Miracle-dz/UniFAR-FTUs huggingface dataset:
Provenance of shales and sedimentary history of the Monte Soro Unit, Sicily

Source area weathering, provenance and tectonic setting from the Lower Cretaceous Monte Soro Unit shales (North-eastern Sicily) have been analysed for mineralogical and chemical compositions. Samples are characterized by high values of MMI index. Among clay minerals I/S mixed-layered and illite are prevailing on chlorite and kaolinite. The amount of illite layers among mixed-layered smectite/illite reflects late diagenetic post depositional conditions reasonably reached during the post-Cretaceous subduction and related collision involving the European and African plate margins. Major and trace elements abuncances indicate the dominant detrital nature of the shales with minimal or absent hydrothermal and biogenic contributions. TiO2/Al 2O3 ratios are distinctive of shales produced by weathering of felsic to intermediate continental crustal rocks. High CIA and CIW values suggest that the source area was subjected to a prolonged and/or intense chemical weathering. The chemical composition and trace element ratios Zr/Y, Zr/Nb, Zr/Ti, Nb/Y, Th/Cr, Th/Co, La/ Co, K/Rb are close to those of the metamorphic basement of the Monti Peloritani segment of the European block. However, in our case, it is difficult to distinguish between a first-cycle sediment production and a recycling of the Upper Triassic - Lower Jurassic Verrucano continental sediments, being the metamorphic basement mainly formed by metagraywakes and metapelites. Riassunto. — L'analisi mineralogica e geochimica delle argille Tardo-Cretaciche dell'Unita di Monte Soro (Sicilia nord-orientale) ha permesso di individuare i complessi processi di alterazione meteorica alla sorgente, la provenienza e l'assetto tettonico di questa Unita. I campioni sono caratterizzati da un elevato valore dell'indice MMI. Tra i minerali argillosi, gli interstratificati I/S e l'illite sono prevalenti su clorite e caolinite. Il contenuto in illite all'interno degli interstratificati illite/ smectite riflette condizioni post-deposizionali tardo- diagenetiche ragionevolmente raggiunte durante il processo subduttivo post-Cretacico e la relativa collisione continentale che hanno coinvolto i margini delle placche Africana ed Europea. Le concentrazioni degli elementi maggiori e in tracce hanno evidenziato la natura detritica delle argille con un contributo idrotermale e biogenico minimo o assente. I valori del rapporto TiO 2/Al 2O3 sono caratteristici di argille originatesi dall'alterazione di rocce crostali di composizione da felsica ad intermedia. Gli alti valori di CIA e CIW suggeriscono che l'area di alimentazione e stata sottoposta ad alterazione chimica intensa e/o prolungata. La composizione chimica e i rapporti Zr/Y, Zr/ Nb, Zr/Ti, Nb/Y, Th/Cr, Th/Co, La/Co, K/Rb sono molto simili a quelli delle rocce del basamento metamorfico costituente il segmento Peloritano del blocco continentale europeo. Tuttavia, dal momento
Coupled lattice Boltzmann method–discrete element method model for gas–liquid–solid interaction problems

Abstract In this paper, we propose a numerical model to simulate gas–liquid–solid interaction problems, coupling the lattice Boltzmann method and discrete element method (LBM–DEM). A cascaded LBM is used to simulate the liquid–gas flow field using a pseudopotential interaction model for describing the liquid–gas multiphase behaviour. A classical DEM resorting to fictitious overlaps between the particles is used to simulate the multiple-solid-particle system. A multiphase fluid–solid two-way coupling algorithm between LBM and DEM is constructed. The model is validated by four benchmarks: (i) single disc sedimentation, (ii) single floating particle on a liquid–gas interface, (iii) sinking of a horizontal cylinder and (iv) self-assembly of three particles on a liquid–gas interface. Our simulations agree well with the numerical results reported in the literature. Our proposed model is further applied to simulate droplet impact on deformable granular porous media at pore scale. The dynamic droplet spreading process, the deformation of the porous media (composed of up to 1277 solid particles) as well as the invasion of the liquid into the pores are well captured, within a wide range of impact Weber number. The droplet spreading dynamics on particles is analysed based on the energy budget, which reveals mechanisms at play, showing the evolution of particle energy, surface energy and viscous dissipation energy. A scaling relation based on the impact Weber number is proposed to describe the maximum spreading ratio.
Perceived Value Mediation Model to Improve Customer Expectation and Loyalty

Consumer loyalty is a determinant of the company's progress. Meanwhile, the number of consumers who come needs to be balanced with full-service provision to increase consumer loyalty. In 2017-2019 Toyota servicing decreased quite drastically. The strategy to maintain loyal customers starts from fulfilling expectations and providing a good value perception to create consumer loyalty to the company. The research technique used in this research is descriptive verification. The sampling technique used is non-probability with the incidental sampling method; the number of samples used is 150 respondents who have used service services at Toyota, Bandung Indonesia. The descriptive research results show that the respondents' responses to the variable expectations, perceived consumer value, and customer loyalty services in Toyota are included in the Good category. Perception of consumer value. The study's findings show that, through the dimensions used, the reliability, responsiveness, assurance, facilities, and employees influence consumer loyalty. Furthermore, it is found that there is an indirect influence between the perceived value of increasing the relationship between customer expectations and customer loyalty. Examining the problem's phenomenon, the researchers focused on studying the impact of customer expectation, perceived value, and customer loyalty.
Electrospun Nanofibrous Conduit Filled with a Collagen-Based Matrix (ColM) for Nerve Regeneration

Traumatic nerve defects result in dysfunctions of sensory and motor nerves and are usually accompanied by pain. Nerve guidance conduits (NGCs) are widely applied to bridge large-gap nerve defects. However, few NGCs can truly replace autologous nerve grafts to achieve comprehensive neural regeneration and function recovery. Herein, a three-dimensional (3D) sponge-filled nanofibrous NGC (sf@NGC) resembling the structure of native peripheral nerves was developed. The conduit was fabricated by electrospinning a poly(L-lactide-co-glycolide) (PLGA) membrane, whereas the intraluminal filler was obtained by freeze-drying a collagen-based matrix (ColM) resembling the extracellular matrix. The effects of the electrospinning process and of the composition of ColM on the physicochemical performance of sf@NGC were investigated in detail. Furthermore, the biocompatibility of the PLGA sheath and ColM were evaluated. The continuous and homogeneous PLGA nanofiber membrane had high porosity and tensile strength. ColM was shown to exhibit an ECM-like architecture characterized by a multistage pore structure and a high porosity level of over 70%. The PLGA sheath and ColM were shown to possess stagewise degradability and good biocompatibility. In conclusion, sf@NGC may have a favorable potential for the treatment of nerve reconstruction.
First-principles study of the conduction mechanism in tantala-based resistive memory devices

Herein, adopting the Pt/Ta2O5/Pt stack, first-principles calculations are carried out to investigate the conduction mechanism of tantala-based resistive switching devices. We first study the formation of conducting filament composed of the TaO2-x from the Ta2O5 matrix by modulating the number of oxygen vacancies and find that the TaO2-x filament formation is initiated from the metal contact through the redox reaction. Subsequent current-bias voltage characteristic analyses reveal that the Ta2O5 matrix with ~1. 4 × 1020 cm−3 oxygen vacancy concentration assumes the low resistance state. It will be emphasized that the conduction mechanism behind the tantala-based resistive switching originates from the carrier concentration variation with the Schottky barrier height modulation according to the oxygen vacancy concentrations. The microscopic understanding accumulated in this study will provide an insight into the development of next-generation tantala-based resistive switching devices.
Exploration of How Polyvagal Theory and Autonomic Nervous System Impact Organizational Performance Through Reduced Employee Turnover and Improved Work Culture

The evolution of corporate practices has made it more challenging for modern workers to remain in their respective roles for long periods, leading to high turnover. Accordingly, the employee turnover problem can be investigated by assessing the factors that encourage employees to shift from the safe and calm state to the fight or flight state. The Polyvagal Theory (PVT) is an innovative framework for understanding the relationship between the autonomic nervous system, emotional regulation, and social engagement. This study’s primary objective is to investigate the emerging trends in modern human resource management practices that encourage workers to resign prematurely. The findings divulge that the PVT can allow organizations to improve employee retention by implementing strategies that appeal to the workers’ psychological and emotional wellbeing.
On the spatio-temporal radial growth response of four alpine treeline species to climate across central Labrador, Canada

This thesis applies standard dendrochronological techniques to contrast and compare between the radial growth responses of alpine treeline species across a climate gradient in central Labrador, Canada. Four species were examined: black spruce (Picea mariana (Mill.) B.S.P.), white spruce (Picea glauca (Moench) Voss), balsam fir (Abies balsamea (L.) Mill. ) and eastern larch (Larix lahcina (DuRoi) K. Koch). Eighteen tree ring width (TRW) chronologies were constructed at five alpine treeline sites across a 600 km long transect from the Labrador Sea to the Quebec border. The effects of climate and forest disturbance events on radial tree growth were examined at various spatial and temporal scales. -- This study is the first to conduct dendrochronological studies on black spruce, fir and larch in Labrador and to use samples from across central Labrador. The chronology statistics are in accordance with regional publications, with the exception of the larch chronologies, which have exceptionally high sensitivity values (MS>0.3). These results suggest that the radial growth response of each one of the tree species sampled is uniquely adapted to monthly and/or seasonal temperature and precipitation values during the growing season and, in some cases, to winter snowfall amounts. These climate sensitivities are also site-specific and indicative of a strong reliance on the proximity of the Labrador Sea. This study simultaneously compares the radial growth patterns of the four alpine treeline species in a region that, to date, has been largely understudied. As a result, this study has identified possible periods of outbreaks of spruce budworm (Choristoneura fumiferana (Clem.)) and larch sawfly (Pristiphora erichsonii Hartig), which are synchronous with outbreaks reported from eastern Quebec. The intensity and frequency of these insect outbreaks is also climate-dependent, and can be so severe as to mask the climate sensitivity in the larch trees. A palaeoclimatic reconstruction (1847-2004) using merged spruce chronologies at one of the sites, the Mealy Mountains, is in accordance with other published records but has weak significance values due to a high degree of variability in climate sensitivity throughout the instrumental record (1942-present). The high degree of variability in the climate sensitivity of spruce trees across central Labrador is partly due to the effect of spruce budworm outbreaks on the radial growth of trees, but other site-specific factors such as moisture effects must also be contributing to the variability. Further studies are needed to determine the cause of this episodic divergence in the relationship between radial tree growth and climate in central Labrador in order to comprehend more fully which environmental factors influence radial tree growth, and how this changes with time.
Ensembled Seizure Detection Based on Small Training Samples

This paper proposes an interpretable ensembled seizure detection procedure using electroencephalography (EEG) data, which integrates data driven features and clinical knowledge while being robust against artifacts interference. The procedure is built on the spatially constrained independent component analysis supplemented by a knowledge enhanced sparse representation of seizure waveforms to extract seizure intensity and waveform features. Additionally, a multiple change point detection algorithm is implemented to overcome EEG signal's non-stationarity and to facilitate temporal feature aggregation. The selected features are then fed into a random forest classifier for ensembled seizure detection. Compared with existing methods, the proposed procedure has the ability to identify seizure onset periods using only a small proportion of training samples. Empirical evaluations on publicly available datasets demonstrated satisfactory and robust performance of the proposed procedure.
A Machine Learning-Based Online Human Motion Recognition System With Multiple Classifier for Exoskeleton

Motion recognition and classification are crucial for exoskeleton applications in rehabilitation, activities of daily living (ADL), and entertainment. Accurate activity analysis is essential to improve human–machine coupling. However, conventional single-task detection systems, which focus on specific requirements, such as finite gait events, mode transitions (such as standing-to-sitting), or locomotion speed, are inadequate and cannot handle the complex and varied walking environments encountered during ADL. This article proposes a real-time, multiclassifier system that incorporates three artificial neural network (ANN) models to simultaneously recognize five gait events, nine activities, and walking speeds ranging from 0 to 8 km/h. Three machine-learning (ML) algorithms were fused and utilized to minimize reliance on manual thresholding methods. The activity detection, speed recognition, and gait detection were performed using a 1-dimension convolutional neural network (1-D-CNN), regression ANN (RANN), and multilayer perceptron (MLP), respectively. The experiment was conducted with five subjects wearing a developing cable-driven exoskeleton. The results demonstrate that the proposed portable motion recognition system accurately detected various movements, including gait events with 99.6% accuracy and a time error of 33 ms, a recognize speed with a mean square error (MSE) of 0.12, and an activity detection with 96.8% accuracy.
Face image generation and feature visualization using deep convolutional generative adversarial networks

Generative Neural Networks (GAN) aims to generate realistic and recognizable images, including portraits, cartoons and other modalities. Image generation has broad application prospects and important research value in the fields of public security and digital entertainment, and has become one of the current research hotspots. This article will introduce and apply an important image generation model called GAN, which stands for Generative Adversarial Network. Unlike recent image processing models such as Variational Autoencoders (VAE), The discriminative network evaluates potential candidates while the GAN generates candidates. As a result, the discriminative network distinguishes created and real candidates, while the generative network learns to map from a latent space to an interest data distribution. In this article, the GAN model and some of its extensions will be thoroughly applied and implemented based on the dataset of CelebA, and details will be discussed through the images and graphs generated by the model. Specific training methods for various models and optimization algorithms can be produced by the GAN framework. The experiment’s findings in this article will show how the framework’s potential may be quantified and qualitatively assessed using the samples that were produced.
Modeling the Pumping Behavior of Macroscopic Lead Structures on Shaft Counterfaces of Rotary Shaft Seals

The leak tightness of the sealing system rotary shaft seal is based on the formation of an active back-pumping effect of the sealing ring. Here, the sealing ring pumps the fluid in the sealing gap back into the housing. However, this active sealing mechanism is disturbed by so-called “lead structures”. Lead structures include all types of directional structures on the sealing counterface which create rotation-dependent axial fluid pumping. Lead-affected sealing counterfaces can thus cause leakage or insufficient lubrication of the sealing contact. To ensure leak tightness, lead must be avoided or tolerated. This article investigates how different structural characteristics of lead affect the amount of fluid pumped by the shaft surface. For this purpose, 26 shafts are subjected to surface analyzing methods and an experimental pumping rate test. The interaction of various geometric features of the lead structures and their combined effect on the pumping capacity is modeled based on the measured data. Appropriated correlation models are discussed and relationships between shaft lead and its pumping effect are shown. The aim is to estimate shaft pumping rates based on surface measurements in future. The results contribute to the derivation of measurable tolerance values for lead and to the prevention of leakage.
Modelling low-frequency volcanic earthquakes in a viscoelastic medium with topography

SUMMARY Magma properties are fundamental to explain the volcanic eruption style as well as the generation and propagation of seismic waves. This study focusses on magma properties and rheology and their impact on low-frequency volcanic earthquakes. We investigate the effects of anelasticity and topography on the amplitudes and spectra of synthetic low-frequency earthquakes. Using a 2-D finite-difference scheme, we model the propagation of seismic energy initiated in a fluid-filled conduit embedded in a homogeneous viscoelastic medium with topography. We model intrinsic attenuation by linear viscoelastic theory and we show that volcanic media can be approximated by a standard linear solid (SLS) for seismic frequencies above 2 Hz. Results demonstrate that attenuation modifies both amplitudes and dispersive characteristics of low-frequency earthquakes. Low frequency volcanic earthquakes are dispersive by nature; however, if attenuation is introduced, their dispersion characteristics will be altered. The topography modifies the amplitudes, depending on the position of the seismographs at the surface. This study shows that we need to take into account attenuation and topography to interpret correctly observed low-frequency volcanic earthquakes. It also suggests that the rheological properties of magmas may be constrained by the analysis of low-frequency seismograms.
Improved 1vs1 Air Combat Model With Self-Play Soft Actor-Critic and Sparse Rewards

The within-visual-range (WVR) air-to-air combat problem, also known as dogfight, has been of great interest to researchers and is known to be challenging to address with reinforcement learning due to its extremely nonlinear dynamics. The previous work on dogfight has required the design of complicated reward functions to control high-dimensional state and action spaces and the design of suitable adversaries to effectively engage any opponent. In this paper, we present a self-play soft actor-critic (SPSAC) framework that is specialized for training one-on-one dogfight. It combines self-play (SP) using the proposed league configuration and dense-to-sparse (D2S) reward based on the recurrent soft actor-critic (RSAC) algorithm. We built a simulated air combat environment to evaluate our method, focusing on whether the trained models are robust against various opponents. SPSAC responds to all opponents more robustly than models trained by a straightforward self-play algorithm and greatly surpasses models trained using conventional reinforcement learning in terms of training performance. Moreover, the inherent nature of SPSAC eliminates the risk of converging to a suboptimal policy after the complex reward function is designed or the risk of overfitting by encountering fewer opponents.
Interception of vertically approaching objects: temporal recruitment of the internal model of gravity and contribution of optical information

Introduction: Recent views posit that precise control of the interceptive timing can be achieved by combining on-line processing of visual information with predictions based on prior experience. Indeed, for interception of free-falling objects under gravity’s effects, experimental evidence shows that time-to-contact predictions can be derived from an internal gravity representation in the vestibular cortex. However, whether the internal gravity model is fully engaged at the target motion outset or reinforced by visual motion processing at later stages of motion is not yet clear. Moreover, there is no conclusive evidence about the relative contribution of internalized gravity and optical information in determining the time-to-contact estimates. Methods: We sought to gain insight on this issue by asking 32 participants to intercept free falling objects approaching directly from above in virtual reality. Object motion had durations comprised between 800 and 1100 ms and it could be either congruent with gravity (1 g accelerated motion) or not (constant velocity or -1 g decelerated motion). We analyzed accuracy and precision of the interceptive responses, and fitted them to Bayesian regression models, which included predictors related to the recruitment of a priori gravity information at different times during the target motion, as well as based on available optical information. Results: Consistent with the use of internalized gravity information, interception accuracy and precision were significantly higher with 1 g motion. Moreover, Bayesian regression indicated that interceptive responses were predicted very closely by assuming engagement of the gravity prior 450 ms after the motion onset, and that adding a predictor related to on-line processing of optical information improved only slightly the model predictive power. Discussion: Thus, engagement of a priori gravity information depended critically on the processing of the first 450 ms of visual motion information, exerting a predominant influence on the interceptive timing, compared to continuously available optical information. Finally, these results may support a parallel processing scheme for the control of interceptive timing.