Miracle-dz/UniFAR-FTUs huggingface dataset:
Weak Shock Propagation with Accretion. III. A Numerical Study on Shock Propagation and Stability

Core-collapse supernovae span a wide range of energies, from much less than to much greater than the binding energy of the progenitor star. As a result, the shock wave generated from a supernova explosion can have a wide range of Mach numbers. In this paper, we investigate the propagation of shocks with arbitrary initial strengths in polytropic stellar envelopes using a suite of spherically symmetric hydrodynamic simulations. We interpret these results using the three known self-similar solutions for this problem: the Sedov–Taylor blast-wave describes an infinitely strong shock, and the self-similar solutions from Coughlin et al. (Papers I and II) describe a weak and infinitely weak shock (the latter being a rarefaction wave). We find that shocks, no matter their initial strengths, evolve toward either the infinitely strong or infinitely weak self-similar solutions at sufficiently late times. For a given density profile, a single function characterizes the long-term evolution of a shock’s radius and strength. However, shocks with strengths near the self-similar solution for a weak shock (from Paper I) evolve extremely slowly with time. Therefore, the self-similar solutions for infinitely strong and infinitely weak shocks are not likely to be realized in low-energy stellar explosions, which will instead retain a memory of the shock strength initiated in the stellar interior.
Cardinality-Based Abnor m al Traffic Classification Using Packet Sa m pling

Network administrators strive to detect abnormal traffic such as distributed denial of service ( DDoS ) attacks and Internet worms, as soon as it occurs. Cardinality analysis is a method to detect such abnormal traffic. Conventional cardinality analysis has a scalability problem as Internet traffic increases beyond the class of 100 Gbps. To address this problem, we propose in this paper cardinality analysis using sampled packets to reduce the amount of packets to be analyzed up to 1/1000. We compared the number of cardinality count reports using sampled Internet traffic and unsampled Internet traffic. The results show that several kinds of abnormal traffic disturbing important communication can be discovered using the sampled packets.
The Role of Social Capital in Competition and Gender-Matching Environments - Evidence from East Asian Countries

This paper investigates the role of social trust in determining one’s willingness to compete in math competitions in school, observing how competitive occupational choices often require higher quantitative skills. Using the data of the PISA test in math, the empirical results highlight that a higher level of trust in school environments promotes a student’s willingness to participate in math competitions. However, this positive effect of trust maintains mainly in mixed-sex competition, but not in single-sex competition. Furthermore, the effect of trust on mixed-sex competition is greater for girls than boys, while the effect is equally insignificant for boys and girls in single-sex competition. These findings suggest the importance of trust in the rules of the game when girls are matched with boys. On the other hand, when they compete with other girls, concerns about fairness do not play a significant role in their consideration. This is possibly because single-sex matches reduce unfairness caused by gender discrimination.
A one-sided version of the Poisson sum formula for semi-infinite array Green's functions

The Poisson sum formula provides an efficient method for transforming many slowly converging infinite summations into equivalent, but more rapidly converging infinite summations. Electromagnetic applications for this result come in the analysis of infinite arrays of periodic scatterers, such as frequency selective surfaces. However, in some applications, such as when one desires the radiation of a semi-infinite array of periodically spaced currents, the original form of the Poisson sum formula is inappropriate. For such applications, we derive a one-sided version of the formula and apply it to the radiation from a semi-infinite array of line sources with currents dictated by Floquet's theorem. The one-sided Poisson sum transformation yields enhanced convergence characteristics for certain regions of application as a result of the inverse bandwidth relationship between Fourier transform pairs. The application of it to a semi-infinite line source array also provides a plane wave representation for the fields, which makes for an extension of the solution to geometries with stratified dielectric media.
Application of Artificial Neural Network and Information Gain in Building Case-Based Reasoning for Telemarketing Prediction

Traditionally, case-based reasoning (CBR) has been used as advanced technique for representing expert knowledge and reasoning. However, for stochastic business data such as customers’ behavior and users’ preferences, the knowledge cannot be extracted directly from data to build the cases in reasoning in making prediction. Artificial Neural Network that is known to be able to build model for predicting unprecedented business data is used together with Shannon Entropy and Information Gain (IG) to identify the key features. 8 attributes have been identified as key features from the 17 attributes which are based on the telemarketing data. These attributes are used to select the key features in building CBR. The weightage for the key features in the cases is obtained from the IG values. The mechanism of creating the cases based on the input from the ANN is discussed and the integration process between ANN and CBR is given. The process of integrating the ANN and CBR shows that both techniques complement each other in building a model in predicting a customer who would subscribe one of the promoted new banking service called “term deposit”.
An Algorithm for Calculations of Low Frequency Transformer Transients

An algorithm for the calculations of low frequency transformer transients such as inrush current and ferroresonance is developed in this paper. The transformer nonlinearity is represented by nonlinear magnetizing inductance in parallel with nonlinear core loss resistance. Nonlinear curves: magnetizing current - flux linkage and core loss current - supply voltage are piecewise linearized. The stiff differential equation system, which describes transients of electrical circuit, is solved by the A and L-stable backward differentiation formulas numerical method. It is shown that the BDF method completely eliminates numerical oscillation events. Simulation results of the developed algorithm are compared with the results obtained by Matlab/Power System Blockset and also with field measurements during a transformer energization. The proposed algorithm could be successfully applied on numerical calculations of transients with some other nonlinear elements such as surge arresters, power electronic elements, etc.
Two-Stage Stochastic Optimization for the Strategic Bidding of a Generation Company Considering Wind Power Uncertainty

With the deregulation of electricity market, generation companies must take part in strategic bidding by offering its bidding quantity and bidding price in a day-ahead electricity wholesale market to sell their electricity. This paper studies the strategic bidding of a generation company with thermal power units and wind farms. This company is assumed to be a price-maker, which indicates that its installed capacity is high enough to affect the market-clearing price in the electricity wholesale market. The relationship between the bidding quantity of the generation company and market-clearing price is then studied. The uncertainty of wind power is considered and modeled through a set of discrete scenarios. A scenario-based two-stage stochastic bidding model is then provided. In the first stage, the decision-maker determines the bidding quantity in each time period. In the second stage, the decision-maker optimizes the unit commitment in each wind power scenario based on the bidding quantity in the first stage. The proposed two-stage stochastic optimization model is an NP-hard problem with high dimensions. To tackle the problem of “curses-of-dimensionality” caused by the coupling scenarios and improve the computation efficiency, a modified Benders decomposition algorithm is used to solve the model. The computational results show the following: (1) When wind power uncertainty is considered, generation companies prefer higher bidding quantities since the loss of wind power curtailment is much higher than the cost of additional power purchases in the current policy environment. (2) The wind power volatility has a strong negative effect on generation companies. The higher the power volatility is, the lower the profits, the bidding quantities, and the wind power curtailment of generation companies are. (3) The thermal power units play an important role in dealing with the wind power uncertainty in the strategic bidding problem, by shaving peak and filling valley probabilistic scheduling.
Indicators of improved learning contexts: a collaborative perspective on educational leadership

The aim of this paper is to illustrate indicators for improved learning contexts based on the notion of collaborative leadership. Collaborative leadership refers to synergetic educational leadership practices through co-performance and a continuous and shared learning process. We argue that the indicators could serve as a bridge between education and the complex and increasing change within the social life and world of work. For finding the indicators, we combined two intertwining dimensions: ‘leadership in education’ and ‘education for leadership’. They were studied by a tested model describing collaborative leadership by a set of attributes. We then applied these attributes to a curriculum reform in a global business school, which aims at educating future leaders. Our data consist of the curricular narrative and interviews with the expert group that designed the curriculum. As a result of a qualitative content analysis, we found altogether 31 indicators, of which we introduce the ones that were related to the most frequent attributes of collaborative leadership. However, we suggest that some or even all of the indicators could be adaptable to other learning contexts at other educational levels.
Numerical Analysis of Thermal Management for High Power LED Array

Adequate thermal management to remove and dissipate the heat produced by the LED is one of the main challenges in designing LED applications. In view of the above problems, this paper analyzed a heat sink as a heat exchanger for the LED array via the experiment combined with the numerical simulation. The results show that the heat sink is necessary for the LED array to guarantee reliable and safe operation. Moreover, the influence of the height of heat sink on the heat transfer of the LED array is also analyzed, and the optimized height of the heat sink for the 20W LED array is 20 mm. Considering the heat transfer and the manufacturing cost, increasing the heat sink area blindly is not the best way to reduce the LED junction temperature, and more specific work should be considered.
Understanding the temporal pattern of spreading in heterogeneous networks: Theory of the mean infection time

For a reliable prediction of an epidemic or information spreading pattern in complex systems, well-defined measures are essential. In the susceptible-infected model on heterogeneous networks, the cluster of infected nodes in the intermediate-time regime exhibits too large fluctuation in size to use its mean size as a representative value. The cluster size follows quite a broad distribution, which is shown to be derived from the variation of the cluster size with the time when a hub node was first infected. On the contrary, the distribution of the time taken to infect a given number of nodes is well concentrated at its mean, suggesting the mean infection time is a better measure. We show that the mean infection time can be evaluated by using the scaling behaviors of the boundary area of the infected cluster and use it to find a nonexponential but algebraic spreading phase in the intermediate stage on strongly heterogeneous networks. Such slow spreading originates in only small-degree nodes left susceptible, while most hub nodes are already infected in the early exponential-spreading stage. Our results offer a way to detour around large statistical fluctuations and quantify reliably the temporal pattern of spread under structural heterogeneity.
Favorable Conditions and Electoral Revolutions

From 1996 to 2005 a wave of electoral revolutions swept through east Central Europe, the Balkans, and Soviet successor states. The success of these revolutions and their concentration in the post-communist world reflect favorable political and social conditions, as well as the fact that the common structures and policies of communist regimes created unusually good conditions for diffusion of the electoral model after communism’s end. These structural conditions led international donors to concentrate democracy assistance in this region. Efforts to support electoral revolutions in countries with less supportive conditions and less favorable attitudes toward the West and democracy are likely to be more problematic
Utilisation of Business Advice in Small Innovative Firms: the Role of Trust and Tacit Knowledge

Objective : The aim of this paper is to expand the understanding of the influence of trust relationship, as well as tacit knowledge on the process of business advice utilisation in small innovative firms. Research Design & Methods : The analysis is based on empirical research conducted with the use of the explorative approach. The multiple case study methodology was used. Findings: Trust relationships as well as tacit knowledge were identified as factors essential to the process of utilisation of business advice in small innovative firms. It was recognised that the significance of both factors is related not only to the process of advice, but also as a purpose of activities happening prior to advice. They are referred to as the initial phase. Implications & Recommendations: The findings provide implications for further research of the structure of the process of business advice utilisation. It is related to the inclusion of the “initial phase”, as well as to the necessity to include both factors in further research. The paper also provides implications related to the measurement of business advice output, and recommendations on the construction of public policy instruments. Contribution & Value Added: The originality of this work lies in using the process perspective and qualitative methodology in the field of the utilisation of business advice. It extends our understanding of the processes of business advice with regard to trust, as well as the use of tacit knowledge. The original contribution is to add the “initial phase” to the description of the business advice process.
Self-definitions of Gang Membership and Involvement in Delinquent Activities

There is significant disagreement among researchers as to the appropriate conceptual and operational definitions of gang membership. One of the key issues involves the validity of allowing respondents to identify themselves as gang members. This research examines the construct validity of gang membership by examining the relationship between various methods of operationalizing gang membership and delinquent involvement. The results demonstrate that there are important consequences to the method utilized to measure gang membership. Individuals reporting membership in organized gangs were far more likely to report that their gangs possess the characteristics typically associated with traditional street gangs. Likewise, the respondent's self-identification had a strong impact on both the group's and the individual's criminal behavior. Overwhelmingly, persons who considered themselves to be members of an organized gang were more apt to engage in all types of delinquent activities.
Effects of Clinacanthus nutans Extracts on Cytokine Secretion in PMA-Induced U937 Macrophage Cells

Background and objectives: Clinacanthus nutans (Burm f.) Lindau (C. nutans) is a well-known traditional medicine in South East Asia and consists of abundant phytomedicinals properties. This study aimed to investigate the effects of C. nutans ethanol and aqueous extracts on interleukin-4 (IL-4) and interleukin-13 (IL-13) cytokines secretion in phorbol 12-myristate 13-acetate phorbol-12-myristate-13-acetate (PMA)-induced U937 macrophages. Methods: Sequential ultrasonic-assisted extraction was carried out using ethanol (ETOH) and water, by applying 1:10 ratio of leaves powder to the solvent volume. U937 cells were incubated with 25 nM PMA for 72 h to induce macrophage differentiation. The macrophage differentiation was assessed based on the cell morphological changes, cell viability and, CD14 and CD11b expression by using flow cytometry. The macrophages were incubated with both ETOH and aqueous extracts at 0.25, 0.5, 1.0, 2.0, 4.0 and 8.0 mg/mL concentration for 48 h. The viability of the extract-treated cells was assessed using PrestoBlue cell viability assay and the IL-4 and IL-13 secretions were assessed by using Enzyme-Linked Immunosorbent Assay (ELISA). Results: PMA stimulation caused morphological changes of U937 cells from round-shaped, non-adherent to larger irregular-shaped, adherent cells, and a reduction of cells viability to 87%. CD14 expression was down-regulated from 7% to 4.5% upon PMA stimulation. CD11b expression was up-regulated from 16% in untreated cells to 38% in PMA-treated cells. ELISA results showed that 1 mg/mL of ETOH and AQ extracts stimulated 1200 and 1800 pg/mL IL-4 secretions, respectively. However, both extracts caused minimal IL-13 secretion. Conclusion: Clinacanthus nutans aqueous extracts stimulated IL-4 production higher than ETOH extract in PMA-induced U937 macrophages.
Optimal harvesting in a changing climate

Choosing the right harvesting strategy is crucial for sustainable utilization of biological resources but is challenging in real systems with fluctuating environments. This challenge is expected to become even greater with global warming, as environmental variability is predicted to increase. Additionally, harvesting strategies based on single-species models have been shown to carry a severe risk of species extinctions when species interactions are ignored, particularly in systems with more than one harvested species. As the climate continues to warm, we therefore need new reference points for harvesting in an ecosystem context with environmental fluctuations. In this paper, we discuss the development of harvesting strategies and suggest that a proportional threshold harvesting framework could be a useful starting point for developing such reference points and tackling the challenge of sustainable harvesting in the future.
Parametrization of quantum states and the quantum state discrimination problem.

A discrimination problem consists of $N$ linearly independent pure quantum states $\Phi=\{\ket{\phi_i}\}$ and the corresponding occurrence probabilities $\eta=\{\eta_i\}$. To any such problem we associate, up to a permutation over the probabilities $\{\eta_i\}$, a unique pair of density matrices $\boldsymbol{\rho_{_{T}}}$ and $\boldsymbol{\eta_{p}}$ defined on the $N$-dimensional Hilbert space $\mathcal{H}_N$. The first one, $\boldsymbol{\rho_{_{T}}}$, provides a new parametrization of a generic full-rank density matrix in terms of the parameters of the discrimination problem, i.e. the mutual overlaps $\gamma_{ij}=\bra{\phi_i}\phi_j\rangle$ and the occurrence probabilities $\{\eta_i\}$. The second one is defined as a diagonal density matrix $\boldsymbol{\eta_p}$ with the diagonal entries given by the probabilities $\{\eta_i\}$ with the ordering induced by the permutation $p$ of the probabilities. $\boldsymbol{\rho_{_{T}}}$ and $\boldsymbol{\eta_{p}}$ capture information about the quantum and classical versions of the discrimination problem, respectively. In this sense, when the set $\Phi$ can be discriminated unambiguously with probability one, i.e. when the states to be discriminated are mutually orthogonal and can be distinguished by a classical observer, then $\boldsymbol{\rho_{_{T}}}\rightarrow \boldsymbol{\eta_{p}}$. Moreover, if the set lacks its independency and cannot be discriminated anymore the distinguishability of the pair, measured by the fidelity $F(\boldsymbol{\rho_{_{T}}}, \boldsymbol{\eta_{p}})$, becomes minimum. This enables one to associate to each discrimination problem a measure of discriminability defined by the fidelity $F(\boldsymbol{\rho_{_{T}}}, \boldsymbol{\eta_{p}})$. This quantity, has the advantage of being easy to calculate and in this respect it can find useful applications in estimating the extent to which the set is discriminable.
A Self-Evolving Fuzzy System Which Learns Dynamic Threshold Parameter by Itself

This paper proposes an online learning algorithm for data streams, namely self-evolving fuzzy system (SEFS). Unlike the fixed control parameters commonly used in evolving fuzzy systems, the SEFS uses online training errors, which measure the quality of an identified model in presenting the dynamics of the data stream, to set a dynamic threshold automatically for rule generation. This self-tuning parameter, which controls the speed and coverage for fuzzy rule generation, helps the SEFS properly deal with the underfitting/overfitting problems relying on two facts: 1) Large training errors present an underfitted model, which is too coarse to represent the highly complicated and rapidly dynamic (e.g., highly nonlinear, nonstationary) behavior of the data segment. Then, finer rules need to be added; and 2) tiny training errors reflect an overfitted model, which can ideally represent any slight dynamic behavior of the data stream. In this case, coarse rule base should be used. Besides, an $L^{2}$-distance-based geometric similarity measure is proposed in the rule merging phase. With this similarity measure, the SEFS computes the similarity between Gaussian membership functions accurately without making an approximation of the Gaussian membership function beforehand. In addition, a weighted recursive least-squares algorithm with a variable forgetting factor, which minimizes the mean square of the noise-free posterior error signal, is applied to learn the consequent parameters. Several benchmark examples across both artificial and real-life datasets verify that the SEFS has the ability to give better performance compared with many state-of-the-art algorithms.
Comparative Study Of Different Hysteresis Modulation Methods Of Multilevel Inverters

The hysteresis modulation for power electronic converters is attractive in many different applications because of its unmatched dynamic response and wide commandtracking bandwidth. Its application and benefits for twolevel converters are well understood, but the extension of this strategy to multilevel converters is still under development. This paper summarizes and reviews the various hysteresis modulation approaches available in the literature for multilevel converters. The pros and cons of various techniques are described and compared for tracking the reference signal in order to attain an adequate switching optimization, excellent dynamic responses and high accuracy in steady-state operation. By using the recently developed multilevel hysteresis modulation approaches, the advantages of using several accessible dc potentials in a multilevel inverter have been fully exploited. All of these hysteresis modulation approaches are tested for tracking a current reference when applied to a five-level inverter. The relevant simulation and experimental results are also presented. This study will provide a useful framework and point of reference for the future development of hysteresis modulation for multilevel converters. Key words—Hysteresis modulation, multiband (MB), multioffsetband (MOB), multilevel converter, time-based