Miracle-dz/UniFAR-FTUs huggingface dataset:
Boson dark matter halos with a dominant noncondensed component

We consider galaxy halos formed by dark matter bosons with mass in the range of about a few tens or hundreds eV. A major part of the particles is in a noncondensed state and described under the Thomas-Fermi approach. Derived equations are solved numerically to find the halo density profile. The noncondensed state is supported in the entire halo except compact gravitationally bounded Bose-Einstein condensates. Although the size of these compact objects, also known as Bose stars, depends on interactions between the particles, its upper limit is only about 100 astronomical units. The Bose stars collect the condensed bosons providing a density cusp avoidance in the halo as well as a natural mechanism to prevent overproduction of small halos. Clusters of the Bose stars can also contribute to the halo density profile. The model is analyzed by confronting its predictions with observations of galaxy rotation curves. We employ 22 low surface brightness galaxies and obtain that the model is consistent with the observational data when the particle mass is in the range above about 50 eV and the best fit corresponds to the mass m = 86 eV. This mass is appropriate for relic dark matter bosons, which decouple just after QCD phase transition.
Efficient edge-preserving methods for dynamic inverse problems

We consider efficient methods for computing solutions to dynamic inverse problems, where both the quantities of interest and the forward operator (measurement process) may change at different time instances but we want to solve for all the images simultaneously. We are interested in large-scale ill-posed problems that are made more challenging by their dynamic nature and, possibly, by the limited amount of available data per measurement step. To remedy these difficulties, we apply regularization methods that enforce simultaneous regularization in space and time (such as edge enhancement at each time instant and proximity at consecutive time instants) and achieve this with low computational cost and enhanced accuracy. More precisely, we develop iterative methods based on a majorization-minimization (MM) strategy with quadratic tangent majorant, which allows the resulting least squares problem to be solved with a generalized Krylov subspace (GKS) method; the regularization parameter can be defined automatically and efficiently at each iteration. Numerical examples from a wide range of applications, such as limited-angle computerized tomography (CT), space-time image deblurring, and photoacoustic tomography (PAT), illustrate the effectiveness of the described approaches.
Circular motion around a regular rotating Hayward black hole

In this paper, we explore the geodesics motion of neutral test particles and the process of energy extraction from a regular rotating Hayward black hole (BH). We analyze the effect of spin as well as deviation parameter [Formula: see text], on ergoregion, event horizon and static limit of the said BH. By making use of geodesic equations on the equatorial plane, we determine the innermost stable circular and photon orbits. Moreover, we investigate the effective potentials and effective force to have information on motion and the stability of circular orbits. On studying the negative energy states, we figure out the energy limits of Penrose mechanism. Using Penrose mechanism, we found expression for the efficiency of energy extraction and observed that both spin and deviation parameters contribute to the efficiency of energy extraction. Finally, the obtained results are compared with that acquired from Kerr and braneworld Kerr BHs.
Factors Affecting the Price of Cost-Equivalent Land: Application of Hierarchical Linear Modeling

This study examined 19 urban land consolidation areas in Kaohsiung City, Taiwan, as well as cost-equivalent lands auctioned off from 2013 to 2019. Hierarchical linear modeling was used for analysis, in which the Level 1b variables pertained to cost-equivalent land and the Level 2 variables pertained to land consolidation areas. According to the empirical results, in terms of the estimation results, there were significant differences between the mean price of each urban land consolidation area. Therefore, HLM is suitable for the subsequent analysis. A total of 76.7% of the differences in the mean land consolidation area price were contributed by the differences between the land consolidation areas. Therefore, it is important to consider the differences generated by the particular features of each area.
Deep Learning Exploration of Agent-Based Social Network Model Parameters

Interactions between humans give rise to complex social networks that are characterized by heterogeneous degree distribution, weight-topology relation, overlapping community structure, and dynamics of links. Understanding these characteristics of social networks is the primary goal of their research as they constitute scaffolds for various emergent social phenomena from disease spreading to political movements. An appropriate tool for studying them is agent-based modeling, in which nodes, representing individuals, make decisions about creating and deleting links, thus yielding various macroscopic behavioral patterns. Here we focus on studying a generalization of the weighted social network model, being one of the most fundamental agent-based models for describing the formation of social ties and social networks. This generalized weighted social network (GWSN) model incorporates triadic closure, homophilic interactions, and various link termination mechanisms, which have been studied separately in the previous works. Accordingly, the GWSN model has an increased number of input parameters and the model behavior gets excessively complex, making it challenging to clarify the model behavior. We have executed massive simulations with a supercomputer and used the results as the training data for deep neural networks to conduct regression analysis for predicting the properties of the generated networks from the input parameters. The obtained regression model was also used for global sensitivity analysis to identify which parameters are influential or insignificant. We believe that this methodology is applicable for a large class of complex network models, thus opening the way for more realistic quantitative agent-based modeling.
Evaluation of an Inflated Beam Model Applied to Everted Tubes

Everted tubes have often been modeled as inflated beams to determine transverse and axial buckling conditions. This paper seeks to validate the assumption that an everted tube can be modeled in this way. The tip deflections of everted and uneverted beams under transverse cantilever loads are compared with a tip deflection model that was first developed for aerospace applications. LDPE and silicone coated nylon beams were tested; everted and uneverted beams showed similar tip deflection. The literature model best fit the tip deflection of LDPE tubes with an average tip deflection error of 6 mm, while the nylon tubes had an average tip deflection error of 16.4 mm. Everted beams of both materials buckled at 83% of the theoretical buckling condition while straight beams collapsed at 109% of the theoretical buckling condition. The curvature of everted beams was estimated from a tip load and a known displacement showing relative errors of 14.2% and 17.3% for LDPE and nylon beams respectively. This paper shows a numerical method for determining inflated beam deflection. It also provides an iterative method for computing static tip pose and applied wall forces in a known environment.
Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning

We investigate the effect of using human demonstration data in the replay buffer for Deep Reinforcement Learning. We use a policy gradient method with a modified experience replay buffer where a human demonstration experience is sampled with a given probability. We analyze different ratios of using demonstration data in a task where an agent attempts to reach a goal while avoiding obstacles. Our results suggest that while the agents trained by pure self-exploration and pure demonstration had similar success rates, the pure demonstration model converged faster to solutions with less number of steps.
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things

As the number of Human-Centered Internet of Things (HCIoT) applications increases, the self-adaptation of IoT services and devices is becoming a fundamental requirement for addressing the uncertainties of their environment in decision-making processes. Self-adaptation of HCIoT aims to manage run-time changes and to adjust the functionality of IoT devices in order to achieve desired goals during execution. SMASH is a semantic-enabled multi-agent system for self-adaptation of HCIoT that autonomously adapts IoT objects to uncertainties of their environment. SMASH addresses the self-adaptation of IoT applications only according to the human values of users, while the behavior of users is not considered. This article presents Q-SMASH: a multi-agent reinforcement learning-based approach for self-adaptation of IoT objects in human-centered environments. Q-SMASH uses Q-Learning and aims to learn the behaviors of users along with respecting human values automatically. The learning ability of Q-SMASH allows it to adapt itself to the behavioral change of users and make more accurate decisions in different states and situations.
A Centralized and Scalable Uplink Power Control Algorithm in Low SINR Scenarios

Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang et al., 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm.
Extended gambler's ruin problem

In the extended gambler's ruin problem we can move one step forward or backward (classical gambler's ruin problem), we can stay where we are for a time unit (delayed action) or there can be absorption in the current state (game is terminated without reaching an absorbing barrier). We obtain absorption probabilities, probabilities for maximum and minimum values of the ruin problem, expected time until absorption and the value of the game. We also investigate asymptotic behavior of absorption probabilities and expected time until absorption. We introduce a conjugate version of our random walk.
DMSO_A_312527 2955..2972

*These authors contributed equally to this work Abstract: GLP-1 receptor agonists (GLP-1RAs) and SGLT-2 inhibitors (SGLT-2is) are novel antidiabetic medications associated with considerable cardiovascular benefits therapying treatment of diabetic patients. GLP-1 exhibits atherosclerosis resistance, whereas SGLT2i acts to ameliorate the neuroendocrine state in the patients with chronic heart failure. Despite their distinct modes of action, both factors share pathways by regulating the central nervous system (CNS). While numerous preclinical and clinical studies have demonstrated that GLP-1 can access various nuclei associated with energy homeostasis and hedonic eating in the CNS via blood–brain barrier (BBB), research on the activity of SGLT-2is remains limited. In our previous studies, we demonstrated that both GLP-1 receptor agonists (GLP1RAs) liraglutide and exenatide, as well as an SGLT-2i, dapagliflozin, could activate various nuclei and pathways in the CNS of Sprague Dawley (SD) rats and C57BL/6 mice, respectively. Moreover, our results revealed similarities and differences in neural pathways, which possibly regulated different metabolic effects of GLP-1RA and SGLT-2i via sympathetic and parasympathetic systems in the CNS, such as feeding, blood glucose regulation and cardiovascular activities (arterial blood pressure and heart rate control). In the present article, we extensively discuss recent preclinical studies on the effects of GLP-1RAs and SGLT-2is on the CNS actions, with the aim of providing a theoretical explanation on their mechanism of action in improvement of the macro-cardiovascular risk and reducing incidence of diabetic complications. Overall, these findings are expected to guide future drug design approaches.
Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion

Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).
Bursting Oscillations and Sliding Motion in Permanent Magnet Synchronous Motor Systems with Friction Factor

The main purpose of this paper is to investigate the qualitative effects of external excitation and friction factor on the response of permanent magnet synchronous motor (PMSM) system. Three different modes of bursting oscillations are found. In particular, the introduction of friction function changes the governing equations from a smooth type to a nonsmooth (Filippov) type in which the special sliding motion is observed. The mechanism, attractor structure, vector field structure, and analytic bifurcation conditions of bursting oscillation and sliding motion are discussed in detail. The validity of theoretical results obtained is verified by numerical simulations and analysis.
Results of a Quantitative Assessment of France’s Garantie Jeunes Programme

France’s Garantie jeunes (“Youth Guarantee”, GJ) is a local support programme that targets young people who are in precarious situations and neither in employment, education, or training. It was set up in October 2013, initially on a trial basis. This article presents the results of a quantitative evaluation of the scheme. A panel survey conducted among young people who participated from the beginning of the trial in the areas first trialling Garantie jeunes reveals a very fragile population. The programme offers a high level of support, especially during the collective phase at the start. The evaluation of the scheme takes into account the fact that it was initially set up in only part of the country. Estimates concerning the participants in the first Garantie jeunes target areas indicate that the programme has had an impact on their life trajectories. It has intensified support and has had an impact on beneficiaries’ employment rates, an impact that continues in the months following the end of support.
CHROMATOGRAPHIC FINGERPRINT ANALYSIS OF FLAVONOIDS OF ACHYRANTHES ASPERA LINN. BY HPTLC

ABSTRACT The study was carried out with an aim to determine the chemical profile and flavanoid composition of the medicinally important plant Achyranthes aspera Linn. from methanolic extract of root, stem and leaf. Preliminary phytochemical screening was done followed by HPTLC studies. Ethyl acetate: Formic acid : Glacial acetic acid : water 10:0.5:0.5:1.3 was used as mobile phase for the separation of flavanoids. The flavanoid fingerprint of leaf exhibited 9 peaks each in methanolic neutral, acidic and basic fractions, while that of root exhibited 8 in neutral, 11 in acidic and 7 in basic methanolic extracts. The fingerprint of stem revealed the presence of 8 peaks in neutral, 9 in acidic and 7 in basic methanolic fractions. The study revealed diverse forms of flavonoids in large number in the root, stem and leaf of Achyranthes aspera Linn. It can be concluded that HPTLC fingerprint analysis of root, stem and leaf extract of Achyranthes aspera Linn. can be used as a diagnostic tool for the correct identification of the plant and it is useful as a phytochemical marker. Key words : Achyranthes aspera Linn. Root, stem, Leaf , Phytochemical Screening, HPTLC Fingerprinting, flavonoids.
THE STRATEGY OF SMALL AND MEDIUM ENTERPRISES (SMEs) IN UPGRADING SOCIAL MEDIA MARKETING TO CONTENT MARKETING

The era of the internet is moving and developing very fast. The use of social media that had only been used to communicate and socialize turned into a marketing campaign tool. On the other hand, the use of the internet to develop content can also be used as a marketing campaign tool and is known as content marketing. It will be seen how social media marketing is also related to content marketing. The resulting conclusions show how content marketing will complement social media marketing so that it can be used for the development of small and medium businesses.
Capacitor theory

A new linear capacitor model is proposed. It is based on Curie's empirical law of 1889 which states that the current through a capacitor is i(t)=U/sub 0//(h/sub 1/t/sup n/), where h/sub 1/ and n are constants, U/sub 0/ is the dc voltage applied at t=0, and 0 >
In-Depth Atomic Structure of the Pentacene/Cu(110) Interface in the Monolayer Coverage Regime: Theory and X-ray Diffraction Results

The atomic structure of the pentacene/Cu(110) interface for coverages at and just below one monolayer has been determined by surface X-ray diffraction (SXRD), supported by state-of-the-art density functional theory (DFT) calculations. The in-depth sensitivity of SXRD to atom positions allows tracking the adsorption-induced distortions down to the fifth substrate layer. The main feature of the DFT model, namely, the buckling induced in the substrate, is fully confirmed by the experiment. The considerable atomic displacements which are the same for the two coverages under investigation are a signature of the strong molecule–substrate interaction, indicative of an adsorption mechanism of chemisorption type.
The influence of gingival phenotype in the morphology of the central maxillary interdental papilla

BackgroundPreservation of interdental papilla is an essential part of the functional and esthetic rehabilitation of dental treatment. It was described that thicker gingival tissues are more resistant to recession. The main objective of this investigation was to analyze if the gingival phenotype represents a potential risk factor affecting interdental papilla fill, height, or width in an esthetic region between central maxillary incisors. The secondary goals were: 1) analyze parameters describing papilla and the influence of a wide papilla basis for the vertical papillary dimension; 2) the correlation between different non-invasive measurements of gingival thickness; 3) comparison among both sexes.MethodsTogether, 54 periodontally healthy students (20–30 years old) were included in the study. Gingival thickness was measured using Pirop Ultrasonic Biometer. Phenotype was also assessed by gingival probe transparency. Papilla parameters - height and width of the interdental papilla were measured, and the degree of papilla recession was classified.ResultsNo significant correlation between papilla height, width, or papilla fill and gingival probe transparency or gingival thickness was found. Both methods of assessments of gingival phenotype showed a significant relationship. There was a significant correlation of papilla height and papilla fill. Papillae, which filled the interdental space completely, seem to be shorter. A strong positive association between papilla height and papilla width was revealed. Papillae were significantly higher in males.ConclusionThe appearance of interdental papillae could be influenced by various factors. Within the limitations of this study, results showed no significant correlation between the morphology of interdental papillae and soft tissue thickness. It seems that there is an influence of a wide papilla basis for its vertical dimension.