Miracle-dz/UniFAR-FTUs huggingface dataset:
Heat Wave Study using Satellite LST and Air Temperature Data over Gujarat Region

Intense, durational, and frequent heat wave becomes a concern in the modern era as they directly effect on human mortality and climate. In this paper, heat wave detection is accomplished through satellite observation and air temperature over the Gujarat region for the period of 15th - 17th March 2022. The Land Surface Temperature (LST) was derived from Indian National Satellite System (INSAT-3D) images for the normal and heat wave year, which indicates severe conditions in some parts of the state. The LST analysis over different land cover types for the year 2019–2022 was observed, which indicates LST intensity on cropland is higher than on other land covers during heat wave. We also qualitatively analyze the correlation between Moderate Resolution Imaging spectroradiometer (MODIS) satellite LST and air temperature over the last 15 years by fitting a linear trend line. It describes the dynamic behavior of land covers over the years. The comparison of an air temperature of normal and heat wave years shows a surge in air temperature during the heat wave in the daytime. This finding shows the potential of satellite remote sensing as it is promising means of accurate study and detection of heat waves from the synoptic view as well as it will help to understand the heat wave pattern.
Anisotropic optical loss and birefringence of anodized alumina film

Anodicallyoxidized alumina has been investigated theoretically and experimentally concerning the anisotropy of its optical loss and refractive index. When the pores in the alumina film are filled with metallic materials, the film yields anisotropy of the optical loss and consequently acts as a polarizer. However, the film acts as a retardation plate when the pores are filled with suitable dielectric materials. It has been demonstrated that the anodized alumina offers efficient optical elements throughout the wide spectral range from the visible to the infrared.
Eigenvalue asymptotics for the Schrödinger operator with homogeneous magnetic potential and decreasing electric potential. I: Behaviour near the essential spectrum tips

We consider the discrete spectrum of the Schrodinger operator with homogeneous magnetic potential and decreasing electric potential. We study the asymptotic behaviour of the isolated eigenvalues of this operator near its essential-spectrum tips. In particular, we investigate the relation between the eigenvalue behaviour and the value of the deficiency index of the magnetic-field tensor On considere le spectre discret de l'operateur de Schrodinger avec un potentiel magnetique homogene et un potentiel electrique decroissant. On etudie le comportement asymptotique des valeurs propres isolees de cet operateur qui sont situes pres de son spectre essentiel. En particulier, on recherche la relation entre le comportement des valeurs propres et la valeur de l'indice du defaut du tenseur du champ magnetique
Statistical Risk Models

We give complete algorithms and source code for constructing statistical risk models, including methods for fixing the number of risk factors. One such method is based on eRank (effective rank) and yields results similar to (and further validates) the method set forth in an earlier paper by one of us. We also give a complete algorithm and source code for computing eigenvectors and eigenvalues of a sample covariance matrix which requires i) no costly iterations and ii) the number of operations linear in the number of returns. The presentation is intended to be pedagogical and oriented toward practical applications.
Q-learning based Handover Algorithm for High-Speed Rail Wireless Communications

High-speed railways (HSRs) has become one of the most preferable modes of transportation. In the evolution of the railway wireless communication system from Long Term Evolution for Railway (LTE-R) to the 5th Generation Wireless System (5G), the rapid increase in the train speed and number of base stations along the railway track led to challenging handover (HO) problems, such as high failure rate and frequent HOs. In order to address this challenge, an improved handover decision strategy is proposed based on Q-learning algorithm. The simulation results demonstrate that our proposed scheme is capable of reducing the number of unnecessary handover and improving the network performance remarkably.
Current Insights into the Significance of the Renal Resistive Index in Kidney and Cardiovascular Disease

Initially, the renal resistive index (RRI) was investigated with the aim of improving diagnosis in kidney diseases, but this goal was not met. Recently, many papers have highlighted the prognostic significance of the RRI in chronic kidney disease: specifically, in estimating the revascularization success of renal artery stenoses or the evolution of the graft and the recipients in renal transplantation. Moreover, the RRI has become significant in the prediction of acute kidney injury in critically ill patients. Studies in renal pathology have revealed correlations of this index with parameters of systemic circulation. The theoretical and experimental premises of this connection were then reconsidered, and studies analyzing the link between RRI and arterial stiffness, central and peripheral pressure, and left ventricular flow were conducted with this purpose. Many data currently indicate that RRI is influenced more by pulse pressure and vascular compliance than by renal vascular resistance—assuming that RRI reflects the complex interplay between systemic circulation and renal microcirculation and should be considered a marker of systemic cardiovascular risk beyond its prognostic relevance for kidney disease. In this review, we overview the clinical research that reveals the implications of RRI in renal and cardiovascular disease.
On the Sample Complexity of Reinforcement Learning with a Generative Model

We consider the problem of learning the optimal action-value function in the discounted-reward Markov decision processes (MDPs). We prove a new PAC bound on the sample-complexity of model-based value iteration algorithm in the presence of the generative model, which indicates that for an MDP with N state-action pairs and the discount factor γ ∈ [0, 1) only O(N log(N/δ)/(1 - γ)3e2)) samples are required to find an e-optimal estimation of the action-value function with the probability 1 - δ. We also prove a matching lower bound of Θ(N log(N/δ)/((1 - γ)3e2)) on the sample complexity of estimating the optimal action-value function by every RL algorithm. To the best of our knowledge, this is the first matching result on the sample complexity of estimating the optimal (action-) value function in which the upper bound matches the lower bound of RL in terms of N, e, δ and 1/(1-γ). Also, both our lower bound and our upper bound significantly improve on the state-of-the-art in terms of 1/(1 - γ).
SUBJECTIVE PROBABILITY AND EXPECTED UTILITY WITHOUT ADDITIVITY

An act maps states of nature to outcomes; deterministic outcomes as well as random outcomes are included. Two acts f and g are comonotonic, by definition, if it never happens that f(s) >- f(t) and g(t) >- g(s) for some states of nature s and t. An axiom of comonotonic independence is introduced here. It weakens the von Neumann-Morgenstern axiom of independence as follows: If f >- g and if f, g, and h are comonotonic, then cff +(l-a)h>-ag+(1 -ac)h. If a nondegenerate, continuous, and monotonic (state independent) weak order over acts satisfies comonotonic independence, then it induces a unique non-(necessarily-)additive probability and a von Neumann-Morgenstern utility. Furthermore, one can compute the expected utility of an act with respect to the nonadditive probability, using the Choquet integral. This extension of the expected utility theory covers situations, as the Ellsberg paradox, which are inconsistent with additive expected utility. The concept of uncertainty aversion and interpretation of comonotonic independence in the context of social welfare functions are included.
Demonstration of Artificial Afferent Nerve Properties With Forming-Free and SiO2-Based Memristive Synapses

The development of artificial nerve systems is considered of great significance for the fabrication of artificial appendages or intelligent robotics with environmental communication and awareness. Along these lines, in this work, the synaptic properties of a SiO2-based conductive bridge random access memory (CBRAM) were thoroughly investigated. Initially, a detailed study was conducted to understand and catalog the short- and long-term properties of the device, and their underlying physical mechanisms. To that end, a numerical model was introduced for interpreting the synaptic pattern within the device and its results were validated with the acquired experimental data. In addition, an artificial afferent nerve concept was demonstrated, comprising a piezoelectric sensory receptor, to generate electrical signals in response to either impulsive (“touch and go”) or continuous external stimuli (“touch and stay”), a microcontroller-emulated leaky-integrate-and-fire neural node converting the signals into spiking action potentials, and the CBRAM element as a terminal synapse, responsible for processing/attenuating the action potentials Throughout these procedures, the synaptic adaptation and the memory consolidation processes were recorded and analyzed.
Detailed study of the binary system LS I +61$^{\circ}$303 in VHE gamma-rays with the MAGIC telescope

The binary star system LS I +61 303, recently discovered by the MAGIC Telescope, was investigated in very high energy (VHE) gamma-rays between 2005 and 2008. The system displays a periodic outburst of gamma-rays with a period of 26.60±0.45 days. This period agrees very good with the orbital period of the binary stars. Thus, LS I +61 303 is established as the second known periodic source of VHE gamma-rays. Furthermore, the system's emissions were investigated for possible correlations between radio frequencies, X-rays and VHE gamma-rays. A flux correlation between X-rays and VHE gamma-rays was revealed. Due to the measurements presented here, the theoretical model descriptions of the VHE gamma-ray emission of LS I +61 303 could be restricted severely.
A fast numerical method for computing the linear and nonlinear mechanical properties of composites

This Note is devoted to a new iterative algorithm to compute the local and overall response of a composite from images of its (complex) microstructure. The elastic problem for a heterogeneous material is formulated with the help of a homogeneous reference medium and written under the form of a periodic Lippman-Schwinger equation. Using the fact that the Green's function of the pertinent operator is known explicitely in Fourier space, this equation is solved iteratively.The method is extended to the case where the individual constituents are elastic-plastic Von Mises materials with isotropic hardening
The impact of foreign direct investment on Namibia’s economic growth: A time series investigation

Abstract The current study investigates the impact of foreign direct investment on the growth of Namibia’s economy from 1990 to 2020 using the ARDL cointegration method. The results reveal that FDI, the interactive variable of FDI and trade openness, and other macroeconomic variables such as domestic investment, government consumption expenditure, human capital, a proxy for economic stability, and return on investment are responsible for Namibia’s economic growth. The article confirms the FDI-led growth and the Bhagwati hypotheses for Namibia as shown by the FDI and the interactive variable of FDI and trade openness, respectively. To reap the full benefits of FDI on economic growth in Namibia, the government must focus on improving physical infrastructure and the quality of human resources. It should also facilitate the development of an entrepreneurship culture, create a stable macroeconomic environment, and improve conditions for productive investments to accelerate economic growth and development.
Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies

Gridded sea surface height (SSH) maps estimated from satellite altimetry are widely used for estimating surface ocean geostrophic currents. Satellite altimeters observe SSH along one‐dimensional tracks widely spaced in space and time, making accurately reconstructing the two‐dimensional (2D) SSH field challenging. Traditionally, SSH is mapped using optimal interpolation (OI). However, OI artificially smooths the SSH field leading to high mapping errors in regions with rapidly‐evolving mesoscale features such as western boundary currents. Motivated by the dynamical relation between SSH and sea surface temperature (SST) and the notion that even the chaotic evolution of mesoscale ocean turbulence may contain repeating patterns, we outline a deep learning (DL) approach where a neural network is trained to reconstruct 2D SSH by synthesizing altimetry and SST observations. In the Gulf Stream Extension region, dominated by mesoscale variability, our DL method substantially improves the SSH reconstruction compared to existing methods. Our SSH map has 17% lower root‐mean‐square error and resolves spatial scales 30% smaller than OI compared against independent altimeter observations. Surface geostrophic currents calculated from our map are closer to surface drifter observations and appear qualitatively more realistic, with stronger currents, a clearer separation between the Gulf Stream and neighboring eddies, and the appearance of smaller coherent eddies missed by other methods. Our map yields significant re‐estimations of important dynamical quantities such as eddy kinetic energy, vorticity, and strain rate. Applying our DL method to produce a global SSH product may provide a more accurate and higher resolution product for studying mesoscale ocean turbulence.
The Black Hole: CAR T Cell Therapy in AML

Simple Summary Unlike B cell malignancies, the progress on generating an adoptive T cell therapy for relapsed/refractory acute myeloid leukemia (AML) remains insufficient. This review focuses on the main challenges in the field and novel strategies to overcome them. Abstract Despite exhaustive studies, researchers have made little progress in the field of adoptive cellular therapies for relapsed/refractory acute myeloid leukemia (AML), unlike the notable uptake for B cell malignancies. Various single antigen-targeting chimeric antigen receptor (CAR) T cell Phase I trials have been established worldwide and have recruited approximately 100 patients. The high heterogeneity at the genetic and molecular levels within and between AML patients resembles a black hole: a great gravitational field that sucks in everything. One must consider the fact that only around 30% of patients show a response; there are, however, consequential off-tumor effects. It is obvious that a new point of view is needed to achieve more promising results. This review first introduces the unique therapeutic challenges of not only CAR T cells but also other adoptive cellular therapies in AML. Next, recent single-cell sequencing data for AML to assess somatically acquired alterations at the DNA, epigenetic, RNA, and protein levels are discussed to give a perspective on cellular heterogeneity, intercellular hierarchies, and the cellular ecosystem. Finally, promising novel strategies are summarized, including more sophisticated next-generation CAR T, TCR-T, and CAR NK therapies; the approaches with which to tailor the microenvironment and target neoantigens; and allogeneic approaches.
Bias and observation error characterization of the Fengyun-2F Stretched Visible and Infrared Spin Scan Radiometer with the aim of its radiance assimilation

ABSTRACT Recent studies start to explore the potential benefits of directly assimilating the radiance from the infrared imagers onboard geostationary satellites to the short-range numerical weather prediction both on the synoptic- and meso-scales, where accurate quantification of the bias and observation error arising from the numerical model and the instrument is a key component in such experiments. This paper investigates both the bias and the observation errors of the Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) onboard Chinese operational geostationary satellite Fengyun-2F, as the first step towards its clear-sky radiance assimilation. In this study, 774,530 observations that passed a five-step quality control procedure during the period of 1–7 July in 2014 are collected to investigate the mentioned two quantities while the synthetic simulated clear radiance is calculated through a numerical weather prediction model and a radiative transfer model. Persistent warm biases are found for FY-2 F S-VISSR channel 3, 4 and 5 during day and night, and they are possibly due to the uncertainty from the instrument calibration process. The observation errors of S-VISSR infrared channels vary with the surface types, generally with larger values over land than over ocean, which could partially be explained by the variation of the land types over land. Besides, whether these biases change with viewing geometry or evolve with time is also examined in our study. Biases from the three surface channels exhibit complex dependence on the scan position and evolve with time, which encourages incorporating both the scan position and temporal corrections into the S-VISSR bias correction scheme in the data assimilation system.
Evaluating university E-assessment in Egypt: A teachers’ perspective

Abstract COVID-19 forced educational institutions to work digitally. Universities were not prepared for online assessment during the lockdown. Teachers faced many challenges when assessing students online; they were afraid that they produced inefficient or fake results. This study aims to understand the factors that affect the efficiency of e-assessment from the teachers’ perspective. Many factors affect the e-exam efficiency, such as university readiness, educator readiness, plagiarism, cheating, type of assessment, and technical issues. Two hundred sixty-eight teachers were invited to fill in the questionnaire. This study concludes that university readiness, educator readiness, and type of assessment have a positive significant impact on e-exam efficiency, while plagiarism, cheating, and technical issues negatively affect e-exam efficiency Implications for practice are discussed.
Acoustofluidics in mirosystems: Theory and simulation

In the recent years lab-on-a-chip devices have won seriously commercial recognition within various application fields. The evolving field of lab-on-a-chip systems has caused an increasing interest in fields like microfluidics and demanded a better theoretical insight. A subset of microfluidics is the field of acoustofluidics which provide the theoretical background for several lab-on-a-chip applications like pumping, separation, sorting, cleaning and mixing. One of the interesting phenomenons observed in acousto fluidic microsystems is acoustic streaming. In earlier studies the fluid viscosity and the boundary layer effect have been investigated as potential sources of streaming but could not account for experimental results. Therefore the attention has turned to energy dissipation to the surroundings as a possible source of streaming. The main goal of this thesis is to investigate how the streaming phenomenon depends on the latter source. The analytical approach is perturbation theory of second order which is applied to the continuity and Navier–Stokes equations. To handle more complex systems numerical computations in COMSOL have been executed. A term coupled to the actual streaming velocity is derived. A comprehensive analysis of this term strongly indicates that energy dissipation to the surroundings in fact is a source of acoustic streaming. Furthermore, numerical calculations indicates how the amount of streaming depends on the energy dissipation. These results have contributed to a better understanding of the streaming phenomenon.
Quantification of data‐related uncertainty of spatially dense soil moisture patterns on the small catchment scale estimated using unsupervised multiple regression

Multiple regression analysis is a valuable method to reduce information gaps in a sparse soil moisture data set by fusing its information content with those of densely mapped data sets. Regression analysis utilizing uncertain data results in an indeterminate regression model and indeterminate soil moisture predictions when applying the regression model. We employ an unsupervised multiple regression approaches, taking optimally located sparse soil moisture measurements directly as coefficients in a linear regression model. We propagate data uncertainties into our probabilistic soil moisture estimation results by embedding the regression in a Monte Carlo approach. The computed uncertainty defines the quantitative limit for information retrieval from the resultant ensemble of soil moisture maps. This raises doubts on the true presence of some prominent channel‐like features of increased soil moisture that are clearly visible in a previously and deterministically derived soil moisture map ignoring the presence of data uncertainty. The approach followed in this work is computationally simple and could be applied routinely to databases of similar size. Insufficient uncertainty communication by the data provider became the biggest obstacle in our efforts and led us to the insight that the geoscientific community may need to revise their standards with regard to uncertainty communication related to measured and processed data.
Risk, Resilience, and Chinese Youth’s Psychosocial Adjustment: The Role of Social Services

Abstract Few empirical studies have examined the impact of the universal social services provided to ordinary youth on their resilience and psychosocial adjustment, especially youth from developing countries like China. Based on a sample of 857 high school students between the ages of 13–19 from Beijing, this article examines the pattern of social services provided to Chinese youth and the impact of these services on their resilience and positive or negative psychosocial adjustment (depression, delinquency, prosocial behavior). The results reveal that: (a) Chinese youth generally make very limited use of services but they report moderate satisfaction; (b) both frequency and satisfaction of service use significantly predicts resilience, and resilience significantly predicts lower depression and higher prosocial behavior; (c) resilience fully mediates the relationships between social service frequency/satisfaction and depression/prosocial behavior. The findings give support to the public health model of social services and reveal that building resilience is one mechanism that universal service contributes to youths’ psychosocial adjustment. Future research may further investigate the relationships between social services, resilience and psychosocial adjustment in other geographical areas and time periods of China, and comparative studies between China and other countries may also be useful.
Some problems in the application of spectrum techniques to strong-motion earthquake analysis

A comparison of analog and digital computation of strong-motion earthquake response spectra is made, and it is shown that the preference as to method will depend mainly on availability of computing equipment. The accuracy of analog response spectrum computations is shown to be compatible with the limitations of the original ground acceleration data. The accuracy of the customary approximate relationship between the displacement response spectrum and the velocity response spectrum is investigated, and the validity of the simplifications are shown for typical strong-motion earthquake applications. The relationship between the response spectrum and the Fourier spectrum is developed, and a comparison as to suitability for earth-quake engineering problems is given.