Miracle-dz/UniFAR-FTUs huggingface dataset:
Conserving and Sustaining Culture through Traditional Dress

The government of Botswana through its National Policy on Culture (2001) and the National Ecotourism Strategy (2002) is committed to preserving national culture and historical heritage. The policy stipulates that valuable heritage must be preserved and developed in order to foster a sense of national identity, pride and unity. It is necessary to reformulate cultural values and valuing processes, in order to better understand the Setswana culture and its meaning in material objects. To this end, dress as one of the valuable material culture objects is essential for signifying and expressing subtle cultural value and social relationships. The intimate link between people and their traditional dress lies at the core of ethnic identity, and has assumed a higher level of significance among consumers and tourists who collect symbolic items. A specific emphasis on a national traditional dress seems to be lacking in Botswana. This paper argues that there is need to restore traditional dress in Botswana, which would serve as a symbol of national identity and cultural heritage. The paper is based on the findings of a study that explored the historical underpinnings of national dress in Botswana, and how national dress could be used to sustain culture.
Small islands and large biogeographic barriers have driven contrasting speciation patterns in Indo-Pacific sunbirds (Aves: Nectariniidae)

Birds of the Indo-Pacific have provided biologists with many foundationalinsights. This study presents evidence for strong phylogeographic structure in two sunbird species from the heart of this region, the olive-backed sunbird, Cinnyris jugularis , and the black sunbird, Leptocoma aspasia . We assessed population divergence using morphological, plumage, bioacoustic and molecular data (mitochondrial ND2 / ND3 ). Our findings indicate that the olive-backed sunbird should be recognized as multiple species, because birds from Sulawesi and the Sahul Shelf are closely related to each other, but widely separated from those in other regions. In addition, we provide evidence for an endemic species on the Wakatobi Islands, an archipelago of deep-sea islands off south-east Sulawesi. That a small bird could exhibit a range all the way from Sulawesi to Australia, while diverging on a small archipelago within this range, illustrates the complex interplay between dispersal and speciation. Our black sunbird genetic data also suggest unrecognized population structure, despite relatively weak plumage divergence. Black sunbirds in Sulawesi are likely to be a separate species from those in New Guinea, with a mean genetic distance of 9.1%. Current taxonomy suggests these sunbird species transcend classic biogeographic barriers, but our results suggest that these barriers are not easily bypassed
Solar atmospheric dynamics. II - Nonlinear models of the photospheric and chromospheric oscillations

The one-dimensional, nonlinear dynamics of the solar atmosphere is investigated, and models of the observed photospheric (300 s) and chromospheric (200 s) oscillations are described. These are resonances of acoustic wave cavities formed by the variation of the temperature and ionization between the subphotospheric, hydrogen convection zone and the chromosphere-corona transition region. The dependence of the oscillations upon the excitation and boundary conditions leads to the conclusion that for the observed amplitudes, the modes are independently excited and, as trapped modes, transport little if any mechanical flux. In the upper photosphere and lower chromosphere, where the two modes have comparable energy density, interference between them leads to apparent vertical phase delays which might be interpreted as evidence of an energy flux.
Undergraduate research: Cyclostationary plot classification using machine learning

Undergraduate students at WVUTech collaborated with Greenbank Observatory, WV to develop models to automatically identify the astronomical data containing radio frequency inference and noise. Normally the identification of RFI signals would be done manually by observing the images built from the data and would take a lot of manual time for processing. A machine learning model could automate the process and save a lot of time in such identification as well as the model can be used in real-time identification. The project was done over the course of two semesters with students from Computer Science majors. Students developed a convolutional neural network model that can run on GPU and obtain an accuracy of more than 90% after training on the available data. This paper discusses in detail the research work done and the results obtained by the students. Students used concepts of image processing and machine learning to develop the model.
0.8-13 Micron Spectroscopy of V838 Monocerotis and a Model for Its Emission

We report on the results of a number of infrared spectra (0.8-2.5, 2.1-4.6, and 3-14 μm) of V838 Monocerotis, taken from a short time after discovery in 2002 January to about 14 months later, in early 2003. The spectrum evolved dramatically, changing from a quasi-photospheric stellar spectrum with weak atomic emission lines (some with P Cygni profiles) to one showing a wide range of deep absorption features indicative of a cool, extended atmosphere with a circumstellar dust shell. The early spectra showed lines of s-process elements, such as Sr II and Ba I. The later spectra showed absorption by gaseous H2O, CO, AlO, TiO, SiO, SO2, OH, VO, and SH, as well as a complex of emission near 10 μm reminiscent of silicate emission, with a central absorbing feature at 10.3 μm. Thus, V838 Mon appears to be oxygen-rich. A simple, spherically symmetric model of the system involving a central star with a two-component expanding circumstellar shell is presented that is able to explain the major molecular features and spectral energy distribution in the object's late stages. The derived shell mass and distance are 0.04 M☉ and 9.2 kpc, respectively.
A Compact Size and High Isolation Dual-band MIMO Antenna Using EMSIW

A simple and low-profile four-elements dual-band multiple-input multiple-output (MIMO) antenna using an eighth mode substrate integrated waveguide (EMSIW) cavity resonator is proposed for 5G new radio. The dual-band operation is achieved by radiating the edge perpendicular to the EMSIW and an extra slot etched out in the proposed structure. Different variations of the size of radiating edges and slots were carried out, and the simulated results were presented. Finally, the optimized antenna is demonstrated to operate at 2.6 GHz (n41) and 3.5 GHz (n78), having 6-dB impedance bandwidths of 6.55 % and 1.91 %, respectively. The four EMSIW elements are counterclockwise arranged to form the MIMO antenna and achieve good diversity performance. Because of the asymmetric EMSIW structure, their radiation patterns are found isolated from each other, thus improving the diversity. As a result, high isolation (>39dB), and envelope correlation coefficient (ECC) is found to be <0.06 without using a complex decoupling network. The prototype of the proposed antenna is developed, and its performances are measured and found in close agreement with the simulated results.
Ensemble model for ECG Classification

Electrocardiography (ECG) is a relatively popular diagnostic technique that does not involve any kind of bodily invasion, and algorithms are increasingly supporting its interpretation. There has been a shortage of relevant datasets for training in this area of work and there will be lack of clearly defined evaluation procedure, which has slowed progress in the field of automated analysis of electrocardiograms (ECGs). These factors have made it difficult to compare the effectiveness of various algorithms. PTB-XL is a clinical 12-lead electrocardiogram dataset that was just released and is easily available. Here, we illustrate a range of results for this dataset. , which covers a classification job based on a variety of ECG statements. This is done so that we may get around the issues that have been brought up. Among the deep learning-based time series classification approaches that were researched and examined, convolutional neural networks, and especially vgg16 and alexnet-based architectures, provide the greatest performance. The vgg16-based convolutional method achieves 0.8710 accuracy and 0.9448 AUC when applied to the ECG dataset. The ensemble model achieves the accuracy of 0.8690 and AUC of 0.9431. In the PTB-XL dataset, we used a 100 Hz sample rate for our training purposes. We split out the PTB-XL dataset into training, which is approx. 70% of the data, testing, which occupies 15% of the dataset, and the remaining 15% is for the validation set. The findings demonstrate that the strategy presented here is not only effective but also accurate when it comes to classifying ECG signals.
Performance augmentation of radiation-resilient III-nitride based multi-junction solar cells with varying composition and thickness of the cap layer

This paper presents performance optimization of III-nitride based multi-junction solar cells using AlGaN cap layer engineering. The Al mole fraction from 0.04 to 0.1 in AlGaN cap layer and its thickness ranging 0.2 – 0.5 µm of solar cell structures are varied and numerical simulation results are obtained using well-calibrated Sentaurus TCAD. The best performance of such solar cells is obtained for Al0.06Ga0. 94N cap material having the thickness of 0.4 µm. Our optimized solar cell yields short-circuit current density of 8.19 mA.cm-2, open circuit voltage of 0.95V, fill-factor of 88% and power conversion efficiency of 6.84 %. Notably, at AM1.5 sun irradiance, the optimized solar cell exhibits about 79% increase in power conversion efficiency over the standard structure.
Containerless undercooling of bulk Fe‐Ni melts

Bulk samples of Fe1−xNix, with x covering the whole concentration range 0≤x≤1, have been undercooled in a containerless state by electromagnetic levitation, yielding undercooling levels not achieved to date by any other technique. Comparison with a theoretical approximation indicates that, despite the large undercoolings attained, the limit of homogeneous nucleation is not yet reached. The solidification of the undercooled melts is initiated rather by heterogeneous surface nucleation originating from surface oxidation processes.
Certain Exact Solutions of the Equations of General Relativity with an Electrostatic Field

Certain exact solutions of the field equations of general relativity for empty space containing an electrostatic field are derived, and a physical interpretation is attempted. The canonical cylinder coordinates of Weyl are used, and all the solutions in which the electrostatic potential depends on only one of the two variables are obtained. Some of these are special cases of a class of solutions previously obtained by Weyl and they are shown to correspond either to a uniform electric field or to the field of a line-charge. Two of the solutions are not members of Weyl's class, and in these the electric field has no analogue in classical electrostatics and possesses the property of generating an orthogonal gravitational field.
Type 1 AGN at low z. I. Emission properties

We analyze the emission properties of a new sample of 3 596 type 1 AGN, selected from the SDSS DR7 based on the detection of broad Hα emission. The sample extends over a broad Hα luminosity LbHα of 1040–1044 erg s−1 and a broad Hα FWHM of 1 000–25 000 km s−1, which covers the range of black hole mass 106 < MBH/M⊙ < 109.5 and luminosity in Eddington units 10−3 < L/LEdd < 1. We combine ROSAT, GALEX and 2MASS observations to form the SED from 2.2 μm to 2 keV. We find the following: 1. The distribution of the Hα FWHM values is independent of luminosity. 2. The observed mean optical-UV SED is well matched by a fixed shape SED of luminous quasars, which scales linearly with LbHα, and a host galaxy contribution. 3. The host galaxy r-band (fibre) luminosity function follows well the luminosity function of inactive non-emission line galaxies (NEG), consistent with a fixed fraction of ∼ 3% of NEG hosting an AGN, regardless of the host luminosity. 4. The optical-UV SED of the more luminous AGN shows a small dispersion, consistent with dust reddening of a blue SED, as expected for thermal thin accretion disc emission. 5. There is a rather tight relation of νLν(2 keV) and LbHα, which provides a useful probe for unobscured (true) type 2 AGN.
Use of the 4∕5 Kolmogorov equation for describing some characteristics of fully developed turbulence

The Kolmogorov equation, which relates the second-order structure function Dll(r) and the third-ordrer structure function Dlll(r), may be presented as a closed linear integrodifferential equation for the probability distribution function W(U,r) of longitudinal velocity difference U. In general, without any restrictions, the function of two variables W(U,r) may be presented as [Dll(r)]−1∕2F(U∕Dll(r),r). As a first approximation, we neglect dependence of F on the second argument r. In this approximation (self-similarity of the probability density function), the integrodifferential equation for W reduces to the ordinary nonlinear differential equation for Dll(r). It follows from this equation that for r→∞ the function Dll(r)∼r2∕3. This consideration does not use the 1941 Kolmogorov hypothesis that is based on dimension analysis. Any deviations from the 2∕3 law, including intermittency effects, must be related to the violation of the above-mentioned self-similarity of W, i.e., with the additional dependence o.
Extremely High-Speed Lattice-Matched InGaAs/InAlAs High Electron Mobility Transistors with 472 GHz Cutoff Frequency

We report extremely high-speed 30-nm-gate InGaAs/InAlAs high electron mobility transistors (HEMTs) lattice-matched to InP substrates with a record cutoff frequency ft of 472 GHz, the highest value yet reported for any transistor. The ft value exceeds 450 GHz and 400 GHz even for 50-nm-gate and 70-nm-gate devices, respectively. This outstanding performance is attributed to the significantly increased gm achieved by reducing the lateral gate-recess length while maintaining a small gate-to-channel separation, which enhances the average electron velocity under the gate.
Query-Efficient Target-Agnostic Black-Box Attack

Adversarial attacks have recently been proposed to scrutinize the security of deep neural networks. Most blackbox adversarial attacks, which have partial access to the target through queries, are target-specific; e.g., they require a well-trained surrogate that accurately mimics a given target. In contrast, target-agnostic black-box attacks are developed to attack any target; e.g., they learn a generalized surrogate that can adapt to any target via fine-tuning on samples queried from the target. Despite their success, current state-of-the-art target-agnostic attacks require tremendous fine-tuning steps and consequently an immense number of queries to the target to generate successful attacks. The high query complexity of these attacks makes them easily detectable and thus defendable. We propose a novel query-efficient target-agnostic attack that trains a generalized surrogate network to output the adversarial directions iv.r. t. the inputs and equip it with an effective fine-tuning strategy that only fine-tunes the surrogate when it fails to provide useful directions to generate the attacks. Particularly, we show that to effectively adapt to any target and generate successful attacks, it is sufficient to fine-tune the surrogate with informative samples that help the surrogate get out of the failure mode with additional information on the target’s local behavior. Extensive experiments on CIFAR10 and CIFAR-100 datasets demonstrate that the proposed target-agnostic approach can generate highly successful attacks for any target network with very few fine-tuning steps and thus significantly smaller number of queries (reduced by several order of magnitudes) compared to the state-of-the-art baselines.
Combination Approaches to Hand Rejuvenation: A Review of the Literature and Discussion

Supplemental Digital Content is Available in the Text. BACKGROUND Various noninvasive/minimally invasive modalities for hand rejuvenation exist, and the efficacy and safety of these procedures as both monotherapy and same-day procedures is increasingly being studied. OBJECTIVE To review data on the efficacy and safety of hand rejuvenation modalities and suggest a practical combination approach for these procedures. METHODS The PubMed database was queried for peer-reviewed articles regarding hand rejuvenation techniques, including chemical peels, laser and light sources, sclerotherapy, autologous fat transfer, and injectable volumetric fillers. RESULTS Chemical peels have been studied the least, with most studies evaluating the use of fillers and laser/light-based devices. Most studies reported overall good results with high patient satisfaction. Satisfaction rates were lower in laser/light-based treatments compared with other modalities. Transient erythema, edema, or pain after procedures was common; most studies did not report serious postprocedure complications. Importantly, there was no significant increase in adverse effects after same-day procedures. CONCLUSION Using same-day procedures allows practitioners to address hand rejuvenation from different aspects, seems to improve outcomes, and reduces time spent in the office for patients. The authors suggest a practical framework for combining cosmetic approaches to achieve the most optimal outcome for hand rejuvenation.
Diagnostic Delays in Sepsis: Lessons Learned From a Retrospective Study of Canadian Medico-Legal Claims

IMPORTANCE: Although rapid treatment improves outcomes for patients presenting with sepsis, early detection can be difficult, especially in otherwise healthy adults. OBJECTIVES: Using medico-legal data, we aimed to identify areas of focus to assist with early recognition of sepsis. DESIGN, SETTING, AND PARTICIPANTS: Retrospective descriptive design. We analyzed closed medico-legal cases involving physicians from a national database repository at the Canadian Medical Protective Association. The study included cases closed between 2011 and 2020 that had documented peer expert criticism of a diagnostic issue related to sepsis or relevant infections. MAIN OUTCOMES AND MEASURES: We used univariate statistics to describe patients and physicians and applied published frameworks to classify contributing factors (provider, team, system) and diagnostic pitfalls based on peer expert criticisms. RESULTS: Of 162 involved patients, the median age was 53 years (interquartile range [IQR], 34–66 yr) and mortality was 49%. Of 218 implicated physicians, 169 (78%) were from family medicine, emergency medicine, or surgical specialties. Eighty patients (49%) made multiple visits to outpatient care leading up to sepsis recognition/hospitalization (median = two visits; IQR, 2–4). Almost 40% of patients were admitted to the ICU. Deficient assessments, such as failing to consider sepsis or not reassessing the patient prior to discharge, contributed to the majority of cases (81%). CONCLUSIONS AND RELEVANCE: Sepsis continues to be a challenging diagnosis for clinicians. Multiple visits to outpatient care may be an early warning sign requiring vigilance in the patient assessment.
Toward Unified Data and Algorithm Fairness via Adversarial Data Augmentation and Adaptive Model Fine-tuning

There is some recent research interest in algorithmic fairness for biased data. There are a variety of pre-, in-, and post-processing methods designed for this problem. However, these methods are exclusively targeting data unfairness and algorithmic unfairness. In this paper, we propose a novel intra-processing method to broaden the application scenario of fairness methods, which can simultaneously address the two bias sources. Since training modern deep models from scratch is expensive due to the enormous training data and the complicated structures, we propose an augmentation and fine-tuning framework. First, we design an adversarial attack to generate weighted samples disentangled with the protected attribute. Next, we identify the fair sub-structure in the biased model and fine-tune the model via weight reactivation. At last, we provide an optional joint training scheme for the augmentation and the fine-tuning. Our method can be combined with a variety of fairness measures. We benchmark our method and some related baselines to show the advantage and the scalability. Experimental results on several standard datasets demonstrate that our approach can effectively learn fair augmentation and achieve superior results to the state-of-the-art baselines. Our method also generalizes well to different types of data.
Temperature dependence Performance Assessment of III-V / Si Heterojunction based Dual Material Stacked Gate Oxide TFET

The bandgap of semiconducting materials changes with temperature, resulting in temperature-dependent variation of the device’s electrical characteristics. Therefore, in this work, we have investigated the temperature dependence performance of the proposed dual-material stacked gate oxide hetero-junction tunnel field-effect transistor (DMSGO-HTFET) structure using III-V material (GaSb) on the source and silicon in the channel and drain regions, respectively. In this context, technology computer-aided design (TCAD) simulations are performed to analyze DC, and analog/radio frequency performance figures of merits such as carrier concentration profile, energy band variation, inter-band tunneling rate, IDS −VGS characteristics, switching ratio (ION/IOFF), average subthreshold swing (SSavg), transconductance (gm), parasitic capacitances (Cgs and Cgd), gain-bandwidth product (GBP), unity gain frequency (fT), transit time (τ) and transconductance frequency product (TFP) considering the impact of temperature variations. Further, the performance of the GaSb-Si heterostructure-based DMSGO-HTFET is also compared to that of the conventional dual-material stacked gate oxide-tunnel field effect transistor (DMSGO-TFET) with Silicon in the drain, source, and channel regions. From the comparative analysis, in terms of % per kelvin variations, DMSGO-HTFET has less temperature sensitivity compared to conventional DMSGO-TFET. Thus, the proposed DMSGOHTFET can be used for low-power switching and analog/RF applications at elevated temperatures.
Classification and analysis of Alzheimer’s Disease using Deep Learning methods on MRI and PET

Alzheimer’s disease (AD) falls in the category of neurodegenerative illness in which an individual loses his or her power to remember things and behaviors. It affects memory in younger patients and as it progresses causes diffuse cortical functions. However, a major issue with the diagnosis and treatment of AD symptoms is that it has complex pathogenesis because of which there is no clinical intervention for its treatment. There is no disease-modifying treatment to cure AD symptoms that increases co-morbidities among the patients. The present research identified this gap and focuses on using Deep Learning methods on MRI and PET data so that there is early diagnosis of AD by healthcare experts and they could propose a better treatment process for reducing AD symptoms. The present research identified that by using deep learning-based approaches particularly ResNet50 architecture, there is the execution of quantitative assessment of brain MRI and PET to acquire insights about the internal abnormalities through self-learning features. It will help in initiating proper treatment and avoiding damage to the brain further.