Miracle-dz/UniFAR-FTUs huggingface dataset:
Galaxy Evolution in Abell 2390

The galaxy population in the intermediate-redshift (z = 0.228) rich cluster Abell 2390 is investigated. We present velocities, colors, and morphological information for an exceptionally large sample of 323 galaxies (216 cluster members) in a 46′ × 7′ (6 h−1 Mpc × 1 h−1 Mpc) strip centered on the cD galaxy. This sample of confirmed cluster members is second only to that for the Coma cluster in terms of sample size and spatial coverage in the cluster rest frame and it is the first to trace the transition between a rich cluster and the field at intermediate redshift. The galaxy population in the cluster changes gradually from a very evolved, early-type population in the inner 0.4 h–1 Mpc of the cluster to a progressively later type population in the extensive outer envelope of the cluster from 1 to 3 h–1 Mpc in radius. Radial gradients in galaxy g - r color, 4000 Å break, Hδ and [O II] line strengths, and morphology are seen in the cluster and are investigated by comparing the data to models computed with the GISSEL spectral synthesis package. The results suggest that the cluster has been built up gradually by the infall of field galaxies over ~8 Gyr and that star formation has been truncated in infalling galaxies during the accretion process. The morphological composition of the cluster is shown to be consistent with such a scenario. If true for other clusters, infall-truncated star formation as seen in Abell 2390 may explain both the Butcher-Oemler effect and the large fraction of S0 galaxies in clusters. Only ≲5% of the galaxies observed in Abell 2390 exhibit evidence for star formation at levels stronger than those seen in typical late-type systems. This suggests that starbursts do not play a major role in driving cluster galaxy evolution at the redshift of Abell 2390, although infall-induced starbursts leading to truncated star formation may have played a role in the earlier history of the cluster. Evidence is found for at least one subcomponent on the west side of the cluster, which is likely to be infalling at the epoch of observation.
Public debt heterogeneity at country level: an empirical analysis

Nowadays, the increase in public debt is aﬀecting economies around the world reaching unprecedented values. In light of that, this paper investigates the eﬀects of taxes and public spending as debt deﬂators. Moreover, it compares how a boost in inﬂation with respect to traditional instruments might help debt-to-GDP reduces. Eventually, this paper highlights the importance of the economy size as a key feature that policymakers should consider in making judgments. For these purposes, we use a ﬁxed eﬀects regression on a balanced panel data from 2005 to 2020 assessing how inﬂation, taxes, and public expenditure impact the dependent variable, namely the public debt-to-GDP ratio. Overall, we found that an increase in inﬂation and tax revenue entails diﬀerent eﬀects on the economic growth for both the United States, the European Union and China. Moreover, a consistent increase in public expenditure causes a decrease in the public debt-to-GDP ratio in all three nations. The divergent political stances and the evolution of the three countries create a huge heterogeneity among them and high values of heterogeneity in terms of public debt within the nations. a historical search for a between high The study carried out on both developed economies and emerging ones. They ﬁnd that both emerging countries and developed ones react in the same way to an increase in public debt to GDP but not in the case of an increase in inﬂation. starting from and Rogoﬀ (2010 a b), shows a diﬀerence between the economic growth both at country level and by time-period considered. Their results show that inﬂation increases in of for
The Rest-Frame Optical Properties of z ≃ 3 Galaxies

We present the results of a near-infrared imaging survey of z ~ 3 Lyman break galaxies (LBGs). The survey covers a total of 30 arcmin2 and includes 118 photometrically selected LBGs with Ks-band measurements, 63 of which also have J-band measurements, and 81 of which have spectroscopic redshifts. Using the distribution of optical ℛ magnitudes from previous work and ℛ-Ks colors for this subsample, we compute the rest-frame optical luminosity function of LBGs. This luminosity function is described by an analytic Schechter fit with a very steep faint-end slope of α = -1.85 ± 0.15, and it strikingly exceeds locally determined optical luminosity functions at brighter magnitudes, where it is fairly well constrained. The V-band luminosity density of only the observed bright end of the z ~ 3 LBG luminosity function already approaches that of all stars in the local universe. For the 81 galaxies with measured redshifts, we investigate the range of LBG stellar populations implied by the photometry that generally spans the range 900-5500 Å in the rest frame. The parameters under consideration are the star formation rate as a function of time, the time since the onset of star formation, and the degree of reddening and extinction by dust. While there are only weak constraints on the parameters for most of the individual galaxies, there are strong trends in the sample as a whole. With a wider wavelength baseline than most previous studies at similar redshifts, we confirm the trend that intrinsically more luminous galaxies are dustier. We also find that there is a strong correlation between extinction and the age of the star formation episode, in the sense that younger galaxies are dustier and have much higher star formation rates. The strong correlation between extinction and age, which we show is unlikely to be an artifact of the modeling procedure, has important implications for an evolutionary sequence among LBGs. A unified scenario that accounts for the observed trends in bright LBGs is one in which a relatively short period of very rapid star formation (hundreds of M☉ yr-1) lasts for roughly 50-100 Myr, after which both the extinction and star formation rate are considerably reduced and stars are formed at a more quiescent, but still rapid, rate for at least a few hundred megayears. In our sample, a considerable fraction (~20%) of the LBGs have best-fit star formation ages ≳1 Gyr, implied stellar masses of ≳1010 M☉, and are still forming stars at ~30 M☉ yr-1.
HOMOLOGY MODELING AND MOLECULAR DOCKING STUDY OF BIOLOGICALLY ACTIVE LIGAND AND SYNTHESIS, CHARACTERIZATION AND INVITRO STUDY OF DERIVATIVES OF METOPROLOL SUCCINATE.

: Beta-1 adrenergic receptor (ADRB1_HUMAN) an enzyme plays a critical role in central to the overall regulation of cardiac function. The latest clinical β-blocker trials Beta-1 adrenergic receptor (β1AR) have been shown to play an important role in cardiac disease and heart failure in particular. Metoprolol succinate is essentially useful in the treatment of chest pain. In the present work Metoprolol succinate Complexes has been prepared with transition metals viz; Cu(II), Zn(II), Co(II), Ni(II) and those checked for homology modeling and molecular docking is done for metoprolol succinate. The Metoprolol succinate base inhibitors towards Beta-1 adrenergic receptor (β1AR) is unknown. Hence, the structural bioinformatics study of Beta-1 adrenergic receptor (β1AR) enzyme from human was carried out using homology modeling and molecular docking techniques. The 3D model of Beta1 adrenergic receptor (β1AR) protein of Homo sapiens were designed using the templet (PDB ID: 2RH1) in Modeller V9.24. The validation of generated model was obtained using several validation methods including Procheck, PROSA, QMean, Errat and Verify3D. The stereo chemical properties of the targeted protein model were validated by using the Ramachandran plot with Procheck server. Towards this end, molecular docking was performed with β1AR and Metoprolol succinate using AutoDock software and Pharmacophore Mapping has been conducted using Accelrys Discovery Studio (DS) to extract a 3D pharmacophore that reflects the important functional groups that are essential for inhibitor binding. This homology modeling and docking study provides clear understanding of β1AR structure and metoprolol succinate, its binding mechanism, thus help in providing the remedial solutions of cardiovascular disease and heart failure caused by malfunctioning of the target protein.
Warwick-Monash Economics Student

We investigate the role of international credit market constraint in a dynamic stochastic general equilibrium model in determining the effect of government spending policies on macroeconomic variables such as consumption and the real exchange rate in a small open economy. The numerical results show that increasing government expenditure under certain economic shocks can increase the value of the real exchange rate and reduce the chance of the small open economy reaching the borrowing limit. Hence, the dynamics of consumption and the real exchange rate can be significantly affected by government spending policies under international credit market constraints. This investigates the effect of changing government spending in a small open economy with and without international credit constraint. The numerical results show that the existence of international credit constraint plays an important role in determining the effect of varying government expenditures in responding to economic shocks. When there is a negative shock in the tradable good sector, the debt-to-GDP ratio will reach the boundary if government does not purchase the non-tradable good.
Associations Between Glycemic Control and Executive Functioning in Type 1 Diabetes: A Systematic Review and Meta-Analysis

Associations Between Glycemic Control and Executive Functioning in Type 1 Diabetes: A Systematic Review and Meta-Analysis Hannah Margot McDaniel Department of Psychology, BYU Master of Science Research conducted with youth with type 1 diabetes (T1D) has shown that glycemic extremes and poorer T1D management are associated with poorer performance on measures of executive functioning (EF) compared to non-T1D counterparts. Deficits in EF may negatively impact the development of healthy T1D management skills. Our primary aim was to conduct a meta-analytic review of previous studies investigating the associations between glycemic control and EF in children with T1D across the developmental span. Comprehensive literature searches were conducted using PubMed, MedLine, PsycInfo, Google Scholar, and EBSCO for articles that met eligibility criteria. Eligibility criteria included correlational, randomized, and non-randomized studies published after 1985 involving participants ages 0-26 with a diagnosis of T1D. Studies evaluated EF using the Behavior Rating Inventory of Executive Functioning (BRIEF) or fMRI and evaluated glycemic control using Hemoglobin A1c. Across twelve studies examining the effect of glycemic control on EF in patients with T1D, 2,204 children, adolescents, and emerging adults were included in the study. Effect sizes (Hedges’ g) were calculated between the T1D group and either control participants or a normative sample. Participant age was included as a moderator to determine whether the hypothesized association varied in magnitude across development. Participant A1c was used as a moderator to determine whether the hypothesized variation varied with diabetic management. A trend towards poorer performance on measures of executive functioning was observed in the T1D group compared to the normative sample (g = -0.22). In comparing age cohorts to the normative sample, a significantly poorer performance on measures of EF was observed in the 911, 15-17, and 18-26 age cohorts (g = -0.201, -0.258, and -0.452, respectively) compared to other age cohorts. Overall, research indicates that poor diabetic management may result in poorer performance on measures of EF compared to non-diabetic participants. These findings imply that this trend of poorer EF in children and adolescents with type 1 diabetes may lead to suboptimal diabetic management. Research suggests that suboptimal diabetic management often persists across the lifespan. Examining this association across critical periods where diabetes management may exert the most influence on the developing brain may provide guidance about when youth may benefit most from T1D management interventions.
THE SIMULTANEOUS SPECTRUM OF SGR A* FROM λ 20CM TO λ 1MM AND THE NATURE OF THE MM-EXCESS

We report results from a multiwavelength campaign to measure the simultaneous spectrum of the super-massive black hole candidate Sgr A* in the Galactic Center from cm to mm-wavelengths using the VLA, BIMA, the Nobeyama 45m, and the IRAM 30m telescopes. The observations conﬁrm that the previously detected mm-excess is an intrinsic feature of the spectrum of Sgr A*. The excess can be interpreted as due to the presence of an ultra-compact component of relativistic plasma with a size of a few Schwarzschild radii near the black hole. If so, Sgr A* might offer the unique possibility to image the putative black hole against the background of this component with future mm-VLBI experiments.
Adoption of Cloud Based E-learning in Developing Countries: A Combination of DOI, TAM and UTAUT

Cloud computing (CC) has created a paradigm shift in using technology. Prior literature focused on the adoption of this technology among business organization while studies that are related to educational institutions are few. The aim of this study is to investigate the factors that affect the cloud based e-learning (CBEL) adoption among students. Based on the literature, it is proposed that relative advantage (RA), perceived ease of use (PEOU), social influence (SI), and user satisfaction affect the behavioural intention (BI). BI is expected to affect the adoption of CBEL. In addition, attitude is proposed to mediate the effect of RA, PEOU, SI and user satisfaction on BI. The population of this study is four universities in Lebanon. Stratified sampling technique was used to collect the data using a questionnaire. A total of 422 students participated in this study. Data was analysed using Smart Partial Least Square (PLS). The findings indicated that the user satisfaction is the most important predictors of BI followed by RA, SI, and PEOU. BI affected use behaviour significantly. Attitude mediated the effects of SI and user satisfaction on BI. Decision makers are recommended to focus on user satisfaction and increase the benefits of CBEL.
Turn of Phrase: Contrastive Pre-Training for Discourse-Aware Conversation Models

Turn of Phrase: Contrastive Pre-Training for Discourse-Aware Conversation Models Roland Laboulaye Department of Computer Science, BYU Master of Science Understanding long conversations requires recognizing a discourse flow unique to conversation. Recent advances in unsupervised representation learning of text have been attained primarily through language modeling, which models discourse only implicitly and within a small window. These representations are in turn evaluated chiefly on sentence pair or paragraph-question pair benchmarks, which measure only local discourse coherence. In order to improve performance on discourse-reliant, long conversation tasks, we propose Turn-of-Phrase Pre-Training, an objective designed to encode long conversation discourse flow. We leverage tree-structured Reddit conversations in English to, relative to a chosen conversation path through the tree, select paths of varying degrees of relatedness. The final utterance of the chosen path is appended to the related paths and the model learns to identify the most coherent conversation path. We demonstrate that our pre-training objective encodes conversational discourse awareness by improving performance on a dialogue act classification task. We then demonstrate the value of transferring discourse awareness with a comprehensive array of conversation-level classification tasks evaluating persuasion, conflict, and deception.
3 Implementing skill-mix changes: lessons for policy and practice

Many countries across Europe are considering implementing skillmix strategies to better meet the needs of their patients, against the backdrop of changing service delivery models, new technologies and financing as well as payment policies. Implementing skill-mix changes in a country, region or setting is a complex intervention and impacts on the education and training of providers, multiprofessional collaboration, work flow and service provision, and ultimately, the patients and families. Skill-mix reforms are often highly controversial and prone to self-interests, influenced by various stakeholders, including different professional associations, payers, regulators or trade unions. Managing the process of implementation therefore requires a good understanding of the influencing factors, potential barriers and pitfalls involved. It also requires anticipating and actively fostering potentially facilitating factors in the process. This chapter provides an overview of the evidence on the implementation of skill-mix changes. It identifies common barriers and facilitators at various levels, from the policy to the organizational level and summarizes the evidence from Chapter 9 (education and workforce planning), Chapter 10 (policy and financing) and Chapter 11 (organizational strategies). Workforce planning and education are key elements influencing skill-mix; they are closely intertwined and therefore covered in one distinct chapter (Chapter 9). With regard to policy, Chapter 10 covers the role of legislation, regulation and other measures as well as financing and payment policies. Chapter 11 addresses the implementation and the uptake by individual health care organizations and the influence of characteristics of the skill-mix innovation and institutional, organizational, individual and process factors.
A comparison of detection thresholds obtained with forced‐choice procedures and with a standard clinical procedure

Detection thresholds, psychometric functions, and response criteria were measured for groups of younger and older normal and hearing‐impared adults (total N = 72). Detection thresholds were obtained at 500 and 4000 Hz using a standard clinical procedure and a two‐interval, forced‐choice (2IFC) adaptive procedure. A measure of response bias was obtained using a yes‐no procedure. Psychometric functions were obtained for all three procedures. The 2IFC adaptive thresholds were approximately 7 dB lower than standard clinical thresholds for all four groups of listeners. Data from the yes‐no procedure indicated that all four groups were equally conservative. There was no correlation between the magnitude of the difference between adaptive and clinical thresholds and the degree of response bias. Psychometric functions for the forced‐choice procedures were generally shallower than those for the standard clinical procedure and were shifted several dB to the left. Criterion effects do not appear to account for the d.
A SYSTEMATIC REVIEW ON EXPLORATIONS IN ARTITIFICAL INTELLIGENCE AND MACHINE LEARNING FOR VARIOUS FIELD OF APPLICATIONS

Machine learning (ML) and other forms of artificial intelligence (AI) are finding more and more uses in production environments. The study provides an in-depth analysis of the current state of machine learning (ML) applications in manufacturing settings. Technology advancements in smart mining have made real-time data production, gathering, and exchange possible. For this reason, the mining sector is actively engaged in ML-based research that makes use of this data. In addition, this research provided a comprehensive review of the uses of data sciences and ML in several areas of petroleum engineering and geosciences, including petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, with special attention paid to the rapidly expanding field of unconventional reservoirs. A look into the future of data science and ML in the oil and gas business is provided, along with an examination of the features of ML that are needed to improve prediction. Different ML methods with application to the oil and gas sector are compared in detail in this paper. This article also discussed how the advent of AI and ML has sparked a paradigm change in healthcare, opening up new avenues of analysis and prediction within the realm of medical data. The application of AI and ML to combat COVID-19 has recently been demonstrated in a number of studies. The purpose of this article is to provide reviewers with a review of recent research that has used AI and ML in various applications.
Arbitrary phases in quantum amplitude amplification

We consider the use of arbitrary phases in quantum amplitude amplification which is a generalization of quantum searching. We prove that the phase condition in amplitude amplification is given by $\tan(\varphi/2) = \tan(\phi/2)(1-2a)$, where $\phi$ and $\phi$ are the phases used and where $a$ is the success probability of the given algorithm. Thus the choice of phases depends nontrivially and nonlinearly on the success probability. Utilizing this condition, we give methods for constructing quantum algorithms that succeed with certainty and for implementing arbitrary rotations. We also conclude that phase errors of order up to $\frac{1}{\sqrt{a}}$ can be tolerated in amplitude amplification.
Forest Ecology and Management Modeling climate and fuel reduction impacts on mixed-conifer forest carbon stocks in the Sierra Nevada, California

Quantifying the impacts of changing climatic conditions on forest growth is integral to estimating future forest carbon balance. We used a growth-and-yield model, modiﬁed for climate sensitivity, to quantify the effects of altered climate on mixed-conifer forest growth in the Lake Tahoe Basin, California. Esti­ mates of forest growth and live tree carbon stocks were made for low and high emission scenarios using four downscaled general circulation model (GCM) projections. The climate scenarios were coupled with a range of commonly-used fuels reduction treatments to quantify the combined effects of these factors on live tree carbon stocks. We compared mid- (2020–2049) and late-21st (2070–2099) century carbon stock estimates with a baseline period of 1970–1999 using common input data across time periods. Recursive partitioning analysis indicates that GCM, forest composition, and simulation period most inﬂuence live tree carbon stock changes. Comparison with the late 20th century baseline period shows mixed carbon stock responses across scenarios. Growth varied by species, often with compensatory responses among dominant species that limited changes in total live tree carbon. The inﬂuence of wildﬁre mitigation treat­ ments was relatively consistent with each GCM by emission scenario combination. Treatments that included prescribed ﬁre had greater live tree carbon gains relative to baseline under the scenarios that had overall live tree carbon gains. However, across GCMs the inﬂuence of treatments varied considerably among GCM projections, indicating that further reﬁnement of regional climate projections will be required to improve model estimates of fuel manipulations on forest carbon stocks. Additionally, had out simulations included the effects of projected climate changes on increasing wildﬁre probability, the effects of management treatments on carbon stocks may have been more pronounced because of the inﬂuence of treatment on ﬁre severity.