Miracle-dz/UniFAR-FTUs huggingface dataset:
Scanning audiences at laser shows: Theory and practice

For more than three decades, the technique of “audience scanning” has been routinely used at laser light shows outside of the United States. Visible beams from continuous-wave lasers are projected towards viewers, to put them inside cones, fans and other moving light shapes set to music. Most commonly, irradiance levels have not been measured by operators; instead they have been set by eye to look “OK”. Since MPE-level irradiance at the audience is somewhat dim, most shows have exceeded the MPE. Estimated irradiance levels range from 5-10 times the MPE, to 100 times or more.Despite this, there have been remarkably few proven or even claimed reports of injuries from deliberate audience scanning. A number of possible reasons are presented, including the greater distance to the audience (e.g., wider beam), the use of moving beams, and viewers who actively take action to reduce exposure. Finally, a compromise proposal is presented for regulators and venue operators, in order to allow audience scanning at measured, reasonable irradiance levels, in return for banning truly unsafe or unknown levels.For more than three decades, the technique of “audience scanning” has been routinely used at laser light shows outside of the United States. Visible beams from continuous-wave lasers are projected towards viewers, to put them inside cones, fans and other moving light shapes set to music. Most commonly, irradiance levels have not been measured by operators; instead they have been set by eye to look “OK”. Since MPE-level irradiance at the audience is somewhat dim, most shows have exceeded the MPE. Estimated irradiance levels range from 5-10 times the MPE, to 100 times or more.Despite this, there have been remarkably few proven or even claimed reports of injuries from deliberate audience scanning. A number of possible reasons are presented, including the greater distance to the audience (e.g., wider beam), the use of moving beams, and viewers who actively take action to reduce exposure. Finally, a compromise proposal is presented for regulators and venue operators, in order to allow audience scanning at meas.
Strain engineering in functional 2-dimensional materials

Two-dimensional (2D) materials have attracted growing interest in the past decade because of their extraordinary properties and great potential in a wide range of applications. Strain is regarded as a simple yet powerful tool to modulate the properties of 2D materials, as it directly affects lattice structures and thus alters electronic structures. In this tutorial, we first discuss the fundamentals of strain engineering in 2D materials from macro and atomic perspective and then review some approaches to strain engineering as well as their merits and drawbacks. After that, we examine in detail how strain modulates physical and chemical properties in various types of 2D materials. In the last section of this tutorial, the applications of strain engineering in functional 2D materials are exhibited.
Prediction of Euroleague Games based on Supervised Classification Algorithm k-Nearest Neighbours

Machine learning becomes one of the top fields in world of ICT (Information and communications technology). In the last few decades machine learning has taken a big boost in sports. Sport fans are using machine learning in sport betting and sport managers are using it for player selection, performance evaluation and even in outcome prediction. This paper gives basketball outcome prediction algorithms using k-nearest neighbours (k-nn). Few methods of preparing data calculated with different k and time period for k-nn algorithm will be presented and closely explained. Feature selection will also be discussed and results based on feature selection will be presented.
Change from aerosol-driven to cloud-feedback-driven trend in short-wave radiative flux over the North Atlantic

Abstract. Aerosol radiative forcing and cloud–climate feedbacks each have a large effect on climate, mainly through modification of solar short-wave radiative fluxes. Here we determine what causes the long-term trends in the upwelling short-wave (SW) top-of-the-atmosphere (TOA) fluxes (FSW↑) over the North Atlantic region. Coupled atmosphere–ocean simulations from the UK Earth System Model (UKESM1) and the Hadley Centre General Environment Model (HadGEM3-GC3.1) show a positive FSW↑ trend between 1850 and 1970 (increasing SW reflection) and a negative trend between 1970 and 2014. We find that the 1850–1970 positive FSW↑ trend is mainly driven by an increase in cloud droplet number concentration due to increases in aerosol, while the 1970–2014 trend is mainly driven by a decrease in cloud fraction, which we attribute mainly to cloud feedbacks caused by greenhouse gas-induced warming. In the 1850–1970 period, aerosol-induced cooling and greenhouse gas warming roughly counteract each other, so the temperature-driven cloud feedback effect on the FSW↑ trend is weak (contributing to only 23 % of the ΔFSW↑), and aerosol forcing is the dominant effect (77 % of ΔFSW↑). However, in the 1970–2014 period the warming from greenhouse gases intensifies, and the cooling from aerosol radiative forcing reduces, resulting in a large overall warming and a reduction in FSW↑ that is mainly driven by cloud feedbacks (87 % of ΔFSW↑). The results suggest that it is difficult to use satellite observations in the post-1970 period to evaluate and constrain the magnitude of the aerosol–cloud interaction forcing but that cloud feedbacks might be evaluated. Comparisons with observations between 1985 and 2014 show that the simulated reduction in FSW↑ and the increase in temperature are too strong. However, the temperature discrepancy can account for only part of the FSW↑ discrepancy given the estimated model feedback strength (λ=∂FSW∂T). The remaining discrepancy suggests a model bias in either λ or in the strength of the aerosol forcing (aerosols are reducing during this time period) to explain the too-strong decrease in FSW↑, with a λ bias being the most likely. Both of these biases would also tend to cause too-large an increase in temperature over the 1985–2014 period, which would be consistent with the sign of the model temperature bias reported here. Either of these model biases would have important implications for future climate projections using these models.
Exploring Daily Activity Pattern Using Spatio-Temporal Statistics with R for Predicting Trip Production

Spatio-temporal data modelling is one of the methods in data analysis that uses space (spatial) and time (temporal) approaches. This study used Spatio-temporal statistical modelling to observe the daily activity patterns of people. Spatio-temporal modelling selected for support activity-based transportation demand. This research identifies community mobility patterns that will provide trip production data for transportation demand prediction. Using Spatio-temporal statistical modelling benefit this study because statistical this model can make model components in a physical system appearing to be random. Even if they are not, the models are helpful as they have accurate and precise predictions. In this study, descriptive analysis was used. Incorporating statistical distributions into the model is a natural way to solve the problem. This research collects daily activity data from 400 respondents recorded every 15 minutes. From this data, a pattern of respondents’ daily activities was formed, which was analyzed using R. Software R also visualizes data on daily activities of the community in Spatio-temporal modelling. This research aims to depict the daily activity patterns to predict trip production. This research found three clusters of trip production patterns with specific groups member and specific patterns between workdays and holidays.
Marketing education for sustainable development

Purpose Given the pressing global challenges underpinning the United Nations’ Sustainable Development Goals, marketing managers can no longer focus only on purely economic outcomes but must simultaneously respond to social and environmental concerns. This requires the teaching of new competencies in marketing education, as also reflected in today’s accreditation requirements for business schools. Therefore, this paper aims to explore how current research into marketing education incorporates sustainable development. Design/methodology/approach Through a bibliometric literature review – examining 71 publications using the bibliographic coupling method – the current research front in marketing education is analysed. Findings This paper identifies seven trending topics in marketing education research that both highlight a currently prevalent sustainability gap in marketing education research and – when combined into a framework – help marketing education researchers and educators to address this gap. Originality/value This paper extends the already established concept of education for sustainable development to include the concept of marketing education for sustainable development (MESD) for the first time. The MESD framework combines its raison d’être with guidance on how sustainable development should be taught and what the learning objectives should be for future marketing managers.
Computer simulation of derivative markets using Black-Scholes model

In recent years diverse mathematical physics models became widely used in practice as a great implement for describing economical and physical phenomena. Modeling of the market of derivatives, in particular case, computation of European option rational price, is carried out with Black-Scholes equation, which in terms of constant volatility and non-dividend situation has the well-known analytical solve. Several generalizations of Black-Scholes model were suggested to reach better conformity between the results of mathematical modeling and the real market figures. In these generalized models volatility depends on the desirable function in a rather complicated way. Surveyed are Leland model, Frey-Patie model and also the model of indefinite volatility, which vary from minimum to maximum value. In order to calculate the numerical solution of this mixed task the scheme with scales, which has the second approximation order on the coordinate and time, is used for the equation of parabolic type. Series of computing experiments which allow to compare the results of analytic and numerical analysis were conducted for the different range of parameters. According to the results of computer simulation, generalized non-linear Black-Scholes equation describes the real market simulation describes the real market situation with a very close approximation.
83.25 Descending to the irrational

values, whereas large numbers of prime factors lead to smaller excessiveness values. But, even just looking at the numbers of the form 2~p above, we see that the excessiveness of trapezoidal numbers fluctuates wildly between large positive and large negative values as larger and larger numbers are considered. It certainly seems plausible that it might occasionally chance upon the values 0 ,1 , 2: can any reader shed light on this?
Support vector regression model with variant tolerance

Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius ε -tube, affording good predictive performance on datasets. However, the fixed radius limitation prevents the adaptive selection of support vectors according to the data distribution characteristics, compromising the performance of the SVR-based methods. Therefore, this study proposes an “Alterable ε i -Support Vector Regression” ( A ε i -SVR) model by applying a novel ε , named “Alterable ε i ,” to the SVR model. Based on the data point sparsity at each location, the model solves the different ε i at the corresponding position, and thus zoom-in or zoom-out the ε -tube by changing its radius. Such a variable ε -tube strategy diminishes noise and outliers in the dataset, enhancing the prediction performance of the A ε i -SVR model. Therefore, we suggest a novel non-deterministic algorithm to iteratively solve the complex problem of optimizing ε i associated with every location. Extensive experimental results demonstrate that our approach can improve the accuracy and stability on simulated and real data compared with the baseline methods.
Pulse Burst Multiwavelength Terahertz-Wave Spectroscopic Measurement Using Parametric Wavelength Conversion

In this study, we developed a multiwavelength spectroscopic measurement system using a pulse burst-pumped injection-seeded terahertz (THz)-wave parametric generator (is-TPG). Real-time spectroscopy using multiwavelength simultaneous generation has been demonstrated; however, the ability to make quantitative measurements with this method has been limited by the gain competition between wavelengths. In this study, we divided the pump beam into multiple paths and created pulse bursts with nanosecond intervals via spatial delays. Quasi-multiwavelength simultaneous generation was achieved by generating different THz wavelengths with each pulse; in principle, this is equal to repetitive single-wavelength generation. The gain competition between multi-wavelengths was suppressed compared to the multiwavelength is-TPG pumped by a single-pump pulse. Pulse-burst pumping improved the “quantitativeness” of real-time spectroscopic measurements using an is-TPG and is expected to expand the scope of THz wave applications.
SYNERGISTIC EFFECT OF COMBINATION BETWEEN CYANOMACLURIN AND ARTOCARPIN FROM ARTOCARPUS HETEROPHYLLUS HEARTWOODS AGAINSTS STREPTOCOCCUS PYOGENES AND STAPHYLOCOCCUS EPIDERMIDIS

Streptococcus pyogenes and Staphylococcus epidermidis are major problem of human health and may generate antibacterial resistance problem. Flavonoid has been used for the treatment of several ailments including bacterial infection. Artocarpus heterophyllus is a potential source of flavonoid compounds such as artocarpin and cyanomaclurin. The study was conducted to observe synergistic effects between flavonoid compounds against S. pyogenes and S. epidermidis. The antibacterial activity of combination of artocarpin and cyanomaclurin isolated from A. heterophyllus heartwoods were evaluated against S. pyogenes and S. epidermidis using broth microdilution methods. Interaction between two compounds in combination was determined using checkerboard assay. Artocarpin showed strong antibacterial activity with MIC value of 1.9 µg/mL. Cyanomaclurin only exhibited moderate activity with MIC value of 15.6 µg/mL. The mixture of compounds in several ratios tended to increase antibacterial activities of cyanomaclurin. There is no antagonistic effect when compounds used together. It can be concluded that flavonoid compounds in combination may enhance antibacterial activity to prevent development of antibacterial resistance.
Exome Sequencing Identifies a Novel SGCA Gene Mutation in an Iranian Family with Limb Girdle Muscular Dystrophy: A Case Report

Introduction: Autosomal recessive limb-girdle muscular dystrophy (LGMD) is a rare congenital muscular dystrophy type. This inherited disease is one of the most common diseases resulting in severe physical health problems for infants. Genetic studies have shown that several gene mutations cause limb-girdle muscular dystrophy. This study aimed to report the case of a 10-year-old boy from an Iranian consanguineous family diagnosed with congenital muscular dystrophy and a causative genetic abnormality. Case Presentation: In this report, whole exome sequencing (WES) followed by Sanger sequencing was performed to diagnose the possible genetic defects in patients with LGMD. A novel homozygous disease-causing SGCA gene mutation (p.A107fs:c.319-329del) was found in exon 10 (NM_000023.4). Both parents were heterozygous for the detected mutation. Conclusions: It was concluded that the identified SGCA gene mutation was a pathogenic variant causing autosomal recessive LGMD.
ONLINE ESTIMATION OF FATIGUE CRACK GROWTH BY MONTE CARLO-BASED FILTERING

For a certain class of industrial and structural components, fatigue crack growth is the typical degradation process [1]. During the operation phase, monitoring of this process allows anticipating the components’ evolution to failure. In practice, a robust prediction of the remaining lifetime of these components, with an estimate of its related uncertainty, may be difficult to obtain since the components’ degradation state may not be directly observable and/or the measurements may be affected by noise and disturbances. In these cases, model-based estimation methods aimed at inferring the dynamic degradation state on the basis of a sequence of noisy measurements, seem to offer interesting potentials for successful applications, ensuring a reliable quantification of the uncertainty associated to the prediction. The soundest approaches of this kind rely on Bayesian methods to combine a prior distribution of the unknown degradation states with the likelihood of the observations collected, to build a posterior distribution [2] [3]. In this setting, the estimation method most frequently used in practice is the Kalman filter, which is optimal for linear state space models and independent, additive Gaussian noises [4]. In this case, the posterior distributions are also Gaussian and computed exactly, without approximations. On the other hand, in most realistic cases the dynamics of degradation is non-linear and/or the associated noises are non-Gaussian. Various approximate methods can be proposed to tackle these cases, e.g. the analytical approximations of the extended Kalman (EKF) and the Gaussian-sum filters and the numerical approximations of the grid-based filters [4]. Recently, Monte Carlo sampling methods are gaining popularity for their flexibility and ease of design [5]. These methods go under the name of particle filtering because the continuous distributions of interest are approximated by a discrete set of weighed particles, where each particle represents a random trajectory of evolution in the state space and the weight is the probability of the trajectory [6] [7].
An aging and values-driven theory of mobility

ABSTRACT Maintaining mobility is crucial for healthy aging in older adults. While the importance of mobility and the availability of various mobility solutions are well-acknowledged, little is known about the values that drive older adults’ maintenance of mobility. By adopting a values-driven perspective, this study argues that understanding these values is essential for promoting the adoption of, and mitigating resistance to, mobility solutions among older adults. In this regard, this study aims to explore the values underpinning the concept of mobility and the notion of aging in older adults. Using a grounded theory methodology, a thematic analysis of interviews with older adults on their experiences with aging and mobility was conducted. This analysis revealed five core values – Go-Go, Resilience, Empowerment, Adaptability, and Technophile (GREAT) – that are intertwined with aging and mobility, leading to a new theory called the aging and values-driven theory of mobility. The insights from this study suggest that a variety of mobility solutions should be available to cater to the diverse mobility conditions experienced by older adults, ultimately creating a supportive ecosystem for healthy aging.
Imagining “national” funding in the Australian federation: The Gonski Review and the Schooling Resource Standard

This paper takes the landmark review into the federal funding of schools in Australia, known as the Gonski Review (2011), as an illustrative case to demonstrate the scalar practices involved in policy production and enactment. Its primary argument is that, while its core recommendation was a needs-based funding model for the federal government funding of schools, the Gonski Review also articulates an aspiration for the translation of this funding model into a comprehensively national approach. This is done, I argue, through important practices of scalar imagining and reasoning (Papanastasiou, 2017b). However, these national aspirations sit uneasily with the realities of schooling and school funding in the Australian federation, which includes constitutional arrangements, legislation, and policy principles that distribute responsibility for funding across multiple spaces of governance. Drawing on documentary evidence, I argue that scalar tensions are produced between these national aspirations and the realpolitik of Australian federalism. By challenging “the national” as a coherent and predetermined “scale,” these findings reinforce the importance of attending to the mediating forces of subnational governments, as well as global policy influences, when thinking about policy mobilities in federations.
Toward Autonomic Internet of Things: Recent Advances, Evaluation Criteria, and Future Research Directions

With the rise of the Internet of Things (IoT), tiny devices capable of computation and data transmission are being deployed across various technological domains. Due to the wide deployment of these devices, manual setup and management are infeasible and inefficient. To address this inefficiency, intelligent procedures must be established to enable autonomy that allows devices and networks to operate efficiently with minimal human intervention. In the traditional client–server paradigm, autonomic computing has been proven effective in minimizing user intervention in computer systems management and will benefit IoT networks. However, IoT networks tend to be heterogeneous, distributed, and resource constrained, mandating the need for new approaches to implement autonomic principles compared to traditional approaches. We begin by introducing the basic principles of autonomic computing and its significance in IoT. We then discuss the self-* paradigm and monitor, analyze, plan, and execute (MAPE) loop from an IoT perspective, followed by recent works in IoT and key enabling technologies for enabling autonomic properties in IoT. Based on the self-* paradigm and MAPE loop analysis from the existing literature, we propose a set of qualitative characteristics for evaluating the autonomy of the IoT network. Finally, we provide a comprehensive list of challenges associated with achieving autonomic IoT and directions for future research.
An adaptive enhancement algorithm for infrared video based on modified k-means clustering

In this paper, we have proposed a video enhancement algorithm to improve the output video of the infrared camera. Sometimes the video obtained by infrared camera is very dark since there is no clear target. In this case, infrared video should be divided into frame images by frame extraction, in order to carry out the image enhancement. For the first frame image, which can be divided into k sub images by using K-means clustering according to the gray interval it occupies before k sub images’ histogram equalization according to the amount of information per sub image, we used a method to solve a problem that final cluster centers close to each other in some cases; and for the other frame images, their initial cluster centers can be determined by the final clustering centers of the previous ones, and the histogram equalization of each sub image will be carried out after image segmentation based on K-means clustering. The histogram equalization can make the gray value of the image to the whole gray level, and the gray level of each sub image is determined by the ratio of pixels to a frame image. Experimental results show that this algorithm can improve the contrast of infrared video where night target is not obvious which lead to a dim scene, and reduce the negative effect given by the overexposed pixels adaptively in a certain range.