Miracle-dz/UniFAR-FTUs huggingface dataset:
PWV FORECAST VALIDATION AT ALMA SITE

In this study, the WRF (Weather Research and Forecasting) model was implemented to predict the atmospheric conditions, particularly the precipitable water vapor (PWV) in the North of Chile. Its performance was evaluated over the ALMA (Atacama Large Millimeter/submillimeter Array) site. Five WRF configurations with different physical options for boundary layer, soil model and microphysics were compared with observations from a radiometer and a weather station from April to December 2007. The results show that all the simulations overestimate PWV values, particularly in summer months. In addition, the microphysics parameterization changes do not notably affect the forecast, observing improved results with the soil model Noah. The errors were smallest with the YSU-Noah configuration, suggesting that it is appropriate to be used in operational forecasting of PWV in ALMA.
Identification and fine-mapping of a major QTL (PH1.1) conferring plant height in broomcorn millet (Panicum miliaceum)

The plant height of broomcorn millet (Panicum miliaceum) is a significant agronomic trait that is closely related to its plant architecture, lodging resistance, and final yield. However, the genes underlying the regulation of plant height in broomcorn millet are rarely reported. Here, an F2 population derived from a cross between a normal variety, “Longmi12,” and a dwarf mutant, “Zhang778,” was constructed. Genetic analysis for the F2 and F2:3 populations revealed that the plant height was controlled by more than one locus. A major quantitative trait locus (QTL), PH1.1, was preliminarily identified in chromosome 1 using bulked segregant analysis sequencing (BSA-seq). PH1.1 was fine-mapped to a 109-kb genomic region with 15 genes using a high-density map. Among them, longmi011482 and longmi011489, containing nonsynonymous variations in their coding regions, and longmi011496, covering multiple insertion/deletion sequences in the promoter regions, may be possible candidate genes for PH1.1. Three diagnostic markers closely linked to PH1.1 were developed to validate the PH1.1 region in broomcorn millet germplasm. These findings laid the foundation for further understanding of the molecular mechanism of plant height regulation in broomcorn millet and are also beneficial to the breeding program for developing new varieties with optimal height.
Voltage dependence of the cannabinoid CB1 receptor

Cannabinoids produce their characteristic effects mainly by binding to two types of G-protein coupled receptors (GPCRs), the CB1 and CB2 cannabinoid receptors. The CB1 receptor is the main cannabinoid receptor in the central nervous system, and it participates in many brain functions. Recent studies showed that membrane potential may serve as a novel modulatory modality of many GPCRs. Here, we used Xenopus oocytes as an expression system to examine whether membrane potential modulates the activity of the CB1 receptor. We found that the potencies of the endocannabinoid 2-AG and the phytocannabinoid THC in activating the receptor are voltage dependent; depolarization enhanced the potency of these agonists and decreased their dissociation from the receptor. This voltage dependence appears to be agonist dependent as the potency of the endocannabinoid anandamide (AEA) was voltage independent. The finding of this agonist-specific modulatory factor for the CB1 receptor may contribute to our future understanding of various physiological functions mediated by the endocannabinoid system.
ON THE INTERACTION BETWEEN TURBULENCE AND A PLANAR RAREFACTION

The modeling of turbulence, whether it be numerical or analytical, is a difficult challenge. Turbulence is amenable to analysis with linear theory if it is subject to rapid distortions, i.e., motions occurring on a timescale that is short compared to the timescale for nonlinear interactions. Such an approach (referred to as rapid distortion theory) could prove useful for understanding aspects of astrophysical turbulence, which is often subject to rapid distortions, such as supernova explosions or the free-fall associated with gravitational instability. As a proof of principle, a particularly simple problem is considered here: the evolution of vorticity due to a planar rarefaction in an ideal gas. Analytical solutions are obtained for incompressive modes having a wave vector perpendicular to the distortion; as in the case of gradient-driven instabilities, these are the modes that couple most strongly to the mean flow. Vorticity can either grow or decay in the wake of a rarefaction front, and there are two competing effects that determine which outcome occurs: entropy fluctuations couple to the mean pressure gradient to produce vorticity via baroclinic effects, whereas vorticity is damped due to the conservation of angular momentum as the fluid expands. Whether vorticity grows or decays depends upon the ratio of entropic to vortical fluctuations at the location of the front; growth occurs if this ratio is of order unity or larger. In the limit of purely entropic fluctuations in the ambient fluid, a strong rarefaction generates vorticity with a turbulent Mach number on the order of the rms of the ambient entropy fluctuations. The analytical results are shown to compare well with results from two- and three-dimensional numerical simulations. Analytical solutions are also derived in the linear regime of Reynolds-averaged turbulence models. This highlights an inconsistency in standard turbulence models that prevents them from accurately capturing the physics of rarefaction–turbulence interaction. In addition to providing physical insight, the solutions derived here can be used to verify algorithms of both the Reynolds-averaged and direct numerical simulation variety. Finally, dimensional analysis of the equations indicates that rapid distortion of turbulence can give rise to two distinct regimes in the turbulent spectrum: a distortion range at large scales where linear distortion effects dominate, and an inertial range at small scales where nonlinear effects dominate.
Essential physics of early galaxy formation

We present a theoretical model embedding the essential physics of early galaxy formation (z ≃ 5-12) based on the single premise that any galaxy can form stars with a maximal limiting efficiency that provides enough energy to expel all the remaining gas, quenching further star formation. This simple idea is implemented into a merger-tree-based semi-analytical model that utilizes two mass and redshift-independent parameters to capture the key physics of supernova feedback in ejecting gas from low- mass haloes, and tracks the resulting impact on the subsequent growth of more massive systems via halo mergers and gas accretion. Our model shows that: (i) the smallest haloes (halo mass Mh ≤ 1010 M☉) build up their gas mass by accretion from the intergalactic medium; (ii) the bulk of the gas powering star formation in larger haloes (Mh ≥ 1011.5 M☉) is brought in by merging progenitors; (iii) the faint-end UV luminosity function slope evolves according to α = -1.75 log z - 0.52. In addition, (iv) the stellar mass-to-light ratio is well fitted by the functional form log M* = -0.38MUV - 0.13 z + 2.4, which we use to build the evolving stellar mass function to compare to observations. We end with a census of the cosmic stellar mass density (SMD) across galaxies with UV magnitudes over the range -23 ≤ MUV ≤ -11 spanning redshifts 5 < z < 12; (v) while currently detected LBGs contain ≈50 per cent (10 per cent) of the total SMD at z = 5 (8), the James Webb Space Telescope will detect up to 25 per cent of the SMD at z ≃ 9.5.
Inductive heating of conductive nanoparticles

This paper presents an analysis and some interesting observations regarding the classical electromagnetic background to the heating of gold nanoparticles (GNPs) in the radio frequency spectrum. Here, it is assumed that the related dipole effects are based solely on homogeneous conducting nanospheres that are immersed in an lossy medium. From this point of view it is concluded that the effect of using a capacitive coupling i.e., a strong electric field to induce electric dipoles can be disregarded unless the volume fraction of the GNPs is unrealistically high, or if there are some other electric dipole mechanisms present which are not taken into account here, such as e.g., with nanospheres coated with ligands providing an electrophoretic movement and associated resonances. On the other hand, a simplified quasi-magnetostatic analysis indicates that an inductive heating (induced eddy currents inside the metal particles) based on magnetic coupling may have the potential to significantly increase the heating locally provided that the supplied magnetic field can be made sufficiently strong at radio frequency. This paper presents a near field optimization approach to study the electromagnetic heating of conductive nanoparticles. An optimization problem is formulated where the power absorption inside the nanoparticles is maximized subjected to power constraints related to the skin effect in the surrounding medium. The analysis shows that when the exterior medium is modelled as salty water the skin effect in the bulk material will render the simple principle of inductive heating of GNPs practically useless at 13.56 MHz.
The impact of self-efficacy perceptions of Imam Hatip Secondary School teachers on students and educational environment

This research was carried out to determine the self-efficacy perceptions of teachers working in Imam Hatip Secondary Schools and to examine the effect of teachers' competencies on students and education environment (relationships with students and teaching-learning processes). “Teacher self-efficacy scale” in the quantitative dimension of the research carried out by the mixed method; “semi-structured interview form” was used in its qualitative dimension. There were 357 teachers in the quantitative part and 86 students in the qualitative part of this research carried out in Istanbul in 2020. According to the results of the research, it was determined that teachers have a high level of self-efficacy. It was observed that gender, seniority and branch variables did not make a significant difference on teachers' self-efficacy perception. In qualitative part, it was concluded that the students confirmed the high efficacy of teachers. It has been determined that high self-efficacy of teachers affects the educational environment positively. As the factors that positively affect the teaching-learning processes; it was determined as giving responsibility, using different teaching methods, cooperating in classroom management, connecting with daily life, following homework outside of school and cooperating with their families. As the factors that negatively affect the teaching-learning processes; teaching the lesson with a single technique, addressing only the relevant students and communicating with the family only in a negative situation. For future research, comparative results can be obtained by evaluating teacher competencies from the eyes of other stakeholders in education, such as school administrators, colleagues, students and parents.
Abstract—The Schottky barrier diodes based on homoepitaxial n-type GaN materials are fabricated for high-power terahertz multiplier applications. The dislocation density of the GaN

The Schottky barrier diodes based on homoepitaxial n-type GaN materials are fabricated for high-power terahertz multiplier applications. The dislocation density of the GaN homoepilayer is estimated to be about 2-3 orders lower than the typical dislocation density of hetero-epitaxial GaN, the defect density and square resistance are also reduced. So, the series resistance of the diodes is decreased. The air-bridge structure and the substrate thinning-down technique were adopted to reduce the parasitic parameters. The cut-off frequency (fc) is improved to be above 1.2THz at zero bias by this method.
On the Multiobjective Optimal Choice of Isolated dc-dc Converter Topology and Design Using Genetic Algorithms

This paper presents a computer-aided design (CAD) tool dedicated to the multiobjective optimal design of isolated dc-dc power converters. This tool, developed in Matlab environment, is based on multiobjective optimization (MO) using Genetic Algorithms (GAs). The design problem requires minimizing the power loss, weight and cost of the converter while ensuring the satisfaction of several constraints. The optimization variables are, as for them, the operating frequency, the current density, the maximum flux density, the transformer dimensions, the wire diameter, the core and conductor materials, the converter topology (among flyback, forward, push-pull, half-bridge (HB) and full-bridge (FB)), the semiconductor devices (among diodes, IGBTs and MOSFETs), the number of these devices associated in parallel, the number of cells associated in series or parallel and the kinds of input and output connections of these cells. Finally, a design example is presented and discussed.
Leptophilic effective WIMPs

Effective WIMP models are minimal extensions of the standard model that explain the relic density of dark matter by the ``WIMP miracle. '' In this paper we consider the phenomenology of effective WIMPs with trilinear couplings to leptons and a new ``lepton partner'' particle. The observed relic abundance fixes the strength of the cubic coupling, so the parameters of the models are defined by the masses of the WIMP and lepton partner particles. This gives a simple parameter space where collider and direct detection experiments can be compared under well-defined physical minimality assumptions. The most sensitive collider probe is the search for leptons + MET, while the most sensitive direct detection channel is scattering from nuclei arising from loop diagrams. Collider and direct detection searches are highly complementary: colliders give the only meaningful constraint when dark matter is its own antiparticle, while direct detection is generally more sensitive if the dark matter is not its own antiparticle.
Primordial helium and the cosmic background radiation

The products of primordial nucleosynthesis, along with the cosmic microwave background (CMB) photons, are relics from the early evolution of the Universe whose observations probe the standard model of cosmology and provide windows on new physics beyond the standard models of cosmology and of particle physics. According to the standard, hot big bang cosmology, long before any stars have formed a significant fraction ( ~ 25%) of the baryonic mass in the Universe should be in the form of helium-4 nuclei. Since current observations of 4He are restricted to low redshift regions where stellar nucleosynthesis has occurred, an observation of high redshift, prestellar, truly primordial 4He would constitute a fundamental test of the hot, big bang cosmology. At recombination, long after big bang nucleosynthesis (BBN) has ended, the temperature anisotropy spectrum imprinted on the CMB depends on the 4He abundance through its connection to the electron density and the effect of the electron density on Silk damping. Since the relic abundance of 4He is relatively insensitive to the universal density of baryons, but is sensitive to a non-standard, early Universe expansion rate, the primordial mass fraction of 4He, Yp, offers a test of the consistency of the standard models of BBN and the CMB and, provides constraints on non-standard physics. Here, the WMAP seven year data (supplemented by other CMB experiments), which lead to an indirect determination of Yp at high redshift, are compared to the BBN predictions and to the independent, direct observations of 4He in low redshift, extragalactic HII regions. At present, given the very large uncertainties in the CMB-determined primordial 4He abundance (as well as for the helium abundances inferred from HII region observations), any differences between the BBN predictions and the CMB observations are small, at a level 1. 5σ.
Dataset Proﬁling - a Guide to Features, Methods, Applications and Vocabularies

The Web of data, in particular Linked Data, has seen tremendous growth over the past years. However, reuse and take-up is limited and focused on a few well-known and established knowledge bases. This can be attributed in parts to the lack of reliable and up-to-date information about the characteristics of available datasets. While datasets vary heavily with respect to features related to quality, coverage, dynamics and currency, reliable information about such features is essential for enabling data and dataset discovery in tasks such as entity retrieval or distributed search. Even though there exists a wealth of works contributing to this central problem of dataset proﬁling, these are spread across a range of communities and disciplines. Here, we provide a ﬁrst comprehensive survey of dataset proﬁling features, methods, tools and vocabularies and also provide an RDF vocabulary for unambiguously identifying dataset features.
Transverse Mode Revival of a Light-Compensated Quantum Memory

A long-lived quantum memory was developed based on light-compensated cold {sup 87}Rb atoms in a dipole trap. The lifetime of the quantum memory was improved by 40 folds, from 0.67 to 28 ms with the help of a compensation laser beam. Oscillations of the memory efficiency due to the transverse mode breathing of the singly excited spin wave have been clearly observed and clarified with a Monte Carlo simulation procedure. With detailed analysis of the decoherence processes of the spin wave in cold atomic ensembles, this experiment provides a benchmark for the further development of high-quality quantum memories.
Machine Learning in Real-Time System: A Review

Machine learning (ML) and deep learning (DL) algorithms are signiﬁcantly evolved and employed in varieties of applications such as computer vision, natural language processing, etc., over the last decade. Usually, ML/DL algorithms based applications have more concerns about accuracy than strict time requirements. With the improvement of hardware technologies, real-time safety-critical embedded and IoT systems such as autonomous driving systems, UVA, drones, security robots, etc., heavily rely on ML/DL based technologies. In these safety-critical systems, the cost of deadline (required time constraint) missed by ML/DL algorithms would be catastrophic. Consequently, researchers from the RTS community address the strict timing requirements of ML/DL technologies to include in real-time systems. This paper will rigorously review the state-of-the-art optimization techniques to meet the worst-case execution time (WCET) and accuracy vs. execution time trade-off for dynamic priority-based systems.
Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) CLOSED AND OPEN STATE CHANNEL MODELS

Background: CFTR, which controls Cl (cid:2) permeation across epithelia, is an ABC transporter whose structure is not known. Results: We modeled CFTR using the crystal structure of four homologous transporters. Conclusion: We identified possible open and closed conformations of CFTR. Significance: The models help the interpretation of experimental data and provide new hypotheses to test with experiments. The cystic fibrosis transmembrane conductance regulator (CFTR) is a member of the ATP-binding cassette (ABC) transporter superfamily. CFTR controls the flow of anions through the apical membrane of epithelia. Dysfunctional CFTR causes the common lethal genetic disease cystic fibrosis. Transitions between open and closed states of CFTR are regulated by ATP binding and hydrolysis on the cytosolic nucleotide binding domains, which are coupled with the transmembrane (TM) domains forming the pathway for anion permeation. Lack of structural data hampers a global understanding of CFTR and thus the development of “rational” approaches directly target-ing defective CFTR. In this work, we explored possible conformational states of the CFTR gating cycle by means of homology modeling. As templates, we used structures of homologous ABC transporters, namely TM(287–288), ABC-B10, McjD, and Sav1866. In the light of published experimental results, structural analysis of the transmembrane cavity suggests that
The Impact of Industry Forces on International SME Performance in Iran: The Mediating Effect of Competitive Advantage

International small and medium-sized enterprises (SMEs) encounter several problems, including how to manage the industry forces in order to achieve their performance goals. Focusing mainly on international SMEs, we explored this issue by examining competitive advantage as a mediator in the relationship between industry forces and international SMEs’ performance. This study was conducted in the context of the Iranian market. Iran offers substantial economic opportunities to international SMEs since it has a population of 85 million people, a range of natural resources, and a sophisticated manufacturing industry. In Iran, an international SME is a firm with less than 100 employees that originated outside of Iran but has an entity inside the country. The partial least squares method was employed in analyzing the survey data collected from 166 international SMEs in Iran. The results indicate that industry forces have significant effects on performance, but competitive advantage does not have a mediating impact. The results of this study reaffirm that international SMEs should be aware of the consequences of industry forces in order to attain their performance goals. This is especially critical for some developing economies which are more characterized by ambiguities.
Uncertainty-based Decision-making in Reinforcement Learning and the Distributed Adaptive Control Cognitive Architecture

This thesis explores the role of uncertainty estimation during training in Reinforcement Learning as a potential way of increasing sample efficiency, acting as a regulator between two subsystems that shape a policy: memory and stimulus-response. Memory-based subsystems are related to Episodic Reinforcement Learning, where exact snapshots or sequences of tuples generated during training are stored and then retrieved to perform the action that maximizes reward based solely on these past experiences. This way of learning is more related to how the hippocampus operates in the brain. In contrast, stimulus-response subsystems can be expressed as models that map states to actions in a model-free fashion. In humans and other animals, the dorsal striatum is responsible for making this stimulus-response mapping. However, this mapping process does not take into account the inherent uncertainty or variability of stimuli (i.e., perceptual uncertainty) in stochastic environments with partial observability and thus sometimes the optimal policy would be to rely more on the sequential feature of (model-based) memory. Several studies have shown that uncertainty plays a significant role in the decision-making process. Therefore we studied how it can arbitrate between the two systems. Concretely, we used an agent based on the Distributed Adaptive Control (DAC-ML) cognitive architecture comprising the two subsystems and an arbitration module that regulated their respective use based on the entropies of the policies. The agent was trained on a foraging task and showed dynamics that are aligned with human behaviour, where the memory-based system dominates at first, and throughout training, the stimulus-response system slowly takes over. This research could potentially lead to more flexible and efficient Reinforcement Learning algorithms that combine different ways of learning and operating depending on the available knowledge about the environment.