Miracle-dz/UniFAR-FTUs huggingface dataset:
Relativistic Feynman-Metropolis-Teller treatment at finite temperatures

The Feynman-Metropolis-Teller treatment of compressed atoms has been recently generalized to relativistic regimes and applied to the description of static and rotating white dwarfs in general relativity. We present here the extension of this treatment to the case of finite temperatures and construct the corresponding equation of state (EOS) of the system; applicable in a wide regime of densities that includes both white dwarfs and neutron star outer crusts. We construct the mass-radius relation of white dwarfs at finite temperatures obeying this new EOS and apply it to the analysis of ultra low-mass white dwarfs with $M\lesssim 0.2 M_\odot$. In particular, we analyze the case of the white dwarf companion of PSR J1738+0333. The formulation is then extrapolated to compressed nuclear matter cores of stellar dimensions, systems with mass numbers $A\approx (m_{\rm Planck}/m_n)^3$ or mass $M_{\rm core}\approx M_{\odot}$, where $m_{\rm Planck}$ and $m_n$ are the Planck and the nucleon mass. For $T \ll m_e c^2/k_B \approx 5.9\times 10^9$ K, a family of equilibrium configurations can be obtained with analytic solutions of the ultra-relativistic Thomas-Fermi equation at finite temperatures. Such configurations fulfill global but not local charge neutrality and have strong electric fields on the core surface. We find that the maximum electric field at the core surface is enhanced at finite temperatures with respect to the degenerate case.
Path Tracking for Unmanned Ground Vehicle Navigation: Implementation and Adaptation of the Pure Pursuit Algorithm

Abstract : Following user defined paths and seeking goal locations is fundamental to Autonomous Unmanned Ground Vehicle (UGV) navigation. This paper summarizes the current state of the art in robotic path tracking for Ackerman steered vehicles and presents results of implementation and adaptation of the Pure Pursuit algorithm at Defence R&D Canada Suffield.
Sustainable materials and processes for electronics, photonics and diagnostics

This paper discusses applicability of sustainable substrate alternatives for printed and other roll-to-roll (R2R) compatible electronics, photonics and diagnostics. Substrate candidates include bio-based plastics and cellulose based substrates, including paper, carton board and nano cellulose. This paper takes a case-specific approach for sustainability and besides changing the substrate also discusses other sustainability aspects for a temperature monitoring intelligent package solution. End-of-life processes, such as recycling, composting and biodegradability, will be paid a due attention. The role of life cycle assessment (LCA) as the main tool for quantifying environmental impact is discussed. The paper will summarize a set of selection criteria that sustainable devices have to fulfil in order to function as part of electronics, photonics and diagnostics products and at the same time be compatible with printing and other R2R compatible processes. Innovative component and device opportunities that increase electronics, photonics and diagnostics product sustainability are defined, and industrial expectations towards sustainability is summarized.
Spatiotemporal Visualization Literature Review

— Spatiotemporal data is data involving location, time, and another value. This type of data exists as social media data, cell phone data, traﬃc data, medical data, and in other domains. To extract insights, visual analytics, topic modeling, anomaly detection, and other techniques are performed on spatiotemporal data. These ﬁndings are then visualized using maps, word clouds, heatmaps, and other techniques. This paper discusses the applications of spatiotemporal data in various domains, related visualization techniques, and future areas of research in this ﬁeld.
Research on machining quality of 304 stainless steel deep hole by changing Angle of gun drill

： With the continuous development of the manufacturing industry, the proportion of deep hole machining in mechanical processing is increasing. Demand for deep hole processing of 304 stainless steel is also increasing year by year. At the same time, due to the physical characteristics of 304 stainless steel and the uncontrollability of gun drill in processing, the quality of deep hole processing is relatively poor. The main research of this paper is as follows. Firstly, the stress model of drill bit is established by analyzing the structure of the drill bit. Secondly, a number of single - factor drilling experiments are carried out to control the cutting force in machining process by changing the forward angle of the inner and outer edges of the gun drill. Thirdly, based on the analysis of test results, this paper discusses the influence of the change of inner and outer edge Angle of gun drill on the surface quality of hole wall and the deviation of hole straightness. Finally, the paper studies the chip forming process of gun drill in deep hole machining of 304 stainless steel, and gives the best choice of cutting edge Angle. edge angle can slow down the increase of offset. The increase of the internal edge angle intensifies the offset of hole straightness. The experimental research shows that when the edge angle is 35 °, the offset of the internal edge angle is better in the range of 20 ° ~ 30 °, and the minimum value is taken when the internal edge angle is 20 °.
Pulsar timing and relativistic gravity

In addition to being fascinating objects to study in their own right, pulsars are exquisite tools for probing a variety of issues in basic physics. Recycled pulsars, thought to have been spun up in previous episodes of mass accretion from orbiting companion stars, are especially well suited for such applications. They are extraordinarily stable clocks, approaching and perhaps exceeding the long-term stabilities of the best terrestrial time standards. Most of them are found in binary systems, with orbital velocities as large as 10-3 c. They provide unique opportunities for measuring neutron star masses, thereby yielding fundamental astrophysical data difficult to acquire by any other means. And they open the way for high precision tests of the nature of gravity under conditions much more ‘relativistic ’ than found anywhere within the Solar System. Among other results, pulsar timing observations have convincingly established the existence of quadrupolar gravitational waves propagating at the speed of light. They have also placed interesting limits on possible departures of the strong-field nature of gravity from general relativity, on the rate of change of Newton’s constant, G, and on the energy density of low-frequency gravitational waves in the universe.
Evaluating the Decent Work Scale Measurement Model in Zimbabwe

The Decent Work Agenda recognises the role of work and work-related contexts in giving meaning, fulfilment, opportunities for development and growth to the employed and those who aspire to work. The Decent Work Scale (DWS) is a commonly referenced measure to appreciate the experience of decent work in organizations. Limited research has examined the quality of the DWS measurement model in the developing countries, especially in sub-Saharan Africa. This study examined the factor structure of the DWS using bifactor modelling. The study also conducted invariance tests using multi-group confirmatory factor analysis. Last, the study evaluated the predictive validity of the decent work construct using covariance-based structural equation modelling. A probability sample from a large manufacturing firm in Zimbabwe informed the analysis. Bifactor analysis suggested the presence of a strong underlying general factor for DWS. The measure was invariant across gender groups. Decent work had significant, positive relationships with organisational learning and work engagement.
Feeding habits influence species habitat associations at the landscape scale in a diverse clade of Neotropical fishes

A primary goal of community ecology is to understand the mechanisms that drive species' spatial distribution and habitat associations. Species' geographic distribution can be influenced by the distribution of their prey partly because consumers' behaviour is oriented to optimal energy use during foraging. We analysed how differences in dietary preferences influence the spatial distribution and habitat associations of species at the landscape scale. We hypothesized that differences in feeding guilds will lead to divergent habitat association patterns among species.
Detailed abundances of 15 stars in the metal-poor globular cluster NGC 4833

We have observed 15 red giant stars in the relatively massive, metal-poor globular cluster NGC 4833 using the Magellan Inamori Kyocera Echelle spectrograph at Magellan. We calculate stellar parameters for each star and perform a standard abundance analysis to derive abundances of 43 species of 39 elements, including 20 elements heavier than the iron group. We derive = -2.25 +/- 0.02 from Fe I lines and = -2.19 +/- 0.013 from Fe II lines. We confirm earlier results that found no internal metallicity spread in NGC 4833, and there are no significant star-to-star abundance dispersions among any elements in the iron group (19 <= Z <= 30). We recover the usual abundance variations among the light elements C, N, O, Na, Mg, Al, and possibly Si. The heavy-element distribution reflects enrichment by r-process nucleosynthesis ([Eu/Fe] = +0.36 +/- 0.03), as found in many other metal-poor globular clusters. We investigate small star-to-star variations found among the neutron-capture elements, and we conclude that these are probably not real variations. Upper limits on the Th abundance, log epsilon (Th/Eu) < -0.47 +/- 0.09, indicate that NGC 4833, like other globular clusters where Th has been studied, did not experience a so-called "actinide boost. "
Algorithms for partially observable markov decision processes

Partially Observable Markov Decision Process (POMDP) is a general sequential decision-making model where the effects of actions are nondeterministic and only partial information about world states is available. However, finding near optimal solutions for POMDPs is computationally difficult. Value iteration is a standard algorithm for solving POMDPs. It conducts a sequence of dynamic programming (DP) updates to improve value functions. Value iteration is inefficient for two reasons. First, a DP update is expensive due to the need of accounting for all belief states in a continuous belief space. Second, value iteration needs to conduct a large number of DP updates before its convergence. This thesis investigates two ways to accelerate value iteration. The work presented centers around the idea of conducting DP updates and therefore value iteration over a belief subspace, a subset of belief space. The first use of belief subspace is to reduce the number of DP updates for value iteration to converge. We design a computationally cheap procedure considering a belief subspace which consists of a finite number of belief states. It is used as an additional step for improving value functions. Due to additional improvements by the procedure, value iteration conducts fewer DP updates and therefore is more efficient. The second use of belief subspace is to reduce the complexity of DP updates. We establish a framework on how to carry out value iteration over a belief subspace determined by a POMDP model. Whether the belief subspace is smaller than the belief space is model dependent. If this is true for a POMDP, value iteration over the belief subspace is expected to be more efficient. Based on this framework, we study three POMDP classes with special problem characteristics and propose different value iteration algorithms for them. (1) An informative POMDP assumes that an agent always has a good idea about the world states. The subspace determined by the model is much smaller than the belief space. Value iteration over the belief subspace is more efficient for this POMDP class. (2) A near-discernible POMDP assumes that the agent can get a good idea about states once in a while if it executes some particular actions. For such a POMDP, the belief subspace determined by the model can be of the same size as the belief space. We propose an anytime value iteration algorithm which focuses the computations on a small belief subspace and gradually expand it. (3) A more general class than near-discernible POMDPs assumes that the agent can get a good idea about states with a high likelihood once in a while if it executes some particular actions. For such POMDPs, we adapt the anytime algorithm to conduct value iteration over a growing belief subspace.
Investigation of sleep quality according to physical activity levels in individuals with lung cancer

Introduction: Lung cancer is a type of cancer that is usually diagnosed late and has a low life expectancy due to its incidence, high mortality, and initial asymptomatic course. Sleep disorders and related fatigue are common complaints for cancer patients. This study aims to examine sleep quality according to physical activity levels in individuals with lung cancer. Methods: This study was carried out between December 2021 and March 2022. Individuals diagnosed with lung cancer in Bolu Abant İzzet Baysal University Faculty of Medicine Chest Diseases Outpatient Clinic were retrospectively analyzed. 100 individuals were included in the study. Individuals diagnosed with lung cancer in the chest diseases department of a tertiary care center were retrospectively analyzed. Physical activity level with the International Physical Activity Questionnaire; sleep quality with the Pittsburg Sleep Quality Index; dyspnea with Modified Medical Research Council Scale; performance status with Eastern Cooperative Oncology Group; functional disability with Karnofsky Performance Status; Pain was evaluated with a numerical rating scale. Results: 52.0% of the individuals included in the study were sedentary. There was no difference between the two groups in terms of mean age (p=0.123), body mass index (p=0.157), disease duration (p=0.342), and length of hospital stay (p=0.273). There was a difference between the two groups in terms of Eastern Cooperative Oncology Group (p=0.001), Karnofsky Performance Status (p=0.001), and total score of Pittsburg Sleep Quality Index (p=0.001). Conclusion: In our study, it was found that the total sleep quality score of the physically active group was better than the sedentary group. Physical activity is a modifiable lifestyle behavior with positive physiological and psychological health consequences and a potential non-pharmacological intervention for poor sleep quality.
Models of stars rotating near the critical limit

When the surface angular velocity is above about 70% of the critical angular velocity, many interesting features appear which may be tested by interferometric observations, like significant deformation of stars, variation of the effective temperature with the latitude. Also polar winds become important and equatorial disks may appear. Near the critical limit, convection is also favored in the outer layers. In the present paper, we emphasize the need for a proper estimate of the critical velocity since this is the ratio of the actual velocity of the star to that critical velocity which determines the amplitude of the above effects. We recall the existence of two critical velocities. The first one, also called the classical critical velocity is the one to consider when the star has an Eddington factor inferior to 0.639, while the second one is the one to be considered when the Eddington factor is above 0.639. The features of the star at these two critical limits may be very different.
Fractional quantum Hall effect and $M$-theory

We propose a unifying model for FQHE which on the one hand connects it to recent developments in string theory and on the other hand leads to new predictions for the principal series of experimentally observed FQH systems with filling fraction $\nu ={n\over 2n\pm1}$ as well as those with $\nu ={m\over m+2}$. Our model relates these series to minimal unitary models of the Virasoro and superVirasoro algebra and is based on $SL(2, {\bf C})$ Chern-Simons theory in Euclidean space or $SL(2,{\bf R})\times SL(2,{\bf R})$ Chern-Simons theory in Minkowski space. This theory, which has also been proposed as a soluble model for 2+1 dimensional quantum gravity, and its N=1 supersymmetric cousin, provide effective descriptions of FQHE. The principal series corresponds to quantized levels for the two $SL(2,{\bf R})$'s such that the diagonal $SL(2,{\bf R})$ has level $ 1$. The model predicts, contrary to standard lore, that for principal series of FQH systems the quasiholes possess non-abelian statistics. For the multi-layer case we propose that complex ADE Chern-Simons theories provide effective descriptions, where the rank of the ADE is mapped to the number of layers. Six dimensional $(2,0)$ ADE theories on the Riemann surface $\Sigma$ provides a realization of FQH systems in M-theory. Moreover we propose that the q-deformed version of Chern-Simons theories are related to the anisotropic limit of FQH systems which splits the zeroes of the Laughlin wave function. Extensions of the model to 3+1 dimensions, which realize topological insulators with non-abelian topologically twisted Yang-Mills theory is pointed out.
The influence of aspect ratio on the properties of cobalt nanowire-based magnetorheological fluids

ABSTRACT The yield stress of magnetorheological (MR) fluids has been shown to depend on particle morphology, but the exact nature of this contribution is still not fully understood. In this study, MR fluids containing 4 vol. % cobalt particles (spherical particles vs. nanowires) suspended in silicone oil were investigated. The influence of the aspect ratio on the rheological properties of suspensions that contained cobalt nanowires with aspect ratios ranging from 10 to 101 in increments of ~6 is described. The cobalt nanowires were fabricated using alumina template-based electrodeposition, producing wires with 305 ± 66 nm diameters. The shear stress was measured as a function of shear rate for increasing applied magnetic fields. The apparent yield stress and viscosity as a function of changing aspect ratio of the nanowire suspensions were determined. At a saturated magnetic flux density, the yield stress was found to increase linearly up to an aspect ratio of 23 (7.1 μm long wires) at which time the yield stress reached a plateau of 3.7 kPa even as the aspect ratio was further increased. As a comparison, suspensions containing 4 vol. % 1.6 μm spherical cobalt particles only reached a maximum yield stress of 1.6 kPa. Graphical abstract
Assessment of deep learning methods for classification of cereal crop growth stage pre and post canopy closure

Abstract. Growth stage (GS) is an important crop growth metric commonly used in commercial farms. We focus on wheat and barley GS classification based on in-field proximal images using convolutional neural networks (ConvNets). For comparison purposes, use of a conventional machine learning algorithm was also investigated. The research includes extensive data collection of images of wheat and barley crops over a 3-year period. During data collection, videos were recorded during field walks at two camera views: downward looking and 45 deg angled. The resulting dataset contains 110,000 images of wheat and 106,000 of barley taken over 34 and 33 GS classes, respectively. Three methods were investigated as candidate technologies for the problem of GS classification. These methods were: (I) feature extraction and support vector machine, (II) ConvNet with learning from scratch, and (III) ConvNet with transfer learning. The methods were assessed for classification accuracy using test images taken (a) in fields on days imagined in the training data (i.e., seen field-days GS classification) and (b) in fields on days not imagined in the training data (i.e., unseen field-days principal GS classification). Of the three methods investigated, method III achieved the best accuracy for both classification tasks. The model achieved 97.3% and 97.5% GS classification accuracy for seen field-day test data for wheat and barley, respectively. The model also achieved accuracies of 93.5% and 92.2% for the principal GS classification task for wheat and barley, respectively. We provide a number of key research contributions: the collection curation and exposure of a unique GS labeled proximal image dataset of wheat and barley crops, GS classification, and principal GS classification of cereal crops using three different machine learning methods as well as a comprehensive evaluation and comparison of the obtained results.
Rapid Production of Accurate Embedded-Atom Method Potentials for Metal Alloys

A critical limitation to the wide-scale use of classical molecular dynamics for alloy design is the limited availability of suitable interatomic potentials. Here, we introduce the Rapid Alloy Method for Producing Accurate General Empirical Potentials or RAMPAGE, a computationally economical procedure to generate binary embedded-atom model potentials from already-existing single-element potentials that can be further combined into multi-component alloy potentials. We present the quality of RAMPAGE calibrated Finnis-Sinclair type EAM potentials using binary Ag-Al and ternary Ag-Au-Cu as case studies. We demonstrate that RAMPAGE potentials can reproduce bulk properties and forces with greater accuracy than that of other alloy potentials. In some simulations, it is observed the quality of the optimized cross interactions can exceed that of the original off-the-shelf elemental potential inputs.
Pressure correction in simulation of incompressible fluid counter flows

The pressure-correction method is discussed in the paper. An algorithm of the method’s implementation is proposed, with a formal proof of its convergence. The efficiency of the algorithm is demonstrated in numerical simulations of counter flows of a Newtonian fluid. The results of computations are analyzed in the context of available experimental data on counter flows of fluids with complex rheology in crossed channels.
AGGRESSIVE BEHAVIOR AND COPING STRATEGIES AMONG CARDIAC PATIENTS WITH TYPE A AND TYPE B BEHAVIOR PATTERN

Objectives: The research aimed to explore the aggression and coping strategies in Type A and Type B cardiac patients. The study also assesses the role of gender and personality type in cardiac patients. Methodology: 101 cardiac patients from different hospitals in Punjab having male (56) and females (45) with an average range of 22-70 were taken for the study. The education range was between middle to master and all were married. All the patients diagnosed with coronary heart disease were referred by their medical staff, with the problems in their ECGs, Echocardiography, and further diagnostic assessments taken as sample. Personality types, and coping styles have been assessed along with their detailed demographics. Results: The mean age range was 45 years, out of the total 101 patients male and female were 55.5% and 44.5%, respectively. Patients of type A and type B personality were 55(45.8%) and 46 (38.3%) respectively. In Cardiac patients avoidant Coping predicted verbal aggression and problem-focused coping predicted physical aggression whereas Problem-Focused and Avoidant Focused negatively predicted aggression among Type A cardiac patients. Gender moderated between aggression and personality type among type A and type B cardiac patients. The interaction effect of gender and personality type on problem-focused, emotion-focused, and avoidant-focused coping is also evident in the results. Conclusion: The study concluded that cardiac patients differ in their coping styles relevant to gender and personality type. Furthermore, gender also affects their level of aggression with the interaction of coping styles among them.