Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigation of Non-CO2 Aviation’s Climate Impact by Changing Cruise Altitudes

Aviation is seeking for ways to reduce its climate impact caused by CO2 emissions and non-CO2 effects. Operational measures which change overall flight altitude have the potential to reduce climate impact of individual effects, comprising CO2 but in particular non-CO2 effects. We study the impact of changes of flight altitude, specifically aircraft flying 2000 feet higher and lower, with a set of global models comprising chemistry-transport, chemistry-climate and general circulation models integrating distinct aviation emission inventories representing such alternative flight altitudes, estimating changes in climate impact of aviation by quantifying radiative forcing and induced temperature change. We find in our sensitivity study that flying lower leads to a reduction of radiative forcing of non-CO2 effects together with slightly increased CO2 emissions and impacts, when cruise speed is not modified. Flying higher increases radiative forcing of non-CO2 effects by about 10%, together with a slight decrease of CO2 emissions and impacts. Overall, flying lower decreases aviation-induced temperature change by about 20%, as a decrease of non-CO2 impacts by about 30% dominates over slightly increasing CO2 impacts assuming a sustained emissions scenario. Those estimates are connected with a large but unquantified uncertainty. To improve the understanding of mechanisms controlling the aviation climate impact, we study the geographical distributions of aviation-induced modifications in the atmosphere, together with changes in global radiative forcing and suggest further efforts in order to reduce long standing uncertainties.
A Novel Probabilistic Label Enhancement Algorithm for Multi-Label Distribution Learning

We propose a novel probabilistic label enhancement algorithm, called PLEA, to solve challenging label distribution learning (LDL) for multi-label classification problems. We adopt the well-known maximum entropy model based label distribution learner. However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the label distribution learner. Specifically, the supervised information in the label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension. Then the robust linear regression is employed to estimate the label distributions associated with the extracted reduced-dimension features. Using the enhanced reduced-dimension features and their associated estimated label distributions in the maximum entropy model, the unknown true label distributions can be estimated more accurately, while imposing considerably lower computational complexity. We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets. Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-label LDL approaches. The results also show that our PLEA compares favourably with the state-of-the-arts multi-label learning algorithms for classification tasks.
Effects of residual biochar amendment on soil chemical properties, nutrient uptake, crop yield and N2O emissions reduction in acidic upland rice of East Lampung

Previous studies have been recognized that biochar addition to poor soil may lead to improve soil and crop productivity, also reduce greenhouse gas (GHG) emissions. However, the results were varied and the mechanisms were not clear. Two rates of rice husk (RH) and cacao shell (CS) biochar namely 0 and 15 t ha−1 were applied to soil, to investigate the effect of RH and CS biochar on soil chemical properties, nutrient uptake, crop yield, and N2O emissions reduction on upland rice at acidic soil of East Lampung. It was residual effect study; biochar was applied only in the first planting season of 2012. In addition, N2O emissions from the soil were monitored with close chamber method. The result showed that 15 t ha−1 CS biochar treatment was the best treatment, resulted 1.21 t ha−1 yield. Meanwhile on 15 t ha−1 RH biochar treatment was only 0.22 t ha−1. However, no significance difference on both nutrient uptake and N2O emissions among the treatments. N2O emissions from RH and CS biochar treatment were reduced 26% and 21% compare to control. Our result proposed that CS biochar may use as soil amendment, to improve crop productivity and also to combat climate change.
Generalized Developable Cubic Trigonometric Bézier Surfaces

This paper introduces a new approach for the fabrication of generalized developable cubic trigonometric Bézier (GDCT-Bézier) surfaces with shape parameters to address the fundamental issue of local surface shape adjustment. The GDCT-Bézier surfaces are made by means of GDCT-Bézier-basis-function-based control planes and alter their shape by modifying the shape parameter value. The GDCT-Bézier surfaces are designed by maintaining the classic Bézier surface characteristics when the shape parameters take on different values. In addition, the terms are defined for creating a geodesic interpolating surface for the GDCT-Bézier surface. The conditions appropriate and suitable for G1, Farin–Boehm G2, and G2 Beta continuity in two adjacent GDCT-Bézier surfaces are also created. Finally, a few important aspects of the newly formed surfaces and the influence of the shape parameters are discussed. The modeling example shows that the proposed approach succeeds and can also significantly improve the capability of solving problems in design engineering.
SAR Parametric Super-Resolution Image Reconstruction Methods Based on ADMM and Deep Neural Network

The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances. However, they face three challenges. First, this kind of method is mainly based on the point scattering model and not suitable for characterizing the line-segment-scattering and surface-scattering features of distributed targets. Second, the hyperparameters in these methods are hard to tune to optimal values. Third, due to a large amount of calculation, these methods are difficult to apply in practice. In this article, to solve these problems, we introduce the line-segment-scatterers (LSSs) and rectangular-plate-scatterers (RPSs) in SAR echo model to develop the SAR hybrid echo model and propose two SAR parametric SR image reconstruction methods based on solving a CS problem, where three penalties are utilized to exploit the sparsity of the point scatterers, LSSs, and RPSs, respectively. At the core of the first method is a direct solver called multicomponent alternating direction method of multipliers (MC-ADMM) solver that solves the CS problem quickly and iteratively based on closed derivative expressions. In contrast, the second method maps the MC-ADMM solver into a deep unfolded neural network, i.e., the parametric SR imaging network (PSRI-Net), which is faster, and the parameters can be automatically set to the optimum. Since all the parameters of the MC-ADMM solver are learned discriminatively through end-to-end training in PSRI-Net. Extensive simulation and practical experiments are carried out to demonstrate the effectiveness of the proposed methods.
Effect of peanut intercropping and mycorrhiza in increasing yield of sweet corn yield

Many researchers reported that mycorrhizal symbiosis could significantly increase crop yield. The objective of this research was to examine the effects of mycorrhiza and intercropping with two peanut varieties on growth and yield of sweet corn. The experiment was conducted following paddy rice crop in Dasan Tebu, West Lombok, Indonesia, in the dry season 2018 and designed according to Split Plot design with four replications and two treatment factors, i.e. application of mycorrhiza-based biofertilizer as the main plots (M0 = without and M1=with mycorrhiza) and intercropping with peanut as the subplots (T0 = without intercropping, T1 = intercropping with peanut var. “Kelinci”, and T2 = intercropping with peanut var. “Bison”). The sweet corn cobs were harvested at the dough maturity stage. Results indicated that application of biofertilizer significantly increased stover dry weight, cob fresh weight, and number of green leaves per plant at harvest. Intercropping with peanuts also affected those observation variables and the height of ear position in the stem, and the “Bison” peanut variety was better than “Kelinci”. Although showing no significant interaction, intercropping with “Bison” did show higher benefits of biofertilization on both dry stover and fresh sweet corn cob weights than those with “Kelinci” variety.
Biodiversity loss leads to reductions in community‐wide trophic complexity

With increasing biodiversity loss occurring worldwide, there is a need to understand how these losses will affect ecosystem structure and function. Biodiversity loss leads to changes in species interactions and alters the trophic complexity of food webs. These alterations to trophic complexity can be described by changes to the diversity of food resources and the diversity of trophic levels. To understand how biodiversity affects trophic complexity of food webs, we used 10 islands across the Aleutian Archipelago to compare the alternate state communities found in kelp forest ecosystems (kelp forest and urchin barren communities) and then compared these to natural reference communities without local benthic production (their associated offshore communities). We constructed food webs for each community across the Aleutian Archipelago using primary producer and consumer carbon (dC, a proxy for food sources to a consumer) and nitrogen (dN, a proxy for consumer trophic level) stable isotope values. Our findings suggest that biodiversity loss (i.e., phase change from kelp forest to urchin barren) leads to reductions in trophic complexity, which was similar to naturally occurring communities with low local resource biodiversity. This was expressed by lower consumer isotopic dietary niche areas, especially omnivores and herbivores, and lower omnivore and carnivore trophic levels within the urchin barren communities. We clarify how biodiversity promotes food resources and increases trophic levels and complexity through critical trophic conduits.
Dyes extracted from Hibiscus Sabdariffa flower and Pandannus amaryllifolius leaf as natural dye sensitizer by using an alcohol-based solvent

The efficiency improvement of Dye sensitized solar cells (DSSC), the fabrication of DSSC by using metal-free organic natural dyes with a different type of solvents for the extraction of dye sensitizer was investigated. The metal-free organic Dye which comes from anthocyanin and chlorophyll dyes were dissolved by using different solvents, which are ethanol, methanol and mixture of ethanol and methanol. Anthocyanin dye was extracted from the petals of Hibiscus Sabdariffa (Roselle), and chlorophyll dye was extracted from the epidermal leaves of Pandannus amaryllifolius (Pandan). The purpose of using different solvents from alcohol-based was to determine which solvents that produce the highest effect in term of efficiency for DSSC. To confirm which solvents that produce the highest efficiency to the DSSC, the photovoltaic measurement was conducted, and the data was collected. From the photovoltaic analysis, the J-V characteristics under illumination are recorded. The sample that used anthocyanin dye extracted from Roselle flower diluted with methanol solvent gave the highest efficiency which is 0.0005% with the following parameters – Voc = 0.419 V, Jsc = 0.0057 mA/cm2 and FF = 0.24. Meanwhile, the sample that used chlorophyll dye extracted from Pandan leaves diluted with a mix of ethanol and methanol solvents gave the highest efficiency which is 0.00014% with the following parameters - Voc = 0.347 V, Jsc = 0.0016 mA/cm2 and FF = 0.25.
Impak Struktur Modal, Struktur Kepemilikkan dan Karakteristik Perusahaan Terhadap Financial Distress dan Financial Health Perusahaan

Corona Virus Pandemic affected the world economy, including Indonesia. Many companies are out of business due to this pandemic.With the background of the conditions mentioned above, the researchers are interested in examining more deeply the variables that determine the level of financial distress and at the same time the financial health of the company. Furthermore, the variables that are used as independent variables and are thought to affect the company's financial performance are capital structure, ownership structure and company characteristics. In assessing financial performance, the Altman Z Score model is used and then to see the impact of the variables that are thought to affect the company's financial performance.The research model used is the Logistic Regression equation.Population and sample are taken from financial data of companies listed on the Indonesia Stock Exchange. Data is taken manually on the website: www.idx. co.id. And the period in this study was taken from 2015-2019. The test results prove that the Capital Structure and Ownership Structure are factors that have a significant influence on the Company's Financial Distress and Financial Health. ABSTRAK Pandemi Virus Corona berimbas pada perekonomian dunia tidak terkecuali pada perekonomian di Indonesia. Banyak perusahaan yang gulung tikar akibat pandemik ini. Dengan berlatar belakang kondisi tersebut diatas maka peneliti tertarik untuk mengkaji lebih dalam menentukkan variabel yang sangat menentukan tingkat Financial Distress dan sekaligus financial health (Kinerja Keuangan) perusahaan. Selanjutnya variabel yang di jadikan variabel independen dan di duga berpengaruh terhadap kinerja keuangan perusahaan adalah struktur modal, struktur kepemilikkan dan Kharakteristik Perusahaan. Dalam menilai kinerja keuangan maka digunakan model Altman Z Score dan selanjutnya untuk melihat dampak variabel yang di duga berpengaruh terhadap kinerja keuangan perusahaan. Model penelitian yang di pakai adalah persamaan Logistic Regression. Model ini kemudian akan di lakukan uji T , Uji F dan Uji Asumsi Klasik sebelum di gunakan dalam melihat signifikasi variabel independen terhadap variabel dependen. Populasi dan sampel diambil dari data keuangan perusahaan yang terdaftar di Bursa Efek Indonesia. Data diambil secara manual di website: www.idx. co.id. Periode pada penelitian ini diambilkan data dari tahun 2015-2019. Hasil Pengujian membuktikan bahwa Struktur Modal dan Struktur Kepemilikkan adalah faktor yang sangat berpengaruh signifikan terhadap Financial Distress dan Financial Health Perusahaan.
Using Multiple Imputation and Inverse Probability Weighting to Adjust for Missing Data in HIV Prevalence Estimates: A Cross-Sectional Study in Mwanza, North Western Tanzania.

Background Population surveys and demographic studies are the gold standard for estimating HIV prevalence. However, non-response in these surveys is of major concern especially if it is not random and complete case analysis becomes an inappropriate method to analyse the data. Therefore, a comprehensive analysis that will account for the missing data must be used to obtain unbiased HIV prevalence estimates. MethodsSerological samples were collected from participants who were resident in a Demographic Surveillance System (DSS) in Kisesa, Tanzania. HIV prevalence was estimated using three methods. Firstly, using the Complete case analysis (CCA), assuming data were Missing Completely at Random (MCAR). The other two methods, multiple imputations (MI) and inverse probability weighting (IPW), assumed that non-response was missing at random (MAR). For MI, a logistic regression model adjusting for age, sex, residence, and marital status was used to impute 20 datasets to re-estimate the HIV prevalence. Propensity for participating in the sero-survey and being tested for HIV given age, sex, residence, and marital status were generated using logistic regression models. Using the propensity scores, inverse probability weights were derived for participants who were tested for HIV.ResultsThe overall CCA HIV prevalence estimate was 6.6% (95% CI: 6.0-7.2), with 5.4% (95% CI: 4.6-6.3) in males and 7.3% (95% CI: 6.6-8.1) in females. Using MI, the overall HIV prevalence was 6.8% (95% CI: 6.2-7.5), 6.2% (95% CI: 5.1-7.3) in males and 7.4% (95% CI: 6.6-8.2) in females. Using IPW the overall HIV prevalence was 6.7% (95% CI: 6.1-7.4), with 5.5% (95% CI: 4.7-6.5) in males and 7.7% (95% CI: 7.0 - 8.6) in females. HIV prevalence differed significantly between age groups (p<0.001), with the highest estimate in males aged 35-39 and females aged 40-44, and lowest in both males and females aged 15-19 years.DiscussionThe results showed that both MI and IPW are reliable methods for estimating HIV prevalence in the presence of missing data. MI is superior to CCA and the IPW approaches as it had smaller standard errors and narrower 95% confidence intervals. Therefore, we recommend use of MI in estimating HIV prevalence to address the problem of varied types of missing data. However, further research is needed to determine the bias in estimates from MI and IPW.ConclusionsComplete case analysis underestimates HIV prevalence compared to methods that adjust for missing data. The best method to adjust for missing data in population surveys is through the use of multiple imputations.
Cardiac Amyloidosis Therapy: A Systematic Review

Heart involvement in Cardiac Amyloidosis (CA) results in a worsening of the prognosis in almost all patients with both light-chain (AL) and transthyretin amyloidosis (ATTR). The mainstream CA is a restrictive cardiomyopathy with hypertrophic phenotype at cardiac imaging that clinically leads to heart failure with preserved ejection fraction (HFpEF). An early diagnosis is essential to reduce cardiac damage and to improve the prognosis. Many therapies are available, but most of them have late benefits to cardiac function; for this reason, novel therapies are going to come soon.
The Ancient Wisdom at Intersection with Modern Cardiac Sciences

Citation: Alabdulgader A. The Ancient Wisdom at Intersection with Modern Cardiac Sciences. Cardiol Vasc Res. 2021; 5(1): 1-13. *Correspondence: Abdullah A. Alabdulgader, MD,DCH,MRCP,FRCP Senior Scientist, Interventional Congenital Cardiologist, Electrophysiologist, cryo and robotic ablation specialist and electrical devices implanter. Prince Sultan Cardiac Center (Founder and Chief Physician). Alhasa, Saudi Arabia].
An integrated structural and GPS study of the Jalpatagua fault, southeastern Guatemala

The Jalpatagua fault in Guatemala accommodates dextral movement of the Central America forearc. We present new global positioning system (GPS) data, minor fault analysis, geochronological analyses, and analysis of lineaments to characterize deformation along the fault and near its terminations. Our data indicate that the Jalpatagua fault terminates at both ends into extensional regions. The western termination occurs near the Amatitlan caldera and the southern extension of the Guatemala City graben, as no through-going structures were observed to continue west into the active volcanic arc. Along the Jalpatagua fault, new and updated GPS site velocities are consistent with a slip rate of 7.1 ± 1.8 mm yr−1. Minor faulting along the central section of the fault includes: (1) N-S–striking normal faults accommodating E-W elongation; and (2) four sets of strike-slip faults (oriented 330°, 020°, 055°, and 295°, parallel to the Jalpatagua fault trace). Minor fault arrays support dextral movement along a major fault in the orientation of the Jalpatagua fault. GPS and fault data indicate that the Jalpatagua fault terminates to the east near the Guatemala–El Salvador border. Data delineate a pull-apart basin southeast of the fault termination, which is undergoing transtension as the Jalpatagua fault transitions into the El Salvador fault system to the east. Within the basin, minor faulting and lineations trend to the NW and accommodate NE-directed elongation. This faulting differs from E-W elongation observed along the Jalpatagua fault and is more similar to minor faults within the El Salvador fault system.
Porosity and compressive strength of PLA-based scaffold coated with hydroxyapatite-gelatin to reconstruct mandibula: a literature review

Ameloblastoma is one of odontogenic tumours that is classified as benign, aggressive, and destructive. Mandibular tumours potentially cause bone disruption or damage, so it is necessary to do a resection to remove the infected part. Treatment done by resection may cause defects starting from the gap in the alveolar bone to the discontinuity of the mandible. Thus, it is necessary to reconstruct the mandible using a scaffold. Scaffold is made by using the PLA 3D-printing with the Fused Deposition Modelling (FDM) method. PLA is non-bioactive and hydrophobic, so the surface needs to be modified by coating PLA with hydroxyapatite (HA) and gelatine. This article aims to examine the effect of PLA surface modification using HA-Gelatine through a review article. The searching strategy for articles is by using databases of internationally reputable journals with certain keywords. From the search, journals were obtained (with status Q3, Q2 and Q1) as reference journals in this review. Literature review shows that the addition of hydroxyapatite can improve the bioactive properties of PLA, while the addition of gelatine can increase its hydrophilicity properties. Therefore, HA-Gelatin coating on 3D PLA has the potential to be used as a scaffold for mandibular reconstruction.
Bayesian inference for the Birnbaum–Saunders autoregressive conditional duration model with application to high-frequency financial data

Abstract Autoregressive conditional duration (ACD) models have been preponderant when the subject is the modeling of high-frequency financial data. A prominent model that has demonstrated great adjustment capacity is the ACD model based on the Birnbaum–Saunders distribution (BS-ACD). Recent works have shown that this model outperforms the existing models in the literature. Nevertheless, these works explore only classical estimation approaches. In this article, we perform a Bayesian approach of the BS-ACD model. The scale parameter was modeled considering a dynamic linear model. Estimation of posterior distribution of parameters was approximated through Markov chain Monte Carlo methods. A simulation study is conducted to evaluate the performance of Bayesian estimators and two applications to real high frequency data illustrate the proposed methodology.
Modulated gravity effects on nonlinear convection in viscoelastic ferromagnetic fluids between two horizontal parallel plates

In this study, the analysis of nonlinear stability with viscoelastic ferromagnetic fluids as working media is performed by finite‐amplitude perturbations. The solution of the resulting nonautonomous system of the Lorenz model (generalized Khayat–Lorenz model of four modes) is obtained numerically to measure the amount of heat transport. The effects of elastic parameters, Prandtl number, modulation parameters, buoyancy magnetic parameter, and nonbuoyancy magnetic parameter on heat transport are studied. Heat transport is quantified through the average Nusselt number, which is determined by solving the scaled Lorenz model. As limiting cases of the study, the results of Newtonian, Maxwell, Rivlin–Ericksen fluids are determined. The results obtained have been presented graphically.