Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies on Impact of Processing on Physiochemical and Biochemical Properties of Osmodehydrated Pineapple (Ananas comosus (L) Merr.) Cuboid and Its Storage Stability

Osmotic dehydration of pineapple cuboids were conducted to study the effect of sugar concentration of osmotic solution on mass transfer, weight reduction, vitamin-C, total phenol content and antioxidant property of samples pretreated with steam blanching and microwave heating. As treatment time went on, there was an increase in water loss, weight loss, and solids accumulation. The sample treated with 60°B experienced the highest mass transfer during the osmotic dehydration of pineapple cuboids, whereas the sample treated with 30°B experienced the lowest mass transfer. The pineapple cuboids immersed in 60˚B sugar syrup and dried in a tray drier resulted maximum weight loss. Microwave heated samples dipped in 60˚B sugar syrup showed better retention of nutritive value(total phenol content, vitamin C and antioxidant activity) as well as better color, texture, taste and mouth feel . According to the sensory analysis, the samples treated with 60°B solution received the highest acceptability for color, flavour, texture, mouth feel, and taste. Osmodried samples were stored for 3 months at ambient condition without any adverse effect on sensory and nutritional parameters.
Stress Testing with Student's T Dependence

In this study we propose the use of the Student's t dependence function to model dependence between asset returns when conducting stress tests. To properly include stress testing in a risk management system, it is important to have accurate information about the (joint) probabilities of extreme outcomes. Consequently, a model for the behavior of risk factors is necessary, specifying the marginal distributions and their dependence. Traditionally, dependence is described by a correlation matrix, implying the use of the dependence function inherent in the multivariate normal (Gaussian) distribution. Recent studies have cast serious doubt on the appropriateness of the Gaussian dependence function to model dependence between extreme negative returns. The student's t dependence function provides an attractive alternative. In this paper, we introduce four tests to analyze the empirical fit of both dependence functions. The empirical results indicate that probabilities assigned to stress tests are largely influenced by the choice of dependence function. The statistical tests reject the Gaussian dependence function, but do not reject the Student's t dependence function.
Deep Learning: A key of Stepping into the Era of Big Data

: With the big data era's coming, deep learning, a new research field of machine learning, is used to analyze and process data efficiently. Firstly we introduce the history of deep leaning development, and compare the differences between shallow and deep structure models. Then we describe the advantages of deep structure model and summarize that the success of deep learning lies in three aspects: big scale data sets, advanced hard-ware facilities and new optimized techniques. Secondly, from the perspective of supervised and unsupervised learning setting, we review the current situation of deep learning research and the basic principles and applications of classical deep structure models Finally, we summarize the challenges and the direction of future research of deep learning.
Does Early Referral Lead to Early Repair? Quality Improvement in Cleft Care

Background Cleft lip and palate is the most common congenital defect of the head and neck, occurring in 1 of 700 live births. Diagnosis often occurs in utero by conventional or 3-dimensional ultrasound. Early cleft lip repair (ECLR) (<3 months of life) for unilateral cleft lip (UCL), regardless of cleft width, has been the mainstay of lip reconstruction at Children's Hospital Los Angeles since 2015. Historically, traditional lip repair (TLR) was performed at 3 to 6 months of life ± preoperative nasoalveolar molding (NAM). Previous publications highlight the benefits of ECLR, such as enhanced aesthetic outcomes, decreased revision rate, better weight gain, increased alveolar cleft approximation, cost savings of NAM, and improved parent satisfaction. Occasionally, parents are referred for prenatal consultations to discuss ECLR. This study evaluates timing of cleft diagnosis, preoperative surgical consultation, and referral patterns to validate whether prenatal diagnosis and prenatal consultation lead to ECLR. Methods Retrospective review evaluated patients who underwent ECLR versus TLR ± NAM from 2009 to 2020. Timing of repair, cleft diagnosis, and surgical consultation, as well as referral patterns, were abstracted. Inclusion criteria dictated: age < 3 months for ECLR or 3 to 6 months for TLR, no major comorbidities, and diagnosis of UCL without palatal involvement. Patients with bilateral cleft lip or craniofacial syndromes were excluded. Results Of 107 patients, 51 (47.7%) underwent ECLR whereas 56 underwent TLR (52.3%). Average age at surgery was 31.8 days of life for the ECLR cohort and 112 days of life for the TLR cohort. Furthermore, 70.1% of patients were diagnosed prenatally, yet only 5.6% of families had prenatal consults for lip repair, 100% of which underwent ECLR. Most patients were referred by pediatricians (72.9%). Significance was identified between incidence of prenatal consults and ECLR (P = 0.008). In addition, prenatal diagnosis was significantly correlated with incidence of ECLR (P = 0.027). Conclusions Our data demonstrate significance between prenatal diagnosis of UCL and prenatal surgical consultation with incidence of ECLR. Accordingly, we advocate for education to referring providers about ECLR and the potential for prenatal surgical consultation in the hopes that families may enjoy the myriad benefits of ECLR.
Upregulation of Receptor Tyrosine Kinase Activity and Stemness as Resistance Mechanisms to Akt Inhibitors in Breast Cancer

Simple Summary Acquired resistance of cancer cells to targeted therapy poses a major clinical problem. Hyperactivation of the PI3K/Akt pathway is common in human malignancies, and numerous Akt inhibitors are undergoing clinical evaluation. However, mechanisms of acquired resistance to Akt inhibitors have not been studied extensively. By using phospho-RTK arrays, we demonstrated hyper-phosphorylation of multiple RTKs in Akt-inhibitor-resistant breast cancer cells. We further showed that the EGFR inhibitor could overcome Akt inhibitor resistance. In addition, our study revealed enhanced cancer stemness in resistant cells, and RNA sequencing identified several stem cell regulators that may contribute to acquired resistance. Abstract The PI3K/Akt pathway is frequently deregulated in human cancers, and multiple Akt inhibitors are currently under clinical evaluation. Based on the experience from other molecular targeted therapies, however, it is likely that acquired resistance will be developed in patients treated with Akt inhibitors. We established breast cancer models of acquired resistance by prolonged treatment of cells with allosteric or ATP-competitive Akt inhibitors. Phospho-Receptor tyrosine kinase (Phospho-RTK) arrays revealed hyper-phosphorylation of multiple RTKS, including EGFR, Her2, HFGR, EhpB3 and ROR1, in Akt-inhibitor-resistant cells. Importantly, resistance can be overcome by treatment with an EGFR inhibitor. We further showed that cancer stem cells (CSCs) are enriched in breast tumor cells that have developed resistance to Akt inhibitors. Several candidates of CSC regulators, such as ID4, are identified by RNA sequencing. Cosmic analysis indicated that sensitivity of tumor cells to Akt inhibitors can be predicted by ID4 and stem cell/epithelial–mesenchymal transition pathway targets. These findings indicate the potential of targeting the EGFR pathway and CSC program to circumvent Akt inhibitor resistance in breast cancer.
Wavelet-based representation of uniform multi-component data

We propose a wavelet-based hierarchical data structure for 2D and 3D data that deals with multicomponent data on a uniform grid. We distinguish two phases: (i) construction and pruning of the wavelet hierarchy, resulting in a block-wise representation of the data, where the resolution of each block is adapted to its content; (ii) visualization, based on a linearly interpolating reconstruction filter. We perform experiments with 2-D timedependent flow data and a 3-D multi-field data set containing vector components and several scalar components of a simulation of hurricane Isabel. For the various data sets, our method approximates the data with the amounts of wavelet coefficients ranging between 10% and 30%. We also show how to obtain gradients directly from the hierarchical representation, without the need to explicitly reconstruct the data first. Our method is not specific to the data we have used for the experiments, and it can be extended to higher dimensions or other data types, such as tensors, in a straightforward manner.
Enhancing Stock Portfolios for Enterprise Management and Investment in Energy Industry

Renewable energy is critical to the energy industry, even more so given the recent surge in oil and gas prices. As a result, the oil and gas sector is constantly preparing for the coming energy transition. This raises the question of when the oil and gas industry should invest in renewable energy sources. This article examines the history of oil company involvement in renewable energy to address this question. It also establishes and quantifies a relationship between renewable energy investment and oil and gas prices, followed by a projection of renewable energy investment based on oil and gas prices. A decision support system based on crude oil prices and renewable energy stocks is developed to predict renewable energy investment decisions. According to the experiments, the prediction model used provides favorable mean square values compared to other models. An algorithm based on the price of oil determines whether to invest in shares of renewable energy companies. The performance of a company is evaluated based on its expected profitability.
Clumpology of starbursts in the WiggleZ Dark Energy Survey

We have observed Halpha emission from a sample of 13 star-forming galaxies from the WiggleZ Dark Energy Survey in the redshift range z~1.2-1.5 and stellar mass range 9.8<log(M_*/M_sun)<11.6 with the integral field spectrograph OSIRIS on Keck, taking advantage of Laser Guide Star Adaptive Optics. We detect multiple emission, 1-2 kpc size sub-components, or 'clumps' within the Halpha spatial emission in 4 galaxies, extended emission in 4 galaxies and compact spheroidal emission in 5 galaxies. Considering our results in the context of unstable disk formation and a merger sequence, we find evidence of ordered orbital motion in the majority of galaxies as would be found in unstable gaseous disks.
The burst mode of accretion in massive star formation with stellar inertia

The burst mode of accretion in massive star formation is a scenario linking the initial gravitational collapse of parent pre-stellar cores to the properties of their gravitationally unstable discs and of their accretion-driven bursts. In this study, we present a series of high-resolution 3D radiation-hydrodynamics numerical simulations for young massive stars formed out of collapsing $100\, \rm M_{\odot }$ molecular cores spinning with several values of the ratio of rotational-to-gravitational energies $\beta =5\%$–$9\%$. The models include the indirect gravitational potential caused by disc asymmetries. We find that this modifies the barycenter of the disc, causing significant excursions of the central star position, which we term stellar wobbling. The stellar wobbling slows down and protracts the development of gravitational instability in the disc, reducing the number and magnitude of the accretion-driven bursts undergone by the young massive stars, whose properties are in good agreement with that of the burst monitored from the massive protostar M17 MIR. Including stellar wobbling is therefore important for accurate modeling disc structures. Synthetic alma interferometric images in the millimeter waveband show that the outcomes of efficient gravitational instability such as spiral arms and gaseous clumps can be detected for as long as the disc is old enough and has already entered the burst mode of accretion.
Physicochemical characterization of levulinate esters with different alkyl chain lengths blended with fuel

Due to the advantages of cellulosic biomass, such as abundant reserves, renewable, and wide sources, the large‐scale development of lignocellulosic biomass fuels has the advantage of relieving energy pressure and promoting energy conservation and emission reduction, of which the levulinate esters are a potential biomass liquid fuel. In this study, different alkyl chain lengths of the levulinate esters were blended with 0# fossil diesel, and the physicochemical properties of the blended fuels, such as mutual solubility, density, distillation range, closed flash point, kinematic viscosity, cold filter plugging point, oxidation stability, and sulfur content and other physical and chemical properties were tested and investigated. The results showed that the longer the alkyl chains of the levulinate esters, the better their stability when mixed with 0# petrochemical diesel. And levulinate esters with short alkyl chains can be easily separated from blended fuels at low temperatures. The physicochemical properties of the blended fuels improved significantly with the increase of carbon chain length and levulinate esters content, low‐temperature flow characteristics, atomization characteristics of spraying, and safety of transportation and storage, and the physicochemical properties of levulinate blended fuels with long alkyl chains were more superior than shorter alkyl chains of the levulinate esters.
Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland

Abstract. Remote sensing of water vapour using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for numerical weather prediction (NWP). However, one of the phenomena rarely studied using GNSS are foehn winds. Since foehn winds are associated with significant humidity gradients between two sides of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. Time series reveal characteristic features like distinctive minima and maxima as well as a significant decrease in the correlation between the stations. However, detecting such signals becomes increasingly difficult for large datasets. Therefore, we suggest the application of machine learning algorithms for the detection and prediction of foehn events by means of GNSS troposphere products. This initial study develops a new, machine learning-based method for detection and prediction of foehn events at the Swiss station Altdorf by utilising long-term time series of high-quality GNSS troposphere products. Data from the Automated GNSS Network Switzerland (AGNES) and various GNSS sites from neighbouring countries as well as records of an operational foehn index are used to investigate the performance of several different classification algorithms based on appropriate statistical metrics. The two best-performing algorithms are fine tuned and tested in four dedicated experiments using different feature setups. The results are promising, especially when reprocessed GNSS products are utilised and the most dense station setup is used. Detection- and alarm-based measures reach levels between 66 %–80 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP. For operational prediction, limitations due to the availability and quality of GNSS products in near-real time (NRT) exist. However, they might be mitigated to a significant extent by provision of additional NRT products and improved data processing in the future. Results also outline benefits for the results when including geographically relevant stations (e.g. high-altitude stations) in the utilised datasets.
Spatial-nonlocality-induced non-Markovian electromagnetically induced transparency in a single giant atom

In recent experiments, electromagnetically induced transparency (EIT) were observed with giant atoms, but nothing unconventional were found from the transmission spectra. In this letter, we show that unconventional EIT does exist in giant atoms, and indicate why it has not been observed so far. Different from these existing works, this letter presents a consistent theory including a real space method and a time delayed master equation for observing unconventional EIT. We discover that this phenomenon is a quantum effect which cannot be correctly described in a semi-classical way as those in recent works. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom, which is crucial for a future experimental observation. This new phenomenon results from inherent non-locality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time. Our theory establishes a framework for application of nonlocal systems to quantum information processing.
Experimental superradiance and slow-light effects for quantum memories

The effects of high optical depth phenomena, such as superradiance, are investigated in potential quantum memory materials. The results may have relevance for several schemes, including controlled reversible inhomogeneous broadening, atomic frequency combs, and quantum memories based on electromagnetically induced transparency, which are based on using ensembles as storage media. It is shown that strong superradiant effects manifested as decay rates larger than 1/T*(2) are present even for moderate values of alpha L <= 5 and increases as a function of alpha L. For even higher alpha L, effects such as off-resonant slow light is demonstrated and discussed and, finally, the efficiency of time-reversed optimized input pulses is tested. A maximum retrieval efficiency of similar to 20% is reached and agreement with the theoretically expected result is discussed.
A Comparative Analysis of the Use of ‘Thereof’ in an English Non-translated Text and the English Machine- and Human-translated Versions of the Hungarian Criminal Code

Owing to the recent rise of neural language translation, a paradigm shift has been witnessed regarding the role of translators and reviewers. As neural machine translation is increasingly more capable of modelling how natural languages work, the traditional tasks of translators are being gradually replaced by new challenges. More emphasis is placed on pre- and post-editing (revision) skills and competences, presumably enabling the production of higher quality and near human-made translations. In my paper, I attempt to demonstrate through the qualitative and quantitative comparison of machine-translated legal texts (acts) to human-translated ones the relevant challenges and dynamic contrasts arising in the process of translating. Through the qualitative and quantitative analysis of the original Hungarian (source language) Criminal Code and its English (target language) machine and human translations, I aim to highlight the peculiar challenges emerging in the process of translation. I also aim to demonstrate what patterns can be observed in translations produced by human and non-human translators.
The Production of 44Ti and 60Co in Supernovae

The production of the radioactive isotopes {sup 44}Ti and {sup 60}Co in all types of supernovae is examined and compared to observational constraints including Galactic {gamma}-ray surveys, measurements of the diffuse 511 keV radiation, {gamma}-ray observations of Cas A, the late-time light curve of SN 1987A, and isotopic anomalies found in silicon carbide grains in meteorites. The revised line flux from {sup 44}Ti decay in the Cas A supernova remnant reported by COMPTEL on the {ital Compton Gamma Ray Observatory} is near the upper bound expected from our models. The necessary concurrent ejection of {sup 56}Ni would also imply that Cas A was a brighter supernova than previously thought unless extinction in the intervening matter was very large. Thus, the reported amount of {sup 44}Ti in Cas A provides very interesting constraints on both the supernova environment and its mechanism. The abundances of {sup 44}Ti and {sup 60}Co ejected by Type II supernovae are such that gamma-radiation from {sup 44}Ti decay SN 1987A could be detected by a future generation of gamma-ray telescopes, and that the decay of {sup 60}Co might provide an interesting contribution to the late-time light curve of SN 1987A and other core collapse supernovae. To producemore » the solar {sup 44}Ca abundance and satisfy all observational constraints, nature may prefer at least the occasional explosion of sub-Chandrasekhar mass white dwarfs as Type Ia supernovae. Depending on the escape fraction of positrons due to {sup 56}Co made in all kinds of Type Ia supernovae, a significant fraction of the steady state diffuse 511 keV emission may arise from the annihilation of positrons produced during the decay of {sup 44}Ti to {sup 44}Ca. The Ca and Ti isotopic anomalies in presolar grains confirm the production of {sup 44}Ti in supernovae and that extensive mixing between zones has occurred, but a quantitative model for this mixing is presently lacking. {copyright} {ital 1996 The American Astronomical Society. }« less
The kinetic Sunyaev–Zel’dovich signal from inhomogeneous reionization: a parameter space study

Inhomogeneous reionization acts as a source of arcminute-scale anisotropies in the cosmic microwave background (CMB), the most important of which is the kinetic Sunyaev–Zel’dovich (kSZ) effect. Observational efforts with the Atacama Cosmology Telescope (ACT) and the South Pole Telescope (SPT) are poised to detect this signal for the first time, with projected 1 μ K2-level sensitivity to the dimensionless kSZ power spectrum around a multipole of l= 3000, [Δl3000]2. Indeed, recent SPT measurements place a bound of [Δl3000 ]2 < 2.8 μ K2 at 95 per cent confidence level, which degrades to [Δl3000 ]2 < 6 μ K2 if a significant correlation between the thermal Sunyaev–Zel’dovich (tSZ) effect and the cosmic infrared background (CIB) is allowed. To interpret these and upcoming observations, we compute the kSZ signal from a suite of ≈100 reionization models using the publicly available code 21cmfast. Our physically motivated reionization models are parametrized by the ionizing efficiency of high-redshift galaxies, the minimum virial temperature of haloes capable of hosting stars, and the ionizing photon mean free path – a parametrization motivated by previous theoretical studies of reionization. We predict the contribution of patchy reionization to the l= 3000 kSZ power to be 1.5–3.5 μ K2. Therefore, even when adopting the lowest estimate in the literature for the post-reionization signal of , none of our models are consistent with the aggressive 2σ SPT bound that does not include correlations. This implies the following: (i) the early stages of reionization occurred in a much more homogeneous manner than suggested by the stellar-driven scenarios we explore, such as would be the case if, e.g. very high energy X-rays or exotic particles contributed significantly and/or (ii) that there is a significant correlation between the CIB and the tSZ. The later is perhaps not surprising, as massive haloes should host both hot gas and star-forming galaxies. On the other hand, the conservative SPT bound of [Δl3000 ]2≲ 6 μ K2 is compatible with all of our models and is on the threshold of constraining physically motivated reionization models. The largest patchy kSZ signals correspond to an extended reionization process, in which the sources of ionizing photons are abundant and there are many recombinations (absorptions in sinks). We point out that insights into the astrophysics of the early Universe are encoded in both the amplitude and shape of the kSZ power spectrum.