Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blockchain Solution for Universal Electronic Health Record: Mexican Healthcare System Case

The Mexican health system cover around 69 million people by public healthcare programs and private institutions. A universal electronic health record (EHR) is a fundamental software to enhance the quality of medical services and administrative procedures, and only until 2013 the law focused in this topic was decreed. Due to this, the EHRs in Mexico are very fragmented and have little coverage throughout the country, also they do not comply the international requirements and the totality of the public standards. In this work the technical, functional and legal requirements for EHR systems are presented and described where blockchain technology arise as a fundamental framework to meet them, also a review of conventional server-client EHRs in Mexico and the most important blockchain based EHR implementations are exposed. Finally, the architecture, the components description and the software design patterns necessary in a universal EHR are exposed and explained, in which the technical and functional requirements are accomplished, and in the case of the Mexican healthcare system the legal requirements are met too.
Logging interpretation method based on Bayesian Optimization XGBoost

With the increasing complexity of geological conditions and labor cost of oil exploitation, processing the obtained geological and logging interpretation information is vital before formal exploitation. The existing logging interpretation technology relies heavily on manual and expert experience, as well as traditional interpretation algorithm has inherent defects of easy over fitting and low accuracy, which is difficult to realize logging interpretation under complex geological conditions. To address these concerns, this paper presents a XGBoost model based on Bayesian optimization for logging interpretation, which used machine learning method to reduce manual dependence and can be used in complex geological conditions. The input features of the proposed model are selected through the correlation ranking of logging interpretation features, and the smaller correlation features are eliminated. The Bayesian optimization algorithm is introduced to optimize the model to the best state. To evaluate the performance of the proposed model, a series of experiments on logging data of sandy conglomerate reservoir are conducted. The results are compared with other machine learning algorithms and indicate that Bayesian optimization XGBoost (BO-XGBoost) is feasible and powerful in the field of logging interpretation.
Research on ADRC strategy of permanent magnet synchronous motor for automated terminal AGV

To overcome the large noise and harmonics in the current waveform of BLDC (Brushless Direct Current, BLDC) motor used in traditional automated guided vehicle (AGV), the permanent magnet synchronous motor(PMSM) is selected as drive motor to be introduced in the AGV. And an improved active disturbance rejection control (ADRC) algorithm are presented in the PMSM vector control. And three parts of ADRC, Tracking Differentiator (TD), Extended State Observer (ESO) and Nonlinear State Error Feedback (NLSEF), are designed in the controller to analyze and process the speed, voltage signal and state estimation values and control and compensate parameter errors. To show the control superiority of ADRC, the PI-based and ADRC-based simulation models are both built and simulated with the specific high and low speed, torque and speed suddenly change. Simulation results show that when the motor suddenly decelerates, PI is stable at 0.1s and ADRC was stable at 0.03s, and the overshoot of PI is higher than ADRC, which verifies that the response speed, anti-disturbance performance and robustness of the improved ADRC are significantly better than PI, which would be helpful to introduce the PMSM and ADRC based algorithm to the AGV and the automated container terminals.
48 V-to-12 V Always-Dual-Path Hybrid DC-DC Converter for Inductor Current Reduction

A novel circuit topology for 48 V-to-12 V always-dual-path hybrid (ADPH) DC-DC converters is proposed using one inductor and two flying capacitors with only two circuit states to reduce the inductor DC current by 50 % compared to a conventional buck converter. The loss analysis shows that the proposed ADPH converter can achieve higher efficiency than the conventional buck converter over the entire output current range of the 48 V-to-12 V conversion. In the measurement, the efficiency at 15 AOUT full load of the prototype converter was improved by 2.3 points from 95.6 % to 97.9 %. Then, the inductor conduction loss due to the inductor DC resistance was reduced by 76.2 % by reducing the inductor DC current by 50.9 % compared to the conventional buck converter.
Underwater Object Detection of AUV based on Sonar Simulator utilizing Noise Addition

Object detection is one of necessary techniques for autonomous underwater vehicles (AUVs) to automate their missions. However, underwater object detection requires a large number of data images of target object. This paper proposes a method to generate highly reliable training images through sonar simulator and background noise templates. Sonar simulator has been developed to generate ideal images of target by modeling imaging mechanism of sonar sensor. To make the image realistic, background noise acquired in the blank water tank are added to the simulated images. Finally, the AUV could detect the target objects at sea using a convolutional neural network trained with the generated images without any field data which is difficult to obtain.
A 6b 800MS/s SAR ADC With Speed-Enhanced SAR Logic and Grouped DAC Capacitors

This paper presents a 6-bit 800MS/s successive approximation register (SAR) analog-to digital converter (ADC) in 28nm CMOS with grouped digital-to-analog converter (DAC) capacitor array. High-speed operation is achieved by improving the control logic and using custom-designed unit capacitor of comb structure. At 800MS/s sampling rate with Nyquist input, the post simulated SNDR and SFDR are 37.68dB and 50.1dB respectively, and the ADC core consumes 1.82mW from 1V supply, achieving 36.5fJ/conversion-step.
Convolutive transfer function-based independent component analysis for overdetermined blind source separation

Frequency domain independent component analysis (FDICA) is a fundamental and widely used method for blind source separation. However, the performance of FDICA degrades in highly reverberant environments because of the limitation of the narrowband assumption. This paper proposes a convolutive transfer function (CTF) based independent component analysis. Compared to the narrowband assumption, the CTF approximation results in fewer errors to represent the time-domain convolutive mixture with long reverberation times, and hence achieves improved separation performance. Moreover, the CTF enables the use of a short frame size to represent long room impulse responses, which increases the accuracy of statistical estimation. We formulate the objective function within the maximum likelihood framework. The optimization of the objective function is accomplished by designing an auxiliary function based on the majorization-minimization principle in the overdetermined case. Finally, the scale-fixed source is recovered by filtering the mixture through a multichannel Wiener filter. In addition, we show that FDICA is a special case of the proposed framework. Experimental results show the efficacy of the proposed method.
A Compact 60 GHz LNA with 22.7-dB Gain and 4.4-dB NF in 40nm CMOS

This paper presents a compact low-noise amplifier (LNA) design for 60 GHz phased-array applications. Utilizing single transformer-based 4th-order resonators for input and inter-stage matching, a compact core area of only 0.08mm2 is achieved. A unit transistor-cell layout design technique for millimeter-wave (mm-Wave) circuit design is adopted to reduce the uncertain high-frequency coupling effects, leading a peak gain of 22.7dB. A 4.4-dB noise figure (NF) and a 3-dB bandwidth from 54 to 63 GHz are achieved based on the post layout simulation results, with a total power consumption of 29.9 mW.
Vibration Characteristics of Induction Motor Considering the Lower-Order Harmonics in Power Supply

To study the influence of low-order harmonic in power suply on vibration characteristics of induction motors, this paper analyzes the electromagnetic force of the induction motor when supply contains the 5th, the 7th and the other low-order harmonics. In order to suppress the vibration caused by the low-order harmonics of the power supply, a low-harmonic winding scheme is proposed. With finite element analysis (FEA), vibration characteristics of the induction motor under different winding design are compared and analyzed, and it is revealed that the low-harmonic winding can suppress the motor vibration effectively. Finally, taking a 32kW induction motor as an example, it is verified that the use of low-harmonic windings can reduce motor vibration.
A Low-Cost Real-time Road Surface Condition Monitoring and Alert System

A good transportation system is essential to modern society. Unfortunately, road networks deteriorate rapidly because of a range of reasons, namely wear and tear caused by the continuous and heavy flow of vehicles, weather elements such as heavy rain and high temperature and various excavation works that are carried out on the roads. Bad roads lead to accidents, damage to vehicles and increased fuel consumption. Therefore, the monitoring of road quality is crucial. This paper proposes a low-cost prototype that can be implemented in any vehicle. The device collects and transmits data on road quality to a cloud platform, where analytics is performed to assess and classify road condition. The pothole locations are viewable on a web page. While travelling, drivers receive real-time sound alerts on their mobile phones to warn them of imminent degradation in the road network, such as potholes. An Android application has been developed to synchronize with the database via the API and send real-time and up-to-date notifications to road users whenever they approach such areas. Authorities can also use the system to grasp the quality of the road network and automatically identify problems that may require immediate repair.
Complete Process Emulation of Integrated Starter/Generator Using Power Electronic Devices

Using motor emulator (ME) to test the motor controller is an emerging technique to optimize the design process of motor drive system. In this paper, an ME is designed to test the control system of integrated starter/generator (ISG). A voltage-in-voltage-out interface is proposed to handle the causality problem caused by operation mode change during the starting/generating process. The LCL interface with predictive voltage closed-loop control is proposed to ensure the emulation accuracy. Experiment has been done to validate the performance of the emulator.
Examination of Phonon Dissipation in 33 GHz Overmoded Bulk Acoustic Resonators

In this work we examine quality factor ($Q$) in an overmoded bulk acoustic resonator (OBAR) operating at 33 GHz through modeling and measurement of fabricated devices to isolate loss mechanisms. Intrinsic and extrinsic loss mechanisms are modeled using a combination of material analytical models and energy distribution determined by finite element analysis. Fabricated devices with varying area and aspect ratio are then measured. Finally, devices are characterized over temperature from 295 K to 12.5 K. By comparing geometric dependencies between models and measurements we conclude anchor and ohmic losses are not significant in these devices. However, much lower thermal dependence in measured devices indicate intrinsic loss models do not fully describe losses in these devices. Further investigation in intrinsic damping in materials at microwave frequencies are needed.
Jolt: Recovering TLS Signing Keys via Rowhammer Faults

Digital Signature Schemes such as DSA, ECDSA, and RSA are widely deployed to protect the integrity of security protocols such as TLS, SSH, and IPSec. In TLS, for instance, RSA and (EC)DSA are used to sign the state of the agreed upon protocol parameters during the handshake phase. Naturally, RSA and (EC)DSA implementations have become the target of numerous attacks, including powerful side-channel attacks. Hence, cryptographic libraries were patched repeatedly over the years.Here we introduce Jolt, a novel attack targeting signature scheme implementations. Our attack exploits faulty signatures gained by injecting faults during signature generation. By using the signature verification primitive, we correct faulty signatures and, in the process deduce bits of the secret signing key. Compared to recent attacks that exploit single bit biases in the nonce that require 245 signatures, our attack requires less than a thousand faulty signatures for a 256-bit (EC)DSA. The performance improvement is due to the fact that our attack targets the secret signing key, which does not change across signing sessions. We show that the proposed attack also works on Schnorr and RSA signatures with minor modifications.We demonstrate the viability of Jolt by running experiments targeting TLS handshakes in common cryptographic libraries such as WolfSSL, OpenSSL, Microsoft SymCrypt, LibreSSL, and Amazon s2n. On our target platform, the online phase takes less than 2 hours to recover 192 bits of a 256-bit ECDSA key, which is sufficient for full key recovery. We note that while RSA signatures are protected in popular cryptographic libraries, OpenSSL remains vulnerable to double fault injection. We have also reviewed their Federal Information Processing Standard (FIPS) hardened versions which are slightly less efficient but still vulnerable to our attack. We found that (EC)DSA signatures remain largely unprotected against software-only faults, posing a threat to real-life deployments such as TLS, and potentially other security protocols such as SSH and IPSec. This highlights the need for a thorough review and implementation of faults checking in security protocol implementations.
Band-Gap Engineering of Layered Perovskites by Cu Spacer Insertion as Photocatalysts for Depollution Reaction

A multi-step ion-exchange methodology was developed for the fabrication of Cu(LaTa2O7)2 lamellar architectures capable of wastewater depollution. The (001) diffraction line of RbLaTa2O7 depended on the guest species hosted by the starting material. SEM and TEM images confirmed the well-preserved lamellar structure for all intercalated layered perovskites. The UV–Vis, XPS, and photocurrent spectroscopies proved that Cu intercalation induces a red-shift band gap compared to the perovskite host. Moreover, the UV–Vis spectroscopy elucidated the copper ions environment in the Cu-modified layered perovskites. H2-TPR results confirmed that Cu species located on the surface are reduced at a lower temperature while those from the interlayer occur at higher temperature ranges. The photocatalytic degradation of phenol under simulated solar irradiation was used as a model reaction to assess the performances of the studied catalysts. Increased photocatalytic activity was observed for Cu-modified layered perovskites compared to RbLaTa2O7 pristine. This behavior resulted from the efficient separation of photogenerated charge carriers and light absorption induced by copper spacer insertion.
Chemistry and composition of fish otoliths: pathways, mechanisms and applications

: The fish otolith (earstone) has long been known as a timekeeper, but interest in its use as a metabolically inert environmental recorder has accelerated in recent years. In part due to technolog-ical advances, applications such as stock identification, determination of rnigralon pathways, recon- struction of temperature and salinity history, age validation, detection of anadromy, use as a natural tag and chemical mass marking have been developed, some of which are difficult or impossible to imple- ment using alternative techniques. Microsamphg and the latest advances in beam-based probes allow many elemental assays to be coupled with daily or annual growth increments, thus providing a detailed chronologcal record of the environment. However, few workers have critically assessed the assumptions upon which the environmental reconstructions are based, or considered the possibility that ele- mental incorporation into the otolith may proceed differently than that into other calcified structures. This paper reviews current applications and their assumptions and suggests future directions. Particu- lar attention is given to the premises that the elemental and isotopic composition of the otolith reflects that of the environment, and the effect of physiological filters on the resulting composition. The roles of temperature, elemental uptake into the fish and the process of otolith crystahzation are also assessed. Drawing upon recent advances in geochemistry and paleoclirnate research as points of contrast, it appears that not all applications of otolith chemistry are firmly based, although others are destined to become powerful and perhaps routine tools for the mainstream fish biologst.
Low-Cost System for Automatic Recognition of Driving Pattern in Assessing Interurban Mobility using Geo-Information

Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving. The system consists of two physical sensors connected to a device node with a display and a speaker. An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style. When an abnormal driving pattern is detected, the speaker will play a warning message. The prototype was assembled and tested using an interurban road, in particular on a conventional road with three driving styles. The gathered data were used to train and validate the ANN. Results, in terms of accuracy, indicate that better accuracy is obtained when the velocity, position (latitude and longitude), time, and turning speed for the 3-axis are used, offering an average accuracy of 83%. If the classification is performed considering just two driving styles, normal and aggressive, then the accuracy reaches 92%. When the geo-information and time data are included, the main novelty of this paper, the classification accuracy is improved by 13%.
Graph-Time Convolutional Autoencoders

We introduce graph-time convolutional autoencoder (GTConvAE), a novel spatiotemporal architecture tailored to unsupervised learning for multivariate time series on networks. The GTConvAE leverages product graphs to represent the time series and a principled joint spatiotemporal convolution over this product graph. Instead of ﬁxing the product graph at the outset, we make it parametric to attend to the spatiotemporal coupling for the task at hand. On top of this, we propose temporal downsampling for the encoder to improve the spatiotemporal receptive ﬁeld without affecting the network structure; respectively, in the decoder, we consider the opposite upsampling operator. We prove that the GTConvAEs with graph integral Lipschitz ﬁlters are stable to relative network perturbations, ultimately showing the role of the different components in the encoder and decoder. Numerical experiments for denoising and anomaly detection in solar and water networks corroborate our ﬁndings and showcase the effectiveness of the GTConvAE compared with state-of-the-art alternatives.
Willingness to Pay for Mobile Health Live Streaming during the COVID-19 Pandemic: Integrating TPB with Compatibility

The COVID-19 pandemic has forced people to limit their physical interactions, which has led to unprecedented growth in mobile health live streaming (MHLS). Such practices have been facilitated by the rapid development of consumers’ willingness to pay for MHLS. However, few efforts appear in the literature to understand this change. This study aims to integrate the theory of planned behavior with compatibility to clarify payment motivations for MHLS during the COVID-19 pandemic in China. Accordingly, the current study used a web-based, self-reported questionnaire to collect data from 506 Chinese consumers. Of the 535 distributed questionnaires, we received 505 valid responses, yielding an effective rate of 94.3%. The valid responses were analyzed using structural equation modeling, and the associated hypotheses were tested using partial least squares regression. The results showed that attitude, self-efficacy, preferred lifestyle, information quality, and interactive immersion (but not subjective norms) significantly predicted consumers’ willngness to pay (WTP). In addition, attitude partially mediated the association between preferred lifestyle, information quality, and WTP, respectively, while the mediating role in the relationship between interactive immersion and WTP was not confirmed. These findings can be used to formulate effective marketing strategies to benefit MHLS services and mobile payment technology in the country.
Generalized Deterministic Traffic Rules

We study a family of deterministic models for highway traffic flow which generalize cellular automaton rule 184. This family is parameterized by the speed limit m and another parameter k that represents a "degree of aggressiveness" in driving, strictly related to the distance between two consecutive cars. We compare two driving strategies with identical maximum throughput: "conservative" driving with high speed limit and "aggressive" driving with low speed limit. Those two strategies are evaluated in terms of accident probability. We also discuss fundamental diagrams of generalized traffic rules and examine limitations of maximum achievable throughput. Possible modifications of the model are considered.
Energy Dissipation at the Termination Shock: 1D PIC Simulation

The Voyager 2 (V2) observations of the heliospheric termination shock indicate that it is heavily influenced by the presence of interstellar pickup ions (PUIs). The V2 instrument measured less solar wind heating than expected and a downstream flow speed that remained supersonic. While PUIs are expected to play a role in energy dissipation at the shock, the details remain unclear. Here, we use particle‐in‐cell (PIC) simulations and show that PUIs indeed play an important role and that the downstream flow speed remains supersonic if the magnetosonic speed were measured without the contribution of PUIs.