Miracle-dz/UniFAR-FTUs huggingface dataset:
GCD+: a new chemodynamical approach to modelling supernovae and chemical enrichment in elliptical galaxies

We have developed a new galactic chemo-dynamical evolution code, called GCD+, for studies of galaxy formation and evolution. This code is based on our original threedimensional tree N-body/smoothed particle hydrodynamics code which includes selfgravity, hydrodynamics, radiative cooling, star formation, supernova feedback, and metal enrichment. GCD+ includes a new Type II (SNe II) and Ia (SNe Ia) supernovae model taking into account the lifetime of progenitor stars, and chemical enrichment from intermediate mass stars. We apply GCD+ to simulations of elliptical galaxy formation, and examine the colour-magnitude relation (CMR), the Kormendy relation, and the [Mg/Fe]–magnitude relation of simulation end-products. GCD+ is a useful and unique tool which enables us to compare simulation results with the observational data directly and quantitatively. Our simulation confirm the results of Kawata (2001) who uses a simpler chemo-dynamical evolution code. We newly find that radiative cooling becomes more efficient and thus the gas infall rate increases,with decreasing mass of galaxies, which contributes to the slope of the CMR. In addition, the sophisticated treatments of both SNe II and SNe Ia in GCD+ show that feedback from SNe Ia plays a crucial role in the evolution of elliptical galaxies. We conclude that the feedback effect of SNe Ia should not be ignored in studying the evolution of elliptical galaxies.
The role of daily adjustment disorder, depression and anxiety symptoms for the physical activity of cardiac patients

Abstract Background Physical activity (PA) is crucial in the treatment of cardiac disease. There is a high prevalence of stress-response and affective disorders among cardiac patients, which might be negatively associated with their PA. This study aimed at investigating daily differential associations of International Classification of Diseases (ICD)-11 adjustment disorder, depression and anxiety symptoms with PA and sedentary behaviour (SB) during and right after inpatient cardiac rehabilitation. Methods The sample included N = 129 inpatients in cardiac rehabilitation, Mage = 62.2, s.d. age = 11.3, 84.5% male, n = 2845 days. Adjustment disorder, depression and anxiety symptoms were measured daily during the last 7 days of rehabilitation and for 3 weeks after discharge. Moderate-to-vigorous PA (MVPA), light PA (LPA) and SB were measured with an accelerometer. Bayesian lagged multilevel regressions including all three symptoms to obtain their unique effects were conducted. Results On days with higher adjustment disorder symptoms than usual, patients engaged in less MVPA, and more SB. Patients with overall higher depression symptoms engaged in less MVPA, less LPA and more SB. On days with higher depression symptoms than usual, there was less MVPA and LPA, and more SB. Patients with higher anxiety symptoms engaged in more LPA and less SB. Conclusions Results highlight the necessity to screen for and treat adjustment disorder and depression symptoms during cardiac rehabilitation.
Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects

With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer’s. Patients with a history of epilepsy, drug abuse, and mental health disorders such as depression have a larger risk of developing Alzheimer’s and other neurodegenerative diseases later in life. Utilizing recordings of patients’ brain waves obtained from the Temple University abnormal electroencephalogram (EEG) corpus, deep leaning long short-term memory neural networks are utilized to classify and predict patients’ brain ages. The proposed deep learning neural network model structure and brain wave-processing methodology leads to an accuracy of 90% in patients’ brain age classification across six age groups, with a mean absolute error value of 7 years for the brain age regression analysis. The achieved results demonstrate that the use of raw patient-sourced brain wave information leads to higher performance metrics than methods utilizing other brain wave-preprocessing methods and outperforms other deep learning models such as convolutional neural networks.
Quantum Ergoregion Instability

We have shown that, as in the case of black holes, an ergosphere itself with no event horizon inside can evaporate spontaneously, giving energy radiation to spatial infinity until the ergoregion disappears. However, the feature of his quantum ergoregion instability is very much different from black hole radiation. It is rather analogous to a laser amplification. This analysis is based on the canonical quantization of a neutral scalar field in the presence of unstable modes characterized by complex frequencies in a simple model for a rapidly rotating star.
Color entanglement like effect in collinear twist-3 factorization

We study the color entanglement like effect for T-odd cases in collinear twist-3 factorization. For an example, we compute the transverse single spin asymmetry for direct photon production in pp collisions in a pure collinear twist-3 approach. By analyzing the gauge link structure of the collinear gluon distribution on the unpolarized target side, we demonstrate how the color entanglement–like effect arises in the presence of the additional gluon attachment from a polarized projectile. The result is consistent with that obtained from a hybrid approach calculation.
A Machine Learning Method for Prediction of Stock Market Using Real-Time Twitter Data

Finances represent one of the key requirements to perform any useful activity for humanity. Financial markets, e.g., stock markets, forex, and mercantile exchanges, etc., provide the opportunity to anyone to invest and generate finances. However, to reap maximum benefits from these financial markets, effective decision making is required to identify the trade directions, e.g., going long/short by analyzing all the influential factors, e.g., price action, economic policies, and supply/demand estimation, in a timely manner. In this regard, analysis of the financial news and Twitter posts plays a significant role to predict the future behavior of financial markets, public sentiment estimation, and systematic/idiosyncratic risk estimation. In this paper, our proposed work aims to analyze the Twitter posts and Google Finance data to predict the future behavior of the stock markets (one of the key financial markets) in a particular time frame, i.e., hourly, daily, weekly, etc., through a novel StockSentiWordNet (SSWN) model. The proposed SSWN model extends the standard opinion lexicon named SentiWordNet (SWN) through the terms specifically related to the stock markets to train extreme learning machine (ELM) and recurrent neural network (RNN) for stock price prediction. The experiments are performed on two datasets, i.e., Sentiment140 and Twitter datasets, and achieved the accuracy value of 86.06%. Findings show that our work outperforms the state-of-the-art approaches with respect to overall accuracy. In future, we plan to enhance the capability of our method by adding other popular social media, e.g., Facebook and Google News etc.
Situations, Actions, and Causal Laws

Abstract : A formal theory is given concerning situations, causality and the possibility and effects of actions is given. The theory is intended to be used by the Advice Taker, a computer program that is to decide what to do by reasoning. Some simple examples are given of descriptions of situations and deductions that certain goals can be achieved.
CODE-SWITCHING AMONG THE INDIGENOUS PEOPLE OF SARAWAK

As a multicultural country, it is common for Malaysians to have the ability to speak two or more languages. Apart from being proficient in their own native language, majority Malaysians are able to speak Bahasa Melayu or English, or even both. In Sarawak, the indigenous people are collectively known as natives, also referred as the people of Dayak and Orang Ulu. The background of the Sarawak community, where the people itself are multilingual, supports the notion that code-switching is a common occurrence in the community’s daily life. This research aims to find out how the natives accommodate their communication behavior when interacting by identifying the occurrence of code-switching and, the factors and reasons contributing to the phenomenon. The research method uses quantitative methods. The data were collected by distributing questionnaires via Google Form. The findings were analyzed using the Communication Accommodation Theory (CAT) by Howard Giles. Based on the findings, there is a significant relationship between the indigenous ethnic groups in Sarawak and the phenomenon of code-switching. Convergence type of accommodation was observed through the study where the natives accommodate their language as a strategy reduce the social differences between themselves and their interactant. It is also observed that the natives also accommodate their language to achieve a clearer and smoother communication.
Improving the Performance of Polymer Solar Cells with Benzo[ghi]perylenetriimide-Based Small-Molecules as Interfacial Layers

In this study, we prepared three benzo[ghi]perylenetriimide (BPTI) conjugated molecules as electron-transporting surface-modifying layers for polymer solar cells (PSCs). These three BPTI derivatives differed in the nature of their terminal functionalities, featuring butylamine (C3NH2), propylammonium iodide (C3NH3I), and butyldimethylamine (C3DMA) units, respectively. We evaluated the optoelectronic properties of PTB7-Th: PC71BM blends modified with these interfacial layers, as well as the performance of resulting PSCs. We used UV–Vis spectroscopy, atomic force microscopy, surface energy analysis, ultraviolet photoelectron spectroscopy, and photoelectric flow measurements to examine the phenomena behind the changes in the optoelectronic behavior of these blend films. The presence of a BPTI derivative changed the energy band alignment at the ZnO–active layer interface, leading to the ZnO film behaving more efficiently as an electron-extraction electrode. Modifying the ZnO surface with the BPTI-C3NH3I derivative resulted in a best power conversion efficiency (PCE) of 10.2 ± 0.53% for the PTB7-Th:PC71BM PSC (cf. PCE of the control device of 9.1 ± 0.13%). In addition, modification of a PM6:Y6:PCBM PSC with the BPTI-C3NH3I derivative increased its PCE from 15.6 ± 0.25% to 16.5 ± 0.18%. Thus, BPTI derivatives appear to have potential as IFLs when developing high-performance PSCs, and might also be applicable in other optoelectronic devices.
Daridorexant

Daridorexant belongs to a class of drugs known as dual orexin receptor (OX1R and OX2R) antagonists. It achieves its function by blocking the binding of wake-promoting neuropeptides orexins. This novel mechanism is believed to turn down overactive wakefulness and as we know so far, most other treatments for insomnia can be sedating.
Microstructural control of microwave dielectric properties in CaTiO3–La(Mg1∕2Ti1∕2)O3 ceramics

High-density xCaTiO3–(1−x)La(Mg1∕2Ti1∕2)O3 ceramics (x=0.0–1.0) were prepared by solid-state reaction. The products were characterized by scanning electron microscopy, x-ray diffraction, and transmission electron microscopy. CaTiO3–La(Mg1∕2Ti1∕2)O3 ceramics formed solid solutions throughout the compositional range with no evidence of a secondary phase. For most ceramics the symmetry was orthorhombic with Pbnm space group. The La(Mg1∕2Ti1∕2)O3 end member exhibited 1:1 ordering of Mg2+ and Ti4+ cations (in octahedral sites) superimposed on the perovskite distortions leading to monoclinic symmetry with P21∕n space group. Long-range 1:1 order of Mg2+ and Ti4+ cations was destroyed at x=0.3. The degree of distortion, unit-cell volume, and crystal lattice stress decreased with increasing CaTiO3 content. Twin and antiphase domains, generated by structural phase transitions on cooling, were observed in all the ceramics. Twin domain density increased with CaTiO3 content but the size of individual domains decreased.
Finding forming globular clusters at high redshifts

The formation of globular clusters (GC) with their multiple stellar populations remains a puzzling, unsolved problem in astrophysics. One way to gather critical insight consists in finding sizable numbers of GC progenitors (GCP) while still near the peak of their star formation phase, at a lookback time corresponding to GC ages (~12.5 Gyr, or z ~ 5). This opportunity is quantitatively explored, calculating how many GCPs could be detected by deep imaging in the optical, near-IR and mid-IR bands. For concreteness, for the imaging camera performances those of NIRCam on board of JWST are adopted. The number of GCPs that could be detected scales linearly with their mass, i.e., on how much more massive GCPs were compared to their GC progeny, and perspectives look promising. Besides providing direct evidence on GC formation, the detection of GCPs, their clustering, with or without a central galaxy already in place, would shed light on the relative timing of GC formation and galaxy growth and assembly. All this, may be the result of dedicated observations as well as a side benefit of deep imaging meant to search for the agents of cosmic reionization.
A HUBBLE SPACE TELESCOPE Survey for Resolved Companions of Planetary Nebula Nuclei

We report the results of a Hubble Space Telescope "snapshot" survey aimed at finding resolved binary companions of the central stars of Galactic planetary nebulae (PNe). Using the the Wide Field and Planetary Camera and Wide Field Planetary Camera 2, we searched the fields of 113 PNe for stars whose close proximity to the central star suggests a physical association. In all, we find 10 binary nuclei that are very likely to be physically associated and another six that are possible binary associations. By correcting for interstellar extinction and placing the central stars' companions on the main sequence (or, in one case, on the white dwarf cooling curve), we derive distances to the objects, and thereby significantly increase the number of PNe with reliable distances. Comparison of our derived distances with those obtained from various statistical methods shows that all of the latter have systematically overestimated the distances, by factors ranging up to a factor of 2 or more. We show that this error is most likely due to the fact that the properties of our PNe with binary nuclei are systematically different from those of PNe used heretofore to calibrate statistical methods. Specifically, our PNe tend to have lower surface brightnesses at the same physical radius than the traditional calibration objects. This difference may arise from a selection effect: the PNe in our survey are typically nearby, old nebulae, whereas most of the objects that calibrate statistical techniques are low-latitude, high surface brightness, and more distant nebulae. As a result, the statistical methods that seem to work well with samples of distant PNe, for example, those in the Galactic bulge or external galaxies, may not be applicable to the more diverse population of local PNe. Our distance determinations could be improved with better knowledge of the metallicities of the individual nebulae and central stars, measurements of proper motions and radial velocities for additional candidate companions, and deeper HST images of several of our new binary nuclei.
Residual iterative schemes for large-scale linear systems

AbstractNew iterative schemes that use the residual direction are proposed and an-alyzed for solving large-scale nonsymmetric linear systems. These schemes areinspired by the success of recently proposed residual methods for nonlinear sys-tems. The convergence is analyzed for general matrices, and strong convergenceis established when the symmetric part of the coeﬃcient matrix is positive (neg-ative) deﬁnite. A preliminar numerical experimentation is included to showthat they are competitive with well-known and well-established Krylov subspacemethods: GMRES and BiCGSTAB, with and without preconditioning.Keywords: Linearsystems, Krylovsubspacemethods, residualiterativeschemes. 1 Introduction We are interested in solving linear systems of equationsAx = b, (1)where A ∈IR n× is not symmetric, b ∈IR , and n is large.Solving linear systems of equations can be seen as a particular (although veryspecial)caseofsolvingnonlinearsystemsofequations. Fornonlinearsystems, somenewiterative schemes have recently been presented that use in a systematic way the residualvectors as search directions [8, 9]. These ideas become eﬀective, and competitive with
BANYAN. XII. New Members of Nearby Young Associations from GAIA–Tycho Data

We present a search for stellar members of young associations within 150 pc of the Sun, based on TGAS and an updated version of the BANYAN Σ software to determine Bayesian membership probabilities that includes Gaia–2MASS color-magnitude diagrams. We identify 32 new F0–M3-type bona fide members of the 10–200 Myr old Sco-Cen, Carina, Tucana-Horologium, Columba, and Octans associations and the AB Doradus, β Pictoris, and Carina-Near moving groups. These new bona fide members have measurements of their full kinematics and literature data consistent with a young age. We also confirm the membership of 66 previously known candidate members using their Gaia–Tycho trigonometric distances or new literature radial velocities, and identify 219 additional new candidate members, most of which do not yet have a radial velocity measurement. This work is the first step toward a completeness-corrected survey of young association members based on Gaia DR2 in the near future.
Complications of Kidney in Severe Malaria

Malaria is an infectious protozoan parasitic disease transmitted by Anopheles spp mosquito. As a disease entity, malaria is still a major global health problem due to its morbidity and mortality that endangered millions of individuals inhabitants in tropical region. Kidney derangement is moderately frequent in severe malaria caused by the species P. falciparum and also P. malariae, but seldom this condition has also been reported in malaria due to P. vivax. Severe or complicated malaria can occur due to delay or failure of treatment. Making correct and early diagnosis is a prerequisite for successful treatment. Actually, a series of events that begins with the entry of blood protozoa into the host which then causes hemodynamic disturbances and loss of normal capillary capacity. Hemodynamic dysfunction, immune response and with addition from the consequences of severe malaria complications in other organs (e.g., liver) leading to worsening of kidney function in severe malaria. This paper cover the recent findings of kidney involvement in severe malaria and other important facts related to this issue.
Identification de modeles arma lineaires a l'aide de statistiques d'ordre eleve. Application a l'egalisation aveugle

Ce memoire traite les problemes de l'identification de modeles autoregressifs a moyenne ajustee (arma) lineaires et de l'egalisation aveugle d'un canal de communication radio. Pour traiter ces problemes, nous utilisons des statistiques d'ordre eleve (soe). Dans la premiere partie de ce travail, une procedure d'estimation des cumulants d'ordres trois et quatre est proposee. Des formules de calcul recursif de ces cumulants sont egalement donnees. Nous developpons aussi une nouvelle relation generale liant les cumulants d'ordres m et (m - i) (m > 2, i = 1,,m-2) aux coefficients de la reponse impulsionnelle (r. I. ) du systeme. Cette relation nous permet de presenter de facon unifiee des methodes decrites dans la litterature pour identifier les coefficients de la r. I. D'un systeme par resolution d'un systeme d'equations algebriques lineaires. Puis, nous proposons une nouvelle solution explicite utilisant plus de cumulants que les solutions du meme type que nous avons trouvees dans la litterature et necessitant moins d'operations arithmetiques que les methodes basees sur la resolution de systeme d'equations algebriques lineaires. Enfin, une synthese de methodes d'identification des parametres a(i) et b(i) est faite. Dans la seconde partie de ce memoire, nous utilisons les estimes des coefficients de la r. I. D'un canal de communication pour faire une egalisation aveugle base sur la minimisation du critere mse. Puis nous proposons un nouveau critere base sur un ajustement de cumulants d'ordres pairs de la sortie de l'egaliseur. Ce critere permet de faire un lien avec les methodes du type bussgang qui utilisent implicitement les soe
Nucleon’s strange electromagnetic and scalar matrix elements

Quenched lattice QCD simulations and quenched chiral perturbation theory are used together for this study of strangeness in the nucleon. Dependences of the matrix elements on strange quark mass, valence quark mass and momentum transfer are discussed in both the lattice and chiral frameworks. The combined results of this study are in good agreement with existing experimental data and predictions are made for upcoming experiments. Possible future refinements of the theoretical method are suggested.
Diesel Engine Fault Diagnosis Method Based on Optimized VMD and Improved CNN

The safe operation of diesel engines performs a vital function in industrial production and life. Because diesel engines often work in harsh environmental conditions, they are prone to failure. Therefore, this paper proposes a fault analysis method based on a combination of optimized variational mode decomposition (VMD) and improved convolutional neural networks (CNN) to address the necessary need for preventive maintenance of diesel engines. The authentic vibration sign is first decomposed by using the (VMD) algorithm, then the greatest range of decomposition layers is decided by using scattering entropy and the useful components are preferentially chosen for reconstruction. The continuous wavelet transform (CWT) records preprocessing method is then delivered to radically change the noise-reduced vibration sign into a time-frequency map, which is fed into the CNN for model coaching and extraction of fault features. Finally, fault classification is realized by support vector machine (SVM) with excellent classification performance. Through preset fault experiments on diesel engines, it is established that the technique proposed in this paper can successfully identify fault states, and the classification accuracy is higher than alternative methods.
Prospective Life Cycle Assessment and Cost Analysis of Novel Electrochemical Struvite Recovery in a U.S. Wastewater Treatment Plant

Nutrient recovery in domestic wastewater treatment has increasingly become an important area of study as the supply of non-renewable phosphorus decreases. Recent bench-scale trials indicate that co-generation of struvite and hydrogen using electrochemical methods may offer an alternative to existing recovery options utilized by municipal wastewater treatment facilities. However, implementation has yet to be explored at plant-scale. In the development of novel nutrient recovery processes, both economic and environmental assessments are necessary to guide research and their design. The aim of this study was to conduct a prospective life cycle assessment and cost analysis of a new electrochemical struvite recovery technology that utilizes a sacrificial magnesium anode to precipitate struvite and generate hydrogen gas. This technology was modeled using process simulation software GPS-X and CapdetWorks assuming its integration in a full-scale existing wastewater treatment plant with and without anaerobic digestion. Struvite recoveries of 18–33% were achieved when anaerobic digestion was included, with a break-even price of $6.03/kg struvite and $15.58/kg of hydrogen required to offset increased costs for recovery. Struvite recovery reduced aquatic eutrophication impacts as well as terrestrial acidification impacts. Tradeoffs between benefits from struvite and burdens from electrode manufacturing were found for several impact categories.