Miracle-dz/UniFAR-FTUs huggingface dataset:
Fitness professionals’ perceptions of acceptability and usability of anti-doping education tools for recreational sports

Abstract Objective The use of performance and image enhancement drugs (PIEDs) in recreational sport is a common practice and there is a need to develop preventive interventions. The study’s aim was to investigate the acceptability and usability of an online e-learning course and an information app designed to support PIED prevention training activities in professionals working with recreational athletes. Design A mixed-method design was used with 51 professionals from Finland, Greece, and Lithuania evaluating the e-learning course through surveys and interviews. Similarly, 19 Greek professionals evaluated the information app. Main outcome measures Participants completed surveys and participated in semi-structured interviews measuring both tool's acceptability, usability, and implementation. Results Participants commented positively on the value and content of both tools. They reported increased knowledge on PIEDs risks and understanding of motives for PIED use. Also, their competence and confidence in interacting with clients and assisting them to quit increased. The e-learning course was perceived as compatible with existing practices. The main barrier was a lack of time in interacting with clients. Conclusion The study provides valuable information on the acceptability and usability of two educational approaches against doping. Anti-doping educational authorities could integrate these tools into their activities to educate fitness instructors about doping.
Predicting Hard Disk Failure by Means of Automatized Labeling and Machine Learning Approach

Today, cloud systems provide many key services to development and production environments; reliable storage services are crucial for a multitude of applications ranging from commercial manufacturing, distribution and sales up to scientific research, which is often at the forefront of computing resource demands. In large-scale computer centers, the storage system requires particular attention and investment; usually, a large number of diverse storage devices need to be deployed in order to match the varying performance and volume requirements of changing user applications. As of today, magnetic drives still play a dominant role in terms of deployed storage volume and of service outages due to device failure. In this paper, we study methods to facilitate automated proactive disk replacement. We propose a method to identify disks with media failures in a production environment and describe an application of supervised machine learning to predict disk failures. In particular, a proper stage to automatically label (healthy/at-risk) the disks during the training and validation stage is presented along with tuning strategy to optimize the hyperparameters of the associated machine learning classifier. The approach is trained and validated against a large set of 65,000 hard drives in the CERN computer center, and the achieved results are discussed.
Adsorption Method for the Remediation of Brilliant Green Dye Using Halloysite Nanotube: Isotherm, Kinetic and Modeling Studies

The first-ever use of halloysite nanotube (HNT), a relatively low-cost nanomaterial abundantly available with minor toxicity for removing brilliant green dye from aqueous media, is reported. The factors affecting adsorption were studied by assessing the adsorption capacity, kinetics, and equilibrium thermodynamic properties. All the experiments were designed at a pH level of around 7. The Redlich-Peterson isotherm model fits best amongst the nine isotherm models studied. The kinetic studies data confirmed a pseudo model of the second order. Robotic investigations propose a rate-controlling advance being overwhelmed by intraparticle dispersion. The adsorbent features were interpreted using infrared spectroscopy and electron microscopy. Process optimization was carried out using Response Surface Methodology (RSM) through a dual section Fractional Factorial Experimental Design to contemplate the impact of boundaries on the course of adsorption. The examination of fluctuation (ANOVA) was utilized to consider the joined impact of the boundaries. The possibilities of the use of dye adsorbing HNT (“sludge”) for the fabrication of the composites using plastic waste are suggested.
Joint Retrieval-Extraction Training for Evidence-Aware Dialog Response Selection

Neural dialog response selection models infer by scoring each candidate response given the dialog context, and the cross-encoder method yields state-of-the-art (SOTA) results for the task. In the method, the candidate scores are computed by feeding the output embedding of the ﬁrst token in the input sequence, which is a concatenation of response and context, to a linear layer for making prediction. However, the embeddings of the other tokens in the sequence are not modeled explicitly, and inferring the candidate scores only with the ﬁrst token makes the result not interpretable. To address the challenge, we propose a Retrieval-EXtraction encoder (REX) for dialog response selection. We augment the existing ﬁrst-token-or sequence-based retrieval approach with an extraction loss. The loss provides gradient signal from each token during training and allows the model to learn token-level evidence and to select response based on important keywords. We show that REX achieves the new SOTA in the dialog response selection task. Also, our qualitative analysis suggests that REX highlights evidence it infers selections from and makes the inference result interpretable.
Krasovskii Passivity and μ-Synthesis Controller Design for Quasi-Linear Affine Systems

This paper presents an end-to-end method to design passivity-based controllers (PBC) for a class of input-affine nonlinear systems, named quasi-linear affine. The approach is developed using Krasovskii’s method to design a Lyapunov function for studying the asymptotic stability, and a sufficient condition to construct a storage function is given, along with a supply-rate function. The linear fractional transformation interconnection between the nonlinear system and the Krasovskii PBC (K-PBC) results in a system which manages to follow the provided input trajectory. However, given that the input and output of the closed-loop system do not have the same physical significance, a path planning is mandatory. For the path planning component, we propose a robust controller designed using the μ-synthesis mixed-sensitivity loop-shaping for the linearized system around a desired equilibrium point. As a case study, we present the proposed methodology for DC-DC converters in a unified manner, giving sufficient conditions for such systems to be Krasovskii passive in terms of Linear Matrix Inequalities (LMIs), along with the possibility to compute both the K-PBC and robust controller alike.
Fast GRNN-Based Method for Distinguishing Inrush Currents in Power Transformers

Differential protection, as the key protection element in the power transformers, has always been threatened with sending false trips subjected to external transient disturbances. As a result, differential protection needs an additional block to distinguish between internal faults and external transient disturbances. The protection system should, first, be able to perform based on raw data, second, be able to learn fully temporal features and sudden changes in the transient signals, and, third, impose no assumption on noise. To address these challenges, a fast RNN, namely fast gated recurrent neural network (FGRNN). By removing reset gate in the gated recurrent unit (GRU), the proposed network is capable of learning abrupt changes in addition to significantly reducing the computational time. Furthermore, a loss function based on an information theory concept is formulated in this article to enhance the learning ability as well as robustness against non-Gaussian/Gaussian noises. A generalized form of mutual information is also adopted to form a noise model-free loss function, then incorporated with the designed deep network. Simulated and experimental examinations engaging various external factors, in addition to comparison between the proposed FGRNN, GRU, and seven firmly established methods indicates the faster and more reliable performance of the proposed algorithm.
A recursive LMI-based algorithm for efficient vertex reduction in LPV systems

This paper proposes a new algorithm to reduce the number of gains of a polytopic LPV controller considering generic tuples of vertices, for which a common controller gain can be used. The use of Frobenius norm and the inclusion of the input matrix in the LMIs perturbation matrix allows decreasing the conservativeness to select vertices which are combinable, with respect to a previous approach based on Gershgorin circles. A combinability metric that can be applied to an arbitrary partition of the set of vertices is defined. Then, a recursive algorithm finds a lesser-fragmented combinable partition at each iteration by combining together two elements of a partition. The algorithm aims at finding combinable partitions with minimal cardinality in fewer attempts, always preserving the original control performance specifications. The proposed method is validated using numerical examples, a twin rotor MIMO system and a two-link robotic manipulator.
Estimation of Distances in 3D by Orthodontists Using Digital Models

In the field of orthodontics, digital dental arch models are increasingly replacing plaster models in orthodontic diagnostics. This change in interface from physical plaster models to digital image-based models raises the question of how orthodontists interpret intra- and inter-arch relationships from an image on a screen. In particular, the issue of the accuracy of the distances estimated on numerical models is crucial since the estimation of distances is the basis of the therapeutic decision-making process. Studies have shown that distances were well estimated on plaster models, but no study has verified this point on numerical models. This is the question that our study addresses. The experimental protocol consisted of collecting estimates of measurements made by orthodontists using digital models. The reliability of these measurements was then assessed by comparing them to the actual physical distances. We asked 31 orthodontists (19 women and 12 men; an average age of 37 years) to generate 3D model-based measurements of seven different elements: mandibular congestion, the maxillary intermolar distance, Spee’s curve, 16/26 symmetry, the right canine class, overbite, and overjet. These values were then compared to the actual measurements calculated using Insignia® software (ORMCO Corporation: Brea, CA, USA), using single sample t-tests. This test makes it possible to compare a distance estimated by the participants with a reference value, which corresponds here to the real distance. The results indicate that, overall, the distance estimates made on the 3D models differ significantly from the actual distances measured using the Insignia® software. This was particularly so for mandibular crowding (test value = 0; t (30) = 10.74; p ≤ 0.01), test value = 1; t (30) = 6.23; p ≤ 0.01). Although no study has focused on distance estimation on numerical models in the field of orthodontics, our results agree with the conclusions of studies showing that distances are not estimated in the same way in real environments and virtual environments. Additional studies will make it possible to identify more clearly the parameters (individual factors, equipment, etc.), which make it possible to improve the estimation of distances in the practice of orthodontics. In any case, these studies are necessary to improve the training of future practitioners in the use of virtual models for decision-making and to support them in the digital transition.
No Gender Differences in Enrollment for Replicated, Minimally-Different ‘Pornographic’ Versus ‘Photographic’ Studies

Abstract Objective: Since women tend to use pornography less, they may enroll less in studies concerning it and/or those who do may be gender-atypical. Methods: One study plus replication, assessed proportion of participants reporting being women, responding to each of two, minimally different (one including the word “pornographic”) study advertisement versions, and their pornography use frequency, Erotophilia-Erotophobia, and Openness to Experience. Results: Proportion responding to each version did not differ. In one sample only, women responding to one version differed in Openness to Experience. Conclusions: Advertising to North American convenience samples using the word “pornographic”, may produce neither self-selection out by, nor over-sampling of gender-atypical, women.
Boundary Enhancement Semantic Segmentation for Building Extraction From Remote Sensed Image

Image processing via convolutional neural network (CNN) has been developed rapidly for remote sensing technology. Moreover, techniques for accurately extracting building footprints from remote sensed images have attracted considerable interest owing to their wide variety of common applications, including monitoring natural disasters and urban development. Extraction of building footprints can be performed easily by semantic segmentation using U-Net-like CNN architectures. However, obtaining precise boundaries of segmentation masks remains challenging due to various impediments surrounding target objects. In this study, we propose a method to elaborate edges of buildings detected in remote sensed images to enhance the boundaries of segmentation masks. The proposed method adopts holistically nested edge detection (HED), which extracts edge features at an encoder of a given architecture. In the proposed boundary enhancement (BE) module, an extracted edge and segmentation mask are combined, sharing mutual information. To enable the proposed method efficiently to adapt to a wide variety of conditions, we design a distinctive approach adopting a HED unit and BE module, which is applicable to various semantic segmentation networks containing encoder-decoder structures. Experiments were conducted on five different datasets (DeepGlobe, Urban3D, WHU [high-resolution (HR), low-resolution (LR)], and Massachusetts). The results demonstrate that our proposed approaches improved on the performance of prior methods for extracting building footprints. Comparative experiments were conducted on various backbone architectures including U-Net, ResUNet++, TernausNet, and U-shape spatial pyramid pooling (USPP) to ensure the effectiveness of the proposed method. Based on various evaluation metrics and qualitative analysis, our results show that the proposed method achieved improved performance compared with prior methods for all datasets and backbone networks.
Establishment of a Real-Time Simulation of a Marine High-Pressure Common Rail System

In this paper, the high-pressure common rail system of the marine diesel engine is taken as case study to establish a real-time simulation model of the high-pressure common rail system that can be used as the controlled object of the control system. On the premise of ensuring accuracy, the real-time simulation should also respond quickly to instructions issued by the control system. The development of the real-time simulation is based on the modular modeling method, and the high-pressure common rail system is divided into submodels, including the high-pressure oil pump, common rail tube, injector, and mass conversion. The submodels are built using the “surrogate model” method, which is mainly composed of MAP data and empirical formulas. The data used to establish the real-time simulation are not only from the empirical research into the high-pressure common rail system, but also from simulations of the high-pressure common rail system undertaken in AEMSim. The data obtained from this real-time simulation were compared with the experimental data to verify the model. The error in fuel injection quality is less than 5%, under different pressures and injection durations. In order to carry out dynamic verification, the PID control strategy, the model-based control strategy, and the established real-time simulation are all closed-loop tested. The results show that the developed real-time simulation can simulate the rail pressure wave caused by cyclic injection according to the control signal, and can feedback the control effect of different control strategies. Through verification, it is clear that the real-time simulation of the high-pressure common rail system can depict the rail pressure fluctuation caused by each cycle of fuel injection, while ensuring the accuracy and responsiveness of the simulation, which provides the ideal conditions for the study of a rail pressure control strategy.
Industrious migrants: gender and the earnings of migrants in Swedish manufacturing around 1900

ABSTRACT Migration played a central role in industrialisation by reallocating labour from the countryside to urban areas and centres of manufacturing where it was in high demand, and better remunerated, with implications for economic growth and individual well-being. We investigate the labour market performance of internal migrants in Sweden around the turn of the last century; a period of industrialisation and increasing migration. We add to the literature in two ways: first by focusing on earnings instead of occupational attainment; second by extending the scope beyond the prevailing focus on men by also considering women. To assess how migrants fared compared to locals, we use detailed matched firm-individual data covering three manufacturing industries which varied in terms of production, organisation, and composition of the workforce. We find that migrants, irrespective of gender, performed well in that their earnings were higher than those of locals in general and of co-workers in the same firm. These premia are consistent with a Roy model in which migrants’ sort into locations where returns to skills match individual ability. An increase in both hours worked and effort further explains the observed earnings premium among female migrants.
Multiband nonthermal radiative model of pulsar wind nebulae: Study of the effects of advection and diffusion

Aims. Nonthermal radiation properties of pulsar wind nebulae (PWNe) are studied in the frame of a time-dependent model with particle advection and di ﬀ usion. Methods. The dynamical and radiative evolution of a PWN was self-consistently solved in the model. The time-dependent lepton (electrons and positrons) equation is described by particle injection, advection, di ﬀ usion, adiabatic loss, and radiative loss. Nonthermal emission from a PWN is mainly produced by the relativistic leptons through synchrotron radiation and inverse Compton process. Results. The e ﬀ ect of particle transport including advection and di ﬀ usion was analyzed, showing that the particle transport process induces a decrease in the nebula ﬂux, and that the total ﬂux decreases with the increase in advection velocity and di ﬀ usion coe ﬃ cient. Meanwhile, the particle transport processes may play an important role in modifying the spectrum of the emitted radiation at X-ray and TeV wavelengths, but only have slightly e ﬀ ects in radio and GeV bands. We applied our model to the three PWNe Crab nebula, 3C 58, and G54.1 + 0.3, and observed that the spectral energy distributions of photon emissions from the three PWNe are reproduced well. Our results indicate that (i) the particle cooling processes are dominated by adiabatic loss in lower-energy bands and synchrotron loss dominates for the higher-energy particles; and (ii) the particle transport processes are advection dominated, and a slow di ﬀ usion may occur within the Crab nebula, 3C 58, and G54.1 + 0.3.
Exploring the Relationship Between Managerial Cognitive Capabilities and Dynamic Managerial Capabilities

The micro-foundations research agenda's primary motivation in strategy is to dissect macro-level constructs in terms of actions and organizational members' interactions to the micro-level. This work seeks to evolve the understanding of these micro-foundations to explain the relationship between Managerial Cognitive Capabilities and Dynamic Managerial Capabilities. We conducted a laboratory experiment with a sample of 111 participants, divided into two groups, containing 57 and 54 participants, each one. The results revealed that Sensing Opportunity and Seizing Opportunity, components of the Dynamic Managerial Capability, and the Language and Communication, which are part of the Cognitive Managerial Capability, can be predictive of the ability to Reconfigure Tangible and Intangible Assets. Our research contributes by extending central literature on micro-foundations through an experiment. We empirically show that managerial and cognitive dynamic capabilities can be a preeminent field to improve the comprehension of dynamic capabilities' micro-foundations.
Sensorless Control of PMSM Based on Backstepping-PSO-Type Controller and ESO-Type Observer Using Real-Time Hardware

In the case of using a Permanent Magnet Synchronous Motor (PMSM) linear model of limited-range parametric variations and of relatively low dynamic of the load torque, the Field Oriented Control (FOC) type strategy ensures good performance of the PMSM control. Therefore, when using a non-linear model of wide-range parametric variations and of high dynamic of the load torque, a backstepping-type controller is proposed, whose tuning parameters are optimized by using a Particle Swarm Optimization (PSO) method. By designing an Extended State Observer (ESO), which provides a good estimate of the PMSM rotor position and speed under uncertainty conditions and with a response time shorter than that of the backstepping-type controller, this observer can be incorporated into the PMSM sensorless control system. The superior performance of the proposed sensorless control system based on the backstepping-PSO-type controller and an ESO-type observer is demonstrated through numerical simulations. Given that the real-time implementation of the control algorithms and observers in an embedded system is a difficult task, consisting of several steps, it is presented after the numerical simulations, which can be assimilated into the Software-in-the-Loop (SIL) step, the Processor-in-the-Loop (PIL) intermediate step, and the Hardware-in-the-Loop (HIL) final step. A comparison between the backstepping-PSO-type controller and the PI-PSO-type controller is presented by means of the real-time implementation of these controllers and demonstrates the superiority of the backstepping-PSO-type controller.