Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Hydrogen Peroxide in Rice Plants

The role of hydrogen peroxide (H2O2) in rice plants under conditions of salinity or cadmium (Cd) stress and abscisic acid (ABA) treatment is reviewed. Evidence is provided to show that H2O2 plays a dual role in the response of rice plants to salinity or Cd stress and ABA treatment. Hydrogen peroxide functions as a damaging toxic molecule, as well as an important signal transduction molecule.
Assessing the AQS/TAS-45 Indonesia Version for Children Attachment in Yogyakarta

Abstract The purpose of this study was to examine the Indonesia versions of the Attachment Q-Sort/AQS-Toddler Attachment Sort-45/TAS-45 (Bimler & Kirkland, 2002). Assessing the child's attachment in Indonesia has not been done lately. Data were collected through observation of 84 Indonesian toddlers residing in Yogyakarta. The validity of the TAS-45 was determined by Kirkland (2008) through mapping TAS-45 for the Indonesian users. This study had the inter-rater reliability of 0.92 that indicated the convincing score. The study finding shows the Indonesian AQS/TAS-45 is reliable and valid in for Indonesia children sample and suggest that the TAS-45 Indonesia version reflect similar concept to those in the original English versions. Keywords : AQS/TAS-45, Child, Attachment
Selective Area Epitaxy of GaAs/Ge/Si Nanomembranes: A Morphological Study

We demonstrate the feasibility of growing GaAs nanomembranes on a plastically-relaxed Ge layer deposited on Si (111) by exploiting selective area epitaxy in MBE. Our results are compared to the case of the GaAs homoepitaxy to highlight the criticalities arising by switching to heteroepitaxy. We found that the nanomembranes evolution strongly depends on the chosen growth parameters as well as mask pattern. The selectivity of III-V material with respect to the SiO2 mask can be obtained when the lifetime of Ga adatoms on SiO2 is reduced, so that the diffusion length of adsorbed Ga is high enough to drive the Ga adatoms towards the etched slits. The best condition for a heteroepitaxial selective area epitaxy is obtained using a growth rate equal to 0.3 ML/s of GaAs, with a As BEP pressure of about 2.5 × 10−6 torr and a temperature of 600 °C.
A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment

In order to solve issues concerning performance induction and in-cylinder heat accumulation of a certain heavy-duty diesel engine in a plateau environment, working state parameters and performance indexes of diesel engine are calculated and optimized using the method of artificial neural network and genetic algorithm cycle multi-objective optimization. First, with an established diesel engine simulation model and an orthogonal experimental method, the influence rule of five performance indexes affected by five working state parameters are calculated and analyzed. Results indicate the first four of five working state parameters have a more prominent influence on those five performance indexes. Subsequently, further calculation generates correspondences among four working state parameters and five performance indexes with the method of radial basis function neural network. The predicted value of the trained neural network matches well with the original one. The approach can fulfill serialization of discrete working state parameters and performance indexes to facilitate subsequent analysis and optimization. Next, we came up with a new algorithm named RBFNN & GACMOO, which can calculate the optimal working state parameters and the corresponding performance indexes of the diesel engine working at 3700 m altitude. At last, the bench test of the diesel engine in a plateau environment is employed to verify accuracy of the optimized results and the effectiveness of the algorithm. The research first combined the method of artificial neural network and genetic algorithm to specify the optimal working state parameters of the diesel engine at high altitudes by focusing on engine power, torque and heat dissipation, which is of great significance for improving both performance and working reliability of heavy-duty diesel engine working in plateau environment.
Escaping Local Minima Using Repulsive Particles in FastSLAM for Space Rover

This paper describes the efficient strategy for guidance and control of a space rover using estimated position and a map acquired by using FastSLAM (Simultaneous Localization and Mapping) algorithm using a particle filter. A guidance law based on potential function method is also designed to cope with topographical change on a planet. Using the method, local minima problem in the potential field are frequently occurred when designing repulsive potential functions so as to avoid obstacles. To overcome the problem, we propose the new method that generates repulsive potential function at particles in FastSLAM for an exploration rover in unknown environment. Numerical results are performed to verify the validity of the proposed method for solving local minimum problem. Adaptive temperature parallel simulated annealing (ATPSA) method that is usually used to escape local minima is also applied to the system for comparison.
Machine Learning Based Resource Allocation of Cloud Computing in Auction

Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to those of the optimal allocation solution. The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.
The Association between Hemoglobin A1c and the Severity of Coronary Artery Disease in Non-diabetic Patients with Acute Coronary Syndrome

Introduction The relationship between the severity of coronary artery disease (CAD) with hemoglobin A1c (HbA1c) levels in diabetic patients is well-understood. However, the association between HbA1c and the severity of CAD in non-diabetics is still controversial. We wanted to find out if HbA1c of the non-diabetic adult population, presenting with an acute coronary syndrome (ACS), had any correlation with the severity of CAD. Methods We selected 119 non-diabetic adults who underwent coronary intervention for clinical reasons during the period of July 2015 to February 2017. The mean age of the patients was 54 ± 10.2 years. All patients were labeled as ‘acute coronary syndrome’, which included unstable angina, non-ST elevation myocardial infarction (NSTEMI), and ST-elevation myocardial infarction (STEMI). We obtained blood samples of patients for laboratory investigations, including HbA1c. We used the SYNTAX score as a tool to classify the severity of CAD, and patients having a SYNTAX score of >22 were considered to be having severe CAD. Results In order to find out the association between HbA1c and CAD, a linear regression analysis of HbA1c with the SYNTAX score was performed, which showed no statistically significant correlation between the SYNTAX score and HbA1c (correlation co-efficient = 0.142; p-value = 0.124). To compare the median value of HbA1c in groups with SYNTAX scores of ≤22 and those with SYNTAX scores of >22, we analyzed the data with the Mann-Whitney U test, which showed no significant difference in HbA1c between the two groups (p-value = 0.771). We determined the independent predictors of the severity of CAD by analyzing all variables with logistic regression, considering a SYNTAX score of >22 as a dependent variable. None of the variables, including HbA1c, proved to be statistically significant in multivariate logistic regression analysis. The unadjusted and adjusted odds ratio (OR) of HbA1c with 95% confidence intervals (CI) were 1.71 (0.47-2.92), p-value = 0.735 and 0.87 (0.33-2.29), and 0.78, respectively. Conclusion In conclusion, we find that HbA1c is not an independent predictor of the severity of CAD in non-diabetic adult patients.
Effect of systemic fungicide (combination of Cyproconazole and propiconazole) newly introduced in Algeria on Septoria of two varieties of wheat (Triticum durum Desf)

The purpose of this study was to test the effect of three doses of a newly introduced fungicide in Algeria, it is the ARTEA 330EC) on Septoria (leaf disease) of two durum wheat varieties (GTAdur and BIDI17 ). The experiment was conducted under natural conditions of the field at the EAC No. 1 Khrouf Bachir El-Karma (W. Annaba) and in the laboratory of the University of Plant El TARF. In the field and to assess the degree of attack of the disease on two varieties of durum wheat used, readings or simultaneous notations were made about the intensity of the disease, a direct reading was made by determining the percentage of the surface of the sheet contested. In the laboratory, the study examined the effect of three doses of the product on the physiology and biochemistry of two durum wheat varieties. To do this, all three doses were applied at early flowering plants, these treatments were compared to a control infected with the disease. The rate of water loss from leaves (RWL), the relative water content (RWC) and chlorophyll content, total carbohydrates, total protein and proline of the extract of leaves of treated plants and infected were determined. The results validated by statistical analysis revealed that the fungicide tested greatly reduces recovery pycnidial and reductions in severity up to 80% compared with control plants. The rate of water loss of treated leaves drop significantly which leads to an increase in their relative water content. Chlorophyll and total carbohydrate were higher in treated plants compared with control plants. Finally, for proline, the treated plants, in general, have a content of this osmolyte, greater than that of control plants especially in the GTA Hard variety.
The effects of tax reform on the capital structure of Listed firms in Norway : an empirical study

The goal of this thesis is to determine how changes in the corporate tax rate affect the capital structures of Norwegian firms, and how such effects can be explained by established economic theories. We have applied propensity score matching, difference-in-differences estimates, and ordinary least squares regressions to determine if the capital structures of Norwegian listed firms have been affected by the recent reductions in the Norwegian corporate tax rate. While our models have found a significant reduction in the development of Norwegian debt to equity ratios compared to the control group during the years 2012 to 2016, we have not been able to isolate these differences to the years where tax reductions have occurred. Furthermore, the small sample of firms, along with macroeconomic factors affecting the Norwegian firms during the time period, make it difficult to conclude any causality between the reductions in corporate tax rate and the changes in capital structure. Considering these issues, we have been unable to determine whether Norwegian corporate behavior most closely resembles the behavior depicted in the trade-off theory, or the behavior depicted in the pecking order theory.
Necessary and sufficient conditions for the conditional central limit theorem

Following Lindeberg's approach, we obtain a new condition for a stationary sequence of square-integrable and real-valued random variables to satisfy the central limit theorem. In the adapted case, this condition is weaker than any projective criterion derived from Gordin's theorem [Dokl. Akad. Nauk SSSR 188 (1969) 739-741] about approximating martingales. Moreover, our criterion is equivalent to the conditional central limit theorem, which implies stable convergence (in the sense of Renyi) to a mixture of normal distributions. We also establish functional and triangular versions of this theorem. From these general results, we derive sufficient conditions which are easier to verify and may be compared to other results in the literature. To be complete, we present an application to kernel density estimators for some classes of discrete time processes.
An Extension of the CONSORT Statement

Piaggio, Gilda; Elbourne, Diana R; Altman, Douglas G; Pocock, Stuart J; Evans, Stephen JW; CONSORT Group; (2006) Reporting of noninferiority and equivalence randomized trials: an extension of the CONSORT statement. JAMA, 295 (10). pp. 1152-1160. ISSN 0098-7484 DOI: https://doi.org/10.1001/jama.295. 10.1152 Downloaded from: http://researchonline.lshtm. ac.uk/id/eprint/12069/ DOI: https://doi.org/10.1001/jama.295. 10.1152
Identify a Spoofing Attack on an In-Vehicle CAN Bus Based on the Deep Features of an ECU Fingerprint Signal

An in-vehicle controller area network (CAN) bus is vulnerable because of increased sharing among modern autonomous vehicles and the weak protocol design principle. Spoofing attacks on a CAN bus can be difficult to detect and have the potential to enable devastating attacks. To effectively identify spoofing attacks, we propose the authentication of sender identities using a recurrent neural network with long short-term memory units (RNN-LSTM) based on the features of a fingerprint signal. We also present a way to generate the analog fingerprint signals of electronic control units (ECUs) to train the proposed RNN-LSTM classifier. The proposed RNN-LSTM model is accelerated on embedded Field-Programmable Gate Arrays (FPGA) to allow for real-time detection despite high computational complexity. A comparison of experimental results with the latest studies demonstrates the capability of the proposed RNN-LSTM model and its potential as a solution to in-vehicle CAN bus security.
Detection of Mycoplasma bovis in Pneumonic Calves

Mycoplasma bovis is a major pathogen in respiratory diseases of calves and cause an excessive economic loses. The current study was a goal to diagnoses bovine Mycoplasma and chiefly M. bovis from an outbreak of pneumonia in calves that occurred in Mosul city and mainly in Gogjaly village. Forty-two lung samples were collected from slaughtered and dead pneumonic calves in seven herds of imported calves. Extraction and amplification for DNA were conduct from all samples for diagnosis of Mycoplasma and M. bovis by PCR technique. The results have recorded the presence of Mycoplasma in 88.1% of examined lungs and M. bovis was diagnosed in 86.5% of the positive Mycoplasma samples. Finally the present study is the first local study at the moment which diagnoses Mycoplasma in general and mainly M. bovis from pneumonic calves, also according to the results it recommended the use of molecular techniques and principally PCR for the diagnosis of Mycoplasma and M. bovis.
DDDT_A_227477 275..284

*These authors contributed equally to this work Purpose: Treatment options for relapsed or refractory diffuse large B-cell lymphoma (RR DLBCL) represent an unmet medical need. Apatinib is a new oral tyrosine kinase inhibitor mainly targeting vascular endothelial growth factor receptor-2 (VEGFR-2) to inhibit tumour angiogenesis. In the present study, we evaluated the efficacy and safety of apatinib for patients with RR DLBCL. Patients and Methods: In this phase II, open-label, single-arm, prospective study, we enrolled patients aged 14–70 years with treatment failure of at least two chemotherapeutic regimens using Simon’s two-stage design. All patients were administered apatinib at an initial dose of 500 mg on a 4-week cycle at home and visited the outpatient clinic every two cycles to evaluate efficacy and to record adverse events. We considered objective response rate (ORR) as the primary end point, and progression-free survival (PFS), and overall survival (OS) plus duration of response (DoR) as the secondary end point. (This trial was registered at ClinicalTrials.gov, identifier: NCT03376958.). Results: From January 2017 to February 2019, we screened 35 patients and enrolled 32 eligible patients. At the cutoff point (April 2019), we noted 2 (6.3%) complete responses, 12 (37.5%) partial responses, and 9 (28.1%) stable diseases, attributing to an ORR of 43.8% and a disease control rate of 71.9%. The median PFS and OS were 6.9 (95% confidence interval [CI], 5.8–7.9) and 7.9 months (95% CI, 7.0–8.7), respectively. The median DoR was 5.0 months (95% CI, 3.5–6.5) for patients who achieved PR. The most common grade 3–4 adverse events (AE) were hypertension (12.6%), hand–foot syndrome (9.4%), and leucopenia (6.3%). No apatinib-related deaths were noted. Conclusion: Home administration of apatinib shows promising efficacy and manageable AEs in patients with RR DLBCL.
Titanium Dioxide Nanoparticles Alter the Cellular Phosphoproteome in A549 Cells

TiO2 nanoparticles (NPs) are one of the most produced NPs worldwide and are used in many consumer products. Their impact on human health, especially through inhalation, has been studied for more than two decades. TiO2 is known for its strong affinity towards phosphates, and consequently interaction with cellular phosphates may be one of the mechanisms driving its toxicity. In the present study, we used a phosphoproteomics approach to document the interaction of TiO2-NP with phosphoproteins from A549 human pulmonary alveolar epithelial cells. Cells were exposed to 21 nm anatase/rutile TiO2-NPs, then their phosphopeptides were extracted and analyzed using shotgun proteomics. By comparing the phosphoprotein content, phosphorylation status and phosphorylation sites of exposed cells with that of control cells, our results show that by affecting the phosphoproteome, TiO2-NPs affect cellular processes such as apoptosis, linked with cell cycle and the DNA damage response, TP53 being central to these pathways. Other pathways including inflammation and molecular transport are also affected. These molecular mechanisms of TiO2-NP toxicity have been reported previously, our study shows for the first time that they may derive from phosphoproteome modulation, which could be one of their upstream regulators.
Confluence of Constraints in Gauge Mediation: The 125 GeV Higgs Boson and Goldilocks Cosmology

Recent indications of a 125 GeV Higgs boson are challenging for gauge-mediated supersymmetry breaking (GMSB), since radiative contributions to the Higgs boson mass are not enhanced by significant stop mixing. This challenge should not be considered in isolation, however, as GMSB also generically suffers from two other problems: unsuppressed electric dipole moments and the absence of an attractive dark matter candidate. We show that all of these problems may be simultaneously solved by considering heavy superpartners, without extra fields or modified cosmology. Multi-TeV sfermions suppress the EDMs and raise the Higgs mass, and the dark matter problem is solved by Goldilocks cosmology, in which TeV neutralinos decay to GeV gravitinos that are simultaneously light enough to solve the flavor problem and heavy enough to be all of dark matter. The implications for collider searches and direct and indirect dark matter detection are sobering, but EDMs are expected near their current bounds, and the resulting non-thermal gravitino dark matter is necessarily warm, with testable cosmological implications.
Ohmic Contacts to 2D Semiconductors through van der Waals Bonding

High contact resistances have blocked the progress of devices based on MX2 (M = Mo, W; X = S, Se, Te) 2D semiconductors. Interface states formed at MX2/metal contacts pin the Fermi level, leading to sizable Schottky barriers for p‐type contacts in particular. It is shown that i) one can remove the interface states by covering the metal by a 2D layer, which is van der Waals‐bonded to the MX2 layer, and ii) one can choose the buffer layer such that it yields a p‐type contact with a zero Schottky barrier height. Possible buffer layers are graphene, a monolayer of h‐BN, or an oxide layer with a high electron affinity, such as MoO3. The most elegant solution is a metallic M′ X′2 layer with a high work function. A NbS2 monolayer adsorbed on a metal yields a high work function contact, irrespective of the metal, which gives a barrierless contact to all MX2 layers.
SiliconPV : March 25 High Resolution Inline Topography of Iron in P Multicrystalline Bricks by MDP

The iron density is one crucial parameter to verify the quality of the materia the material quality right at the beginning of the production line. This paper presents contact less high resolution inline topographic measurements of the iron density in multicrystalline silicon bricks by MDP (microw photoconductivity). The measurement procedure is fully automatic and takes less than 5 obtained were compared to QSSPC (quasi steady state photoconductivity) measurements, however, with a loss in spatial resolution. Furthermore, passivated wafers were prepared from the bricks after the measurements and investigated individually. The consistency of all the data is remarkable. © 2013 The Authors. Published by Elsevier Selection and/or peer-review under responsibility of conference
Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation

Satellite-derived estimates of downward surface shortwave radiation (SSR) and photosynthetically active radiation (PAR) are a part of the surface radiation budget, an essential climate variable (ECV) required by climate and vegetation models. Ground measurements are insufficient for generating long-term, global measurements of surface radiation, primarily due to spatial limitations; however, remotely sensed Earth observations offer freely available, multi-day, global coverage of radiance that can be used to derive SSR and PAR estimates. Satellite-derived SSR and PAR estimates are generated by computing the radiative transfer inversion of top-of-atmosphere (TOA) measurements, and require ancillary data on the atmospheric condition. To reduce computational costs, often the radiative transfer calculations are done offline and large look-up tables (LUTs) are generated to derive estimates more quickly. Recently studies have begun exploring the use of machine-learning techniques, such as neural networks, to try to improve computational efficiency. Here, nine machine-learning methods were tested to model SSR and PAR using minimal input data from the Moderate Resolution Imaging Spectrometer (MODIS) observations at 1 km spatial resolution. The aim was to reduce the input data requirements to create the most robust model possible. The bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression, and Neural Network yielded the best results with minimal training data requirements: an R 2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W / m 2 , and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W / m 2 , respectively, for all-sky condition total surface shortwave radiation and viewing angles less than 55°. Viewing angles above 55° were excluded because the residual analysis showed exponential error growth above 55°. A simple, robust model for estimating SSR and PAR using machine-learning methods is useful for a variety of climate system studies. Future studies may focus on developing high temporal resolution direct and diffuse estimates of SSR and PAR as most current models estimate only total SSR or PAR.