Miracle-dz/UniFAR-FTUs huggingface dataset:
PAGWAS: a manually curated web-based knowledge database of GWAS pathway analysis

Genome-wide association studies (GWAS) have been widely used to investigate the pathogenesis of human complex diseases, and have yielded important new insights into the genetic mechanisms. However, the newly identified susceptibility loci exert very small risk effects, and cannot fully explain the underlying genetic risk. Fortunately, the existing large-scale GWAS datasets provide strong support for the investigation of human complex disease mechanisms using pathway analysis methods. Here, we developed a web-based knowledge database named PAGWAS to provide a comprehensive catalog of published pathway analysis of GWAS. It provides a convenient way to understand the associations between pathways or genes in these pathways and the given disease or trait of interest. PAGWAS incorporates knowledge from 164 published pathway analysis of GWAS papers and manually curated 5769 pathways and 89 diseases. Also, PAGWAS provides two types of network visualization function to illustrate the relationships between different diseases or pathways. We hope PAGWAS will be a useful tool with great potential for researches on pathogenesis of human complex diseases. Database URL: PAGWAS can be accessed at http://www.bio-annotation.cn:18080/GWASDisease/.
Power Factor Pre-regulation in Interleaved Luo Converter Fed Electric Vehicle Battery Charger

This work deals with the design and implementation of a new interleaved Luo power factor pre-regulator (PFP) based electric vehicle (EV) battery charger, which is suitable for low cost and improved power quality (PQ) based EV charging. By interleaving the two Luo converter cells, the input current is divided between them, which provides reliable and efficient charging to the battery at high-power level. The interleaved Luo converter incorporates low input and output current ripple, which reduces the switch conduction loss due to reduced current stress and thus, the charger efficiency is improved. Moreover, the operation of converter in DCM (Discontinuous Conduction Mode) is achieved to provide inbuilt zero current switching and circuit diodes show good reverse recovery characteristics. The proposed charger operates in constant current mode upto certain battery state of charge (SOC). However, for higher SOC range, it maintains constant battery voltage using flyback converter. Therefore, an inherent PF (power factor) pre-regulation is obtained at input mains over a wide range of supply voltages and the total harmonic distortion of input current is obtained as per the PQ regulations.
DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information

The prediction of precise protein-ligand binding activities can accelerate drug discovery by virtual screening—a computational technique that predicts whether a small molecule ligand is able to bind to a specific target. Thus, it is crucial to improve the performance of virtual screening. However, previous models for solving this problem are either ligand-based or structure-based. In this paper, we propose a universal deep neural network model called DeepDock that predicts protein-ligand interaction by using both ligand and structure information. Using the combination of two types of information, our model consists of embedding, convolution, max pooling, and fully-connected layers. In particular, different types of inputs are concatenated before being fed into the fully-connected layers. In the experiments, we compare our approach to the competing methods against two benchmark datasets under different settings. The experiment results have demonstrated that DeepDock can improve predictive performance by more than 4% on both DUD-E and MUV datasets in terms of AUPR
Application of Foil Based Directly Coupled Inductor for Interleaved DC-DC Converter with GaN Transitors

The paper describes design and application of foil based directly coupled inductor for two phase DC-DC converter with GaN transistors. The main advantages of the interleaved multiphase DC-DC converters over single-phase converters are reduced current stress and reduced output current ripple. Low on state resistances and low switching losses of GaN transistors allows increase current without significant increase in losses but anyway for higher switching frequencies some soft switching techniques and specific magnetics components are needed. The integrated magnetic structure can be used to achieve higher power density. The paper shows that foil based directly coupled inductor can be an option when designing such converter. PSIM software and FEMM simulation software based results are given and experimental tests are provided. It is concluded that directly coupled inductor is worth considered when designing interleaved high frequency converter, further research is needed to introduce best soft switching method for transistors and further optimize magnetics.
Stress Recognition with State Classification Considering Temporal Variation of Stress Responses

To avoid and manage stress-related problems, several works have investigated how to recognize stress states of people by exploiting the machine learning. Most of works utilize the supervised learning which requires labels for data to be trained and tested. Thus, labels which represent stress state of each data such as stressed or not become definitely important. Conventional stress state classification methods assign labels to unit data per a certain experimental period by applying stressor appearances or self-evaluation scores. However, those methods ignore temporal variations in stress responses which are involuntarily triggered inside the body within a shorter period of time than an experimental period. Therefore, we propose a stress state classification method by considering not only user's subjective evaluations but also temporal changes of stress responses in short periods. For the demonstration, we label our experimental data of 40 subjects by using our proposed classification method and conventional ones, respectively. Then, we train and test stress recognition models with 6 machine learning algorithms and our implemented neural network ones based on the labeled data. Finally, binary stress recognition with our proposed classification method improves the recognition accuracy by up to 31.6% as compared to those with conventional techniques.
Who is Alice? Privacy Risk, the Case of Regression (Extended Abstract)

While there seems to be no turning back from data becoming a commodity, news headlines and scandals recurrently exacerbate a general concern for privacy. Can Alice’s data be collected, analysed and the results meaningfully published and shared without compromising Alice's privacy? How can organizations assess the privacy risk before disclosing data or the results of their analysis of data? We discuss two streams of research that study the assessment of privacy risk: statistical disclosure and differential privacy. We critically discuss their similarities and dissimilarities. We comparatively and empirically evaluate the two approaches with a canonical dataset and the scenario of synthetic data generation with linear regression.
Microscopic Modeling of Metasurfaces by the Mixed Finite Element Numerical Mode-Matching Method

A semi-analytical finite element numerical mode-matching (FNMM) method is developed to simulate microscopic structures of metasurfaces on the interfaces of the 3-D multilayered structure. Similar to the recently developed spectral element NMM method, the FNMM method reduces the original 3-D problem into a series of 2-D waveguide eigenvalue problems plus a 1-D layered medium problem, which can be analytically described by a recursion procedure. The 2-D eigenvalue problems in the horizontal directions are solved numerically by the mixed finite element method (MFEM) to remove the spurious modes in the conventional FEM. Therefore, it can effectively and accurately simulate electromagnetic wave interactions with the detail metasurfaces, especially for thick layers. Metasurfaces embedded on the interfaces of 3-D structures are treated by the generalized impedance boundary conditions, thus enabling their rapid yet accurate computation. This method is applied to characterize metasurfaces consisting of microscopic patterns with complex geometric construction with details much smaller than the wavelength. Numerical experiments indicate that the FNMM method is highly efficient and accurate for the microscopic metasurface models.
On the effect of KInSe2 (KIS) layer on Cu(In,Ga)Se2 solar cell performance: a numerical study

In this work we use numerical simulations to investigate the potential impact of the presence of a KInSe2 (KIS) layer on the CIGS surface due to KF post-deposition treatment (PDT). Although we focus on a KIS layer, due to the generality of the approach our results can be extended to other Alk-InSe2 of similar energy gap (larger than the CIGS bandgap), since no hypotheses have been made on the layer composition except for the energy gap being large enough to make it transparent to the AM1.5G spectrum. We consider different values for the KIS electron affinity, resulting in a range of conduction band offsets with CIGS and CdS, in the case of trap-free KIS or KIS with acceptor traps. We also evaluate the effect of the KIS layer on the cell current-voltage characteristics at low temperature.
Ultra-Low Power and Minimal Design Effort Interfaces for the Internet of Things: Invited paper

This paper reviews the results of recent researches aimed to extend the standard-cell based digital design flow to analog building blocks, so that to enhance scalability, reconfigurability and portability across technology nodes and to reduce design effort, time-to-market and costs. In this framework, the application of the proposed fully digital design approach to a wake up oscillator and to a Digital-to-Analog Converter, which are two building blocks widely employed in IoT sensor nodes, is illustrated in detail.
A Vision-Guided Robotic Grasping Method for Stacking Scenes Based on Deep Learning

Serious occlusion among parts in industrial stacking scenes brings a significant challenge to the automatic sorting. This paper proposes a 3D vision-guided robotic grasping method to solve part sorting problem in stacking scenes. Firstly, a large-scale synthetic dataset is generated automatically by the simulation technology to train a Point-wise Pose Regression Network (PPRNet), which is the extension of the Pointnet++ with the Hough voting theory. Then, the trained PPRNet is used to realize instance segmentation and pose estimation simultaneously. Finally, according to the pose estimation of segmented instances in the stacking scene, a grasp pose selection model is established to determine the optimal grasp target and corresponding grasp pose. We carried out 200 grasp experiments on random stacking scenes of single-class objects and obtained success rate of 78.0%. The result shows that PPRNet improves the efficiency and stability of the robotic grasping method, which basically meets the requirements of industrial application.
Blockchain-Based Distributed Trust and Reputation Management Systems: A Survey

Distributed Ledger Technologies (DLTs), like Blockchain, are characterized by features such as transparency, traceability, and security by design. These features make the adoption of Blockchain attractive to enhance information security, privacy, and trustworthiness in very different contexts. This paper provides a comprehensive survey and aims at analyzing and assessing the use of Blockchain in the context of Distributed Trust and Reputation Management Systems (DTRMS). The analysis includes academic research as well as initiatives undertaken in the business domain. The paper defines two taxonomies for both Blockchain and DTRMS and applies a Formal Concept Analysis. Such an approach allowed us to identify the most recurrent and stable features in the current scientific landscape and several important implications among the two taxonomies. The results of the analysis have revealed significant trends and emerging practices in the current implementations that have been distilled into recommendations to guide Blockchain’s adoption in DTRMS systems.
Design of Human Health Monitoring System Based on NB-IoT

As the number of elderly and sub-healthy people increases, people need systems that can monitor their health in real time. As an emerging communication technology, Narrowband Internet of Things (NB-IoT) technology has the characteristics of super coverage, massive connections, and low power. In this paper, we designed a human health monitoring system based on NB-IoT technology. Mainly completed the following work: 1) We designed a terminal device containing STM32 microprocessor to collect pulse, body temperature and geographical location. 2) Combined with the existing NB-IoT network, we built a Tomcat server in the cloud to stored the uploaded information in the MySQL database. We designed the alarm service. In an emergency, the system will send the GPS location information of the user to the set caregiver by mail. 3) We designed a web application. A doctor or family member can use the web page to view a line chart of past data. The experimental results show that the designed human health monitoring system can realize the function of real-time monitoring.
Study and Analysis of the cascoded LNA with and without using inductive degeneration at the common gate for WLAN applications

This paper discusses about the study and analysis of the cascoded low noise amplifier (LNA) with and without using inductive degeneration at the common gate. The LNA design used here is for Wireless Local Area Network (WLAN) applications i.e. for 2.4 GHz. Gain, noise figure (NF) and power consumption are the vital LNA constraints. In this paper, a brief comparison between the LNA parameters for two different instances is done. This LNA design is simulated in Cadence Virtuoso RF Spectre in 180 nm technology. The results of the simulations for both the cases shows that the gain is 20.623 dB and 17.064 dB, NF is 0.85 dB and 1.011 dB, S11is −31.599 dB and −18.67 dB, power consumption is 1.2511 mW and 1.5916 mW respectively.
An Art of Speech Recognition: A Review

Speech recognition is conversion of human speech in to the text or control signal by the means of intelligent algorithms. Speech recognition plays vital role in many biometric authentication systems and voice controlled automation systems. Speech recognition is challenging task due to variation in recording devices, speakers, contexts, and environment. Speech recognition consists of three major steps such as speech preprocessing, feature extraction, and speech classification. This paper provides literature review on the various feature extraction and classification methods used in speech recognition system.
mROBerTO 2.0 – An Autonomous Millirobot With Enhanced Locomotion for Swarm Robotics

Numerous millirobots were developed in the past decade for autonomous swarm systems that aim to utilize large numbers of these units in space-constrained environments. However, the size limitation of these robots has often resulted in their reduced computational, sensing, and locomotion capabilities. mROBerTO (milli-ROBot-TOronto) was developed in response to such limitations. Despite its enhanced features, the reliable and repeatable locomotion of mROBerTO has still been of some concern due to lack of effective closed-loop motion control – as is the case with all other similar millirobots. In this letter, we present the next version of mROBerTO with a new locomotion mechanism that utilizes stepper motors, capable of micro-stepping down to 1/32 of a full step, to yield a millirobot with maneuvering capabilities superior to current similar-sized robots. mROBerTO 2.0 is novel in that it utilizes these stepper motors without relying on a separate processor for controlling them. This letter also presents a complementary new algorithm for efficiently converting desired trajectories into robot-motion commands. The proposed algorithm was developed to allow millirobots to execute complex trajectories reliably in an open-loop manner.
Development of A Novel Bio-inspired Asymmetric Dual Arm Robot

Dual arm robots can imitate human’s behavior, thus a good alternative for labors, especially in tedious or dangerous application. Traditional dual arm robots have symmetric structures and the same DOF. In this paper, a new type of bio-inspired dual arm robot is designed with the characteristic of asymmetric structure. Firstly, the kinematic model is deduced. Then the working space of both arms are calculated by kinematic model. Finally, the dexterity and numerical condition are analyzed. Results show that our asymmetric dual arm robot has large workspace and high dexterity for its right arm and has high robust and accuracy for its left arm, thus suitable for real industrial application.
Efficient PID Controller based Hexapod Wall Following Robot

This paper presents a design of wall following behaviour for hexapod robot based on PID controller. PID controller is proposed here because of its ability to control many cases of non-linear systems. In this case, we proposed a PID controller to improve the speed and stability of hexapod robot movement while following the wall. In this paper, PID controller is used to control the robot legs, by adjusting the value of swing angle during forward or backward movement to maintain the distance between the robot and the wall. The experimental result was verified by implementing the proposed control method into actual prototype of hexapod robot.
An Attention-based Semi-supervised Neural Network for Thyroid Nodules Segmentation

Image segmentation based on deep learning has greatly promoted the development of the field of computer-aided diagnosis. However, the large scale medical annotation of ground truth is so difficult that it directly affects the performance of existing segmentation models. In this work, an Attention based Semi-supervised Neural Network is proposed, which can complete end-to-end segmentation task of thyroid ultrasound image with weakly annotated classification data and a small amount of fully annotated segmentation data. Two kinds of attention modules are proposed to improve network performance through the trainable feedforward structure of bottom-up and top-down so as to suppress or activate the feature channels and image regions respectively. The experimental results show that when there is only 13% of fully annotated data, the Jaccard similarity coefficient of thyroid nodule segmentation is 74.91%, 4.97% higher than VGG-based semi-supervised model. The classification accuracy of benign and malignant is increased from 91.67% to 95.00%. Equally important, with the same number of fully annotated data, our model has better generalization than that of the supervised segmentation models.
Effect of I Shaped Periodic Structures over Collinear Arms of 150 Degree Bend Substrate Integrated Waveguide

A Substrate integrated waveguide (SIW) wideband band pass filter with 150 degree bend is presented in this paper. Attenuation of higher transmission frequencies are achieved by using array of I-shaped periodic band gap structures at the collinear arms. Six I-shaped slots (three in each arm) are etched over the top conducting layer to achieve the purpose. Several major parameters of these I-shaped structures are studied and presented with detailed outcome. Insertion loss < 0.9dB over the entire pass band has been achieved by this technique. Moreover significant attenuation at the stop band frequencies (> 4GHz) is observed. The filter finds application in microwave X-band and Ku-band radar and satellite systems.
Energy-based model of a solar-powered pumped-hydro storage system

This document presents a port-Hamiltonian model of a pumped-hydro storage system, using Photo Voltaic energy as the primary source. Matlab simulation results show that the model is functional under ideal conditions of constant solar radiation. It also graphically demonstrate the relationship between input solar power and the accumulation of energy at the upper reservoir. This work is a fundamental step towards a tool for the analysis and design of optimized and fully automated system.
K value selection method for test data similarity division based on K-Means algorithm

The scale of test data can not be underestimated in the analysis of large-scale integrated circuit test,for the characteristics of K-Means algorithm the similarity of large amounts of data can be processed. It is convenient for subsequent operations to be processed according to the similarity such as compression, encoding, etc. The selection of its center and the determination of the cluster number (K value) are the core of the K-Means algorithm, the selection of the K value is difficult to estimate. This paper has divided targeted and similarity for the generated exhaustive or pseudo-random test data. The K-means algorithm is combined with the constraints proposed in this paper to constrain the generation of suitable K values, and select appropriate range of K value. The experimental results based on a large number of random data have shown that when using the method in the text, the range of K values is selected in the 4-8 categories, the trend of each index is gentle, and the similarity between data is well.
Failure Analysis on TiAl Metallization Process for Ohmic Contact on 4H-SiC pMOSFET

SiC is suitable for high-power and high-temperature applications due to its’ wide energy bandgap and high thermal conductivity. Most literature focus on SiC nMOSFET due to higher electron mobility than hole. In this work, we fabricated 4HSiC pMOSFET using TiAl alloy as contact metal to reduce the contact resistivity. However, ultrahigh leakage current was measured among all terminals of the pMOSFET. By comparing with different contact schemes, the failure mechanism is attributed to Al spiking into the underneath poly-Si and SiO2 during the metallization process. Using suitable blocking layer such as LPCVD Si3N4 (300 nm) or PECVD Si3N4 (100 nm) on SiO2 (200 nm) can avoid Al spiking so that conventional pattern topology and TiAl metallization process can be used on device fabrication.
IoT Platform for the Control and Monitoring of Physical Variables with Open Hardware Technology

The control and monitoring of physical variables in real time is very important to the industry to guarantee the quality of products, industrial safety, the operation of the machines and the manufacturing process. Nowadays, the Salvadoran industry must adopt technologies associated with the Internet of Things. Understanding as Internet of Things the possibility to provide Internet connection to any object, giving the user the possibility to make physical measures or remote controlling. In this case, knowing that the impact that represent for industry the control and monitoring of physical variables is very important, ITCA FEPADE Regional Center of Santa Ana City has designed and implemented a IoT platform to monitor the physical variables with educational purposes. For the designing of the platform some open source tools were use. The hardware tools used were atmega 328p and xbee antennas. PHP, Java and C were the programming language used for the interface design. This IoT platform can control the electric charges and monitoring variables like: electric current and power, electrical energy and temperature. For statistical measurement the computer center 2 and classroom 103 were chosen. For using this software, the user just needs a smartphone, a computer or a tablet to connect to the IoT platform. The user can visualize and have control of physical variables using the platform interface. Both devices (user device and the server) must be connected to the same network, in this case ITCA Santa Ana's network.