Miracle-dz/UniFAR-FTUs huggingface dataset:
Small-Signal Modeling of LLC Converters Using Homopolarity Cycle

The widespread use of the LLC converter in dc–dc applications has increased the importance of small-signal modeling to control the converter below- to above-resonant frequency. The analysis of LLC converters is complicated, since they process the electrical energy through a high-frequency resonant tank that causes excessive nonlinearity. As a result of this complexity, small-signal modeling of the LLC converter is traditionally performed using empirical methods, iterative simulation approaches, or theory limited to the vicinity of the resonant frequency. Often, such approaches may lead to limited insight (just empirical trends) or low accuracy below and above resonance. This article proposes a new average small-signal modeling technique for the LLC converter, and it is performed in the time domain. The proposed technique is based on the analysis of the homopolarity cycle and accurately predicts not only the small-signal dynamic behavior of the LLC converter at resonance, but also below and above resonance. By using the homopolarity cycle, the theoretical analysis of the LLC converter is significantly simplified to a level that the small-signal dynamic behavior of the LLC converter is expressed by two second-order circuit models. Experimental and simulation results of a 650-W LLC converter are provided to verify the theoretical analyses and accuracy of the circuit models. The results have shown that the proposed small-signal circuit models can accurately predict the small-signal dynamic behaviors of the LLC converter from below- to above-resonant operations.
$\Sigma$ -Scan: A Mobile Beacon-Assisted Localization Path-Planning Algorithm for Wireless Sensor Networks

Deploying static beacons to help sensor localization is a common approach in wireless sensor network. Alternatively, adopting a mobile beacon to travel along a specific trajectory and to broadcast its locations at specific points is more efficient. Therefore, path planning of the mobile beacon originally focused on finding shortest path length and minimum broadcast points, such as Scan and Hilbert, to decrease power consumption. More recently, path planning increasingly attempted to achieve higher accuracy, such as Double-scan and Z-scan. However, optimal trade-off between power efficiency and localization accuracy is yet to be achieved. In this paper, we designed <inline-formula> <tex-math notation="LaTeX">$\Sigma $ </tex-math></inline-formula>-Scan which aims to reach high accuracy and coverage with short path length by combining the advantages of Scan and Z-scan. <inline-formula> <tex-math notation="LaTeX">$\Sigma $ </tex-math></inline-formula>-Scan provides three kinds of unit to constitute arbitrary rectangles and minimize the minimum bounding rectangle. Compared to them, <inline-formula> <tex-math notation="LaTeX">$\Sigma $ </tex-math></inline-formula>-Scan is more applicable, accessible and easy to implement. The simulation results show that compared to other previous approaches, <inline-formula> <tex-math notation="LaTeX">$\Sigma $ </tex-math></inline-formula>-Scan has the highest ratio of accuracy and coverage to path length and also decreases the incidence of collinearity in time-priority trilateration (TPT).
Model-based and machine learning techniques for nonlinear image reconstruction in diffuse optical tomography

Diffuse optical tomography (DOT) is a low cost and noninvasive 3D biomedical imaging technique to reconstruct the optical properties of biological tissues. Image reconstruction in DOT is inherently a difficult problem, because the inversion process is nonlinear and ill-posed. During DOT image reconstruction, the optical properties of the medium are recovered from the boundary measurements at the surface of the medium. In this work, two approaches are proposed for non-linear DOT image reconstruction. The first approach relies on the use of iterative model-based image reconstruction, which is still under development for DOT and that can be found in the literature. A 3D forward model is developed based on the diffusion equation, which is an approximation of the radiative transfer equation. The forward model developed can simulate light propagation in complex geometries. Additionally, the forward model is developed to deal with different types of optical data such as continuous-wave (CW) and time-domain (TD) data for both intrinsic and fluorescence signals. First, a multispectral image reconstruction algorithm is developed to reconstruct the concentration of different tissue chromophores simultaneously from a set of CW measurements at different wavelengths. A second image reconstruction algorithm is developed to reconstruct the fluorescence lifetime (FLT) of different fluorescent markers from time-domain fluorescence measurements. In this algorithm, all the information contained in full temporal curves is used along with an acceleration technique to render the algorithm of practical use. Moreover, the proposed algorithm has the potential of being able to distinguish more than 3 FLTs, which is a first in fluorescence imaging. The second approach is based on machine learning techniques, in particular deep learning models. A deep generative model is proposed to reconstruct the fluorescence distribution map from CW fluorescence measurements. It is the first time that such a model is applied for fluorescence DOT image reconstruction. The performance of the proposed algorithm is validated with an optical phantom and a fluorescent marker. The proposed algorithm recovers the fluorescence distribution even from very noisy and sparse measurements, which is a big limitation in fluorescence DOT imaging.
An Integrated Micromachined Thermopile Sensor With a Chopper Interface Circuit for Contact-Less Temperature Measurements

This paper presents a sensor and its readout circuit suitable for contact-less temperature measurements. The sensor, designed employing a MEMS technology provided by STMicroelectronics, consists of a miniaturized micromachined silicon thermopile with 180-V/W responsivity, 540- $\text{k}\Omega $ output resistance, and 0.64-mm2 active absorbing area. The interface circuit, fabricated in a standard 130-nm CMOS process, employs chopper technique in order to provide amplification of the sensor output signal, which behaves substantially as a DC while minimizing offset and noise contributions at low frequency. Given the sensor characteristics, a single-ended architecture was preferred over the most straightforward fully-differential approach. The interface circuit reduces the offset by a factor 255, achieving an input referred offset standard deviation equal to 1.365 $\mu \text{V}$ , measured across 29 samples. The thermopile sensor and the interface circuit, integrated in two separate test-chips, were characterized and extensively measured both as stand-alone devices and together as a system to perform contact-less temperature measurements. Adding a metal cap to the thermopile sensor in order to reduce the environmental noise, a measurement accuracy of approximately ±0.2 °C was obtained, thus verifying the system suitability for human body temperature detection.
A Family of Stateful Memristor Gates for Complete Cascading Logic

The conditional switching of memristors to execute stateful implication logic is an example of in-memory computation to potentially provide high energy efficiency and improved computation speed by avoiding the movement of data back and forth between a processing chip and memory and/or storage. Since the first demonstration of memristor implication logic, a significant goal has been to improve the logic cascading to make it more practical. Here, we describe and experimentally demonstrate nine symmetry-related Boolean logic operations by controlling conventional Ta/TaOx/Pt memristors integrated in a crossbar array with applied voltage pulses to perform conditional SET or RESET switching involving two or three devices, i.e., a particular device is switched depending on the state of another device. We introduce a family of four stateful two-memristor logic gates along with the copy and negation operations that enable two-input-one-output complete logic. In addition, we reveal five stateful three-memristor gates that eliminate the need for a separate data copy operation, decreasing the number of steps required for a particular task. The diversity of gates made available by simply applying coordinated sequences of voltages to a memristor crossbar memory significantly improves stateful logic computing efficiency compared to similar approaches that have been proposed.
Fermat's least-time principle and the embedded transparent lens

We present a simplified version of the lowest-order embedded point mass gravitational lens theory and then make the extension of this theory to any embedded transparent lens. Embedding a lens effectively reduces the gravitational potential's range, i.e., partially shields the lensing potential because the lens mass is made a contributor to the mean mass density of the universe and not simply superimposed upon it. We give the time-delay function for the embedded point mass lens from which we can derive the simplified lens equation by applying Fermat's least-time principle. Even though rigorous derivations are only made for the point mass in a flat background, the generalization of the lens equation to lowest-order for any distributed lens in any homogeneous background is obvious. We find from this simplified theory that embedding can introduce corrections above the few percent level in weak lensing shears caused by large clusters but only at large impacts. The potential part of the time delay is also affected in strong lensing at the few percent level. Additionally we again confirm that the presence of a cosmological constant alters the gravitational deflection of passing photons.
Constraining the star formation history withFermi-LAT observations of the gamma-ray opacity of the universe

The star formation history (SFH) of the Universe is of fundamental importance to cosmology, not only to galactic formation itself but also for ongoing efforts to determine cosmological parameters and matter content of the Universe [1]. Measurements of the extragalactic background light (EBL) as a function of redshift can constrain models of the SFH, including the initial mass function(IMF) and dust extinction [1]. The gamma-ray spectra of AGN allows us to study the extragalactic background light (EBL) through γ − γ absorption of high-energy photons. In this work, we will produce constraints on models of SFH using a sample of AGN observed by the Fermi-LAT instrument by investigating the imprint of the EBL on the observed spectra of high-redshift Fermi-LAT AGN above ∼ 10 GeV.
The Effect of Nutritional Education Based on Health Belief Model on Nutritional Knowledge, Health Belief Model Constructs, and Dietary Intake in Hemodialysis Patients

Background: Based on the results of many studies on the relationship between nutritional status and clinical implications in dialysis patients, malnutrition is one of the most important factors associated with mortality in these patients. The current study examined the effect of nutritional education based on Health Belief Model (HBM) on nutritional knowledge, HBM constructs, and dietary intake in Hemodialysis (HD) patients. Materials and Methods: One-hundred chronic HD patients entered to this randomized clinical trial in 2017 in Iran; 41 in control group and 45 in intervention group completed the study. Demographic data and four 24-h recalls were collected. To evaluate the nutritional knowledge and HBM constructs, a researcher-made questionnaire was used. Patients were evaluated before, immediately after, and 3 months after intervention. Eight 1-h education sessions in 4 weeks were considered for intervention group. Independent samples t-test, Chi-square test, and repeated measures ANOVA were used to analyze the data. Results: Repeated measures ANOVA test showed significant increases in scores of the nutritional knowledge test, perceived susceptibility, perceived severity, perceived barriers (p = < 0.001), perceived benefits (p = 0.010), and self-efficacy (p = 0.019) after the study in the intervention group. There were no significant differences between two groups in energy, protein, High Biologic Value (HBV) protein, carbohydrate, fat, cholesterol, fiber, vitamin B2, B3, B6, B12, E, calcium, phosphorus, and potassium intake. Conclusions: It seems that education based on HBM can improve nutritional knowledge but in order to influence on dietary intake, longer interventions that are more comprehensive are needed.
An overview of adaptive e-learning systems

Due to the emergence of information and communication technologies in various fields, which have also affected the educational sector, adaptive e-learning systems are recognized as one of the most interesting research areas in distance web-based education. This research direction enables developers to build a model of goals, preferences and knowledge of each individual user in order to adapt the learning to his/her needs and characteristics. The objective of this paper is to present the state of the art in adaptive e-learning systems as an alternative to the traditional learning by describing its dimensions, design, architecture and theoretical approaches. We also highlight some prospects for our future work by studying, analyzing and criticizing existing systems.
FEM-Based Compression Fracture Risk Assessment in Osteoporotic Lumbar Vertebra L1

This paper presents a finite element method (FEM)-based fracture risk assessment in patient-specific osteoporotic lumbar vertebra L1. The influence of osteoporosis is defined by variation of parameters such as thickness of the cortical shell, the bone volume–total volume ratio (BV/TV), and the trabecular bone score (TBS). The mechanical behaviour of bone is defined using the Ramberg–Osgood material model. This study involves the static and nonlinear dynamic calculations of von Mises stresses and follows statistical processing of the obtained results in order to develop the patient-specific vertebra reliability. In addition, different scenarios of parameters show that the reliability of the proposed model of human vertebra highly decreases with low levels of BV/TV and is critical due to the thinner cortical bone, suggesting high trauma risk by reason of osteoporosis.
Textile-Friendly Interconnection between Wearable Measurement Instrumentation and Sensorized Garments—Initial Performance Evaluation for Electrocardiogram Recordings

The interconnection between hard electronics and soft textiles remains a noteworthy challenge in regard to the mass production of textile–electronic integrated products such as sensorized garments. The current solutions for this challenge usually have problems with size, flexibility, cost, or complexity of assembly. In this paper, we present a solution with a stretchable and conductive carbon nanotube (CNT)-based paste for screen printing on a textile substrate to produce interconnectors between electronic instrumentation and a sensorized garment. The prototype connectors were evaluated via electrocardiogram (ECG) recordings using a sensorized textile with integrated textile electrodes. The ECG recordings obtained using the connectors were evaluated for signal quality and heart rate detection performance in comparison to ECG recordings obtained with standard pre-gelled Ag/AgCl electrodes and direct cable connection to the ECG amplifier. The results suggest that the ECG recordings obtained with the CNT paste connector are of equivalent quality to those recorded using a silver paste connector or a direct cable and are suitable for the purpose of heart rate detection.
Effect of Adapted Game-Aided Physical Education Program on the Motor Skills of Children with Autism Spectrum Disorders: Longitudinal Case Study

The aim of this study is to reveal the effect of long term adapted game aided physical education intervention programconducted on an individual with ASD and to conserve the achieved skills. The participant was fourteen years oldduring study and was fifteen years old when research was completed. The study took 15 months in total including 12months of implementation process and 3 months of interval before retention test conducted on the participant. TheShort Form of 2nd version of Bruininks-Oseretsky Motor Proficiency Test (BOT-2 SF) was used as a measuring set.Obtained data were evaluated graphically. According to the obtained data, we determined a development but BOT-2SF started in different periods in 8 sub-dimensions under the title of fine and gross motor skills. When permanencytest was obtained after an interval of 3 months for examined intervention program, it was clear that all skills exceptpower sub-dimension were conserved. There was a decrease in a certain amount only in power sub-dimension. In thelight of all these consequences, we can say that even though it is late, adapted intervention programs conducted onthe individuals with ASD affect motor skill levels positively and it can be said that this development will be effectivein increasing life quality of the individuals with ASD.
The Association Between Eating-Compensatory Behaviors and Affective Temperament in a Brazilian Population

Eating-compensatory behaviors are associated with biological and psychological complications, with high rates of morbidity and mortality. Different elements may contribute to the development of eating-compensatory behaviors, such as genetic, physiological, environmental, and temperamental factors. Therefore, the aim of this study was to examine the association between affective temperaments and eating-compensatory behaviors. A sample of 27,501 volunteers, between 18 and 55 years old, mean age 28.9 ± 8.7 years (69.6% women), were assessed by the Brazilian Internet Study on Temperament and Psychopathology (BRAINSTEP). The results showed that eating-compensatory behaviors were associated with distinctive affective temperaments. Cyclothymic types were more associated with eating-compensatory behaviors. The avoidant and irritable types presented lower percentages of eating-compensatory behaviors in women and men, respectively. In conclusion, this study highlighted that participants who adopted frequent eating-compensatory behaviors are more likely to have dysfunctional affective traits. Consequently, the affective temperaments should be considered as a strategy to build capacity for prevention, treatment, and care of eating-compensatory behaviors.
Image-Guided Registration of Unordered Terrestrial Laser Scanning Point Clouds for Urban Scenes

This paper presents an image-guided end-to-end registration approach for globally consistent 3-D registration of unordered terrestrial laser scanning (TLS) point clouds. The proposed method can handle arbitrary point clouds with reasonable pairwise overlap without knowledge about their initial position and orientation, without requiring artificial targets, and without needing to record the order of the scanning. One of the novel contributions of the proposed approach lies in the optimization of a scanning network. We retrieve the similarities of all scans based on a vocabulary tree using both the geometrically rectified panorama images and the corresponding 3-D point clouds. The approach also highlights the integral optimization in both the coarse and fine registration. A pose graph is introduced to realize global optimization at the end of the coarse step without primitives. After that, the results act as the inputs to start the pairwise fine registration, which is then followed by the minimum loop expansion (MLE) refinement. Comprehensive experiments demonstrated network optimization rates of over 60% using the image-guided strategy. Using the pose-graph optimization method, successful registration rates (SRRs) increased to 100% for all tested cases. The MLE not only accelerates the speed of the convergence but also improves registration accuracy, which reached 0.1 m and 0.1° in the translation and rotation angles, respectively.
Animal Models Used to Simulate Retinal Artery Occlusion: A Comprehensive Review

Purpose To present an overview of animal models of retinal artery occlusion (RAO). Methods Through a systematic literature search in PubMed and Embase, papers describing methods of inducing RAO in animal models were included. The identified methodologic approaches were presented in a narrative synthesis and compared with RAO in humans. Results In total, 83 papers reporting on 88 experiments were included. Six different species were used with rodents and monkeys being the most common, and a minority were performed using cats, dogs, rabbits, or pigs. The anatomy of pigs and monkeys resemble that of humans most closely. The two most frequently used methods were laser-induced occlusion or ligation of the arteries. Other methods included raised intraocular pressure, arterial clamping, administration of vasoconstricting agents, the use of an occluder, embolization, and endovascular approaches to induce occlusion. In general, occlusions lasted for only 30 to 90 minutes, often followed by reperfusion. Conclusions Although a broad range of methods have previously been used, they all have limitations. Preferably, the methods should imitate the human disease as closely as possible and avoid damaging other structures. Therefore, monkeys followed by pigs are to be preferred and ligation or clamping may be a suitable model in larger animals as there is a potential to isolate and occlude the retinal artery only. Being less invasive, laser-induced occlusion is another suitable approach. Translational Relevance This review aims at assisting researchers in deciding on the most ideal experimental setting, and thereby increase the translational value to human disease.
The Impact of Microphysics Parameterization in the Simulation of Two Convective Rainfall Events over the Central Andes of Peru Using WRF-ARW

The present study explores the cloud microphysics (MPs) impact on the simulation of two convective rainfall events (CREs) over the complex topography of Andes mountains, using the Weather Research and Forecasting- Advanced Research (WRF-ARW) model. The events occurred on December 29 2015 (CRE1) and January 7 2016 (CRE2). Six microphysical parameterizations (MPPs) (Thompson, WSM6, Morrison, Goddard, Milbrandt and Lin) were tested, which had been previously applied in complex orography areas. The one-way nesting technique was applied to four domains, with horizontal resolutions of 18, 6, and 3 km for the outer ones, in which cumulus and MP parameterizations were applied, while for the innermost domain, with a resolution of 0.75 km, only MP parameterization was used. It was integrated for 36 h with National Centers for Environmental Prediction (NCEP Final Operational Global Analysis (NFL) initial conditions at 00:00 UTC (Coordinated Universal Time). The simulations were verified using Geostationary Operational Environmental Satellites (GOES) brightness temperature, Ka band cloud radar, and surface meteorology variables observed at the Huancayo Observatory. All the MPPs detected the surface temperature signature of the CREs, but for CRE2, it was underestimated during its lifetime in its vicinity, matching well after the simulated event. For CRE1, all the schemes gave good estimations of 24 h precipitation, but for CRE2, Goddard and Milbrandt underestimated the 24 h precipitation in the inner domain. The Morrison and Lin configurations reproduced the general dynamics of the development of cloud systems for the two case studies. The vertical profiles of the hydrometeors simulated by different schemes showed significant differences. The best performance of the Morrison scheme for both case studies may be related to its ability to simulate the role of graupel in precipitation formation. The analysis of the maximum reflectivity field, cloud top distribution, and vertical structure of the simulated cloud field also shows that the Morrison parameterization reproduced the convective systems consistently with observations.