Miracle-dz/UniFAR-FTUs huggingface dataset:
MASSIV: Mass Assembly Survey with SINFONI in VVDS - VI. Metallicity-related fundamental relations in star-forming galaxies at 1 < z < 2

Aims. The MASSIV (Mass Assembly Survey with SINFONI in VVDS) project aims at finding constraints on the different processes involved in galaxy evolution. This study proposes to improve understanding of the galaxy mass assembly through chemical evolution using metallicity as a tracer of the star formation and interaction history. Methods. We analysed the full sample of MASSIV galaxies for which a metallicity estimate has been possible, the 48 star-forming galaxies at z similar to 0 : 9 1 : 8, and computed the integrated values of some of the fundamental parameters, such as stellar mass, metallicity, and star formation rate (SFR). The sample of star-forming galaxies at similar redshifts from zCOSMOS was also combined with the MASSIV sample. We studied the cosmic evolution of the mass-metallicty relation, together with the effect of close environment and galaxy kinematics on this relation. We then focussed on the so-called fundamental metallicity relation and other relations between stellar mass, SFR, and metallicity. We investigated whether these relations are really fundamental, i.e. if they do not evolve with redshift. Results. The MASSIV galaxies follow the expected mass-metallicity relation for their median redshift. We find, however, a significant difference between isolated and interacting galaxies as found for local galaxies: interacting galaxies tend to have a lower metallicity. The study of the relation between stellar mass, SFR, and metallicity gives such large scattering for our sample, even combined with zCOSMOS, that it is difficult to confirm any fundamental relation.
Optimization of fast‐charging strategies for lithium‐ion batteries with Kriging‐Assisted evolution

Reducing the charging period while maximizing battery cycle life is a major objective in the development of fast charging technologies. However, the optimization of fast‐charging strategies and evaluation of lifetime necessitate substantial time‐consuming experiments. To decrease both the time and number of tests needed, we propose an experimental learning optimization method to efficiently optimize the parameter space of 10‐min, five‐step fast‐charging strategies. Two components are combined to minimize the optimization cost. On the one hand, the cycle life is evaluated by a deep learning method. It only requires 10 cycles of charging data to predict the cycle life with high accuracy, so can reduce lifetime experiments by over 99%. On the other hand, the optimization of the charging strategy is experimentally carried out based on the Kriging‐assisted population evolution algorithm (KAPEA). It is employed for the first time in this field to select promising strategies for experiments and narrow down the experimental search space remarkably. And the procedure period can be significantly saved, 98.7% less than with conventional approaches. Furthermore, validated by experiments, the battery's cycle life under the strategy recommended by our method is the highest among all the strategies evaluated.
Representative Points of Two-dimensional Uniform Distribution

Representative point is to use discrete point set to represent a continuous random vector. The set of k points optimally summarizes a distribution, which is called the set of k representative points. It is a mean generalization from one point to multiple points, especially useful for two-dimensional distribution, and it has important practical significance. In this paper, the representative points on two-dimensional uniform distribution are studied. Taking isosceles triangle and rectangle as examples, the exact calculation results of two representative points of isosceles triangle are given, and some new interesting properties are given.
A Data-Driven Predictive Model for Speed Control in Automotive Safety Applications

The importance of transportation to society and daily life makes safe driving an important responsibility to reduce the occurrence of accidents. Among the accident factors, overspeeding led to a maximum probability of road crashes and death. As the number of vehicles increases, manually monitoring the overspeed offense is laborious and time-consuming. The existing context-aware-based speed control strategy is effective but pricey. Therefore, in this article, a cost-effective, compact, and simple design-oriented speed recommendation and control mechanism is designed to avoid accidents caused by excessive speed. The proposed system leverages the potential of the decision tree regression model to do data analysis and find out the recommended speed for a particular location at a specific time. In the implementation process, the trained regression model is stored in the Heroku web server to obtain the speed. To support the recommendation, model of a new dataset is created from the Gray Spot dataset and stored in the Storage bucket of Firebase (Open Source), which comprises the time, location, weather, and speed attributes. The vehicle user receives speed recommendations from the Heroku server for every location; consequently, the speed controller regulates the speed of the motor through a motor driver’s electrical signal. The proposed decision tree regressor achieved 92.3% prediction accuracy during processing with 30 locations. The efficiency of the model showed that this model is a good assistant to car drivers and an optimal solution to reduce accidents on a larger level.
Heritage language communities as sites of struggle and identity negotiation: a case of Korean–American university students

ABSTRACT This study explores how three Korean-American university students engaged with the linguistic and cultural practices of two subgroups in their ethnic community - a Korean church context and an ethnic peer group - where different language and cultural ideologies coexisted. The participants' narratives showed complex experiences of heritage language (HL) use and identity construction in these two contexts, with the participants frequently experiencing tensions between their desire to use their HL and pressure to adopt traditional Korean cultural and linguistic norms. They thus solidified their individual and group identities through processes of negotiation and self-positioning within a multicultural environment.
Comment on \Observing the Average Trajectories of Single Photons in a Two-Slit Interferometer"

Department of Chemistry and Biochemistry and Institute for Theoretical Chemistry1 University Station A5300, University of Texas at Austin, Austin, TX 78712, USA(Dated: September 22, 2011)Kocsis et al. (Science, Reports 3 June 2011, p. 1170) state that the experimen-tally deduced average photon trajectories are identical to the particle trajectories ofBohm’s quantum mechanics. No supporting evidence, however, was provided. Thephoton trajectories presented in their report do not converge to high probabilityregions, a familiar and necessary behavior of Bohm trajectories. We reanalyze theirdata and calculations, conclude that the average photon trajectories do indeed agreewith Bohm, and discuss possible interpretations of this result.
Analysis of Deep Learning Methods for Early Wildfire Detection Systems: Review

A wildfire is an uncontrollable fire that arises outside of a specific focus, damages property, and poses a threat to human life and health. Thousands of fires are started every year for a variety of reasons, including dry seasons, thunderstorms, and volcanic activity. However, in recent years, the human factor has emerged as the main reason for irreparable forest fires. In order to address the problem of wildfire detection, deep learning model-based wildfire detection and recognition have been attracted by researchers. Deep learning models using Convolutional Neural Network (CNN) are essentially required several dataset samples to train the network with high accuracy. Meanwhile, the common wildfire datasets are small, repetitious and collected as images or videos samples from the Internet and experiments and there’s no standard dataset to adopt in the training mode and performance evaluation task. This paper presents a literature review of fire detection methods based on Computer Vision systems. It’s focused on recent machine learning models used in addition to the datasets required to construct future research projects for the wildfire detection system. Further, a comparison study is performed to highlight the strength and weaknesses key points of current methods including detection accuracy.
Content Distribution in Private Networks

Today, many companies want to serve a large number of internal applications not exposed to the open Internet. They need to set up, configure, and maintain VPN solutions for such a purpose. To provide all of the required internal services adjusted to their needs, the underlying network needs to be reliable, secure, and efficient. However, a significant part of the customers that need such a solution is international corporate companies having their branch offices across continents and employing a mass of people. Such circumstances require a feasible and versatile infrastructure. The main goal of our work is to identify suitable use cases, design and implement network components capable of serving content in private networks effectively, and demonstrate the advantages of the proposed solution. In our work, we also introduced other solutions in our area. In the end, we compared our solution with systems currently used in corporate networks and discussed our acquisition in this domain.
Water in low-mass star-forming regions with Herschel - The link between water gas and ice in protostellar envelopes

Aims: Our aim is to determine the critical parameters in water chemistry and the contribution of water to the oxygen budget by observing and modelling water gas and ice for a sample of eleven low-mass protostars, for which both forms of water have been observed. Methods: A simplified chemistry network, which is benchmarked against more sophisticated chemical networks, is developed that includes the necessary ingredients to determine the water vapour and ice abundance profiles in the cold, outer envelope in which the temperature increases towards the protostar. Comparing the results from this chemical network to observations of water emission lines and previously published water ice column densities, allows us to probe the influence of various agents (e.g., FUV field, initial abundances, timescales, and kinematics). Results: The observed water ice abundances with respect to hydrogen nuclei in our sample are 30-80ppm, and therefore contain only 10-30% of the volatile oxygen budget of 320ppm. The keys to reproduce this result are a low initial water ice abundance after the pre-collapse phase together with the fact that atomic oxygen cannot freeze-out and form water ice in regions with T(dust)>15 K. This requires short prestellar core lifetimes of less than about 0.1Myr. The water vapour profile is shaped through the interplay of FUV photodesorption, photodissociation, and freeze-out. The water vapour line profiles are an invaluable tracer for the FUV photon flux and envelope kinematics. Conclusions: The finding that only a fraction of the oxygen budget is locked in water ice can be explained either by a short pre-collapse time of less than 0.1 Myr at densities of n(H)~1e4 cm-3, or by some other process that resets the initial water ice abundance for the post-collapse phase. A key for the understanding of the water ice abundance is the binding energy of atomic oxygen on ice.
Development of a Wireless Electromyography System

Upper-limb amputation imposes a significant burden on amputees thereby restricting them from fully exploring their environments during activities of daily living. Thus, the quality of hand prostheses for patients with upper limb amputation has improved dramatically in recent years. Driving the prosthetic arm automatically by the users is the main challenge that faced the researchers in this field of study. Electromyography (EMG) signals produced by muscle contractions are one important typical control approach. In this paper, a sensor was developed to capture the electrical signals in the muscle of a patient using electromyography sensors. The proposed system has the ability for wirelessly transferring the captured data from the sensory system to computer, which represents the controlling and processing zone. The measured signals are then used to control the prosthesis arms. To achieve this, first the hardware and firmware of the sensor are developed and documented. Two Matlab scripts were developed to display the measured signals in the time and frequency domain. Finally, the characteristics of the developed sensors were verified by means of measurements. These measurements have shown that it is possible to distinguish whether a muscle is flexed or relaxed by means of the measured values captured with the sensor.
Exposure to Cigarette Smoke and Daily Physical Activity Associated with Asthma

Background: Asthmatic sufferers complain of sudden relapse when performing daily activities. exposure to cigarette smoke makes asthmatics where the longer the exposure is experienced by the patient, the more frequent asthma exacerbations are experienced. For people with asthma, avoiding excessive physical activity and exposure to cigarette smoke is very necessary to prevent recurrence of symptoms. So it is necessary to control the level of asthma, which is influenced by factors that trigger asthma recurrence. Aim: The purpose of the study was to determine the relationship between daily physical activity and exposure to cigarette smoke on asthma recurrence in asthmatic patients Methods: This type of research uses observational analytic using a cross sectional approach. Sampling in this study using purposive sampling obtained the number of respondents 358 with a vulnerable age of 18-45 years. This research was conducted for 6 months. The measuring instrument for exposure to cigarette smoke uses a questionnaire containing the categories of exposure to cigarette smoke less than 1 hour, exposure to cigarette smoke 1-4 hours and exposure to cigarette smoke more than 4 hours. Physical activity measurement tool using the International Physical Activity Questionnaire (IPAQ). Asthma recurrence was measured by using a questionnaire that refers to the Asthma Control Test (ACT). The research procedure was carried out by researchers by ensuring that respondents were in good health and signed the informed consent provided. Data analysis using Chi-Square test. Result: The results of the Chi Square test with a significance value of = 0.05, p value = 0.000, which means p < then there is a relationship between daily physical activity and exposure to cigarette smoke on asthma recurrence in asthmatic patients. Conclusions: There is a relationship between exposure to cigarette smoke and daily physical activity on asthma relapse in asthmatic patients. It is expected that asthmatics should avoid triggering factors for asthma relapse to achieve controlled asthma levels.
Dielectric properties of metal-organic chemical vapor deposited highly textured Pb(ScTa)1−xTixO3 (x=0–0.3) relaxor ferroelectric thin films on LaNiO3 electrode buffered Si

Highly (100) textured Pb(ScTa)1−xTixO3 (x=0–0.3) thin films were grown on LaNiO3/Pt/Ti electrode-coated Si substrate using metal-organic chemical vapor deposition at 685 °C. Ti addition was introduced to modify the dielectric properties. Diffuse phase transition, typical of relaxor ferroelectrics was noticed. As Ti content increased from 0% to 30%, the phase transition temperature (Tmax) gradually shifted from −10 to 120 °C with the dielectric constant at Tmax increased from 1397 to 1992 (1 kHz). Loss tangent values are generally below 0.025.
Cosmological constraints from the anisotropic clustering analysis using BOSS DR9

Our observations of the Universe are fundamentally anisotropic, with data from galaxies separated transverse to the line of sight coming from the same epoch while that from galaxies separated parallel to the line of sight coming from different times. Moreover, galaxy velocities along the line of sight change their redshift, giving redshift space distortions. We perform a full two-dimensional anisotropy analysis of galaxy clustering data, fitting in a substantially model independent manner the angular diameter distance D_A, Hubble parameter H, and growth rate ddelta/dln a without assuming a dark energy model. The results demonstrate consistency with LCDM expansion and growth, hence also testing general relativity. We also point out the interpretation dependence of the effective redshift z_eff, and its cosmological impact for next generation surveys.
Investigation of The Influence of Data Acquisition Conditions on the Quality of the CNN-Based Vision System

Practically applied vision systems can be operated in various conditions, while their reliable operation must be ensured, regardless of the operating conditions. Currently, convolutional neural networks (CNN) are widely used in vision systems for various purposes. Since the generalizing ability of neural networks does not allow to work equally well with images whose characteristics are different from the training sample, there is often a decrease in the quality of functioning of the CNN-based vision system when weather conditions, time of day, camera position, etc. change. At the same time, it is not always obvious how much the quality will drop if the listed conditions change and within what limits these changes are permissible. In this paper, the decrease in the quality of the CNN-based vision system is investigated under the influence of unspecified changes in weather conditions, time of day and size of objects. Several well-known CNN architectures have been selected for experiments. As data for experiments, an open data set Berkeley Deep-drive Dataset was used, which has a markup of shooting conditions. As a result of experiments, it was revealed that the least effect on the quality of the vision system with a pre-trained CNN is affected by changing weather conditions, and the biggest influence is the change in the size of objects (the more the size differed from the training ones, the greater the drop in accuracy). It is worth noting that CNN InceptionV3 have greater resistance to image size changes among the popular CNN architectures under consideration.
Separating intrinsic alignment and galaxy-galaxy lensing

The coherent physical alignment of galaxies is an important systematic for gravitational lensing studies as well as a probe of the physical mechanisms involved in galaxy formation and evolution. We develop a formalism for treating this intrinsic alignment (IA) in the context of galaxy-galaxy lensing and present an improved method for measuring IA contamination, which can arise when sources physically associated with the lens are placed behind the lens due to photometric redshift scatter. We apply the technique to recent Sloan Digital Sky Survey (SDSS) measurements of Luminous Red Galaxy lenses and typical ( ~ L*) source galaxies with photometric redshifts selected from the SDSS imaging data. Compared to previous measurements, this method has the advantage of being fully self-consistent in its treatment of the IA and lensing signals, solving for the two simultaneously. We find an IA signal consistent with zero, placing tight constraints on both the magnitude of the IA effect and its potential contamination to the lensing signal. While these constraints depend on source selection and redshift quality, the method can be applied to any measurement that uses photometric redshifts. We obtain a model-independent upper-limit of roughly 10% IA contamination for projected separations of rp ≈ 0.1–10 h−1Mpc. With more stringent photo-z cuts and reasonable assumptions about the physics of intrinsic alignments, this upper limit is reduced to 1–2%. These limits are well below the statistical error of the current lensing measurements. Our results suggest that IA will not present intractable challenges to the next generation of galaxy-galaxy lensing experiments, and the methods presented here should continue to aid in our understanding of alignment processes and in the removal of IA from the lensing signal.
The logic of language

1. Logic and Entailment 2. Logic: a New Beginning 3. Natural Set Theory, Cognition, and Natural Logic 4. Logical Power, Abelard, and Empirical Success Rates 5. Aristotle, the commentators and Abelard 6. The Functionality of the Square and of BNPC 7. The Context-Sensitivity of Speech and Language 8. Discourse Incrementation 9. Primary and Donkey Anaphora 10. Presupposition and Presuppositional Logic 11. Topic-Comment Modulation Bibliography Index
Linear programming with a feasible direction interior point technique for smooth optimization

We propose an adaptation of the Feasible Direction Interior Points Algorithm (FDIPA) of J. Herskovits, for solving large-scale linear programs. At each step, the solution of two linear systems with the same coefficient matrix is determined. This step involves a significant computational effort. Reducing the solution time of linear systems is, therefore, a way to improve the performance of the method. The linear systems to be solved are associated with definite positive symmetric matrices. Therefore, we use Split Preconditioned Conjugate Gradient(SPCG) method to solve them, together with an Incomplete Cholesky preconditioner using Matlab’s ICHOL function. We also propose to use the first iteration of the conjugate gradient, and to presolve before applying the algorithm, in order to reduce the computational cost. Following, we then provide mathematica proof that show that the iterations approach Karush-Kuhn-Tucker points of the problem under reasonable assumptions. Finally, numerical evidence show that the method not only works in theory but is also competitive with more advanced methods.
Impact of Antenna Distribution on Spectral and Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control Algorithms

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G (B5G). CF-mMIMO systems with multiple-antenna APs have been investigated from various viewpoints recently. However, no comprehensive analysis of the impact of antenna distribution on CF-mMIMO system performance has been done so far, which is important for practical deployment. Besides spectral efficiency, in B5G, energy efficiency of user equipments is one of the key indicators because various kinds of battery-limited devices connect to the network. Thus, this paper provides a comprehensive performance analysis of the impact of antenna distribution on the performance indicators, while considering several combining/precoding schemes and transmit power control algorithms. For uplink maximal-ratio combining, the concentrated deployment has the best performance thanks to the channel hardening and favorable propagation phenomena. On the other hand, the concentrated deployment prominently suffers from shadowing effects. For uplink/downlink minimum mean-square error combining with transmit power control, the semi-distributed deployments show the best performance, and it implies that we can reduce the number of APs to 1/4 for uplink and to 1/2 for downlink while keeping the same performance as the fully-distributed deployment.