Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrical and electroluminescence properties of As-doped p-type ZnO nanorod arrays

We have reported the growth of p-type As-doped ZnO nanorod (NR) arrays and their electrical and electroluminescence (EL) properties. Well-directed ZnO NR arrays were grown on the Si substrate. The GaAs wafer was employed as an arsenic resource. Thermal anneal was performed to diffuse an As element into a ZnO NR. The As content increased with the increase in annealing time and temperature, which was measured utilizing energy dispersive x-ray spectroscopy in transmission electron microscopy (TEM). When enough As was doped, ZnO NR arrays began to show p-type behaviour and formed good p–n heterojunctions with the n-type Si substrate. The high-resolution TEM (HRTEM) was employed to measure the lattice structure change in the ZnO NR before and after As doping. The ultraviolet EL spectrum of the n-Si/p-ZnO NR arrays' light emitting diode device was measured at room temperature and a peak at a wavelength of about 380 nm was observed in the spectrum.
Involvement of Nitric Oxide in Methyl Jasmonate-Mediated Regulation of Water Metabolism in Wheat Plants under Drought Stress

Drought is a serious challenge that causes significant crop loss worldwide. The developmental processes of plants are regulated by phytohormones and signaling molecules that crosstalk together in signaling cascades. We suppose that nitric oxide (NO) is a secondary messenger of the JAs signaling pathway, as 10−7 M methyl jasmonate (MeJA) pretreatment regulates NO accumulation in wheat plants under drought stress, modulated by 12% polyethylene glycol (PEG), and in control plants. This study aimed to compare 2 × 10−4 M nitric oxide donor sodium nitroprusside (SNP) and MeJA pretreatments in regulating growth and water balance parameters at the vulnerable initial first-leaf stage of wheat growth. The application of 12% PEG decreased transpiration intensity twofold, relative water content (RWC) by 7–9%, and osmotic potential of cell sap by 33–40% compared with those of control plants. Under drought, MeJA- and SNP-pretreated plants decreased transpiration intensity by 20–25%, RWC by 3–4%, and osmotic potential of cell sap by 16–21% compared with those of control plants, and enhanced the proline content by 25–55% compared with MeJA- and SNP-untreated plants. Our results suggest that pretreatment with MeJA as well as SNP could mitigate drought stress in wheat plants. Similarities in MeJA- and SNP-induced shifts in plant water balance suggested that NO is a mediator of MeJA-induced regulation of wheat water content during water deficit.
Periodisation of Eccentrically-Integrated Resistance Training during a National Rugby League Pre-Season

Successful performances in rugby league require the ability to engage in repeated contact efforts with minimal recovery while maintaining a high running intensity. The capacity to express high levels of time-limited force appears to underlie many important physical attributes required to meet the repeated-effort demands of rugby league play. If appropriately periodised and integrated into the training plan, resistance exercise that sufficiently loads the eccentric phase of movement may provide a beneficial stimulus to improve players’ force-generating capacity. Comprehensive reviews relating to the adaptive effects of eccentric training and the methods most commonly prescribed in practical environments are available and may provide context for applying these strategies. However, no literature to date has specifically discussed the planning and programming of eccentric resistance exercise to enhance force production characteristics in elite athletes. Therefore, this narrative review focuses on the periodisation of eccentrically-integrated resistance training during a 17-week National Rugby League pre-season phase. To help guide programming during the pre-season period, the 17-week timeline is divided into several phases (i.e., general preparation, special preparation, active rest, and pre-competition). Within the periodised model, eccentric exercise parameters (i.e., volume, load [% 1RM]) are manipulated to progressively increase the rate of muscle lengthening velocity over the pre-season phase and sequentially elicit changes in muscle-tendon properties and neural function that culminate in improving muscular strength expression.
Treatment and outcomes of peritonitis due to Rothia species in patients on peritoneal dialysis: A systematic review and multicentre registry analysis

Peritoneal dialysis (PD)-associated peritonitis remains a severe complication of PD. Although peritonitis due to Rothia spp. is rare, the treatment recommendations and outcomes are uncertain. Our study aims to review (1) published literature on peritonitis caused by Rothia spp. and (2) reported cases of peritonitis due to Rothia spp. in patients on PD in Australia and New Zealand. A literature search of PubMed, Scopus, Embase and Google Scholar for articles published between January 1949 and February 2022 was conducted. To be eligible, articles had to describe antibiotic therapy and treatment outcomes in all PD patients for peritonitis caused by Rothia or Stomatococcus spp. Data from the Australia and New Zealand Dialysis and Transplant (ANZDATA) registry of PD patients who developed peritonitis due to Rothia spp. between July 2011 and May 2020 were also reviewed. A total of 12 articles and 28 episodes were identified from the literature search and ANZDATA registry analysis, respectively. Over 60% of the peritonitis cases due to Rothia spp. were from the Rothia mucilaginosa species (8/12 and 17/28, respectively), while Rothia dentocariosa was the second most commonly identified species in both the literature search and the ANZDATA registry analysis (4/12 and 5/28, respectively). A majority 8 (66.7%) of the articles in the literature search employed a combination antibiotic regimen, while the remaining 4 (33.3%) used a single antibiotic regimen. In contrast, most of the episodes, 22 (78.6%) described in the ANZDATA registry analysis, employed a single antibiotic regimen, and only 6 (21.4%) episodes were treated with a combination antibiotic regimen. The duration of antibiotic therapy ranged from 2 to 3 weeks in the literature search, and 1 to 3 weeks in the ANZDATA registry. While no deaths within 30 days of developing peritonitis were reported, catheter removal was reported in three (25%) and two (7.1%) episodes in both the literature search and the ANZDATA registry analysis, respectively, of which the majority occurred in patients treated for ≤2 weeks. PD-associated peritonitis due to Rothia spp. is uncommon and associated with relatively good outcomes. Antibiotic treatment for 3 weeks is associated with better outcomes.
Non-albuminuric Diabetic Kidney Disease Phenotype: Beyond Albuminuria

Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease worldwide. Its pathogenic mechanism is complex, and it can affect the entire structures of the kidneys such as the glomerulus, tubules and interstitium. Currently, the urinary albumin excretion rate and the estimated glomerular filtration rate are widely accepted as diagnostic criteria. However, some studies have reported a different or non-classical clinical course of DKD, with some patients showing declined kidney function with normal levels of albuminuria, known as the ‘non-albuminuric DKD’ phenotype. The pathogenesis of this phenotype remains unclear, but some clinical and pathological features have been postulated. This review explores the evidence regarding this topic.
Robust Estimates for ARMA Models

Abstract Two new classes of robust estimates for ARMA models are introduced: estimates based on residual autocovariances (RA estimates), and estimates based on truncated residual autocovariances (TRA estimates). A heuristic derivation of the asymptotic normal distribution is given. We also perform a Monte Carlo study to compare the robustness properties of these estimates with the least squares, M, and GM estimates. In this study we consider observations that correspond to a Gaussian model with additive outliers. The Monte Carlo results show that RA and TRA estimates compare favorably with respect to least squares, M, and GM estimates.
Differential Beat Accuracy for ECG Family Classification Using Machine Learning

Holter systems record the electrocardiogram (ECG), which is used to identify beat families according to their origin and severity. Many systems have been proposed using signal conditioning and machine learning (ML) classification algorithms for beat family recognition. However, the design stage of these systems does not always consider the impact that tuning the intermediate blocks has on the beat family classification and the overall accuracy. We propose to use a new index based on the confusion matrices and bootstrap resampling to summarize the global performance for all family beats, so-called differential beat accuracy (DBA), which is obtained as the total number of beats correctly classified in each class minus the total number of beats incorrectly classified. We addressed the sensitivity of the different subblocks when creating a simple beat family classifier consisting of signal preprocessing blocks and a simple k-Nearest Neighbors classifier. The MIT-BIH Arrhythmia database was used for this purpose, following existing literature on the field. We benchmarked two implementations, one for biclass classification (supraventricular vs. non-supraventricular origin) and another for multiclass beat labeling. The usual preprocessing stages were scrutinized with the DBA to evaluate their impact on the quality of the complete ML system, such as signal detrending and filtering, beat balancing, or inter-beat distance. With the support of the DBA, our methodology was able to detect significant differences in terms of some of the options in the algorithm design. For instance, balancing the number of beats in each class for training significantly improved the classification accuracy of the minority classes at 3.22% for the multiclass dataset but not for the biclass dataset. Also, accuracy improved significantly by about 6% for the biclass regrouping without data normalization, whereas overall accuracy improved significantly by about 7% for the multiclass regrouping with data normalization. In addition, the analysis of the statistical dispersion of confusion matrices showed that this database should be considered with caution when training ML-based family classifiers. We can conclude that the proposed DBA can provide us with statistically principled criteria for designing ML-based classifiers and reducing their bias in strongly unbalanced beat family datasets.
An Inverse Optimal Control Approach for Learning and Reproducing Under Uncertainties

This letter presents a novel inverse optimal control (IOC) approach that can account for uncertainties in measurements and system models. The proposed IOC approach aims to recover an objective function including a time-varying term, called variability, from a given demonstration. All uncertainties of the demonstration and the system model can be lumped into the variability such that the optimality condition violation is further reduced. The inferred objective function including the variability has two advantages over the objective function inferred by existing IOC approaches: first, the variability can enhance the capability of describing the given demonstration since it represents how the uncertainties of the system affect the objective function; and second, the proposed IOC approach can reproduce the trajectories such that we can predict the behavior of the system even with system modeling errors. We show that the variability exists and is unique under attainable assumptions. Illustrative numerical examples are presented to demonstrate the proposed method.
NGC 1333 IRAS 4 : a very young, low-luminosity binary system

IRAS 4 is a cold, low luminosity source in the active star formation region NGC 1333. The present sub-mm photometry and high spatial resolution 800- and 450-micron maps resolve this source into a binary system whose components are separated by about 31 arcsec and connected by a bridge of faint dust emission. The dust masses associated with the binary component are by far the largest seen toward any star of comparable luminosity, and are a factor of 10 higher than predicted by protostar models. It is suggested that these observations indicate a very young stellar system, possibly an actual protostar, in which most luminosity is generated by accretion. 27 refs.
Single-Sample Finger Vein Recognition via Competitive and Progressive Sparse Representation

As an emerging biometric technology, finger vein recognition has attracted much attention in recent years. However, single-sample recognition is a practical and longstanding challenge in this field, referring to only one finger vein image per class in the training set. In single-sample finger vein recognition, the illumination variations under low contrast and the lack of information of intra-class variations severely affect the recognition performance. Despite of its high robustness against noise and illumination variations, sparse representation has rarely been explored for single-sample finger vein recognition. Therefore, in this paper, we focus on developing a new approach called Progressive Sparse Representation Classification (PSRC) to address the challenging issue of single-sample finger vein recognition. Firstly, as residual may become too large under the scenario of single-sample finger vein recognition, we propose a progressive strategy for representation refinement of SRC. Secondly, to adaptively optimize progressions, a progressive index called Max Energy Residual Index (MERI) is defined as the guidance. Furthermore, we extend PSRC to bimodal biometrics and propose a Competitive PSRC (C-PSRC) fusion approach. The C-PSRC creates more discriminative fused sample and fusion dictionary by comparing residual errors of different modalities. By comparing with several state-of-the-art methods on three finger vein benchmarks, the superiority of the proposed PSRC and C-PSRC is clearly demonstrated.
Amplification process in a high-Q cryogenic whispering gallery mode sapphire Fe3 + maser

This paper reports on the analysis of a high-Q (109) cryogenic whispering gallery mode (WGM) sapphire resonator maser oscillator based on residual impurities of the Fe3 + ion (1 ppm). A theoretical model is developed to determine the evolution of the maser output power versus the pump power and the resonator output coupling coefficient. Comparison of the theoretical model and experimental data shows a good agreement.
Antioxidant, Cytotoxicity, Apoptotic Properties of Extracts of Andricus sternlichti galls and Their Phenolic Characterisation by HPLC

Quercus L. galls have been used in Western and Eastern cultures for various diseases in traditional medicine. Galls are also used in the East for many purposes, including consumption as food, commercial inks, leather tanning. In the current study, Andricus sternlichti Bellido, Pujade‐Villar & Melika, 2003 galls were extracted in different solvents. The possible antioxidant effects of gall extracts were determined using 7 different methods (β‐carotene‐linoleic acid assay, Phosphomolybdenum assay, DPPH and ABTS radical scavenging activity, CUPRAC and FRAP assay, Metal Chelating activity) to support each other. Total phenolic, flavonoid and tannin amounts of extracts are calculated by using standard curves. In addition, HPLC method used to characterize the phenolic component with 15 different standards. The MIA PaCa‐2 cell lines was preferred to identify possible cytotoxic activities of galls. Expression of some genes (Bax, Bcl‐2, FAS, BID, caspase‐3, caspase‐8, caspase‐9, caspase‐10, FADD, TRADD) role in the apoptosis was determined to investigate apoptotic effects of extracts. According the results, the gall extracts of A. sternlichti may be considered as a potential source of biological agents for their antioxidant capacity and rich bioactive compounds. The gall extracts exhibit antiproliferative activity via regulating expressions of apoptotic genes.
Modeling of BN-Doped Carbon Nanotube as High-Performance Thermoelectric Materials

Ternary BNC nanotubes were modeled and characterized through a periodic density functional theory approach with the aim of investigating the influence on the structural, electronic, mechanical, and transport properties of the quantity and pattern of doping. The main energy band gap is easily tunable as a function of the BN percentage, the mechanical stability is generally preserved, and an interesting piezoelectric character emerges in the BNC structures. Moreover, C@(BN)1−xCx double-wall presents promising values of the thermoelectric coefficients due to the combined lowering of the thermal conductivity and increase of charge carriers. Computed results are in qualitative agreement with the little experimental evidence and therefore can provide insights on an atomic scale of the real samples and direct the synthesis towards increasingly performing hybrid nanomaterials.
A Numerical Study for Robust Active Portfolio Management with Worst-Case Downside Risk Measure

Recently, active portfolio management problems are paid close attention by many researchers due to the explosion of fund industries. We consider a numerical study of a robust active portfolio selection model with downside risk and multiple weights constraints in this paper. We compare the numerical performance of solutions with the classical mean-variance tracking error model and the naive portfolio strategy by real market data from China market and other markets. We find from the numerical results that the tested active models are more attractive and robust than the compared models.
High-density analog image storage in an analog-valued non-volatile memory array

Data stored in the cloud or on mobile devices reside in physical memory systems with finite sizes. Today, huge amounts of analog data, e.g. images and videos, are first digitalized and then compression algorithms (e.g. the JPEG standard) are employed to minimize the amount of physical storage required. Emerging non-volatile-memory technologies (e.g. phase change memory (PCM), resistive RAM (RRAM)) provide the possibility to store the analog information in a compressed format directly into analog memory systems. Here, we demonstrate with hardware experiments an image storage and compression scheme (joint source-channel coding) with analog-valued PCM and RRAM arrays. This scheme stores information in a distributed fashion and shows resilience to the PCM and RRAM device technology non-idealities, including defective cells, device variability, resistance drift, and relaxation.
Multihop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of $300\times300$ m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions.
Dynamics of spontaneous heart rate turbulence in patients with pure chronic aortic regurgitation

Background: Biphasic responses to ventricular premature contractions (VPCs) cause heart rate turbulence (HRT), which is primarily mediated by the baroreceptors. HRT is useful for cardiovascular risk stratification of different cardiac diseases. Aim and objectives: The purpose of this research was to analyze HRT in patients with chronic Aortic regurgitation (AR) and determine whether there is a correlation between HRT parameters and echocardiographic data. Subjects and methods: This case control study was carried out in Specialized Medical Hospital, Cardiology Department, Mansoura University on 30 aortic regurgitation patients and other 30 control individuals. HRT parameters were compared in both groups. Result: There was a positive correlation between turbulence slope (TS) and ejection fraction (EF), Fractional shortening (FS). There was a positive correlation between turbulence onset (TO) and min heart rate (HR), and TS with time domain SDANN. Conclusion: Patients with chronic AR had significantly impaired HRT parameters compared to normal subjects.
PRESSURES GENERATED BY A BUBBLE CLOUD COLLAPSE

The collapse of a low density cloud of bubbles is studied using matched asymptotic expansions. Equations describing the motion and deformation of an individual bubble accounting for its interaction with the other bubbles in the cloud are derived. Numerical results for the case of a symmetrical cloud configuration show a dramatic increase of the pressure generated at the end of the collapse due to collective effects. This explains the observed high erosion associated with cloud cavitation.
On approximating the distribution of indefinite quadratic forms

This paper provides a simple methodology for approximating the distribution of indefinite quadratic forms in normal random variables. It is shown that the density function of a positive definite quadratic form can be approximated in terms of the product of a gamma density function and a polynomial. An extension which makes use of a generalized gamma density function is also considered. Such representations are based on the moments of a quadratic form, which can be determined from its cumulants by means of a recursive formula. After expressing an indefinite quadratic form as the difference of two positive definite quadratic forms, one can obtain an approximation to its density function by means of the transformation of variable technique. An explicit representation of the resulting density approximant is given in terms of a degenerate hypergeometric function. An easily implementable algorithm is provided. The proposed approximants produce very accurate percentiles over the entire range of the distribution. Several numerical examples illustrate the results. In particular, the methodology is applied to the Durbin–Watson statistic which is expressible as the ratio of two quadratic forms in normal random variables. Quadratic forms being ubiquitous in statistics, the approximating technique introduced herewith has numerous potential applications. Some relevant computational considerations are also discussed.