show considerable promise towards seamless adaptation of gold-standard ECG trained models for non-ambulatory AF detection with consumer wearable devices through HRV-based knowledge transfer.