Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of mindfulness training in early adolescence on affective executive control, and on later mental health during-COVID-19: A randomised controlled trial

SART-RTV-NEU, STR-NEU-RT (RT on the Stroop task for the neutral stimuli), and WMC-NEU represent the underlying task performance in the non-affective context. All 4 indices correlate also significantly with age, r s>|. 15|, p s<. 01, and verbal IQ on the Cattell Culture Fair Test at Baseline, r s>|. 12|, p s<. 01, with older age and higher IQ associated with better performance, as would be expected. anxiety obsessive compulsive disorder.
Package ‘pmsampsize’

February 12, 2022 Version 1.1. 2 Date 2022-02-12 Title Calculates the Minimum Sample Size Required for Developing a Multivariable Prediction Model Maintainer Joie Ensor <j.ensor@keele.ac. uk> Depends R (>= 2.1) Suggests stats Description Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi:10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-toevent) outcomes. Riley et al. (2018) <doi:10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model. License GPL (>= 3) RoxygenNote 7.1. 2 Encoding UTF-8 NeedsCompilation no Author Joie Ensor [aut, cre], Emma C. Martin [aut], Richard D. Riley [aut] Repository CRAN Date/Publication 2022-02-12 17:00:02 UTC
Degradation Tendency Measurement of Aircraft Engines Based on FEEMD Permutation Entropy and Regularized Extreme Learning Machine Using Multi-Sensor Data

As the core component and main power source for aircrafts, the reliability of an aero engine is vital for the security operation of aircrafts. Degradation tendency measurement on an engine can not only improve its safety, but effectively reduce the maintenance costs. In this paper, a hybrid method using multi-sensor data based on fast ensemble empirical mode decomposition permutation entropy (FEEMD-PE) and regularized extreme learning machine (RELM), systematically blending the signal processing technology and trend prediction approach, is proposed for aircraft engine degradation tendency measurement. Firstly, a synthesized degradation index was designed utilizing multi-sensor data and a data fusion technique to evaluate the degradation level of the engine unit. Secondly, in order to eliminate the irregular data fluctuation, FEEMD was employed to efficiently decompose the constructed degradation index series. Subsequently, considering the complexity of intrinsic mode functions (IMFs) obtained through sequence decomposition, a permutation entropy-based reconstruction strategy was innovatively developed to generate the refactored IMFs (RIMFs), which have stronger ability for describing the degradation states and contribute to improving the prediction accuracy. Finally, RIMFs were used as the inputs of the RELM model to measure the degradation tendency. The proposed method was applied to the degradation tendency measurement of aircraft engines. The results confirm the effectiveness and superiority of the proposed method, and it is more suitable for actual applications compared with other existing approaches.
Damage of the Bone-Cement Interface in Finite Element Analyses of Cemented Orthopaedic Implants

In orthopedic surgery and particularly in total hip arthroplasty, fixation of femoral implant is generally made by the surgical cement. Bone–cement interface has long been implicated in failure of cemented total hip replacement (THA), it is actually a critical site that affect the long-term stability and survival of prosthetic implants after implantation. The main purpose of this study is to investigate the effect of cement penetration into the bone on damage scenario at the interface. Previously most researchers have been performed to study damage accumulation in the cement mantle for different amount of cement penetration. In this work, bone–cement interface integrity has been studied for different mechanical properties. Cohesive traction separation law is used to detect contact damage between cement and bone. Results showed that a larger debonded area was predicted proximally and distally. Adhesion between bone and cement is affected mainly by cement penetration into the bone. Higher cement penetration into the bone leads to a good load transfer. A lower strength of the bone–cement interface due to a lower mechanical property results in faster interface damage. So we advise surgeons to well perpetrate the bone for long-term durability of cemented THA.
TECHNOLOGY OF CANDY CARAMEL WITH DIETARY-FUNCTIONAL PROPERTIES

for dietary-functional purpose using monosaccharide fructose and sweeteners of the new generation  polyols isomaltitol and maltitol. The feature of this technology is the lack of starch molasses in the formulation. The physical- chemical, structural-mechanical and sorption-desorption properties of the obtained caramel samples in comparison with caramel produced on the basis of traditional 97 sugar technology are investigated. The content of reducing agents in the developed caramel samples is 6.5 and 6.7%, respectively, for samples on isomaltitol and maltitol, which is more than 3 times less than in caramel based on sucrose and starchy molasses. Isotherms of adsorption-desorption of moisture showed that the equilibrium moisture of samples at а w 0.7−0.75 varies within 2.0−2.5%, which contributes to the absence of violation of the amorphous structure during storage, despite the reduced content of reducing agents. Calculation of glycemic index and energy values indicates that new types of caramel deserve the labeling of "Product with reduced glycemicity" and "Low calorie product". On the basis of the research, new types of candy caramel "Fruity lightness" on the basis of isomaltitol and "Nasoloda" on the basis of maltitol were developed.
The 1982-1984 Eclipse of Epsilon Aurigae

A workshop proceedings concerned with the new data collected during the 1982-1984 eclipse period of the 27-year system Epsilon Aurigae is presented. This binary star has been a classic problem in astrophysics because the opaque eclipsing object is nonstellar, and probably disk shaped. Invited papers concerning the history of the system, optical, infrared and ultraviolet photometry, optical polarimetry and ultraviolet spectroscopy are included. An invited paper concerning comprehensive theoretical interpretation in the context of stellar evolution also is included. The information collected herein is unparalleled in scope and will remain a standard reference until the next eclipse cycle in the year 2009 A.D., in all probability.
International Journal of Science and Business

Lung cancer is a sort of dangerous cancer and difficult to detect. It usually causes death for both gender men & women therefore, so it is more necessary for care to immediately & correctly examine nodules. Accordingly, several techniques have been implemented to detect lung cancer in the early stages. In this paper a comparative analysis of different techniques based on machine learning for detection lung cancer have been presented. There have been too many methods developed in recent years to diagnose lung cancer, most of them utilizing CT scan images and some of them using x-ray images. In addition, multiple classifier methods are paired with numerous segmentation algorithms to use image recognition to identify lung cancer nodules. From this study it has been found that CT scan images are more suitable to have the accurate results. Therefore, mostly CT scan images are used for detection of cancer. Also, marker-controlled watershed segmentation provides more accurate results than other segmentation techniques. In Addition, the results that obtained from the methods based deep learning techniques achieved higher accuracy than the methods that have been implemented using classical machine learning techniques. IJSB Literature Review Accepted 6 February 2021 Published 12 February 2021 DOI: 10.5281/zenodo.4536818
University of Birmingham “We were fighting for our place”

This is the peer reviewed version of the following article: Jones S, Martinez Dy A, Vershinina N. ‘We were fighting for our place’: Resisting gender knowledge regimes through feminist knowledge network formation. Gender Work Organ. 2018, which has been published in final form at: https://doi.org/10.1111/gwao.12288. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
tens of

Whether dinosaurs were in a long-term decline, or whether they were reigning strong right up to their final disappearance at the Cretaceous-Paleogene (K-Pg) mass extinction event 66 million years ago (Ma), has been debated for decades with no clear resolution. The dispute has continued unresolved because of a lack of statistical rigor and appropriate evolutionary framework. Here, for the first time, we apply a Bayesian phylogenetic approach to model the evolutionary dynamics of speciation and extinction through time in Mesozoic dinosaurs, properly taking account of previously ignored statistical violations. We find overwhelming support for a long-term decline across all dinosaurs and within all three dinosaurian subclades, Ornithischia, Sauropodomorpha and Theropoda, where speciation rate slowed down through time and was ultimately exceeded by extinction rate tens of millions of years before the K-Pg boundary. The only exceptions to this general pattern are the morphologically specialized herbivores, the Hadrosauriformes and Ceratopsidae, which show rapid proliferations throughout the Late Cretaceous instead. Our results highlight that, despite some heterogeneity in speciation dynamics, dinosaurs showed a marked reduction in their ability to replace extinct species with new ones, making them vulnerable to extinction and unable to respond quickly to, and recover from, the final catastrophic event.
Airport Expansion and Endangered Bats: Development and Mitigation Actions Near the Indianapolis International Airport

Economic prosperity and globalization are major drivers for development of international airports, but aviation-oriented businesses and residential developments are a by-product of airport business models. Among the multitude of planning and development considerations is the habitat needs of endangered wildlife species. Foraging data were analyzed from 57 bats during three time periods (1998–1999: pre-mitigation; 2005–2006: during mitigation, and 2014–2016: post-mitigation) of a long-term study of Indiana bats (Myotis sodalis) near the Indianapolis International Airport. At this site, both developed land cover and forested land cover increased between 1998 and 2016 (34.1% and 3.3%, respectively). Mitigation actions included converting 323 ha of residential lots back to forest, and creation of a 56 ha wetland and an 85 ha multi-use park. Bat use of landscape cover types was related to changes in land cover during each period and competing hypotheses were compared to explain changes in bat foraging space use. With the addition of a major highway interchange where the colony foraged, bats increased foraging ranges, presumable in search of new habitat. In all periods, bats selected for forested habitat; as trees in replanted forest and designated parks aged, bats reduced their foraging ranges. Restoring hardwood forest and setting aside parklands were effective proactive mitigation measures for the colony of Indiana bats near the Indianapolis International Airport, and similar actions should benefit other wildlife where human development and habitat needs intersect.
Nonexpansive mappings and hyperconvex spaces

A guard for swimming pool cleaning hoses includes a spool having a hole therethrough, the hole dimensioned so as to receive the hose. The periphery of the spool is provided with a concavity, with a plurality of individual roller segments rotatably mounted in the cavity about a shaft, whereby the roller segments produce frictional contact between the hose and a pool being cleaned.
RBOUNDS: Stata module to perform Rosenbaum sensitivity analysis for average treatment effects on the treated

rbounds calculates Rosenbaum bounds for average treatment effects on the treated in the presence of unobserved heterogeneity (hidden bias) between treatment and control cases. rbounds takes the difference in the response variable between treatment and control cases as input variable varname. The procedure then calculates Wilcoxon signrank tests that give upper and lower bound estimates of significance levels at given levels of hidden bias. Under the assumption of additive treatment effects, rbounds also provides Hodges-Lehmann point estimates and confidence intervals for the average treatment effect on the treated. If installed, the input variable varname may be generated from psmatch or psmatch2. Currently, rbounds implements the sensitivity tests for matched (1x1) pairs only.
A recipe of training neural network-based LDPC decoders

—It is known belief propagation decoding variants of LDPC codes can be unrolled easily as neural networks after assigning differed weights to message passing edges ﬂexibly. In this paper we focus on how to determine these weights, in the form of trainable parameters, within a framework of deep learning. Firstly, a new method is proposed to generate high-quality training data via exploiting an approximation to the targeted mixture density. Then the strong positive correlation between training loss and decoding metrics is fully exposed after tracing the training evolution curves. Lastly, for the purpose of facilitating training convergence and reducing decoding complex- ity, we highlight the necessity of slashing the number of trainable parameters while emphasizing the locations of these survived ones, which is justiﬁed in the extensive simulation.
Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations

This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study (<inline-formula><tex-math notation="LaTeX">$\mathrm{n}=150$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="tvcg-abdelaal-3209427-eqinline-1-small.tif"/></alternatives></inline-formula>), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.
Towards a General Pre-training Framework for Adaptive Learning in MOOCs

Adaptive learning aims to stimulate and meet the needs of individual learners, which requires sophisticated system-level coordination of diverse tasks, including modeling learning resources, estimating student states, and making personalized recommendations. Existing deep learning methods have achieved great success over statistical models; however, they still lack generalization for diverse tasks and suffer from insufficient capacity since they are composed of highly-coupled task-specific architectures and rely on small-scale, coarse-grained recommendation scenarios. To realize the idea of general adaptive systems proposed in pedagogical theory, with the emerging pre-training techniques in NLP, we try to conduct a practical exploration on applying pre-training to adaptive learning, to propose a unified framework based on data observation and learning style analysis, properly leveraging heterogeneous learning elements. Through a series of downstream tasks of Learning Recommendation, Learning Resource Evaluation, Knowledge Tracing, and Dropout Prediction, we find that course structures, text, and knowledge are helpful for modeling and inherently coherent to student non-sequential learning behaviors and that indirectly relevant information included in the pre-training foundation can be shared across downstream tasks to facilitate effectiveness. We finally build a simplified systematic application of adaptive learning and reflect on the insights brought back to pedagogy. The source code and dataset will be released.
Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors

Nondestructive testing (NDT) is widely applied to defect identiﬁcation of turbine components during manufacturing and operation. Operational efﬁciency is key for gas turbine OEM (Original Equipment Manufacturers). Automating the inspection process as much as possible, while minimizing the uncertainties involved, is thus crucial. We propose a model based on RetinaNet to identify drilling defects in X-ray images of turbine blades. The application is challenging due to the large image resolutions in which defects are very small and hardly captured by the commonly used anchor sizes, and also due to the small size of the available dataset. As a matter of fact, all these issues are pretty common in the application of Deep Learning-based object detection models to industrial defect data. We overcome such issues using open source models, splitting the input images into tiles and scaling them up, applying heavy data augmentation, and optimizing the anchor size and aspect ratios with a differential evolution solver. We validate the model with 3 -fold cross-validation, showing a very high accuracy in identifying images with defects. We also deﬁne a set of best practices which can help other practitioners overcome similar challenges.
Predicting the minimum control time of quantum protocols with artificial neural networks

Quantum control relies on the driving of quantum states without the loss of coherence, thus the leakage of quantum properties into the environment over time is a fundamental challenge. One work-around is to implement fast protocols, hence the Minimal Control Time (MCT) is of upmost importance. Here, we employ a machine learning network in order to estimate the MCT in a state transfer protocol. An unsupervised learning approach is considered by using a combination of an autoencoder network with the k-means clustering tool. The Landau–Zener (LZ) Hamiltonian is analyzed given that it has an analytical MCT and a distinctive topology change in the control landscape when the total evolution time is either under or over the MCT. We obtain that the network is able to not only produce an estimation of the MCT but also gains an understanding of the landscape’s topologies. Similar results are found for the generalized LZ Hamiltonian while limitations to our very simple architecture were encountered.
Object Detection with Deep Reinforcement Learning

Object localization has been a crucial task in computer vision ﬁeld. Methods of localizing objects in an image have been proposed based on the features of the attended pixels. Recently researchers have proposed methods to formulate object localization as a dynamic decision process, which can be solved by a reinforcement learning approach. In this project, we implement a novel active object localization algorithm based on deep reinforcement learning. We compare two different action settings for this MDP: a hierarchical method and a dynamic method. We further perform some ablation studies on the performance of the models by investigating different hyperparameters and various architecture changes.
Generalization and Overfitting in Matrix Product State Machine Learning Architectures

While overfitting and, more generally, double descent are ubiquitous in machine learning, increasing the number of parameters of the most widely used tensor network, the matrix product state (MPS), has generally lead to monotonic improvement of test performance in previous studies. To better understand the generalization properties of architectures parameterized by MPS, we construct artificial data which can be exactly modeled by an MPS and train the models with different number of parameters. We observe model overfitting for one-dimensional data, but also find that for more complex data overfitting is less significant, while with MNIST image data we do not find any signatures of overfitting. We speculate that generalization properties of MPS depend on the properties of data: with one-dimensional data (for which the MPS ansatz is the most suitable) MPS is prone to overfitting, while with more complex data which cannot be fit by MPS exactly, overfitting may be much less significant.
Velocity distributions of particles sputtered from supported two-dimensional 
MoS2
 during highly charged ion irradiation

The interaction of highly charged ions (HCI) with solids leads to particle sputtering, which can be used for defect-mediated engineering of the properties of the material. Ions can store energy in the form of kinetic and potential energy (sum of the ionization energies) and transfer it to the solid upon impact. The interaction and sputtering mechanisms depend significantly on the projectile energies. However, the relevance of various interaction mechanisms is unknown. Here we show that for slow HCI (5 keV) the interaction mechanisms leading to particle emission by electronic excitation and transferred kinetic energy are independent from each other, which is consistent with our atomistic simulations. We have irradiated substrate supported (Au, SiO$_2$) monolayers of MoS$_2$ with highly charged xenon ions (charge state: 17$+$ - 40$+$), extracted the emitted neutral, post-ionized Mo particles into a time-of-flight mass spectrometer and determined their velocity distributions. We find two main contributions, one at high velocities and a second one at lower velocities, and assign them to kinetic and potential effects respectively. Our data suggests that the dominant mechanism for potential sputtering is related to electron-phonon coupling, while non-thermal processes play no significant role. We anticipate that our work will be a starting point for further experiments and simulations to determine whether the different processes resulting from E$_{pot}$ and E$_{kin}$ can be separated or whether synergistic effects play a role.
Adaptive Zeroth-Order Optimisation of Nonconvex Composite Objectives

In this paper, we propose and analyze algorithms for zeroth-order optimization of non-convex composite objectives, focusing on reducing the complexity dependence on dimensionality. This is achieved by exploiting the low dimensional structure of the decision set using the stochastic mirror descent method with an entropy alike function, which performs gradient descent in the space equipped with the maximum norm. To improve the gradient estimation, we replace the classic Gaussian smoothing method with a sampling method based on the Rademacher distribution and show that the mini-batch method copes with the non-Euclidean geometry. To avoid tuning hyperparameters, we analyze the adaptive stepsizes for the general stochastic mirror descent and show that the adaptive version of the proposed algorithm converges without requiring prior knowledge about the problem.