Miracle-dz/UniFAR-FTUs huggingface dataset:
Melt-enhanced strain localization and phase mixing in a large-scale mantle shear zone (Ronda peridotite, Spain)

Abstract. Strain localization in upper-mantle shear zones by grain size reduction and the activation of grain-size-sensitive deformation mechanisms is closely linked to phase mixing. With its mylonitic grain size (50–100 µm) and well-mixed phase assemblage, the kilometer-scale shear zone at the northwestern boundary of the Ronda peridotite is, in this respect, no exception. In transects across the high-strain mylonitic into the low-strain tectonitic part of this shear zone, the following four dominant microstructural domains were identified: (1) olivine-rich matrix, (2) mixed matrix, (3) neoblast tails of clinopyroxene porphyroclasts, and (4) neoblast tails of orthopyroxene porphyroclasts. In these domains, phase mixing and its impact on strain localization were investigated by a combination of microstructural (optical microscopy), textural (EBSD), and geochemical (EPMA) analysis. The dominant microstructural domain of all samples is the mixed matrix composed of olivine, orthopyroxene, and clinopyroxene. Its homogenous distribution of interstitial pyroxenes contradicts mechanical mixing. Instead, extensive phase mixing under near-steady-state conditions is documented by the constant grain size and by phase boundary percentages > 60 % for the entire mylonitic unit and all the microstructural domains. Lobate phase boundaries, homogenous phase mixing, and secondary-phase distribution, as well as continuous geochemical trends that are independent of the microstructural domain, point to a reaction-driven, metasomatic formation of the mixed matrix and pyroxene porphyroclast tails in the entire shear zone. An OH-bearing metasomatism by small fractions of evolved melts is indicated by amphibole abundance in pyroxene neoblast tails, olivine B-type-crystallographic-preferred orientations (CPOs), and the microstructural consistency of the garnet–spinel (grt–spl) mylonites from both major peridotite massifs of the Gibraltar arc, Ronda, and Beni Bousera (Morocco). The established syn-deformational temperature of 800–900 ∘C at 1.95–2.00 GPa suggests that the metasomatism did not reset the equilibrium temperatures. Consistent geochemistry and phase assemblage in mylonites and tectonites but a change from equiaxial (tectonites) to wedge-shaped pyroxenes aligned parallel to the foliation (mylonites) point to a pre- to syn-deformational metasomatism, with the potential annealing of the tectonites. For the mylonitic mixed matrix, wedge-shaped pyroxenes, and neoblast tail formation in pyroxene porphyroclast stress shadows point to the activity of incongruent dissolution–precipitation creep. Apart from the dissolution–precipitation creep, strong CPOs of all major phases (ol, opx, and cpx) suggest dislocation creep as being the major deformation mechanism in the entire shear zone.
Solving a seismic mystery with a diver’s camera: A case of shallow water T -waves in the Persian Gulf

Sparse offshore coverage of seismic networks has hindered detailed studies of sub-marine earthquakes and their associated seismic hazard. We present results of our analysis of a diver’s recording of acoustic signals from an M L = 5 . 6 earthquake in the Persian Gulf. We model the signals as a set of several shallow water T phases the frequency and group velocity of which are determined by bathymetry. We show that the audio track from this recording can provide reliable estimates of earthquake location and seismic moment. We also show that the reported shaking in the southern Persian Gulf, > 170 km from the source of this small earthquake could result from T waves traveling through the entire width of the basin. Our results point to rudimentary and affordable underwater microphones similar to those used in the divers’ cameras as tools to build efficient, low-cost networks for the study of offshore events and early warning.
from a Survey Experiment in Turkey

This paper examines the role of social norms and political polarization in shaping vaccination attitudes and behaviors in the context of the Covid-19 pandemic. Using a large-scale representative survey experiment in Turkey, we first show that political affiliation is a strong predictor of attitudes towards vaccination. We then use standard economic games to measure the extent of polarization caused by subjects’ attitudes towards vaccination. We find that pro-and anti-vaxxers discriminate each other substantially. Furthermore, when pro-and anti-vaxxers perceive a political difference between them, this polarization is exacerbated. Finally, using randomized informational treatments, we show that the promotion of a broadly shared social identity might mitigate this outgroup discrimination.
Record statistics of a strongly correlated time series: random walks and Lévy flights

We review recent advances on the record statistics of strongly correlated time series, whose entries denote the positions of a random walk or a Lévy flight on a line. After a brief survey of the theory of records for independent and identically distributed random variables, we focus on random walks. During the last few years, it was indeed realized that random walks are a very useful ‘laboratory’ to test the effects of correlations on the record statistics. We start with the simple one-dimensional random walk with symmetric jumps (both continuous and discrete) and discuss in detail the statistics of the number of records, as well as of the ages of the records, i.e. the lapses of time between two successive record breaking events. Then we review the results that were obtained for a wide variety of random walk models, including random walks with a linear drift, continuous time random walks, constrained random walks (like the random walk bridge) and the case of multiple independent random walkers. Finally, we discuss further observables related to records, like the record increments, as well as some questions raised by physical applications of record statistics, like the effects of measurement error and noise.
Training in technological tools and performance of employees of a microfinance institution

The objective of this study was to identify the most appropriate training program in different software tools for employees of the commercial department of Compartamos Financiera S.A. in the regions of East Lima, Southern Lima, and Piura. The research was carried out during 2022, taking into account the latest trends and technological progress that influence this process. The data collection instrument used was a questionnaire, prepared with Google Forms, which consisted of 25 questions on a Likert scale. A total of 232 surveys were collected from a total of 250 employees. Internal consistency was determined with Cronbach’s alpha, and the relationship between the two variables was established by Spearman’s correlation hypothesis tests and Pearson’s chi-square test. The aforementioned tests found a positive relationship between the variables training in technological tools and
Enhanced net CO2 exchange of a semideciduous forest in the southern Amazon due to diffuse radiation from biomass burning

Abstract. Carbon cycling in the Amazon fundamentally depends on the functioning of ecosystems and atmospheric dynamics, which are highly intricate. Few studies have hitherto investigated or measured the radiative effects of aerosols on the Amazon and Cerrado. This study examines the effects of atmospheric aerosols on solar radiation and their effects on net ecosystem exchange (NEE) in an area of semideciduous tropical forest in the north of Mato Grosso. Our results show that for a relative irradiance (f) 1.10–0.67, a decrease in incident solar radiation is associated with a reduction in the NEE. However, an average increase of 25 %–110 % in NEE was observed when pollution levels and aerosol optical depth (AOD) were above ≈ 1.25 and f < 0.5. The increase NEE was attributed to the increase of up to 60 % in the diffuse fraction of photosynthetically active radiation. The change in AOD and f was mainly attributable to biomass burning organic aerosols from fires. Important influences on vapor pressure deficit (VPD) as well as air temperature (Tair) and canopy (LCT), induced by the interaction between solar radiation and high aerosol load in the observation area, were also noticed. On average, a cooling of about 3–4 ∘C was observed for Tair and LCT, and a decrease of up to 2–3 hPa was observed for VPD. Given the long-distance transport of aerosols emitted by burning biomass, significant changes in atmospheric optical properties and irradiance will impact the CO2 flux of semideciduous forests distributed in the region.
Consumer behaviour in e-Tourism: Exploring new applications of machine learning in tourism studies

Digital markets have altered how economic agents interact and have changed the behaviour of tourists. In addition, the COVID-19 pandemic has shown that it is necessary to constantly monitor the evolution of digital consumer behaviour and the factors that influence it, as they are dynamic elements that evolve over time. This paper analyses digital inequalities and validates the main factors influencing tourists to book online tourism services. This research uses a set of microdata with 69,752 and 23,779 observations to analyse the booking mode of accommodation and transportation services, respectively, obtained from the Resident Travel Survey of the National Statistics Institute of Spain during the period 2016-2021. The article confirms variations in the online consumer profile and in the trip's characteristics. One of the most relevant findings is the narrowing of the generational gap in the online contracting of tourist services. However, there are remaining digital inequalities, such as regional inequalities and others based on the education level and income of tourists. It is also highlighted that different types of trips, depending on the destination, the type of accommodation or transport have a different propensity to be booked through digital purchase channels. The accessibility to big data sources and recent advances in machine learning models have also made the methodologies for analysing digital consumer behaviour evolve and must be incorporated into tourism studies. This study compares the predictive performance of different methodologies in the context of e Tourism. In particular, we evaluate the potential predictive power that could be obtained using machine learning techniques to explain consumer behaviour in e-Tourism and use it as a benchmark to compare it with the results obtained using traditional statistical methods. The selected predictive evaluation metrics show that the logistic regression statistical model outperforms the predictive power of the Multilayer Perceptron neural network and presents values very close to the maximum predictive power achieved by the Random Forest algorithm.
The Clouds with the Shape of Karman Vortex Street in the Wake of Cheju Island, Korea

The properties of the clouds with the shape of Karman vortex street in the wake of Cheju Island, Korea are studied using satellite pictures which are taken approximately four hours apart. The results indicate that non-dimensional numbers and physical quantities obtained from the cloud vortex street satisfy theoretically as well as experimentally obtained criteria of Karman vortex street. The equation of the displacement of the cloud vortex street is derived through drag consideration. The displacement speed obtained from this equation agrees fairly well with the observed value. The speed is approximately 76 % of the undisturbed flow when the ratio between the spacing of two rows and that of individual vortex is 0.332. It is foundd that such a cloud vortex street appears in the wake of Cheju Island from late autumn to early spring when steady northerly wind of more than moderate intensity (above 10 kts) prevails and a well defined inversion is found at around 1000 meters or at about half the height of the mountain located in the center of the Island.
Determinants of Using Digital Learning Platforms in Higher Tourism Education Environment

The aim of this study is to identify the factors that affect students' engagement and satisfaction with digital learning platforms and to examine how these factors affect their intentions to use the digital learning platforms. Data were obtained from 324 undergraduates from different tourism and hotel faculties who went through a fully online learning experience during the COVID19 pandemic. Therefore, the study focused on digital textbook systems that were initiated during the COVID-19 outbreak and are still in use today. The findings showed that academic flexible material delivery and the teaching staff's role have a positive impact on perceived usefulness and that they also indirectly affect students' intentions to use digital learning platforms regularly. Additionally, it was discovered that the perceived usefulness had a direct positive influence on students' engagement, satisfaction, and intention to use the digital learning platforms continuously, as well as an indirect impact on usage intention via the students' engagement and satisfaction. These findings imply that, in light of student intention to use these platforms in perpetuity, flexible content delivery, a favorable teaching staff role, the perceived usefulness, student engagement, and student satisfaction should be considered in the design and development of the DLPs.
Development and validation of a reversible prototype system to test 1.5 kW SOC stacks

Reversible solid oxide cell technology is an effective solution to deal with the intermittent nature of renewable energy sources, by producing hydrogen during excess of electricity generation and performing the reverse operation when electricity is needed. Many modelling studies of reversible systems are available in literature but few of them have been effectively built and tested. This work presents the development of a compact reversible system demonstrator able to operate SOC stacks up to 1.5 kW. The balance of plant has been designed to optimize the heat management, by integrating two heat exchangers to pre-heat the inlet gases with the exhaust ones. A special attention has been paid to ensure the compactness and reduce the cost of the system, fitting all the equipment (furnace, heat exchangers, steam generator, condenser, load, power supply, gas panel and control system) into two standard racks. The prototype has been validated with a commercial SOC stack, leading to the production of 7 Nm 3 /day of H 2 and an electric production of 0.5 kW. Different operating regimes as well as the transition dynamics from
Inverse images of stable L\'evy processes

We establish a uniform Hausdorff dimension result for the inverse image sets of real-valued strictly $\alpha$-stable L\'evy processes with $1< \alpha\le 2$. This extends a theorem of Kaufman for Brownian motion. Our method is different from that of Kaufman and depends on covering principles for Markov processes.
Next Generation Video Compression Standards – Performance Overview

This paper offers a comparative experimental study of the performances of several novel video compression standards. The standards included in the survey are H.265 (HEVC), AOMedia Video (AV1), and H.266 (VVC), all of them proposed relatively recently, and still developing. For a comparison reasons, the legacy H.264 (MPEG-4 AVC) is also included in the study. The performances are measured by a rate-distortion grading, utilizing different bitrates for each standard, and obtaining several objective and subjective measures for the output video quality. The results generally reveal the expected performance differences between standards, corresponding to their complexity; however, there are also some interesting and surprising findings.
Comparing image processing techniques for bubble separation and counting in underwater leaks

This work aims to present a study realized to improve the counting of bubbles in underwater videos for applications of oil spill or natural exudation detection. It explains all the accomplished results through used techniques. The numbers of bubbles and their positions are compared versus human observations and with previous techniques. It shows an improvement in the detection, separation and counting of very close bubbles that are presented in terms of accuracy, sensitivity, specificity, predictivity, efficiency, Youden index and F-Score. The used video and frames are available for future comparison.
Accelerating Packet Processing in Container Overlay Networks via Packet-level Parallelism

Overlay networks serve as the de facto network virtualization technique for providing connectivity among distributed containers. Despite the flexibility in building customized private container networks, overlay networks incur significant performance loss compared to physical networks (i.e., the native). The culprit lies in the inclusion of multiple network processing stages in overlay networks, which prolongs the network processing path and overloads CPU cores. In this paper, we propose mFlow, a novel packet steering approach to parallelize the in-kernel data path of network flows. mFlow exploits packet-level parallelism in the kernel network stack by splitting the packets of the same flow into multiple micro-flows, which can be processed in parallel on multiple cores. mFlow devises new, generic mechanisms for flow splitting while preserving in-order packet delivery with little overhead. Our evaluation with both micro-benchmarks and real-world applications demonstrates the effectiveness of mFlow, with significantly improved performance – e.g., by 81% in TCP throughput and 139% in UDP compared to vanilla overlay networks. mFlow even achieved higher TCP throughput than the native (e.g., 29.8 vs. 26.6 Gbps).
Nonsingular Terminal Sliding-mode Lateral Control of an Autonomous Vehicle with Global Fixed-time Stability Guarantees

The present paper develops an output feedback fixed-time sliding mode control (FxTSMC) approach for lateral trajectory tracking of an autonomous vehicle subject to disturbances. Firstly, a nonsingular terminal sliding manifold is constructed to force the orientation error and lateral deviation to converge to desired values in finite time. Secondly, using variable exponent coefficients, a switching control law is proposed to provide fixed-time stability against external disturbances. By adjusting the control design parameters, the upper bound of the settling time for the closed-loop lateral system can be attained arbitrarily minimal without depending on the systems initial conditions. The effectiveness of FxTSMC is demonstrated by comparing the performance of closed-loop systems of the supplied controller and other existing controllers.
In-Vivo Validation of Smart Device for on Body Hydration Monitoring

Dehydration can lead to serious health complications if not timely prevented, especially in the elderly. In-house point-of-care hydration monitoring devices can help to predict and detect such conditions. However, at the moment, a fully wearable medical healthcare device for dehydration monitoring is not available. We propose here the design and the preliminary in-vivo experiments of a multisensory wearable device for continuous dehydration monitoring, which includes inkjet printed flexible sensors on a bracelet-shaped substrate for temperature, body impedance, and skin hydration monitoring, combined with a small-size, low-power, fully custom-designed front-end read-out circuit. In particular, after shortly presenting the calibration and the performances of the sensors in-vitro, we report an in-vivo characterization and application case studies showing the use of the proposed platform to discriminate different hydration levels. Overall, the reported results represent interesting insights into in-vivo usability of the proposed approach to discriminate different levels of hydration.
Retail Demand Forecasting Using Sequence to Sequence Long Short-Term Memory Networks

Demand forecasting is crucial for a retail business as it can greatly affect everything ranging from promotion, pricing, product assortment and inventory. Building a reliable and useful demand forecasting model is still a challenging task. Machine learning techniques used for demand forecasting including Random Forest Regressor and Support Vector Regressor are inadequate when dealing with time series. Recent works show that Long Short-Term Memory (LSTM) networks can learn non-linear relationships and time-series specific information from retail time series data. In this paper, a methodology based on Sequence to Sequence Long Short-Term Memory (Seq2Seq LSTM) network is proposed to tackle short-term retail demand forecasting problem. The Seq2Seq architecture commonly used for language translation is adapted to retail demand forecasting to improve LSTM's ability of learning long-range temporal dependencies from retail time series data. Experiments are evaluated with different input sequence lengths on store item sales dataset with daily resolution data. Bayesian Optimization is conducted to tune models' hyperparameters and examine whether it could enhance the prediction accuracy of the models. In order to gauge the robustness of the proposed forecasting model, it is compared against Standard LSTM and Vanilla RNN.
Cloud-to-Ground Lightning Activity over Land and Ocean in Peninsular Malaysia

This paper presents an investigation on the characteristics of cloud-to-ground (CG) lightning over land and ocean in Peninsular Malaysia. The study aims to investigate the characteristics of negative and positive CG in Peninsular Malaysia, particularly over land and ocean on the west coast region and over the Malacca Straits. By analyzing 2020 lightning data recorded by the lightning locating system (LLS) operated by TNB Research (TNBR), we analyzed the negative and positive CG in terms of their occurrences, stroke density, and the peak currents. Six circular regions with a 15 km radius were selected: three (3) over the land and three (3) over the ocean, each having distinct characteristics from the others. We found that most of the CG lightning occurred over land than ocean but there is only a slight difference of 2.4% between the two. We also found that the average peak current for negative CG is higher over the ocean with a geometric mean of 25.7 kA whereas for land, the average value is 21.9 kA. For positive CG, we found that the average peak current is also higher over the ocean with a geometric mean of 32.3 kA whereas for land, the average value is 28.7 kA. The higher occurrence of the CG lightning over land could be due to the heat effect caused by the increase in the development over the land region. The higher intensity of the CG peak currents over the ocean could be due to larger aerosol emission in the area and the higher conductivity of the seawater.
Robust PWA control Based on State Space Partitions

Based on a convex lifting approach for the state partitions, the present paper studies the sensitivity of piecewise affine (PWA) control laws for linear discrete-time systems with state and input constraints. In particular, the goal is to study the impact of perturbations on the state space partitions. Unlike the current results, which concentrate on the local disturbance of vertex or hyperplane representations, each state partition in this work is considered to be potentially affected. The aim is to characterize the admissible perturbation. Practically, a collection of maximal admissible regions (CMAR) is defined to characterize the maximum perturbation for each set within the original state partition. Based on CMAR, which guarantees the positive invariance of the original controller, and using the induced fragility margin for the associated convex lifting (FMCL) is proposed to realize a PWA control in an implicit (region-free) form. A numerical example illustrates the effectiveness of the new PWA controller.