Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformative teacher leadership experiences in schools in creating an innovative educational culture: The case of Lithuania

Abstract The importance of teacher leaders has been increasingly emphasized in innovative educational practices. In this study, we present the experiences of innovative teacher leaders (N = 10) in Lithuania. The sample of respondents is qualitative-purposive. Teachers who have been awarded for their innovative practice took part in the study. In addition, teachers nominated by their school communities as innovative educators who are making a difference in their schools were also included. The data analyzed by the method of critical analysis of qualitative research discourse will help the reader understand what helps teacher leaders develop innovative educational culture practices. Additionally, the data reveal how teacher leaders themselves understand leadership, which motivates them to work innovatively. We found that innovative teachers in Lithuania perceive the phenomenon of transformational leadership in four categories, namely: attention to others, initiating change, community building, development, and improvement. The participants in the study also described the meaning of innovative teacher behavior, which is revealed in the analysis of two categories: quality of education and educational change. The third component, innovative organizational culture, was discussed by presenting teachers’ insights on three categories: planning, implementation, and motivation. Teacher leaders support, create and nurture a culture of innovation by motivating and encouraging their colleagues to improve. The findings of this study are useful for school communities wanting to make changes, as they provide answers to what motivates teachers to change their practices and to mobilise other educators for common goals.
Simultaneous inference of multiple plasma parameter profiles by utilizing transport properties

Numerical techniques for the computation of posterior distribution ease restrictions on inference models, and multiple plasma parameters can be simultaneously inferred with detailed modelling of uncertainties. In addition, data that depend on multiple parameters in a complex manner can also be utilized. This paper discusses frameworks that further expand the inference capability by including transport properties in plasmas. A prior belief based on a transport equation helps to localize the posterior distribution, and when ample diagnostic capability is available, even unknown parameters in the transport model can be estimated. Furthermore, these transport parameters can be directly inferred when the profiles of the measurable plasma parameters can be easily calculated for the given transport properties.
Transient dynamics of a massive structure on the seismically oscillating foundation

In the present paper we study an anti-plane nonstationary (transient) problem for a massive structure above the oscillating foundation. The latter is taken as an elastic half-space with an oblique plane seismic wave incoming from below to the boundary surface. The structure is assumed to be considerably more rigid than the foundation. The analytical treatment is achieved by applying the Laplace transform over time. After that the solution in the half-space is constructed by using the Fourier transform along the horizontal coordinate. By satisfying the joining boundary conditions between the rectangle and the foundation, the problem is reduced to an integral equation along the contact zone, which contains the Laplace parameter. We propose a new numerical approach to solve the dual Volterrà—Fredholm integral equations of the arising type. Then we investigate the dynamic properties of the structure for various combinations of physical and geometric parameters.
Change in the dominant decadal patterns and the late 1980s abrupt warming in the extratropical Northern Hemisphere

Widespread abrupt warming in the extratropical Northern Hemisphere (NH) occurred in the late 1980s. This warming was associated with a change in the relative influence of the Pacific Decadal Oscillation (PDO)‐like pattern and the Arctic Oscillation (AO)‐like pattern. The AO‐like pattern has had a dominant influence on the NH‐mean temperature since the late 1980s, whereas the influence of the PDO has weakened. The AO‐like mode appears as part of natural variability in the pre‐industrial simulations of the CMIP3/IPCC climate models. However, its emergence in the late 1980s was not simulated by most models with or without the observed increasing greenhouse effect in the 20th century. Copyright © 2010 Royal Meteorological Society
Why the Cross-Lagged Panel Model Is Almost Never the Right Choice

The cross-lagged panel model (CLPM) is a widely used technique for examining reciprocal causal effects using longitudinal data. Critics of the CLPM have noted that by failing to account for certain person-level associations, estimates of these causal effects can be biased. Because of this, models that incorporate stable-trait components (e.g., the random-intercept CLPM) have become popular alternatives. Debates about the merits of the CLPM have continued, however, with some researchers arguing that the CLPM is more appropriate than modern alternatives for examining common psychological questions. In this article, I discuss the ways that these defenses of the CLPM fail to acknowledge well-known limitations of the model. I propose some possible sources of confusion regarding these models and provide alternative ways of thinking about the problems with the CLPM. I then show in simulated data that with realistic assumptions, the CLPM is very likely to find spurious cross-lagged effects when they do not exist and can sometimes underestimate these effects when they do exist.
Parallel Computation of the Flow of Integral Viscoelastic Fluids On a Heterogeneous Network of Workstations

We consider the parallel computation of flows of integral fluids on a heterogeneous network of workstations. The proposed methodology is relevant to computational mechanics problems which involve a compute-intensive treatment of internal variables (e.g. fibre suspension flow and deformation of viscoplastic solids). The main parallel computing issue in such applications is that of load balancing. Both static and dynamic allocation of work to processors are considered in the present paper. The proposed parallel algorithms have been implemented in an experimental, parallel version of the commercial POLYFLOW package developed in Louvain-la-Neuve. The implementation uses the public domain PVM software library (Parallel Virtual Machine), which we have extended in order to ease porting to heterogeneous networks. We describe parallel efficiency results obtained with three PVM configurations, involving up to seven workstations with maximum relative processing speeds of five. The physical problems are the stick/slip and abrupt contraction flows of a K.B. K.Z. integral fluid. Using static allocation, parallel efficiencies in the range 67%-85% were obtained on a PVM network with four workstations having relative speeds of 2:1:1:1. Parallel efficiencies higher than 90% were obtained on the three PVM configurations using the dynamic load-balancing schemes.
Methods and Applications of Power Series

solutions. The series solution, with its great accuracy, permits study of the analytic properties of the solution to an extent which is unachievable with a finite difference solution; and the series solution can be used as an intermediate result which can be integrated and differentiated easily. If a finite difference solution is only an intermediate step iil the solution of a problem, computer storage problems can be a major concern. Derivatives and interpolated values of the difference solution can be very unreliable. In this paper we intend to show how many of the disadvantages of power series can be overcome by automatic coding procedures and to indicate some of their useful properties and results. In this paper we use the convention that a sum is zero if the upper limit is less than the lower. Let P(I) denote the coefficient of x' 1 in the polynomial P(x) = EJ= x and let Q(I, J) denote the coefficient of x'1lyJ-l in the polynomial Q(x, y) = N =1 EN 1 Qu7 i)XI-1 J-1 The following formulas for integration and differentiation are well known: If Q(x, y) = fA P(x, y) dx or R(x, y) = ' P(x, y) dy then
12W CW operation of 640nm-band laser diode array

A high-power and short-wavelength GaInP/AlGaInP quantum-well laser diode array was designed and fabricated. Because a conduction band offset of this material system is small, a carrier leakage from an active layer is an important limiting factor of the maximum light output. In this work, long cavity length of 1.5 mm, high front facet reflectivity of 18% and AlInP cladding layers were adopted to reduce the leakage. An evaluation test of the fabricated array was performed under CW operation. At 15°C, high light output of 12W was obtained with injection current of 16A. The lasing wavelength was 643.3 nm. Moreover, high wall-plug efficiency of 34% was achieved. These excellent characteristics are considered to be due to the effective suppression of the carrier leakage.
Wipe Scene Change Detection in Object-Camera Motion Based on Linear Regression and an Inflated Spatial-Motion Neural Network

To facilitate content-based video analysis, automatic scene change detection (SCD) with large-scale motion activity is an essential fundamental step for locating a transition from one video scene to another. With the exponential increase in digital media usage, SCD has become more challenging in processing large motion content with minimal information loss and maximum perseverance. Wipe SCD in object-camera motion is noticeable evidence of this issue. Wipe transitions, which are a type of gradual transition, have diverse motion pattern changes when influenced by object-camera motion (camera pan, large-object, and zoom-in/out), creating a velocity imbalance in the same frame. Furthermore, this motion imbalance leads to false detection. Due to the loss of motion information and longer processing time of existing frameworks, we propose a novel method of wipe scene change detection (WSCD) based on deep spatial-motion feature analysis. First, large input videos are segmented into shots using dimensionality reduction and adaptive threshold. Secondly, linear regression is used to compute slope angle changes in shots for candidate selection and wipe localization. Finally, only selected candidates are processed to extract features using a two-stream inflated 3D-convolutional neural network for RGB stream and optical flow velocity for motion stream network (I3DCNN) and then classified into wipe in-motion and no-motion clips. The experimental results are obtained by classifying wipe patterns using a detection reviewing and merging strategy on corresponding wipe frames. The average improvement in wipe scene change detection accuracy evaluated on the benchmark TRECVID dataset is 11.9%, demonstrating the efficacy of our proposed method.
Contribution of polar plumes to fast solar wind

Context. Several physical properties of solar polar plumes have been identified by different published studies, however such studies are rare and sometimes in disagreement. Aims. The purpose of the present work is to analyze a set of SOHO/UVCS data dedicated to the observation of plumes and to obtain a picture of the physical properties of plumes in the intermediate solar corona through a self-consistent analysis. Methods. We applied the Doppler Dimming technique to data acquired by SOHO/UVCS in April 1996, which was during the very early phases of the mission. From this we derived outflow speeds and electron densities. We used SOHO/LASCO images as context data in order to better identify plume and interplume regions in the UVCS field of view. Results. The results we obtain demonstrate that in three cases out of four plumes expand with outflow speeds comparable to those of interplumes, and in a single case with lower speeds. We estimate that the contribution of plumes to the wind coming from the solar poles is about 20%, and that different plumes provide a different contribution, possibly according to different stages of their evolution. Conclusions. We conclude that plumes are not static structures, and that they contribute significantly to the wind coming from the solar poles.
Analysis of Super-Solar Integrated Patch Antenna for Sub-6 GHz and Beyond 6 GHz Millimeter Wave 5G Applications

This article describes a new compact parasitic patch-loaded transparent patch antenna with a copper ground plane for wireless-fidelity (Wi-Fi) and 5thgeneration (5G) millimeter-wave (mm-wave) applications. The proposed antenna uses two rectangular parasitic patches with a rectangular main radiation patch. The L-shaped strips are also added to the main radiation patch and one of the rectangular parasitic patches to cover both the sub-6 GHz and beyond 6 GHz mm-wave 5G frequency spectrums. The same transparent patch antenna with a solar ground plane is built, and its effect is parametrically studied alongside the integration of a polycrystalline silicon solar cell. The proposed antennas with a dimension of 42x30x2 mm23 are fabricated and experimentally validated for impedance and radiation characteristics. In terms of impedance bandwidth, the proposed copper ground plane antenna offers 36.89% (5.04-7.32 GHz), 5.15% (14.35-15.11 GHz), 6.23% (27.08-28.79 GHz), and 21.34% (31.64-39.81 GHz). The solar cell serves as both a photovoltaic generator and the ground plane of the transparent antenna. The same radiating patch with a solar ground plane offers impedance bandwidth of 36.03% (4.47-6.56 GHz), 14.4% (9.6-11.12 GHz), 2.55% (22.14-22.71 GHz), and 27.9% (28.79-39.05 GHz) for 5G applications.
Quantitative reverse stress testing, bottom up

We propose a bottom-up quantitative reverse stress testing framework that identifies forward-looking fragilities tailored to a bank's portfolio, credit and funding strategies, models, and calibration constraints. Thus, instead of relying on historical events, we run a Monte Carlo simulation, and we mine those future states that contribute the most to a bank's cost of capital expressed in terms of scenario differential. This approach allows identifying both the systemic and idiosyncratic weaknesses of the bank's portfolio, with applications that include solvency risk, extreme events hedging, liquidity risk management, trading and credit limits, model validation and model risk management.
Expertise on Offer: Why Isn’t Anyone Buying?

It is a much-lamented fact that research with the potential to inform or influence education policy instead remains policy inert. There are many reasons for this frustrating state of affairs, including a lack of strategic thinking on the part of researchers on how to successfully accomplish outreach—as opposed to communication with peers (in-reach). Another, and a principal focus of this article, is the failure of researchers to appreciate the power of employing compelling narratives to bring their findings to the attention of policymakers and other stakeholders. Accordingly, this article presents some examples of narratives specifically designed for outreach and discusses some of their features. It also considers the challenges in gaining traction with counternarratives once a particular narrative has achieved currency. Researchers should also be mindful of the tenor of the times, with experts now often viewed with skepticism, if not downright hostility. In some quarters, excessive reliance on technocrats is even seen as a threat to democratic governance. The article concludes with some recommendations on how to appropriately enhance the role of research in education policymaking.
Equity-Market-Neutral Strategy Portfolio Construction Using LSTM-Based Stock Prediction and Selection: An Application to S&P500 Consumer Staples Stocks

In recent years, a great deal of attention has been devoted to the use of neural networks in portfolio management, particularly in the prediction of stock prices. Building a more profitable portfolio with less risk has always been a challenging task. In this study, we propose a model to build a portfolio according to an equity-market-neutral (EMN) investment strategy. In this portfolio, the selection of stocks comprises two steps: a prediction of the individual returns of stocks using LSTM neural network, followed by a ranking of these stocks according to their predicted returns. The stocks with the best predicted returns and those with the worst predicted returns constitute, respectively, the long side and the short side of the portfolio to be built. The proposed model has two key benefits. First, data from historical quotes and technical and fundamental indicators are used in the LSTM network to provide good predictions. Second, the EMN strategy allows for the funding of long-position stocks by short-sell-position stocks, thus hedging the market risk. The results show that the built portfolios performed better compared to the benchmarks. Nonetheless, performance slowed down during the COVID-19 pandemic.
A DMP-Based Motion Generation Scheme for Robotic Mirror Therapy

Hemiparesis has become prevalent in recent years, and robot-assisted rehabilitation provides great therapeutic potential. Robotic mirror therapy is a promising approach for hemiplegic patients by transmitting the motion of the healthy limb (HL) to the impaired limb (IL), where the wearable robot assists the IL to mimic the HL's action to stimulate the active participation of the injured muscles. However, complete replication of the HL's movement trajectory without considering the IL's muscle strength is not satisfactory for rehabilitation. In this article, a motion generation scheme is proposed for robotic mirror therapy. The robot's movement trajectory is modeled with dynamic movement primitive (DMP), and the physical human–robot interaction is formulated as an impedance model and coupled with the DMP model. The subject's muscle strength is evaluated with skin surface electromyography signals and transferred to the robot's joint stiffness. In order to adapt the human–robot coupled DMP model parameters to varying subject performance, a reinforcement learning algorithm is designed to optimize the robot's movement trajectory and stiffness profile, where the training safety and rehabilitation improvement are both guaranteed. The proposed method is validated using a lower extremity rehabilitation robot, and experimental results demonstrate its feasibility and superiority.
Integrating Theory with Education about Genetic Risk for Alcohol Use Disorder: The Effects of a Brief Online Educational Tool on Elements of the Health Belief Model

Introduction: The utility of genetic risk information relies on the assumption that individuals will use the information to change behavior to reduce risk of developing health problems. Educational interventions designed to target elements of the Health Belief Model have shown to be effective in promoting behaviors for positive outcomes. Methods: A randomized controlled trial (RCT) was conducted in 325 college students to assess whether a brief, online educational intervention altered elements of the Health Belief Model that are known to be associated with motivations and intentions to change behavior. The RCT included a control condition, an intervention condition that received information about alcohol use disorder (AUD), and an intervention condition that received information about polygenic risk scores and AUD. We used t tests and ANOVA methods to compare differences in beliefs related to the Health Belief Model across study conditions and demographic characteristics. Results: Providing educational information did not impact worry about developing AUD, perceived susceptibility and severity of developing alcohol problems, or perceived benefits and barriers of risk-reducing actions. Individuals in the condition that received educational information about polygenic risk scores and AUD reported higher perceived chance of developing AUD than individuals in the control condition (adj. p < 0.01). Sex, race/ethnicity, family history, and drinking status were associated with several components of the Health Belief Model. Conclusion: Findings from this study demonstrate the need to better design and refine the educational information intended to accompany the return of genetic feedback for AUD to better promote risk-reducing behaviors.
3D finite-element simulations of enhanced light transmission through arrays of holes in metal films

Light transmission through a 2D-periodic array of small rectangular apertures in a film of highly conductive material is simulated using a finite-element method. It is demonstrated that well converged results are obtained using higher-order finite-elements. The influence of the array periodicity and of corner roundings on transmission properties is investigated.
Substation priority maintenance planning based on genetic ant colony algorithm

In the treatment of substations, it is very crucial to make a reasonable arrangement of route used for the maintenance of each substation. Moreover, given the urgency degree of different substations, the priority of each substation should be carefully considered for a good arrangement of route used for the maintenance. In this paper, considering the complexity of the routing arrangement, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) were adopted with the designed priority coding methods and priority constraints for a more reasonable arrangement of route. Moreover, with the analysis of the performances of GA and ACO on the priority-based routing arrangement, a fused method was designed to obtain a good routing arrangement in an efficient manner. The experimental results show that, with the designed priority coding method and the priority constraints, a more reason result can be obtained by the fusion-based method.