Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustically driven emission of light in granular and layered semiconductors: recent advances and future prospects

Acoustic driving techniques can produce luminescence in semiconductors quite efficiently. A mixture of grain particles, for example, emits light when driven by sufficiently intense acoustic fields. This effect is therefore particularly interesting for exploring the dynamics of granular solids. By applying this novel technique, further insight is gained when monitoring the driving-induced evolution of dense particle arrangements. Another subject in this article is the description of the basic physics that governs acoustically driven relaxation of photoexcited carriers in quantum well systems. Due to these properties, µs-prolonged recombination processes and periodic pumping of exciton emission in a quantum dot are observed, which offer new possibilities for tailoring multilayer structures of semiconductors. A variety of potential applications using these phenomena are envisaged.
A review on linear mixed models for longitudinal data, possibly subject to dropout

Many approaches are available for the analysis of continuous longitudinal data. Over the last couple of decades, a lot of emphasis has been put on the linear mixed model. The current paper is dedicated to an overview of this approach, with emphasis on model formulation, interpretation and inference. Advantages as well as drawbacks are discussed, and guidelines are given for general statistical practice. Special attention is given to the problem of missing data, i.e., the case where not all data are present as planned in the original design of the study.
Finite element approximations with quadrature for second‐order hyperbolic equations

In this article, the effect of numerical quadrature on the finite element Galerkin approximations to the solution of hyperbolic equations has been studied. Both semidiscrete and fully discrete schemes are analyzed and optimal estimates are derived in the L∞(H1), L∞(L2) norms, whereas quasi‐optimal estimate is derived in the L∞(L∞) norm using energy methods. The analysis in the present paper improves upon the earlier results of Baker and Dougalis [SIAM J Numer Anal 13 (1976), pp 577–598] under the minimum smoothness assumptions of Rauch [SIAM J Numer Anal 22 (1985), pp 245–249] for a purely second‐order hyperbolic equation with quadrature. © 2002 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 18: 537–559, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/num.10022
Cluster synchronization in star-like complex networks

The cluster synchronization of a star-like complex network which consists of N nodes coupled with a common environment is discussed. By using the matrix theory and the Lyapunov function approach, a sufficient condition about the existence and asymptotic stability of a cluster synchronization invariant manifold is derived. The effectiveness of the sufficient condition is illustrated by two examples. In addition, the two examples also show that, in the star-like complex network in which the individual node is chaotic (or non-chaotic), the dynamics of some nodes in the cluster synchronization invariant manifold can be non-chaotic (or chaotic).
Nanoscale probing of surface potential landscape at MoS2/BP van der Waals p–n heterojunction

2D van der Waals heterostructure paves a path towards next generation semiconductor junctions for nanoelectronics devices in the post silicon era. Probing the band alignment at a real condition of such 2D contacts and experimental determination of its junction parameters is necessary to comprehend the charge diffusion and transport through such 2D nano-junctions. Here, we demonstrate the formation of the p–n junction at the MoS2/Black phosphorene (BP) interface and conduct a nanoscale investigation to experimentally measure the band alignment at real conditions by means of measuring the spatial distribution of built-in potential, built-in electric field, and depletion width using the Kelvin probe force microscopy (KPFM) technique. We show that optimization of lift scan height is critical for defining the depletion region of MoS2/BP with nanoscale precision using the KPFM technique. The variations in the built-in potential and built-in electric field with varying thicknesses of MoS2 are revealed and calibrated.
The spiral of state capitalism: labour transformations or the ‘whip of external necessity’?

This article opens a conversation between uneven and combined development (U&CD) and state capitalism studies. It reflects on the potential of U&CD to answer the following questions: why has there been an aggregate expansion of the state’s role as promoter, supervisor and owner of capital across the world capitalist economy since the turn of the millennium, and why has this taken an uneven and combined form? I argue that U&CD as an evolving research programme holds great potential in creatively expanding the study of state capitalism and its multi-scalar geographies. It is particularly well positioned to help us elucidate the multilinear and interactive character of present-day state capitalism. U&CD’s heuristics of ‘societal multiplicity’, ‘combination’ and ‘hybridisation’ can help us trace the combinatorial dynamics which result in the cumulative expansion of state prerogatives and their cascading impacts across policy realms and geographic space. Yet the causal mechanisms identified by U&CD (the ‘whip of external necessity’, ‘the privilege of historic backwardness’ and ‘the simultaneity of the non-simultaneous’) are unable to grasp contemporary state capitalist transformations in their historical and geographical fullness, notably due to a tendency to misread spatial unevenness as temporal dislocation. Theorising state capitalism requires that we examine the role of multiple and interacting capitalist states in politically mediating historically determinate transformations in the capitalist labour process, and their ramifications in terms of the temporal and geographical dynamics of value production and circulation. Thus, looking at U&CD from the perspective of the new state capitalism reveals both its analytical strengths and limitations for GPE.
Elastic moduli of two‐dimensional composite continua with elliptical inclusions

The elastic field around an elliptical inclusion in two dimensions is obtained. This result is then used to compute the effective moduli of a composite medium containing many randomly positioned and oriented elliptical objects. Two different self‐consistent methods are described and the special cases of voids and rigid reinforcement are considered in detail. The asymmetric self‐consistent method shows that the Young’s modulus E goes to zero as E=E1(p−pc)/(1−pc), where 1−p is the concentration of the voids and subscript one denotes the void free material. The Poisson ratio σ is also linear in p and goes to a value σc at pc that is independent of the initial value of Poisson’s ratio σ1. Unlike the corresponding three‐dimensional case, the two elastic moduli decouple in this special case. The corresponding elastic threshold is pc=[1+ab/(a2+b2)], where a and b are the major semiaxes. The symmetric self‐consistent method yields a different pc =2[1+(2(a+b)2/(a2+b1))1/2]−1 and a different concentration dependenc.
Two-stage Shortest Path Algorithm for Solving Optimal Obstacle Avoidance Problem

Abstract In most of the path-planning applications, the controlled object (mobile robot) is expected to reach its predetermined target by following the shortest path and avoiding the obstacles. This navigation problem is also called optimal obstacle avoidance. In this work, obstacles are assumed to be motionless circles in different sizes. The object is supposed to be a point robot. The two-stage algorithm is proposed to And a numerical solution to the problem. At first stage, the method, which is optimal for one step, is applied iteratively. In every step of the method the first obstacle on the straight line between the current position and the target is assumed to be a single obstacle. The proposed method is realized using geometric representations. Some evaluations are made to prove that the method is convergent. The path obtained at the first stage might not be optimum. However, its length can be used to limit the feasible region through an ellipse, which contains the shortest path. Thus, the reduced search space makes the next stage more efficient and endurable for real-time applications. In the second stage of the algorithm, the elliptic region is meshed with squares, the side length of which is set in agreement with the minimum distance between obstacles. It is prohibited to pass through the squares that intersect obstacles. Thus, by discretization the problem becomes the shortest path problem in a graph, and is solved by applying the Dijkstra’s algorithm. The proposed two-stage algorithm is verified with numerical simulations. Obstacles are chosen randomly. A target position is selected and fixed. For different starting points, the algorithm is tested repeatedly. The results show that the proposed algorithm can be applied to find an optimal solution for the obstacle avoidance problem.
Adaptive predictive control of periodic non-linear auto-regressive moving average systems using nearest-neighbour compensation

Many practical non-linear systems can be described by non-linear auto-regressive moving average (NARMA) system models, whose stabilisation problem is challenging in the presence of large parametric uncertainties and non-parametric uncertainties. In this work, to address this challenging problem for a wide class of discrete-time NARMA systems, in which there are uncertain periodic parameters as well as uncertain non-linear part with unknown periodic time delays, we develop adaptive predictive control laws using the key ideas of `future outputs prediction' and `nearest-neighbour compensation', among which the former is carried out to overcome the non-causalness problem and the latter novel idea is proposed to completely compensate for the effect of non-linear uncertainties as well as unknown time delays. To achieve the desired asymptotic tracking performance in the presence of semi-parametric uncertainties with time delays, an ` n -step parameter update law' is first designed, based on which an `one-step update law' is then elaborately constructed to obtain smoother closed-loop signals. This study in general develops a systematic adaptive control framework for periodic NARMA systems with guaranteed boundedness stability and asymptotic tracking performance, which are established by rigorous theoretic proof and verified by simulation studies.
Characterization of Laminar Transient Flow Regimes and Mixing in T-shaped Micromixers

Convective micromixers create vortices in curved channel elements allowing characteristic mixing times below 1 millisecond for gaseous and liquid media. This contribution gives an overview about the flow regimes of symmetrical 1:1 mixing in T-shaped micromixers with rectangular cross-sections for Reynolds numbers (Re) from 0.01 to 1000 in the mixing channel. First, symmetrical vortices are formed for Re > 10 in the so-called Dean flow. At Re > 140, fluid from one side swaps to the opposite side and creates a double vortex, which enhances mixing. For Re > 240, the flow becomes unsteady, and a kind of wake flow establishes. From 240 < Re < 500, the wake flow is periodic with a Strouhal number of about 0.23. The mixing quality also shows a periodic behavior and reaches its maximum at this point. With further increasing the Re number, the flow becomes chaotic, and the two components are often flowing parallel in the mixing channel, which decreases the mixing quality. Besides detailed CFD simulations, the periodic flow is observed in experimental studies with stroboscopic imaging. The decreasing mixing quality is also reflected in a lower selectivity of parallel chemical test reactions for Re > 500. With the knowledge of the flow regimes in microchannels, design criteria can be formulated for efficient mixing devices.
A Bayesian Lifetime Model for the “Hot 100” Billboard Songs

People have long been enamored by ranked lists of celebrities (e.g., “best-dressed” lists), places (e.g., best cities to live in), things (e.g., most popular songs, books, and movies), and countless other entities. Likewise, people are equally interested in watching these rankings evolve over time and speculating about possible future changes (e.g., who will be ##1 next week? ) We focus on a popular, but fairly typical ranked list (the Billboard “Hot 100” songs) to explain and model the simultaneous movement of multiple items (songs) up and down the chart over time. Although our interest in Billboard data partly reflects the glamour of the music industry, these charts provide a very rich and general data structure. Surprisingly little research has been done on time-series models for ranked objects. We further enrich the dataset by adding covariates (e.g., artist history) to capture additional sources of variation across songs and over time. We posit a model for the time series of charts based on a latent lifetime (worth) process. Specifically, the latent popularity of each song is assumed to follow a generalized gamma (GG) lifetime curve with double exponentially (DE) distributed errors. The immense flexibility in the GG family allows the mean of a song's latent worth process to follow an exponential, Weibull, lognormal, or gamma curve (among others), reflecting the many possible paths that it might take through the chart. The DE error structure is used for convenience, as it leads to a well-established “exploding” multinomial-logit likelihood. This framework is embedded in a Bayesian structure in which parameters of the GG curve are song specific, with means related to observed covariates, and assumed to come from a multivariate lognormal prior. Inferences from the model are obtained from posterior samples using Markov chain Monte Carlo techniques.
Genetics and Philosophy: An Introduction

through the puzzles of causation—can be expected to stimulate further discussion, and possibly further reflections on their relevance for non-fictional situations, and for illuminating actual case studies from the sciences. The volume can also be expected to fuel reflections on how ‘doing metaphysics can be understood as a contribution to our overall understanding of the world’ (Paul 2012, 24), and more work on whether a single account of causation will ever be able to fit all varieties of causal relations.
A Certain Class of Weighted Approximations for Integrable Functions and Applications

A certain class of weighted approximations, which extends the results of Masjed-Jamei [6] is introduced for integrable functions and some of upper bounds are obtained for the absolute value of the errors of such approximations in two L1[a, b] and L∞[a, b] spaces. As the main motivation for introducing the aforesaid class, it is shown that many new inequalities can be generated from the given error bounds. Some illustrative examples are presented in this sense. Moreover, by using the obtained error bounds, a nonstandard type of three-point weighted quadrature rules is introduced and its error bounds are computed.
Multiscale modeling of microtubules and actin filaments

Abstract Computational methods, as Molecular Dynamics (MD) simulations, have been used for years to gain insight into the properties of small fractions of microtubules (MTs) and actin filaments (F-actins). However, when considering an entire cytoskeleton filament, MD simulations lack of sufficient sampling of the phase space. In order to acquire adequate amount of data with statistical validity, a faster sampling technique is here developed and the bending stiffness of entire MTs and F-actins is computed. Our approach allows to propagate the information obtained at a fine-grained atomistic scale to a coarse grained (CG) scale. We perform MD simulations on representative units of the MT and F-actin. Then, we describe each monomer of these representative units with few interacting grains and employ them to build CG filaments: (1) entire MTs with lengths from ~300 nm to ~1.5 μm and (2) an entire actin filament of ~500 nm. The dynamics of these CG systems is analyzed using Brownian Dynamics (BD) simulations, allowing to achieve time and length scales hundreds of times higher than MD simulations. For our BD simulations an ad hoc force field is used, that matches MD atomistic fluctuations on the interaction potentials between pairs of CG beads. Results from the here proposed combination of atomistic MD simulations with coarse grained BD simulations are in agreement with experimental finding, providing more informative proofs of concept on how thermal fluctuations change interaction potentials among beads and on how the molecular rearrangements affect MT and F-actin rigidities.
Midwifery care providers’ childbirth and immediate newborn care competencies: A cross-sectional study in Benin, Malawi, Tanzania and Uganda

Evidence-based quality care is essential for reducing sub-Saharan Africa’s high burden of maternal and newborn mortality and morbidity. Provision of quality care results from interaction between several components of the health system including competent midwifery care providers and the working environment. We assessed midwifery care providers’ ability to provide quality intrapartum and newborn care and selected aspects of the working environment as part of the Action Leveraging Evidence to Reduce perinatal morTality and morbidity (ALERT) project in Benin, Malawi, Tanzania, and Uganda. We used a self-administered questionnaire to assess provider knowledge and their working environment and skills drills simulations to assess skills and behaviours. All midwifery care providers including doctors providing midwifery care in the maternity units were invited to take part in the knowledge assessment and one third of the midwifery care providers who took part in the knowledge assessment were randomly selected and invited to take part in the skills and behaviour simulation assessment. Descriptive statistics of interest were calculated. A total of 302 participants took part in the knowledge assessment and 113 skills drills simulations were conducted. The assessments revealed knowledge gaps in frequency of fetal heart rate monitoring and timing of umbilical cord clamping. Over half of the participants scored poorly on aspects related to routine admission tasks, clinical history-taking and rapid and initial assessment of the newborn, while higher scores were achieved in active management of the third stage of labour. The assessment also identified a lack of involvement of women in clinical decision-making. Inadequate competency level of the midwifery care providers may be due to gaps in pre-service training but possibly related to the structural and operational facility characteristics including continuing professional development. Investment and action on these findings are needed when developing and designing pre-service and in-service training. Trial registration: PACTR202006793783148—June 17th, 2020.
Predicting Alzheimer's Disease Using Multi-Omic Data: A Systematic Review

Background and Purpose: Alzheimer's Disease (AD) is a complex neurodegenerative disease that has been becoming increasingly prevalent in recent decades. Efforts to identify predictive biomarkers of the disease have proven difficult. Advances in the collection of multi-omic data and deep learning algorithms have opened the possibility of integrating these various data together to identify robust biomarkers for predicting the onset of the disease prior to the onset of symptoms. This study performs a systematic review of recent methods used to predict AD using multi-omic and multi-modal data. Methods: We systematically reviewed studies from Google Scholar, Pubmed, and Semantic Scholar published after 2018 in relation to predicting AD using multi-omic data. Three reviewers independently identified eligible articles and came to a consensus of papers to review. The Quality in Prognosis Studies (QUIP) tool was used for the risk of bias assessment. Results: 22 studies which use multi-omic data to either predict AD or develop AD biomarkers were identified. Those studies which aimed to directly classify AD or predict the progression of AD achieved area under the receiver operating characteristic curve (AUC) between . 70 - . 98 using varying types of patient data, most commonly extracted from blood. Hundreds of new genes, single nucleotide polymorphisms (SNPs), RNA molecules, DNA methylation sites, proteins, metabolites, lipids, imaging features, and clinical data have been identified as successful biomarkers of AD. The most successful techniques to predict AD have integrated multi-omic data together in a single analysis. Conclusion: This review has identified many successful biomarkers and biosignatures that are less invasive than cerebral spinal fluid. Together with the appropriate prediction models, highly accurate classifications and prognostications can be made for those who are at risk of developing AD. These early detection of risk factors may help prevent the further development of cognitive impairment and improve patient outcomes.
Resampling interpolation algorithm for Fresnel telescopy imaging system

Fresnel telescopy full-aperture synthesized imaging ladar is a new high resolution active laser imaging technique. In the operational mode with moving target by beam scanning, spatial distributions of the complex data of the returned signals are not regular or uniform. In order to use fast Fourier transform (FFT) algorithm in signal processing, algorithm for Fresnel telescopy imaging system should include resampling interpolation step to change sampling data into uniform and quadratic spatial distribution. We choose suitable resampling interpolation algorithm for Fresnel telescopy, and get computer simulation of area target. The work is found to have substantial practical value and offers significant practical benefit for high resolution imaging in Fresnel telescopy imaging ladar.
Are Chronic Pain Syndromes the Reason for Statin-associated Muscle Symptoms?

BACKGROUND Statin-induced myalgia is defined as muscle pain without elevation of serum creatine phosphokinase levels and is a well-known complaint among statin users. Chronic pain syndromes affect a high percentage of the population. These pain syndromes may confound the reports of statin-induced myalgia. OBJECTIVES To compare the occurrence of chronic pain among patients on statin therapy who developed myalgia with those who did not. METHODS This study included 112 statin-treated patients, who were followed at the lipid center at Sheba Medical Center. Fifty-six patients had a diagnosis of statin-associated muscle symptoms (SAMS) and 56 did not. Verified questionnaires were used to assess the diagnoses of fibromyalgia, pain intensity, functional impairment, anxiety, and depression in the study population. RESULTS Patients with statin myalgia were more likely to fulfil the diagnostic criteria for fibromyalgia than patients without statin myalgia (11 [19.6%] vs. 0, respectively). Patients in the SAMS group exhibited higher levels of anxiety and depression compared with the control group. Female sex, higher scores on the Brief Pain Inventory pain intensity scale, and a Hamilton rating scale level indicative of an anxiety disorder were found to be significant predictors for fibromyalgia in patients presenting with statin myalgia. CONCLUSIONS A significant percentage of patients diagnosed with statin myalgia fulfilled the diagnostic criteria for fibromyalgia depression or anxiety disorder. Detection of these patients and treatment of their primary pain disorders or psychiatric illnesses has the potential to prevent unnecessary cessation of effective statin therapy.