Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Built-in Polarization Field on the Optical Properties of AlGaN/GaN Quantum Wells

AlGaN/GaN quantum well (QW) structures were grown entirely by molecular-beam epitaxy on c-plane sapphire substrates. Reflection high-energy electron diffraction intensity oscillations, which testify a smooth growth front surface at the molecular monolayer scale, were used to precisely measure in situ both the growth rate and the Al content in AlGaN alloys. High-qualily AlGaN/GaN QWs with thicknesses varying from 10 to 80 Angstrom were achieved. Their photoluminescence (PL) linewidth is 10 to 30 meV, and the Stokes shift, determined by temperature-dependent FL, also ranges between 10 to 30 meV. The sample geometry and the Al composition in the barriers were varied in order to assess the built-in electric Grid present in wurtzite nitride-based structures. The magnitude of the deduced electric field points out the role of spontaneous polarization.
The extended variable space approach to density functional theory in the canonical ensemble

Density functional theory (DFT) of classical fluids in the canonical ensemble (CE) is studied by means of Legendre transform techniques in an extended variable space. The constraint that arises from having a fixed number of particles N is incorporated into the theory by considering N and its conjugate, the CE chemical potential μ, as additional variables in the DFT analysis of the problem. The method allows for obtaining an Ornstein–Zernike (OZ) equation in the CE. A comparison is made with other approaches to the problem. In particular, μ is identified with the Lagrange multiplier related to the fixed-N constraint and a further Legendre transform leads to an equivalent OZ equation equal to that of the grand canonical ensemble (GCE) in terms of functional derivatives stripped off from their asymptotic behaviour. The CE analogous to the GCE direct correlation function is introduced as an excess (over ideal) quantity in terms of which we obtain a CE compressibility equation.
Planar Ultra-Wideband Antenna with Added GSM Frequency Bands

A planar antenna design for ultra-wideband (UWB) communication with two additional GSM bands is presented in this study. A beveled-shaped patch with a quarter-wavelength transformer is utilized to provide UWB response. Two capacitive-loaded resonators (CLRs) are utilized in conjunction with a partial ground plane to create resonance in the GSM frequency bands (900 and 1800 MHz). The designed antennas’ prototype is fabricated and measured, and it is noticed that both the results are well in agreement. Furthermore, the proposed antenna design produces good radiation characteristics for the bands of interests.
An Analog Nearest Class with Multiple Centroids Classifier Implementation, for Depth of Anesthesia Monitoring

Monitoring the Depth of Anesthesia on a patient is crucial to maintain a safe sedation state during a surgical operation. A high dosage can directly affect the patient's health, while a low one may disrupt the operation and, in turn, lead to unavoidable damage. To this end, this work proposes a novel, low power $(1.7\mu W)$, low voltage (0.6V) analog architecture of a Nearest Class with Multiple Centroids classifier for depth of anesthesia monitoring. The architecture consists of a bell-shaped function circuit and the Lazzaro argmax operator circuit. To verify the proper operation of the proposed classifier a real-world depth of Anesthesia dataset is utilized. Post-layout simulation results were compared with software-based ones to confirm the high accuracy of the proposed design. The implemented architecture was realized and simulated in a TSMC 90nm CMOS process, using the Cadence IC Suite.
Scattering mechanisms and defects in InP epitaxially grown on (001) Si substrates

Carrier concentration and mobility of unintentionally doped InP layers, grown directly on Si using metal‐organic vapor‐phase epitaxy, have been studied. The formation of antiphase domains (APDs) was found to depend on annealing of the Si substrate in an AsH3 flow prior to epitaxial growth. Dislocation densities determined by the wet chemical delineation technique were (8±1)×107 cm−3, seemingly uncorrelated to APDs in the layers. In addition to a shallow donor and a compensating acceptor, a deep donor was observed affecting the temperature dependence of the free‐electron concentration between 77 and 300 K. The electron mobility in this temperature range could be described in terms of the scattering mechanisms which are dominant in homoepitaxial InP, namely, scattering due to polar optical phonons, to ionized impurities, and to space charges. Electron scattering due to either of these mechanisms was strongly influenced by the occurrence of antiphase boundaries (APBs). The space‐charge density as well as the.
Parameters Estimation for PEDOT:PSS Based UV Light Sensors Using an Improved Numerical Platform

This work proposes a combined mathematical-numerical approach based on modeling, simulation, and optimization procedures to numerically evaluate the photoelectrical performances of innovative nanomaterials based UV-photodetectors (PDs). The improved platform is based on the Lambert W function combined with the enhanced Nelder–Mead (NM) algorithm and the root mean squares error (RMSE) function. The novelty of the proposed platform lies primary in the improvement of the classical principles of the NM algorithm search direction: 1) optimization of the initial guess choice by integrating two different choice strategies; 2) incorporating of complementary conditions to prevent obtaining unacceptable negative values of resistances; and 3) this combination reflects the simplicity and the efficiency of the technique more specifically, for complex and multidimensional problems. It is also relevant for incomplete and undefined curves but requires in return a good choice of initial parameters. The proposed algorithm was applied for several zinc oxide nanorods (ZnO NRs) based hybrid structures such as Poly(3, 4ethylenedioxythiophene):Poly(styrenesulfonate) (PEDOT: PSS/ZnO NRs) and Poly(1,4-phenylenevinylene) (PPV-<inline-formula> <tex-math notation="LaTeX">$\text{C}_{{6}}$ </tex-math></inline-formula>) derivative (PPV-<inline-formula> <tex-math notation="LaTeX">$\text{C}_{{6}}$ </tex-math></inline-formula>/ZnO NRs) based devices. More importantly, high accuracy (<inline-formula> <tex-math notation="LaTeX">$\text {RMSE} \sim {10}^{-{6}}$ </tex-math></inline-formula>) in terms of extracted electrical parameters was obtained thanks to the proposed improvements at the numerical level of the platform, which translates a better fitting quality between the theoretical and experimental <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> curves in both operating range (forward and reverse bias). Therefore, the algorithm seems to be quite promising for providing accurate simulations of PDs characteristics.
The Electricity Price Prediction of Victoria City Based on Various Regression Algorithms

Precise electricity price prediction is extremely important for all markets especially for families' life conditions because the more demand the more electricity price increases, therefore it is vital to keep the balance between demand and supply. It is crucial to know how much electricity is needed for the future as it has a remarkable impact on economic circumstances. This article proposes four productive methods in order to forecast high-precision results. In the regression algorithms, it is used several methods which are called decision tree regressions, random forest regression, gradient boosting regression, and linear regression algorithms. The dataset is divided into three parts. Training, validation, and test are split into %70, %10, and %20 respectively. The empirical and efficient results show that these methods can be used and reduce errors. The article demonstrates that a novel forecasting model can be designed for the future.
A Machine Learning Approach To The Prediction Of Bank Customer Churn Problem

Customers in the banking industry nowadays have many options when deciding where to invest their money. Customer retention and churn have thus emerged as crucial challenges for the majority of banks. This research tries to solve the customer churn problem by applying machine learning algorithms. Classifiers such as Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and Light Gradient Boosting are employed. To this end, first, the irrelevant features are removed by applying a feature selection technique, and the most pertinent features are identified, second, the resulting data set is balanced using the SMOTE method. The results of experiments on classifiers with balanced data and with imbalanced data are compared in terms of accuracy, recall, precision, and performance. In the case of imbalanced data (before the SMOTE is applied) no classifier over-performed others. In the case of balanced data (after SMOTE is applied) the Random Forest classifier outrightly overperformed other classifiers.
A Review of Effort Estimation in Agile Software Development using Machine Learning Techniques

Nowadays organizations are heading towards the agile software development instead of traditional development models due to the flexibility and acceptance of change in the agile software development approach. Estimation of time, effort, and size of a project is a challenging task in agile software development due to the changing requirement during development. So to overcome the challenges in effort estimation different Machine Learning (ML) techniques such as Random Forest, Decision tree, SGB, and Neural Network models are used along with story points. In this paper, a literature review of all these ML techniques is carried out for effort estimation of agile projects.
Robust Adaptive Neural Sliding Mode Method for Coupled-Tank System Intelligent Control

This paper introduces an advanced adaptive neural sliding mode controller (ANSMC) applied for precisely tracking control the liquid level of the coupled tank system. The controlled plant is a highly nonlinear time-delayed system which contains the uncertainties such as noise measurement, disturbances of output valves, etc. The proposed ANSMC controller includes a combined neural sliding mode controller permitting to generate adaptive learning rule which can adaptively estimate the plant parameters and compensate the uncertainties of the plant regardless of the knowledge concerning the controlled plant information. The effectiveness of the proposed control scheme is convincingly proved through the simulation results applied for a highly nonlinear coupled tank system.
Point defects and electrical properties of ice

This paper is a brief summary of a review lecture in which the essential features of the standard theory of electrical properties of ice were described and attention was drawn to current problems concerning the nature of protonic point defects on which the theory is based. PACS Nos.: 61.72Ji, 66.10Ed, 66.30Dn, 77.22
Exact relation between nonlinearity and algebraic immunity

Boolean functions have found a widespread use in cryptography. In connection with the advent of the 'algebraic' attack on stream ciphers, the Boolean functions, which are used in these ciphers as nonlinear filters, have to possess, among other properties, high algebraic immunity. One more cryptographically significant property of Boolean functions, especially of those utilised in stream ciphers, is nonlinearity. In this connection, the question arises about relations between the nonlinearity of a Boolean function and its algebraic immunity. In this research we obtain a lower bound for nonlinearity in terms of algebraic immunity and present functions at which this bound is attained for any admissible values of the parameters.
UAV-assisted Uplink NOMA Networks: UAV Placement and Resource Block Allocation

This paper studies an uplink network in which a hovering unmanned aerial vehicle (UAV) serves as a flying base station and multiple ground users access different resource blocks (RBs) with the aid of power-domain non-orthogonal multiple access (NOMA). We aim to maximize the sum of information rate of the network through appropriate UAV placement and RB allocation. The mixed integer nonconvex problem is decomposed into two layers. The inner layer, RB allocation given the position of the UAV, is solved by hill-climbing. The outer layer, UAV placement given the result of RB allocation of the inner layer, is solved by particle swarm optimization. Simulation results show that the proposed layered scheme outperforms existing resource allocation strategies.
Comparative Study of Outer Rotor Field Excited Flux Switching Machine with Feasible Rotor Poles For EV and HEV Application

This paper proposes a novel Outer Rotor Field Excited Flux Switching Machine (OR-FEFSM) for electric and hybrid electric vehicles. First, the design topology and operating principles is discussed. In order to investigate the effectiveness of the proposed OR-FEFSM, a detailed study on rotor pole combinations is performed. Based on simplified mathematical formulation, 12S/7P (stator slot/rotor poles), 12S/11P, 12S/13P and 12S/17P are the most feasible combination. Moreover, the developed OR-FEFSM offers improved average torque and ripples. Finite Element Analysis (FEA) based on comprehensive electromagnetic performance are opted on the aforesaid combination and found that 12S/13P offers the highest average torque of 4.62 Nm whereas 3.72 Nm, 2.72Nm, and 2.103 Nm average torque are offered by 12S/17P, 12S/7P and 12S/11P respectively.
X-ray spectroscopy of the ultrasoft transient 4U 1543 - 47

The X-ray transient 4U 1543 - 47 was observed in August 1983 by the Exosat observatory near the maximum of an outburst. The X-ray spectrum was measured using a gas scintillation proportional counter (GSPC) and a transmission grating spectrometer (TGS). A broad (FWHM about 2.7 keV) line at 5.9 keV is detected in the GSPC, which is interpreted as a redshifted and broadened iron K-alpha line. The line broadening and redshift may arise from either Compton scattering in a cool plasma with small optical depth and/or from Droppler and relativistic effects in the vicinity of compact object. The spectrum below 2 keV, obtained with the TGS, shows evidence for a broad emission feature at 0.74 keV, which may be an iron L-transition complex. However, such an emission feature could be an artifact caused by an anomalously low interstellar absorption by neutral oxygen. The contimuum emission is extremely soft and is well described by an unsaturated Comptonized spectrum from very cool plasma (kT = 0.84 keV) with large scattering depth. 30 refs.
Use of Deep Learning Application in Medical Devices

Artificial Intelligence (AI) is a system that helps machines to march with human abilities within daily lifestyles. Deep learning supported by AI can be an effective application within healthcare sector. This research has explained various aspects of Deep learning application that can be a major area of concern for pushing the development process of Indian medical sector that have lack of infrastructure and lack of capacity, to take less time to optimise the medical diagnosis process. This research has also investigated the advantages and disadvantages that medical sector might face while using deep learning applications. Deep learning applications under AI systems are used to classify objects. CNN model, Machine-learning tools, and other tools that use deep learning approach are effective to diagnose any disease and in medical image analysis process. Deep learning techniques are also used to detect heart disease and manage the data regarding the patients of heart diseases. Secondary data collection method has been used and a thematic analysis has been conducted in this research to describe and find various challenges that might have been engaged within deep learning process used in medical sectors of India. It has been found that, Deep Learning is used widely for COVID-19 medical image processing through a fully connected CNN model. As a result, the main finding states that deep learning application creates a major scope for the improvement in Indian medical sector.
Multi-target Search Using Teams of Robots Based on Thompson Sampling

Different Solutions and algorithm to the Multi-Armed Bandit (MAB) problem using method of exploration and exploitation trade-off, including Thompson Sampling. Which proved to have competitive results compared to popular methods of solution. Formulated MAB problem into Multi-robot Search problem. Showed data solving multi-robot search problem with Dynamic Thompson sampling.
Design and performance study of a BMI-based hand-assisted robot

Forty-six percent of stroke patients, of whom 80 percent are not treated in a timely manner, have hand paralysis syndrome that lasts a lifetime. The integration and control of external rehabilitation devices based on EEG signal collection is gaining more attention and research, and this type of research has a wide range of directions to improve the quality of life for users. The research aims to study the design and performance of BMI-based hand-assisted robots. Existing robotics and equipment are relatively expensive, and the programmed procedures are not well adapted to different stroke users. Through experimental development and research of innovative BMI brain-computer interface devices, customized programming of experimental protocols, situational awareness of assistive robotics system data processing, brain-computer driven device testing and human-computer interaction methods, and finally after 510 practical tests concluded that patients can get significant rehabilitation process, especially for in the clinical state. The innovative system-on-a-chip research and ergonomic design confirmed that it meets the standards of rehabilitation practice and can significantly improve the patient's cognitive abilities during learning and rehabilitation and more efficiently facilitate the patient's recovery.
Enhanced coherent terahertz emission from indium arsenide

Abstract We present results showing enhancement of terahertz (THz) emission from indium arsenide (InAs) which demonstrate that at 170 K and magnetic fields (B) up to B = 8 T, increased visible to terahertz conversion efficiencies are possible. With an average total power of 1μW at B = 0 T and 12μW at B = 8 T, we have observed an order of magnitude increase in total conversion efficiency with no visible saturation. The InAs sample was placed within an optical cryostat and illuminated by Ti:Sapphire laser pulses with duration 100 fs. The terahertz powers were recorded using a calibrated He-cooled bolometer. Free space electro-optic sampling (EOS) measurements confirmed the coherent nature of this radiation over the field and temperature range investigated. The optical cryostat could produce magnetic fields of B = 8 T at temperatures from 3 to 300 K. We therefore present a comprehensive study of the terahertz generation as a function of both these variables and a brief comparison with an alternative material system, InSb.
Comparative Analysis of Ant Colony Optimization and Particle Swarm Optimization for Test Case Prioritization

To boost the efficiency of testing and save time and money in the construction of the testing program, the test case prioritization technique prioritizes a subset of the full test suite and optimizes the execution order of the test cases. The goal of this paper is to classify and compare the performance of two nature-based test case prioritization techniques: To overcome the problem of needing to perform the whole test suite at some point, resulting in time and expense limits, we used Ant Colony Optimization and Particle Swarm Optimization. We chose a sample test suite and prioritized the test cases for our research. An experimental investigation of the acquired data will be useful in selecting the best prioritizing technique under various environmental constraints. For our analysis, we selected a sample test suite and prioritized the test cases. In various environmental restrictions, an experimental study of the findings collected will be valuable in determining the optimum prioritization strategy. The findings of both algorithms showed high global optimization abilities, with the ant colony strategy outperforming the particle swarm optimization approach.
Real-time Guidance for Powered Landing of Reusable Rockets via Deep Reinforcement Learning

Powered landing of reusable rocket is an advanced technology to achieve pinpoint landing (the norm of position error < 5 m and velocity error < 2 m/s) while satisfying a series of highly nonlinear constraints. A major challenge is guaranteeing fuel-optimal and convergence when solving rocket powered landing problem. In this manuscript, a real-time feedback guidance algorithm based on deep reinforcement learning is developed. The proposed method maps state directly to thrust control commands. The first contribution of this paper is to use multi-stage reward function to eliminate the negative effects triggered by design guidance law, thereby significantly enhancing fuel-optimal performance. Another contribution is that a model pre-training framework based on imitation learning is presented to improve model convergence by fitting optimal data. Numerical experiments show that the nearly fuel-optimal trajectories generated by the proposed algorithm successfully achieve pinpoint landing from random initial states.
Precise Determination of the Valence-Band Edge in X-Ray Photoemission Spectra: Application to Measurement of Semiconductor Interface Potentials

A highly precise method for locating the valence-band edge in x-ray photoemission spectra is reported. The application to measuring semiconductor interface potentials is discussed. X-ray photoemission-spectroscopy experiments on Ge and GaAs(110) crystals have given Ge $3d$, Ga $3d$, and As $3d$ core level to valence-band edge binding-energy differences of 29.55, 18.81, and 40.73 eV to a precision of \ifmmode\pm\else\textpm\fi{}0.02 eV. For illustration, the valence-band discontinuity at an abrupt Ge/GaAs(110) heterojunction is determined to be 0.53\ifmmode\pm\else\textpm\fi{}0.03 eV.
Subjective Overview of Accelerated Aging in Schizophrenia

Schizophrenia, like many other human diseases, particularly neuropsychiatric diseases, shows evidence of accelerated brain aging. The molecular nature of the process of aging is unknown but several potential indicators have been used in research. The concept of accelerated aging in schizophrenia took hold in 2008 and its timing, pace, determinants and deterrents have been increasingly examined since. The present overview of the field is brief and selective, based on diverse studies, expert opinions and successive reviews. Current thinking is that the timing of age acceleration in schizophrenia can occur at different time periods of the lifespan in different individuals, and that antipsychotics may be preventive. The majority opinion is that the cognitive decline and premature death often seen in schizophrenia are, in principle, preventable.