Miracle-dz/UniFAR-FTUs huggingface dataset:
SOI technology for high-temperature applications

This work investigates and demonstrates the potential of Silicon-On-Insulator (SOI) MOSFETs for high-temperature analog and digital applications. The small area of junctions in SOI/MOS devices reduces the high-temperature leakage currents by as much as 3 to 4 orders of magnitude over regular (bulk) MOS devices. The threshold voltage variation with temperature is 2 to 3 times smaller than in bulk devices, and the output conductance of SOI MOSFETs actually improves as temperature is increased. These properties enable the fabrication of digital and analog SOI/CMOS circuits operating up to over 300 degrees C with little performance degradation. This paper describes the high-temperature performances of small SOI/CMOS circuit blocks such as static and dynamic logic gates, frequency dividers, and operational amplifiers. <<ETX>>
Motivation Analysis of Technological Startups Business Models Based on Intelligent Data Mining and Analysis

To improve the motivation analysis effect of the technological startups business model, this study combines intelligent data mining technology to analyze related factors and proposes a method of adjusting parameters of machine-learning model based on Bayesian optimization algorithm. Moreover, this study integrates the prior information and sample information of the machine learning algorithm performance function and builds an intelligent analysis model based on the elements of the technological startups business model. Finally, this article designs experiments to evaluate the effect of intelligent data mining of the system model in this study. The research shows that the motivation analysis model of the technological startups’ business model based on intelligent data mining analysis proposed in this study meets the basic requirements of the design system of this study.
Weighted maximum likelihood estimation for individual growth models

We apply a class of stochastic differential equations to model individual growth in a randomly fluctuating environment using cattle weight data. We have used maximum likelihood theory to estimate the parameters. However, for cattle data, it is often not feasible to obtain animal's observations at equally spaced ages nor even at the same ages for different animals and there is typically a small number of observations at older ages. For these reasons, maximum likelihood estimates can be quite inaccurate, being interesting to consider in the likelihood function a weight function associated to the elapsed times between two consecutive observations of each animal, which results in the weighted maximum likelihood method. We compare the results obtained from both methods in several data structures and conclude that the weighted maximum likelihood improves the estimation when observations at older ages are scarce and the observation instants are unequally spaced, whereas the maximum likelihood estimates are recommended when animals are weighted at equally spaced ages. For unequally spaced observations, a bootstrap estimation method was also applied to correct the bias of the maximum likelihood estimates; it revealed to be a more precise alternative, except when the available data only has young animals.
Quad‐element multi‐band antenna array in the smart mobile phone for LTE MIMO operations

In this paper, a quad‐element MIMO antenna array in the smart phone has been developed for multi‐wideband operations covering 750–960 MHz and 1700–3600 MHz. The proposed antenna element consists of two parts: one is a dielectric resonator that operates in a TE11δ mode and generates the antenna's upper band, and the other is a radiating patch with a meander line connected to the ground plane, which achieves composite right‐ and left‐handed (CRLH) metamaterial transmission lines structure and contributes the antenna's lower band. Each of four antenna elements fitting in a small region of 25 × 16 × 4 mm3 are located on a circuit board of 125 × 75 × 0.8 mm3. The slots are etched on the metallic ground plane to guarantee good isolation between four antenna elements. The MIMO antenna array is fabricated, and the simulated and measured results show good performance in terms of S parameters, radiation pattern, total efficiency, and envelope correlation coefficient (ECC). © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2619–2626, 2016
Toward efficient runs of nonlinear gyrokinetic simulations assisted by a convolutional neural network model recognizing wavenumber-space images

A neural-network based innovative model recognizing the wavenumber space images has been developed to accurately forecast when the saturation of turbulent heat fluxes commences, i.e., the saturation time, in nonlinear gyrokinetic simulations. The wavenumber space images of the perturbed distribution function are focused on, which better represent the characteristics of turbulence. The model exploiting the state-of-the-art convolutional neural network model is capable of detecting minuscule differences between the images. Once the wavenumber space image is fed into the developed model, it can quickly and almost perfectly classify which phase of the turbulence evolution in nonlinear gyrokinetic simulations the image is in: the linearly and nonlinearly growing phases and the saturation phase. It can also predict the simulation time at which the image was processed with significantly high accuracy. The model enables us to forecast the saturation time of the gyrokinetic simulation in question by feeding an image at an early stage of the simulation and receiving the degree of progress toward the saturation. The ability of the model makes it possible to easily search out a desirable initial condition that rapidly conducts the simulation to a saturation phase. Such a pre-prediction model is important for running long time simulations on a large scale supercomputer like Fugaku in view of the efficient use of computational resources. In order to improve the predictive capability for the simulation that is going to be performed, several prediction models are trained by data with different major instabilities. The best predictor is selected to be in use based on the result of the pre-performed linear stability calculation with low computational cost.
Efficacy of promoting awareness in ELF communicative strategies through PBLAcademic Year 2014-2015

At the Center for English as a Lingua Franca (CELF), students interact with teachers and tutors who have different first languages and cultural backgrounds. The CELF goals are to raise student awareness of the use of language in such ELF contexts and to develop an ELF-oriented curriculum. ELF research has put forth a range of pedagogical implications. Suggestions by ELF researchers include exposing students to a “wide range of English” (Björkman, 2013, p. 191), and “promoting interactions among students themselves in the classroom” (Matsumoto, 2011, p. 110). Students should be encouraged with opportunities to engage in meaningful tasks which promote the use of such strategies as repetition, paraphrasing, clarification checks and “collaborative completion of utterances in their interactions as they negotiate meaning and co-construct understanding in English” (Kaur, 2014, p. 159). A research project incorporating these ELF insights has been developed and project-based learning (PBL) was chosen as the pedagogical approach as it provides for communication and group collaboration similar to real-world situations. Student interaction with tutors and in focus group discussions (FGD) from two classes were recorded and transcribed for analysis of their use of strategies in order to maintain efficacy in communication. Results from preand post-project questionnaires on student beliefs regarding their use of strategies are also discussed.
Fintech: Financial Inclusion or Exclusion?

ABSTRACT: This paper examines the role of Fintech in financial inclusion. Using Global Findex data and emerging fintech indicators, we find that Fintech has a higher positive correlation with digital financial inclusion than traditional measures of financial inclusion. In the second stage of our empirical investigation, we examine the key factors that are correlated with the Fletcher School’s three digital divide – gender divide, class (rich-poor) divide and rural divide. The results indicate that greater use of fintech is significantly associated with a narrowing of the class divide and rural divide but there was no impact on the gender divide. These findings imply that Fintech alone may not be sufficient to close the gender gap in access to financial services. Fintech development may need to be complemented with targeted policy initiatives aimed at addressing the gender gap directly, and at changing attitudes and social norms across demographics
Real-Time Distributed Cooperative Adaptive Cruise Control Model Considering Time Delays and Actuator Lag

Real-time control of a fleet of Connected and Automated Vehicles (CAV) for Cooperative Adaptive Cruise Control (CACC) is a challenging problem concerning time delays (from sensing, communication, and computation) and actuator lag. This paper proposes a real-time predictive distributed CACC control framework that addresses time delays and actuator lag issues in the real-time networked control systems. We first formulate a Kalman Filter-based real-time current driving state prediction model to provide more accurate initial conditions for the distributed CACC controller by compensating time delays using sensing data from multi-rate onboard sensors (e.g., Radar, GPS, wheel speed, and accelerometer), and status-sharing and intent-sharing data in BSM via V2V communication. We solve the prediction model using a sequential Kalman Filter update process for multi-rate sensing data to improve computational efficiency. We propose a real-time distributed MPC-based CACC controller with actuator lag and intent-sharing information for each CAV with the delay-compensated predicted current driving states as initial conditions. We implement the real-time predictive distributed CACC control algorithms and conduct numerical analyses to demonstrate the benefits of intent-sharing-based distributed computing, delay compensation, and actuator lag consideration on string stability under various traffic dynamics.
Validation of TEKNOsim 5: Verification Against Different Standards and Building Energy Simulation Tools

The growth of building energy simulation tools has provided users with a variety of alternatives to model and simulate buildings. The algorithms used in each building energy simulation software are different and range from simple to highly complex. TEKNOsim is a simple design tool based on analytical and empirical algorithms. It is used for calculating building thermal loads and simulating indoor thermal climate. A new version of TEKNOsim has been released (Version 5) with a new interface and feature enhancements. This thesis aims to validate the algorithms used in TEKNOsim 5. The objective is to demonstrate the accuracy and reliability of the results that TEKNOsim 5 provides. The thesis is divided into three main parts: 1) Qualitative analysis of features of TEKNOsim 5 through a survey performed on a sample of users, 2) Analytical verification against three European Standards through verification procedures proposed by these Standards, and 3) Comparative verification against several building energy simulation tools through the Building Energy Simulation Test (BESTEST) cases. The survey results indicate that TEKNOsim 5 is perceived as an efficient tool with good functionality and a user-friendly interface with easy navigation. The survey results also suggest some future developments to make TEKNOsim more attractive to users. The analytical and comparative verifications indicate that TEKNOsim is a reliable and accurate tool for the evaluation of indoor thermal climate. It provides an accurate estimation of the peak sensible cooling load with relatively short model setup and simulation time. The peak heating loads are somewhat overestimated by TEKNOsim, but no more than 12 % compared to other simulation tools. A possible change, to reduce the deviation of TEKNOsim 5 results in comparison to other simulation tools, is to modify the way outdoor climate is defined and setup.
A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning

Multiple sclerosis (MS), a disease of the central nervous system, affects the white matter of the brain. Neurologists interpret magnetic resonance images that are often complicated, time-consuming, and contradictory. Using EEG signals, this disease can be analyzed and diagnosed more accurately. However, it is imperative that MS be diagnosed by specialists using assistive technology. Until now, a few methods have been proposed in this field that are sometimes associated with different challenges in analysis. This paper presents a hybrid approach to MS diagnosis in order to decrease classification error rates. Using the proposed method, EEG descriptors in both the time and frequency domains are analyzed. After the feature extraction stage, a modified version of the ant colony optimization method (m-ACO) was used to select the appropriate subset of features. Then, the support vector machine is used to determine whether the disease exists. A metaheuristic algorithm adjusts the support vector machine's parameters to withstand overfitting challenges. Despite a limited number of input channels, significant classification accuracy has been achieved using wavelet analysis techniques, dividing all five subbands of EEG signals, signal windowing, and extracting efficient features from the data. Additionally, alpha, beta, and gamma bands of the signal are important, and the accuracy, sensitivity, and specificity levels are higher than 98.5%. Compared to similar MS diagnostic methods, the proposed method achieved significantly higher diagnostic accuracy. Application and implementation of this method can be effective in treating neurological diseases, including multiple sclerosis.
Rapid Quantification of the Binocular Visual Field for Clinical Trials: Performance of a Modified Esterman Supra-Threshold Test Implemented with the Open Perimetry Interface

Purpose We aimed to assess the performance of the modified-Esterman test (mET) as a rapid suprathreshold binocular quantification tool for the assessment of peripheral visual fields. The mET consists of an even spread of test points across the visual field. Materials and Methods The mET was implemented on the Octopus 0900 perimeter using the Open Perimetry Interface (OPI) and consisted of 160 points. Patients with choroideremia, a rod-cone dystrophy, Stargardt disease, a cone-rod dystrophy, and healthy volunteers underwent both the mET and the standard Esterman tests twice. Disease severity (mild/moderate/severe) was graded on both tests independently. Voronoi tessellation was utilised to compare the tests. Results The Voronoi visualisation was able to demonstrate that the mET was able to provide more information about the disease state at all stages of diseases. This was confirmed by the agreement statistic, which showed that the mET detected 27% more points of visual field loss compared to the Esterman test, being most useful in patients with rod-cone dystrophies. Conclusion The mET provides a speedy quantitative measure of the peripheral visual field loss, which can be used in clinical trials to monitor longitudinal assessment of peripheral visual function. The mET provides a more even coverage across the visual field compared to the Esterman test points, making it more suitable for this purpose. This is a key part of safety monitoring in retinal clinical trials. The mET can easily be implemented on commercially available perimeters that allow Open Perimetry.
Use of Boost Power Stage for Static Voltage Gain Improvement of a Non-isolated Continuous Input Current Three-Level DC-DC Converter

The work presented in this paper describes the steady state analysis of a non-isolated and continuous conduction mode (CCM) operated three-level DC-DC converter with input boost power stage for achieving high static voltage gain. The proposed converter configuration is a double boost DC-DC conversion scheme. A boost power module consisting of a power switch, an inductor, a pre-charge diode, and a diode rectifier is introduced as the input stage in the conventional three-level DC-DC converter configuration to obtain the DC-DC converter topology suggested in this research work. The role of the input boost power stage is to extend the voltage gain of the conventional three-level boost converter. The proposed topology employs two power switches in addition to a boost power switch with simple control strategy so that the switching losses are reduced. The switches are subjected to low voltage and current stresses. The suggested non-isolated converter is operated under continuous inductor current mode in open loop configuration. The converter configuration has been simulated in MATLAB / SIMULINK platform. The input boost power stage receives a voltage of 24 V from a DC source. With low duty ratio (= 0.8) of the power switches, a DC voltage of around 208 V is obtained at the load end. The passive components of the converter are so designed that the filtering requirements on the DC output voltage and current waveforms are reduced. The voltage gain of the proposed converter is compared with that of the three-level boost converter without input boost power stage. The various modes of operation of the suggested converter are discussed with relevant equivalent circuits. The proposed system is validated using a developed hardware prototype model of 100 W converter with a low cost KA3525A PWM controller. Both simulation and prototype model results demonstrate that the proposed converter suits high voltage gain applications.
A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

For the sensing layer of the Internet of Things, the mobile wireless sensor network has problems such as limited energy of the sensor nodes, unbalanced energy consumption, unreliability, and long transmission delay in the data collection process. It is proved by mathematical derivation and theory that this is a typical multiobjective optimization problem. In this paper, the optimization goal is to minimize the energy consumption and improve the reliability under time-delay constraints and propose a path optimization mechanism to optimize the mobile Sink of mobile wireless sensor networks based on the improved dragonfly optimization algorithm. The algorithm takes full advantage of the abundant storage space, sufficient energy, and strong computing power of the mobile Sink to ensure network connectivity and improve network communication efficiency. Through simulation comparison and analysis, compared with random movement method, artificial bee colony algorithm, and basic dragonfly optimization algorithm, the energy consumption of the network is reduced, the lifespan of the network is increased, and the connectivity and transmission delay of the network are improved. The proposed algorithm balances the energy consumption of the sensors nodes to meet the network service quality and improve the reliability of the network.
Impasses of Affective Inquiry in Pandemic Times

This paper puzzles through the doubled impasses of our affective inquiry into learning communities in a college setting. In so doing, we take on the incommensurabilities of our field site and post qualitative inquiry, providing an example of critical qualitative inquiry where theory and practice are made to work together at their limits, both compromising with each other, neither subordinated to the other. Affective inquiry, an instantiation of concept as method inquiry, itself was a search for the impasses of learning community life. When our observations were moved to Zoom as a result of the COVID-19 pandemic, we were faced with another impasse: how to attune to affect in this new and thoroughly datafied space? We open by theorizing affective inquiry and our doubled impasse, and we then explore three locations of these impasses in our fieldwork. We close with a reaffirmation of relentless experimentation.
Precise Elevation Thresholds Associated with Salt Marsh–Upland Ecotones along the Mississippi Gulf Coast

Coastal marshes provide essential ecosystem services related to biodiversity, water quality, and protection from erosion. As increasing rates of relative sea-level rise affect many coastal marsh systems, a thorough understanding of marsh responses to sea-level change, particularly the migration of marsh–upland boundaries, becomes essential. The goal of this study was to determine precise elevation thresholds associated with coastal marsh, the marsh–upland ecotone, and upland plant communities along Mississippi’s Gulf of Mexico coast (diurnal, microtidal). Elevations (NAVD88) were measured using survey-grade Global Navigation Satellite System solutions integrated with high-precision leveling. Plant species were sampled at approximately 1-m intervals along each of thirty-three transects extending from intermediate marsh through the marsh–upland ecotone. Elevation thresholds associated with plant community change were determined based on relevant quartiles of the data. Probabilities of occurrence of each plant community type were computed for elevations at the centimeter scale. Results indicated transitions from marsh to ecotone and ecotone to upland at elevations of approximately 0.40 m and 0.60 m, respectively. Understanding the precise nature of these centimeter-scale dependencies of marsh vegetation on coastal elevation will facilitate spatial modeling of marsh transgression in response to sea-level rise, subsidence, changes in sediment flux, and land use change.
Inter-set rest configuration effect on acute physiological and performance-related responses to a resistance training session in terrestrial vs simulated hypoxia

Background Metabolic stress is considered a key factor in the activation of hypertrophy mechanisms which seems to be potentiated under hypoxic conditions.This study aimed to analyze the combined effect of the type of acute hypoxia (terrestrial vs simulated) and of the inter-set rest configuration (60 vs 120 s) during a hypertrophic resistance training (RT) session on physiological, perceptual and muscle performance markers. Methods Sixteen active men were randomized into two groups based on the type of hypoxia (hypobaric hypoxia, HH: 2,320 m asl; vs normobaric hypoxia, NH: FiO2 of 15.9%). Each participant completed in a randomly counterbalanced order the same RT session in four separated occasions: two under normoxia and two under the corresponding hypoxia condition at each prescribed inter-set rest period. Volume-load (load × set × repetition) was calculated for each training session. Muscle oxygenation (SmO2) of the vastus lateralis was quantified during the back squat exercise. Heart rate (HR) was monitored during training and over the ensuing 30-min post-exercise period. Maximal blood lactate concentration (maxLac) and rating of perceived exertion (RPE) were determined after the exercise and at the end of the recovery period. Results Volume-load achieved was similar in all environmental conditions and inter-set rest period length did not appreciably affect it. Shorter inter-set rest periods displayed moderate increases in maxLac, HR and RPE responses in all conditions. Compared to HH, NH showed a moderate reduction in the inter-set rest-HR (ES > 0.80), maxLac (ES > 1.01) and SmO2 (ES > 0.79) at both rest intervals. Conclusions Results suggest that the reduction in inter-set rest intervals from 120 s to 60 s provide a more potent perceptual, cardiovascular and metabolic stimulus in all environmental conditions, which could maximize hypertrophic adaptations in longer periods of training. The abrupt exposure to a reduced FiO2 at NH seems to reduce the inter-set recovery capacity during a traditional hypertrophy RT session, at least during a single acute exposition. These results cannot be extrapolated to longer training periods.