Miracle-dz/UniFAR-FTUs huggingface dataset:
A model for the study of the Shubnikov-de Haas and the integer quantum Hall effects in a two dimensional electronic system

Up to know all the experimental results concerning the integer and fractional quantum Hall effect are related to semiconductor heterostructures (and more recently with graphene). The common characteristic of all these systems is the presence of a reservoir of electrons, which, in fact, in the initial stage is the source of the electrons, providing the two-dimensional electron gas (2DES). Then, any physical realization of a 2DES is necessarily embedded in a 3D structure, which establishes the Fermi level. Hence, the 2DES appears to be an open system. In this paper we present an analytical approach to the integer quantum Hall effect (IQHE) and the Shubnikov-de Haas (SdH) phenomena in the 2DES, basing us in fundamental principles and showing the secondary role of the localized electron states in both phenomena. In fact, we show that the IQHE is a consequence of the fluctuations of electrons in the 2DES. Once we obtain the density of states of the 2DES under the application of a magnetic field we calculate both magnetoconductivities (diagonal and Hall) deducing them from the Boltzman semiclassical equation. The model proposed reproduces both phenomena, the width of the Hall plateaus (with the precision reached in the experimental measurements, of the order of 10-8-10-9) and the corresponding minima of the diagonal magnetoresistivity, and also the dependence with temperature of the IQHE and SdH.
Nonlinear dynamics at the interface of two-layer stratified flows over pronounced obstacles

The flow of a two--layer stratified fluid over an abrupt topographic obstacle, simulating relevant situations in oceanographic problems, is investigated numerically and experimentally in a simplified two--dimensional situation. Experimental results and numerical simulations are presented at low Froude numbers in a two-layer stratified flow and for two abrupt obstacles, semi--cylindrical and prismatic. We find four different regimes of the flow immediately past the obstacles: sub-critical (I), internal hydraulic jump (II), Kelvin-Helmholtz at the interface (III) and shedding of billows (IV). The critical condition for delimiting the experiments is obtained using the hydraulic theory. Moreover, the dependence of the critical Froude number on the geometry of the obstacle are investigated. The transition from regime III to regime IV is explained with a theoretical stability analysis. The results from the stability analysis are confirmed with the DPIV measurements. In regime (IV), when the velocity upstream is large enough, we find that Kelvin-Helmhotz instability of the jet produces shedding of billows. Important differences with flows like Von Karman's street are explained. Remarkable agreement between the experimental results and numerical simulations are obtained.
On the correlation structure of continuous and discrete point rainfall

This paper proposes new analytical results based on the theory of stochastic processes that establish a theoretical dependence of point rainfall variance on the scale of observation under somewhat general assumptions. It is shown that power law scaling, commonly assumed by analyses and models of point rainfall, cannot hold over all scales of observation and that an “inner” regime exists for small sampling intervals where the variance is a quadratic function of interval length. A power law scaling regime is shown to exist for larger aggregation intervals, whose characteristics depend on the memory of the continuous rainfall process. The presence of a “transition” regime between the inner and the scaling regime is also shown and may explain the deviations from a power law scaling behavior in observed rainfall reported in the literature. Furthermore, the application of the theory to rainfall data from a representative variety of climate types shows that fractal and multifractal analysis techniques and models may indeed fail to capture observed rainfall properties over wide ranges of aggregation scales. Finally, correlation structures with “short‐” and “long‐”term memory are considered and different theoretical relationships expressing rainfall variance as a function of aggregation interval are derived and tested against rainfall observed at different locations with aggregation timescales ranging from 15 min to 2 days. The theoretical and observational analyses show that both finite and infinite memory rainfall processes may be observed in nature.
Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system’s accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before.
Applications of biopolymer coatings in biomedical engineering

In this paper, a comprehensive overview of recent developments in the many different types of biopolymer coatings used in biomedical applications is presented. Biopolymer coatings have tremendous promise in various biological applications due to their adaptability. Polymer coatings may be used to improve surface qualities to fulfill certain criteria or to include extra capabilities for various biomedical applications. This analysis focuses mostly on certain polymers for usage in coating applications as well as a variety of polymer coatings that provide enhanced functionalization. The most recent findings from relevant research have been presented on using polymer coatings on nanoparticles for biological applications. Moreover, the latest research on biopolymer coatings for im­proved tissue engineering and drug delivery on various substrates and nanoparticles has been reported. Additionally, the most recent scientific breakthroughs have been compiled.
A Review of Path-Planning Approaches for Multiple Mobile Robots

Numerous path-planning studies have been conducted in past decades due to the challenges of obtaining optimal solutions. This paper reviews multi-robot path-planning approaches and decision-making strategies and presents the path-planning algorithms for various types of robots, including aerial, ground, and underwater robots. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. The decision-making strategies mainly consist of centralized and decentralized approaches. The trend of the decision-making system is to move towards a decentralized planner. Finally, the new challenge in multi-robot path planning is proposed as fault tolerance, which is important for real-time operations.
Hierarchical Pointing on Distant Displays with Smart Devices

Abstract Large display interaction has undergone considerable growth, whereas distant pointing poses challenges due to distance limitations and input devices. Smart devices with superior computing power and rich input capabilities aim to surmount these restrictions. However, pointing interactions in single-layer have limited accuracy in large display scenarios. We investigate a hierarchical pointing approach that couples multiple control modes of rotation attitude, touch operation, and pressure simulation with different cursor strategies. Specifically, it covers the dual-layer cursor with the hierarchical structure, the pressure-dependent multistage gain cursor, and the cursor projection technique based on the target acquisition mechanism. We contrast seven distant pointing techniques in two groups of experiments. Our results indicate that the hierarchical interaction strategy improves user performance, and cursor projection achieves an optimal trade-off between velocity and accuracy for high Fitts’ index of difficulty (ID) contexts.
Projected dynamics of kinetic equations with energy diffusion in spaces of orthogonal polynomials

We examine a partial differential equation describing energy diffusion in velocity space, analyzing its projected dynamics in finite-dimensional subspaces of orthogonal polynomials. Comparison with an expensive but highly accurate spectral transform approach reveals that for a large class of (mildly singular) initial conditions, using polynomials that are orthogonal with respect to a non-classical weight function can be many orders of magnitude more accurate than classical Hermite polynomials for the same computational work. For the new polynomials, the exact solution is found to leave the subspace for a time, but return (up to roundoff accuracy) to the same point evolved to by the projected dynamics in that time. By contrast, using classical polynomials, the exact solution differs significantly from the projected dynamics solution when it arrives in the subspace. We also explore the connection between eigenfunctions of the projected evolution operator and (non-normalizable) eigenfunctions of the full evolution operator, as well as the effect of truncating the computational domain. Techniques for suppressing floating-point arithmetic errors are discussed, including use of the singular value decompositon to solve a better-conditioned version of the stiffness-mass generalized eigenvalue problem.
Probability of Study Success (PrSS) Evaluation Based on Multiple Endpoints in Late Phase Oncology Drug Development

Abstract Phase 2 Oncology clinical trials are increasingly designed with multiple primary endpoints, such as progression-free survival (PFS) and overall survival (OS). While this gives a clearer picture of treatment benefit than trials with a single endpoint, it complicates the decision to begin a registration (phase 3) trial following a phase 2 trial. Methods for calculating the probability of study success (PrSS) assume the distribution of OS is well understood, but rarely consider the case of multiple primary endpoints. We introduce the BAMBOO method (BAyesian Model that Brings phase 2 cOmposite endpOints) to address this gap. We propose modeling the phase 2 log hazard ratios for PFS and OS as a bivariate normal random variable, with the unknown correlation parameter estimated with a meta-analysis or via simulation. The PrSS of a phase 3 study with multiple primary endpoints study can then be obtained using the posterior predictive distribution of this bivariate normal random variable. We provide methods to include additional surrogate endpoints, such as objective response rate, by extending the model to a multivariate normal of arbitrary dimension. Simulation results suggest the proposed method can better address the known prior-data conflict issue comparing to existing approaches, while borrowing historical data with caution is generally recommended under high discrepancy and small sample size. Finally, we provide an example of how BAMBOO can aid in planning a phase 3 trial.
The Use of Yeast in Biosensing

Yeast has been used as a model for several diseases as it is the simplest unicellular eukaryote, safe and easy to culture and harbors most of the fundamental processes that are present in almost all higher eukaryotes, including humans. From understanding the pathogenesis of disease to drug discovery studies, yeast has served as an important biosensor. It is not only due to the conservation of genetics, amenable modification of its genome and easily accessible analytical methods, but also some characteristic features such as its ability to survive with defective mitochondria, making it a highly flexible microbe for designing whole-cell biosensing systems. The aim of this review is to report on how yeasts have been utilized as biosensors, reporting on responses to various stimuli.
Achieving an Active Lifestyle within a Walkable Environment: Assessment of Before and During the COVID-19 Pandemic

Having an active lifestyle has been regarded as one of the main factors for maintaining a healthy and happy life. However, previous studies have pointed out that most Malaysian adults are reported to be physically inactive due to lifestyle changes such as increasingly sedentary lifestyles, decreased physical activity (PA), and growing reliance on automobiles for mobility. Subsequently, the COVID-19 pandemic has further impacted the quality of the urban population’s active lifestyles, particularly the disruption resulting from movement restriction control orders which has limited the ability to maintain an active lifestyle. This scenario has the potential to negatively impact the health and mental state of the population if it continues without intervention by relevant stakeholders. Therefore, this study examined the patterns of PA and body mass index (BMI) and assessed the association between PA and a walkable environment to maintain an active lifestyle in Kuala Lumpur city centre before and during the COVID-19 pandemic. In this study ‘walkable environment’ refers to a physical attribute that encourages walking for functional and recreational purposes and ‘active lifestyle’ refers to walking being the main physical PA that contributes to health as part of people’s regular routine. This study adopted the mixed method approach through an online questionnaire survey, combined with the observation of selected areas and content analysis of written documents. The findings show that most respondents perceived that walking was the main PA that contributes to health. In terms of effects, walkable environments have been shown to have a positive impact on PA and health by encouraging an active lifestyle.
Rural electrification in Africa: a case study of Yebu community solar minigrid

Using field observation and focus group semi-structured interviews, we explored rural electrification dynamics within a rural community setting. We investigated the issues surrounding the emergence, deployment, operation and management of solar minigrid technology within Yebu community in the Federal Capital Territory, north-central Nigeria, and the associated issues that emerged over time. The study revealed that: (1) many solar minigrid energy access interventions do not have a viable business and sustainability plan that guarantees long term impact; (2) a lack of participatory decision making by all concerned stakeholders (from the project conception stage) has hindered the potential of many solar minigrid energy access projects to achieve their intended developmental and economic benefits. The paper concludes by highlighting the key recommendations that can help address these challenges.
Dissertation: Geodesics of Random Riemannian Metrics

We introduce Riemannian First-Passage Percolation (Riemannian FPP) as a new model of random differential geometry, by considering a random, smooth Riemannian metric on $\mathbb R^d$. We are motivated in our study by the random geometry of first-passage percolation (FPP), a lattice model which was developed to model fluid flow through porous media. By adapting techniques from standard FPP, we prove a shape theorem for our model, which says that large balls under this metric converge to a deterministic shape under rescaling. As a consequence, we show that smooth random Riemannian metrics are geodesically complete with probability one. In differential geometry, geodesics are curves which locally minimize length. They need not do so globally: consider great circles on a sphere. For lattice models of FPP, there are many open questions related to minimizing geodesics; similarly, it is interesting from a geometric perspective when geodesics are globally minimizing. In the present study, we show that for any fixed starting direction $v$, the geodesic starting from the origin in the direction $v$ is not minimizing with probability one. This is a new result which uses the infinitesimal structure of the continuum, and for which there is no equivalent in discrete lattice models of FPP.
EFL Students’ Perceptions of Collaborative Writing during Remote Teaching

Whether online or face-to-face, research on collaborative writing has been widely conducted to explore its theoretical and pedagogical merits for language learning. Despite positive evidence on its benefits, more studies need to explore its implementation under specific circumstances such as teaching and learning during the pandemic Covid-19. This paper reports the EFL pre-service teachers’ perceptions and attitudes of their experiences and perceived affordances of collaborative writing toward their language and soft skill development. Data were gathered from the three questionnaires administered after students finished their collaborative writing projects: poetry infographic, descriptive text, and discussion text. Additionally, students’ reflective notes after completing all tasks were used to triangulate the data. The findings showed students’ positive perceptions of their collaborative writing experiences across the three text genres. The study also revealed students’ agreement on the affordances of collaborative writing for their language and soft skill development. Pedagogical implications are also discussed.
Seed positioning in extruded pellets: does it matter?

Extruded pellets containing activated carbon (AC) can be used to sow native seeds while simultaneously applying herbicide to control invasive species. Incorporating AC in pellets has been demonstrated to protect native seeds; however, there may be unintended detrimental impacts to seedling emergence. We aimed to optimize seed position within pellets to maximize emergence and survival of the perennial shrub Jacksonia furcellata. Seeds were positioned at 2 mm (top), 6 mm (middle), and 12 mm (bottom) within pellets (with or without AC), sown on or below the soil surface, and compared to non‐pelleted seeds sown under the soil surface in the equivalent positions (2, 6, and 12 mm depth). Trays were treated with a pre‐emergent herbicide (Simazine) or left unsprayed. Emergence (without herbicide) was significantly higher from seeds positioned at the bottom of pellets without AC sown on the soil surface (70%), compared to non‐pelleted seeds sown at the bottom (12 mm below the soil surface; 57%). However, emergence was inhibited when seeds were positioned in the middle (6 mm) of pellets with AC (32%). When treated with Simazine, survival was highest from seeds positioned at the bottom of pellets with AC (60%), compared to pellets without AC (15%) and non‐pelleted seeds sown at the bottom (12 mm below the soil surface; 15%). Jacksonia furcellata seeds positioned at the bottom of pellets, sown on the soil surface, shows promise to minimize negative impacts to emergence, and maximize herbicide protection. Further testing with additional species is required to refine pellet production (e.g. recipe, extrusion, and shape) for optimal emergence.
Monodromy calculatons of fourth order equations of Calabi-Yau type

This paper contains a preliminary study of the monodromy of certain fourth order differential equations, that were called of Calabi-Yau type in math.NT/0402386. Some of these equations can be interpreted as the Picard-Fuchs equations of a Calabi-Yau manifold with one complex modulus, which links up the observed integrality to the conjectured integrality of the Gopakumar-Vafa invariants. A natural question is if in the other cases such a geometrical interpretation is also possible. Our investigations of the monodromies are intended as a first step in answering this question. We use a numerical approach combined with some ideas from homological mirror symmetry to determine the monodromy for some further one-parameter models. Furthermore, we present a conjectural identification of the Picard-Fuchs equation for 5 new examples from Borcea's list and one constructed by Tonoli and conjecture the existence of some new Calabi-Yau three folds. The paper does not contain any theorems or proofs but is, we think, nevertheless of interest.
Discrete-Time Chebyshev Neural Observer for Twin Rotor MIMO System

In this paper, we investigate the problem of designing a Neural Network (NN) observer for the Euler discretized model of Twin Rotor Multi-Input-Multi-Output (MIMO) system which belongs to a class of nonlinear system. The observer is based on a Chebyshev Neural Network (CNN), trained by using Extended Kalman Filter (EKF) learning algorithm. The state estimation error and output error are guaranteed to be semiglobally uniformly ultimately bounded (SGUUB) and neural network weights to be bounded. Simulation results are also included to illustrate the applicability of the proposed observer. Keywords-Chebyshev Neural Network, Discrete–Time Nonlinear System, Extended Kalman Filtering, Neural Observer, Twin Rotor MIMO System.
Remote Learning Experience and Adolescents’ Well-Being during the COVID-19 Pandemic: What Does the Future Hold?

Background. Major shifts within the education system have taken place during the COVID-19 pandemic; frontal teaching was often replaced with remote learning, which has affected students in many ways. We investigated the associations and predictors of perceptions of the remote learning experience on well-being (life satisfaction, self-rated health, psychosomatic, and psychological symptoms). Methods. We conducted a cross-sectional research study consisting of 1019 school students in Israel aged 11–18 (53.5% girls, 46.7% boys). Questionnaires were distributed from May–July 2021 during school time. The percentages of participants with various levels of well-being (WB) and remote learning experience were compared. Multiple regression procedures were used to analyze factors predicting wellbeing. Results. All of the remote learning items had statistically significant positive correlations with life satisfaction and self-rated health (i.e., better overall WB was associated with a more positive perception of the remote learning experience). Male gender, high socioeconomic status, greater involvement in lessons in the past year, and connection to the pedagogical team/school and peers predicted better overall WB (F-ratio = 14.03; p < 0.01; adjusted R2 = 0.08). Conclusions. Our results highlight the need for schools to target youths’ coping skills, which may lead to better remote learning experiences. These findings also provide several implications for the need to support children and adolescents through positive activities, relaxation/mindfulness, and cognitive coping to deal with the psychosomatic symptoms during remote learning periods.