Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of the valence band offset of MOVPE-grown In 0.48 Ga 0.52 P ∕ Ga As multiple quantum wells by admittance spectroscopy

The valence band discontinuity of the lattice matched ${\mathrm{In}}_{0.48}{\mathrm{Ga}}_{0.52}\mathrm{P}∕\mathrm{Ga}\mathrm{As}$ heterostructure was determined through a careful analysis of the temperature and frequency dependence of the admittance of ${p}^{+}∕\mathrm{MQW}∕{n}^{+}$ structures, formed by a nominally undoped $\mathrm{In}\mathrm{Ga}\mathrm{P}∕\mathrm{Ga}\mathrm{As}$ multiple quantum well region, interposed between ${p}^{+}$ and ${n}^{+}$ GaAs layers. The heterostructures were grown through metal organic vapor phase epitaxy by using tertiary butyl arsine and tertiary butyl phosphine as alternative precursors for the V-group elements. The growth conditions were optimized for obtaining sharp interfaces and negligible ordering effects in the cation sublattice. Accounting for the temperature dependence of the Fermi energy and the calculated confining energy $(10\phantom{\rule{0.3em}{0ex}}\mathrm{meV})$ of the heavy holes in the wells, a valence band offset $\ensuremath{\Delta}{E}_{V}=(356\ifmmode\pm\else\textpm\fi{}5)\phantom{\rule{0.3em}{0ex}}\mathrm{meV}$ was derived from the temperature variation of the resonance frequency at which the isothermal conductance over frequency $G(\ensuremath{\omega})∕\ensuremath{\omega}$ curves show a maximum. The experimental uncertainty of this result is significantly low if compared with the wide range $(240\char21{}400\phantom{\rule{0.3em}{0ex}}\mathrm{meV})$ of the previously reported $\ensuremath{\Delta}{E}_{V}$ values. By considering the band gap difference between InGaP and GaAs, a conduction band offset $\ensuremath{\Delta}{E}_{C}=119\phantom{\rule{0.3em}{0ex}}\mathrm{meV}$ was estimated. The accuracy of the experimental procedure and the reliability of the main assumptions of the admittance spectroscopy measurements were accurately checked. The obtained results were discussed in light of the large and growing amount of literature data by taking into account the influence of the growth conditions on the physical properties of the $\mathrm{In}\mathrm{Ga}\mathrm{P}∕\mathrm{Ga}\mathrm{As}$ quantum wells.
Multiple Festo Robotino Navigation using Gazebo-ROS Simulator

: Two robotino are implemented on this study, the idea is to control two robotino as an Autonomous Robot to achieve various tasks by using Robot Operating System (ROS) as their platform. Autonomous robots are widely used in the unstructured environment and a robust system required to safely and efficiently achieve the various tasks such as logistical task. By having a reliable system, many industries can be benefited by using autonomous robot especially for open source system. The cost of building the robot itself has become a burden, by using an open source operating system, maintenance cost can be reduced. The robots are using motion planning to achieve their goal position using bug algorithm. This system is implemented on Gazebo simulation workspace and can be developed further to the real robotino or other omnidirectional robot by providing different namespace on ROS topics. The robots are able to be controlled by a ground station such as Personal Computer or a Laptop through wireless communication. The result of this study shows effectiveness of the algorithm that are being used by both robots in a certain environment.
Good reduction of elliptic modules

In this paper we give a criterion for good reduction of elliptic modules (Theorem 1, Section 2) which is an analogue of the criterion of N\’eron-Ogg\v{S}afarevi\v{c} for abelian varieties, cf. [7]. In the rest of the paper we give applications to elliptic modules of rank one over global function fields: In Section 3, the main theorem of complex multiplication of elliptic modules ([3] and [5]) is reformulated in a more relevant form to our subject (Theorem 2). Then, to each elliptic module we can associate the “Hecke character” (Theorem 3) so that the elliptic module has good reduction at a place $v$ if and only if the Hecke character is unramified at $v$ . In Section 4, we give a classification theoran (Theorem 4) by means of the Hecke characters. Ag an application, it will be shown that each rank-one elliptic module over a global function field $K$ has a K-form which has good reduction everywhere (Theorem 5).
Electromagnetic field of a vertical dipole over an imperfectly conducting half‐space

The electromagnetic field generated by a vertical electric dipole on or near the surface of the Earth is studied for continuous-wave excitation. The analytical foundation established by Sommerfeld and developed by many others is reviewed, and simple explicit new formulas are derived for the three components of the electromagnetic field and represented graphically. Their range of validity is defined. They show that the surface waves constitute the entire far field along the air-earth boundary.
General Theory of Nonuniform Fluids: From Hard Spheres to Ionic Fluids

Title of dissertation: GENERAL THEORY OF NONUNIFORM FLUIDS: FROM HARD SPHERES TO IONIC FLUIDS Yng-gwei Chen, Doctor of Philosophy, 2004 Dissertation directed by: John D. Weeks Department of Physics and Department of Chemistry and Biochemistry Institute for Physical Science and Technology The exclusion effects of repulsive intermolecular potential cores are often modeled by hard sphere fluids, for which an accurate Hydrostatic Linear Response (HLR) equation was previous developed by Katsov in 2001 for computing the density response to general external fields. In this dissertation the HLR equation is combined with various thermodynamic integration pathways to investigate the solvation free energy of cavity insertion which characterizes the entropic cost of solvating molecules in a fluid. A Shifted Linear Response (SLR) equation is developed to build in the exact limits of external fields varying in very small ranges and fluids confined in narrow spaces, where the HLR fails qualitatively. The SLR is derived from an expansion truncated at linear order about a reference density, and an Insensitivity Criterion (IC) is proposed for determining an optimal reference density. The slow 1/r decay of the Coulomb potential is characteristically long-ranged, but it also becomes strong at short distances. The structure of ionic systems exhibits an intricate interplay between the short and long length scales of their molecular potentials. A strategy is proposed for separating the Coulomb interaction between general charge distributions into a short-ranged piece u0(r) and a slowly varying piece u1(r). In the strong coupling states of the ionic systems that we have studied, mimic systems with only the short-ranged part u0(r) are found to show very similar correlation functions. The slow decays of ion-ion and ion-dipole interactions give rise to unique long-wavelength constraints on ionic fluid structure. Local Molecular Field Theory (LMF), which maps an external field in the full system to a mimic system in the presence of a renormalized field, can correct the mimic correlations by embodying contributions from u1(r). The LMF has been applied to both uniform and nonuniform model ionic systems, and accurate results for bulk correlation functions, internal energy and the density distribution in a confined system are obtained. For a system of counterions confined by charged walls, the LMF and the mimic system have especially helped shed light on many phenomena that had previously lacked coherent physical interpretations and consistent approximations. GENERAL THEORY OF NONUNIFORM FLUIDS: FROM HARD SPHERES TO IONIC FLUIDS
Biodiversity of butterflies (Lepidoptera: Rhopalocera) in the protected landscape of Nandhour, Uttarakhand, India

An appraisal of butterfly species composition in terms of comparative diversity richness in various habitat types was conducted in and around the Nandhour Wildlife Sanctuary of Terai Arc landscape in Uttarakhand. During the two years of survey period (March 2018–February 2020), a total of 89 species of butterflies belonging to families Nymphalidae 43 species, Pieridae 15 species, Lycaenidae 13 species, Hesperiidae eight species, Papilionidae seven species, and Riodinidae three species were recorded, of which 46 species represented new records for the Nandhour Landscape. Butterfly diversity and richness were highest in dense moist & open dry riverine forests and lowest in human settlements & agricultural land. No significant differences in the number of species were found in moist mixed deciduous forest, subtropical Chir Pine forest, moist Bhabar Sal forest, moist Shiwalik Sal forest, and plantation forest. Eight species are endemic to the Indian Himalayan Region.
On p-harmonic functions, convex duality and an asymptotic formula for injection mould filling

This work treats the injection of certain thermoplastics into a planar mould cavity. The problem is to determine the filling pattern. It is assumed that the thermoplastic can be modelled as a non-Newtonian fluid of power-law type whose power-law exponent is relatively small (the pseudo-plastic case). The dependence of the viscosity on thermal variations is neglected. The mathematical description leads to a moving boundary problem, for which an asymptotic solution is found. According to this solution, the expansion of the polymer melt follows the level sets of an interior distance function, which is determined by the geometry of the mould, and the position of the injection point. The solution is easily computed and results of numerical experiments are given.
Manifold Curvature Estimation for Neural Networks

This paper introduces a novel method for creating a metric to measure curvature of a discretized manifold. For each data point xi on a manifold, a subspace ${{\mathcal{S}}_i}$ is matched using a number of neighboring points of xi. A local subspace is also matched to each neighboring point of xi. Then, a set of weighted angles between ${{\mathcal{S}}_i}$ and each neighboring local subspace are computed and the minimum of those weighted angles is used as a measure of curvature of the manifold at xi. The average curvature for all data points on the manifold is used as metric for the manifold’s curvature estimation. This research also uses the proposed metric to show that each layer of a neural network maps an input manifold to a flatter manifold during the training process. It is observed that each successive block in a neural network generates a manifold with less curvature than that of the previous layer. Another observation is that convolutional layers always generate flatter manifolds. Furthermore, it is shown that this metric can be used as a robustness measure for a neural network. The method has been tested successfully using two datasets MNIST and Extended YaleB datasets.
Errors in Maximum-Entropy Charge-Density Distributions Obtained from Diffraction Data

A scheme to estimate errors in maximum-entropy method (MEM) charge-density distributions via Monte Carlo simulations is presented. Knowledge of the errors in the density allows evaluation of the reliability of fine features in MEM electron densities. As a test example, the errors in the MEM electron-density distribution of metallic beryllium are examined based on calculations using both uniform and non-uniform prior distributions. The study shows that the MEM introduces systematic bias in the density and that the bias is closely related to the fact that after a MEM optimization most of the χ2 value is carried by a few low-order reflections. An iterative procedure to estimate the bias is presented and this allows a correction of the MEM density to be performed. The systematic bias is in some regions an order of magnitude larger than the random error in the density. The bias-corrected MEM density has a more uniform residual distribution than the uncorrected density. The topological features of the electron-density distribution in metallic beryllium are discussed based on the bias-corrected MEM densities.
Influence of macrophage polarization in herniated nucleus pulposus tissue on clinical efficacy after lumbar discectomy

Low back pain or sciatic pain because of lumbar intervertebral disc herniation (LDH) is caused by mechanical compression and/or an inflammatory component on the nerve root. However, it is difficult to define to what extent each component contributes to the pain. This study attempted to explore the effects of macrophage polarization on clinical symptoms in patients experiencing LDH after surgery, and investigated the association between macrophage cell percentages and clinical efficacy.
Interoperable Reference Model for Public Data in Medical Imaging

In the past few years, the use of big data has been important for public health. However, it was difficult to use medical imaging as public data. Several problems, such as data privacy and inconsistent measurement system, were not suitable for the interoperability in big data. In this paper, we proposed the reference model that satisfies the interoperability of medical imaging data and able to be shared in public data server. The de-identification method minimizes personal information, in order to protect the confidentiality of patients. The quantification method for medical imaging was to calibrate the measured values using a reference material. Finally, de-identified and quantified medical imaging data was shared with a public server.
Increasing Alaskan river discharge during the cold season is driven by recent warming

Arctic hydrology is experiencing rapid changes including earlier snow melt, permafrost degradation, increasing active layer depth, and reduced river ice, all of which are expected to lead to changes in stream flow regimes. Recently, long-term (>60 years) climate reanalysis and river discharge observation data have become available. We utilized these data to assess long-term changes in discharge and their hydroclimatic drivers. River discharge during the cold season (October–April) increased by 10% per decade. The most widespread discharge increase occurred in April (15% per decade), the month of ice break-up for the majority of basins. In October, when river ice formation generally begins, average monthly discharge increased by 7% per decade. Long-term air temperature increases in October and April increased the number of days above freezing (+1.1 d per decade) resulting in increased snow ablation (20% per decade) and decreased snow water equivalent (−12% per decade). Compared to the historical period (1960–1989), mean April and October air temperature in the recent period (1990–2019) have greater correlation with monthly discharge from 0.33 to 0.68 and 0.0–0.48, respectively. This indicates that the recent increases in air temperature are directly related to these discharge changes. Ubiquitous increases in cold and shoulder-season discharge demonstrate the scale at which hydrologic and biogeochemical fluxes are being altered in the Arctic.
A Risk-Averse Adaptive Stochastic Optimization Method for Transactive Energy Management of a Multi-Energy Microgrid

This paper proposes a new energy management method for a multi-energy microgrid (MEMG) which supplies both electrical and thermal energies. Based on the transactive energy (TE) concept, the problem is formulated as a Stackelberg game-theoretic bi-level optimization model. The MEMG operator optimizes the energy scheduling and pricing strategies at the upper level, and the industrial, commercial and residential agents optimize their energy trading strategies at the lower level. The equivalent single-level mixed-integer linear program (MILP) reformulation is then derived for computational tractability. To coordinate the strategies made in the day-ahead and intra-day energy markets, an adaptive stochastic optimization (SO) approach is adopted, by which a day-ahead stochastic MILP and an intra-day deterministic model are formed. Furthermore, the conditional value-at-risk (CVaR) measure is incorporated in the day-ahead stage for risk aversion of the MEMG operator towards uncertainties. To solve the models, an adaptive Progressive Hedging (PH) algorithm is developed to decompose the day-ahead stochastic MILP into multiple scenario-based subproblems which can be solved in parallel, and an outer approximation (OA) algorithm is adopted in the intra-day stage to linearize the bilinear objective function. Finally, the simulation results verify the effectiveness of the proposed energy management method and the efficiency of the solution algorithms.
Applied modelling for bio and lean gas fired micro gas turbines

This contribution presents the outcome of applied Computational Fluid Dynamics for analysis of combustion technologies to find an efficient firing mode for use of bio and low calorific gaseous fuels in micro gas turbine combustors. The combustion technologies considered are based on the new concepts of Flameless Oxidation and Continued Staged Air. To optimise these concepts to burn low calorific gaseous fuels manifold numerical simulations were carried out using the CFD code FLUENT. The results achieved showed the influence of the fuel compositions on the flow behaviour inside the combustion chamber, reaction zone, flame structure and pollutant emissions.
Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care

Sepsis is an extreme inflammatory response of the body to an infection. It is one of the leading causes of death in critical care and ICUs worldwide, resulting in approximately 25% mortality in critically ill populations. Early identification and intervention are crucial to reducing sepsis-associated mortality and improving patient prognosis because severe sepsis cases can lead to organ failure and other life-threatening complications. Diagnosis of Sepsis is challenging in terms of diagnostic accuracy and timeliness due to ambiguous symptoms and individual differences which are captured in heterogeneous varied data sources with irregular time sequences. In recent years, numerous efforts have been made using machine learning methods for sepsis prediction. However, there are still very limited successful industry scale implementations due to limitations of consistency of input data, which often does not fit the characteristics of uncertain time intervals and large number of missing values in real-world critical care settings. In this study, we demonstrate an innovative approach to predict sepsis occurrence in real-time, on heterogeneous sets of variables with multiple time-granularity, using a flexible graph structure to model patient health records. Our modeling task is to predict future sepsis risk at any time after the first 48 hours of admission using observations data from any time window within the past 12 hours. To the best of our knowledge, our proposed approach is the first ever implementation that is dynamic, uses a graph representation to overcome the problem of irregular input features, and offers continuous risk prediction, thereby improving the compatibility of the model in clinical settings. Unlike prior efforts that report results on de-identified curated public datasets with synthetic data filters, our experiments and results are validated by clinical experts, and are based on a large level-1 trauma center’s multi-year real-world longitudinal data.
Filter design: a finite dimensional convex optimization approach

We consider the design of transfer functions (filters) satisfying upper and lower bounds on the frequency response magnitude or on phase response, in the continuous and discrete time domains. The paper contribution is to prove that such problems are equivalent to finite dimensional convex optimization problems involving linear matrix inequality constraints. At now, such optimization problems can be efficiently solved. Note that this filter design problem is usually reduced to a semi infinite dimensional linear programming optimization problem under the additional assumption that the filter poles are fixed (for instance, when considering FIR design). Furthermore, the semi infinite dimensional optimization is practically solved, using a gridding approach on the frequency. In addition to be finite dimensional, our formulation allows to set or not the filter poles. These problems were mainly considered in signal processing. Our interest is to propose an approach dedicated to automatic control problems. In this paper, we focus on the following problems: design of weighting transfers for H∞ control and design of lead‐lag networks for control. Numerical applications emphasize the interest of the proposed results. Copyright © 2003 John Wiley & Sons, Ltd.
Joint Optimization of Transmission and Computing Resource in Mobile Edge Computing Systems with Multiple Base Stations

In the power grid networks, mobile edge computing (MEC) is a critical technology to tackle the insufficient processing capacity and limited resources of smart devices. This paper considers a smart grid system, where MEC servers are used to collect and process the information obtained by the power terminals. To guarantee energy conservation, we aim to minimize total energy consumption by jointly optimizing base stations association, transmission power, bandwidth allocation and com-pression rate allocation. By the problem decomposition, four sub-problems are derived and solved iteratively. The simulation results show that the proposed MEC system can significantly reduce the total energy consumption.
Robust Object Pose Estimation Based on Improved Point Pair Features Method

In the robot grasping system, it is necessary to estimate the pose of the object. The common method of 3D pose estimation is to use the CAD model of the original object. However, sometimes the 3D model of the object is not easy to obtain. We propose a new method, which obtains the three-dimensional point cloud of the template object captured by depth camera. The improved method filters out redundant candidate poses, and uses the proposed pose verification method to improve the accuracy of pose estimation. Compared with the original PPF method, we get a better recognition rate in a large number of workpieces pose recognition tasks.
Dynamic Power Reduction of TCAM Using Selective Precharging of Match Lines

In this paper, we propose a power-efficient memory operation, selective match line precharge, based on analysis of power consumption in ternary content addressable memory(TCAM). A statistical study reveals that 58% on average of power dissipation related to the match line operation is saved by deactivating unnecessary swing of match lines. The improvement has been simulated and proved using 180nm CMOS technology.
Dietary Patterns, Occupational Stressors and Body Composition of Hospital Workers: A Longitudinal Study Comparing before and during the COVID-19 Pandemic

This longitudinal study aimed to evaluate the association between dietary patterns and the body composition of hospital workers subjected to occupational stressors before and during the COVID-19 pandemic. Data on sociodemographic, occupational, lifestyle, anthropometric, food consumption and occupational stress were collected before and during the COVID-19 pandemic. A total of 218 workers from a private hospital in Santo Antônio de Jesus, Bahia, Brazil were included in the study. After evaluating the normality of the data, parametric or non-parametric tests were used to characterize the sample. Dietary pattern was defined with Exploratory Factor Analysis and Structural Equation Modeling was used to test the desired association. During the pandemic, work per shift increased by 8.2% (p = 0.004) and working hours > 40 h/week increased by 9.2% (p = 0.006). Despite the higher prevalence of low occupational stress (85.8% vs. 72.1%), high stress increased by 13.7% from 2019 to 2020 (p < 0.001) and 30.3% reported a positive mediating effect on the variables of body composition, body mass index (b = 0.478; p < 0.001), waist circumference (b = 0.395; p = 0.001), fat-free mass (b = 0.440; p = 0.001) and fat mass (b = −0.104; p = 0.292). Therefore, a dietary pattern containing high-calorie foods was associated with changes in the body composition of hospital workers, including occupational stressors as mediators of this relationship.