Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Optimization of Monolayer 1T/1T’-2H MoX2 Lateral Heterojunction Transistors

Recently, 2-D transition metal dichalcogenides (TMDs) lateral heterojunction field-effect transistors (FETs) have been demonstrated experimentally, in which metallic TMDs were used for the source/drain. In this work, we systematically investigate the contact property and device performance of monolayer 1T/1T<inline-formula> <tex-math notation="LaTeX">$^{\prime }$ </tex-math></inline-formula>-2H MoS<sub>2</sub>, MoSe<sub>2</sub>, and MoTe<sub>2</sub> FETs. Schottky barrier (SB) heights are extracted from density functional theory calculations, and nonequilibrium Green’s function transport simulations have been performed to study device characteristics. Our simulation results show that the inherent SB strongly affects the overall performance of these devices. Here, we optimize the performance of TMD lateral heterojunction FETs by using two different approaches. First, we have improved the electrostatic control by scaling equivalent oxide thickness and gate underlap, which boosts both ON- and OFF-state characteristics, making the device suitable for high-performance applications. On the other hand, moderate doping has been used in the gate underlap region, improving ON/OFF current ratio with negligible impacts on ON-state characteristics, and this approach is more preferred for low-power applications. This study reveals that 1T<inline-formula> <tex-math notation="LaTeX">$^{\prime }$ </tex-math></inline-formula>-2H MoTe<sub>2</sub> FET shows the highest ON current (<inline-formula> <tex-math notation="LaTeX">$\sim 1$ </tex-math></inline-formula> mA/<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>) among the three with a reasonably small subthreshold swing (80 mV/dec) if properly scaled, while 1T-2H MoS<sub>2</sub> FET exhibits the highest <inline-formula> <tex-math notation="LaTeX">${I} _{\scriptscriptstyle{\text{ON}}}/{I} _{\scriptscriptstyle{\text{OFF}}}$ </tex-math></inline-formula> (~10<sup>7</sup>) when ohmic contact is established with moderate doping in the gate underlap region. This study not only provides physical insight into the electronic devices based on novel TMD heterostructures but also suggests engineering practice for device performance optimization in experiments.
A Low Voltage Rectifier for Piezo-Electric Energy Harvesting Designed in CMOS Technology

A low voltage pMOS based rectifier with moderate power conversion efficiency (PCE) for harvesting of Piezo Electric energy is presented in this work. The topology is achieved by utilizing the pair of pMOS devices with their gate terminals cross coupled in addition to another pair of pMOS transistors diode connected whose body terminals are tied to their own well. Based on standard 45nm CMOS technology, the simulations for the proposed work are performed in Tanner T-SPICE. Comparison of the proposed work with other existing topologies are also performed which shows VCE is close to 44.5% and 43.1% respectively, for an input voltage equals to 1Vp-p, 100kHz with load resistance 10kΩ and capacitor of 150nF.
Research on the impact of smart city pilot on the quality of foreign investment in China based on a difference-in-differences model

In order to analyze the impact of "smart city" pilot policy on the quality of foreign investment accurately in China, based on the panel data of 226 prefecture level cities from 2006 to 2017. This paper constructs a difference-in-differences model by Stata software, taking the quality of foreign investment as the research object, through the level of economic development, scientific and technological innovation and other variables for empirical analysis. The research shows that the implementation of the "smart city" pilot policy has significantly improved the quality of foreign investment in pilot cities, and the results are still valid after a series of robustness tests. Based on the above results, this paper puts forward some policy suggestions for the future construction of "smart city" and the optimization of urban foreign investment attraction mode.
Performance Analysis and Reduction of Torque Ripple of Axial Flux Permanent Magnet Synchronous Motor Manufactured for Electric Vehicles

Axial flux permanent magnet synchronous motors (AFPMSMs) have advantages over other motor types in terms of efficiency, vibration, and noise. However, torque ripple is an important problem for axial flux permanent magnet (AFPM) motors. One of the methods recommended as a solution to this problem is the skew operation. The skew can be applied to the magnets or the stator. However, for these motors, this process is mostly applied to magnets because the skew applied to the stator makes it difficult to manufacture. In this article, a solution to the torque ripple of an in-wheel AFPMSM for the electric vehicle is sought. For this purpose, designs, optimization, and 3-D analyzes have been carried out. This solution has been applied to magnets designed by skewing for ease of production. 3-D analyses have been carried out by applying skew of up to 40° with an angle of 5° to the magnets in the rotor. In light of the analysis, the most appropriate skew angle that does not affect motor performance has been determined. As a result of the analysis of the motor model obtained with the determined skew angle, it is observed that the torque ripple decreased by 86.71%. Finally, production has been carried out according to the analysis results obtained.
Analysis of Special Adders for Digital System

Addition operation is the basic process for digital system, digital signal processing or control system. In this paper, we have compared different special adder's topologies and parameters like area, power and delay. All the adders are simulated in Xilinx and Cadence tool for comparison. We showed the comparison in terms of LUTs, Slices and also in cells. As per our comparison carry select Adder shows better result with high speed and less power. Obtained parameters for carry select adder is 2.69 ns delay and $4.7\ \mu\mathrm{W}$ power for high-speed application. Adders are designed using Cadence and Xilinx tool. For the analysis and comparison Verilog design module and test bench module are considered.
Fix Director for Automated Program Repair

As the scale of software increases, automated program repair (APR) is required as an essential technology to reduce bug-fixing manual effort. One of the most effective APR approaches is the template-based APR. Although this data- driven approach has made many improvements in fixing bugs, there is still a lack of information utilization at each stage of the program repair. In this paper, we propose a fix pattern prioritization APR technique. We do not just use the test cases to determine whether the source file is fault or not, but as a material for why the bug occurred. With this information, we select a more appropriate fix action and apply it first to increase the efficiency of the automated program repair.
Driving Maneuver Classification Using Domain Specific Knowledge and Transfer Learning

With the increasing number of vehicles, the usage of technology has also been increased in the transportation system. Although automobile companies are using advanced technologies to develop high performing transports, traffic safety still remains to be a concerning issue. Drivers’ driving behavior is considered as one of the key factors of the traffic safety, which could be monitored from their individual driving maneuvers. In this paper, we present a supervised learning model and a semi-supervised transfer learning model for the classification of driving maneuvers from the sensor fusion time series data. The semi-supervised model consists of an unsupervised long-short term memory (LSTM) autoencoder and a supervised LSTM classifier. The supervised model consists of a supervised LSTM model. Because of using LSTM, both of the models can analyze time-series data. In the semi-supervised model, the LSTM encoder learns from unlabeled data as a compressed low dimensional feature vector, which then transfers the learning to the supervised LSTM classifier to classify the driving maneuvers. With the proposed models, we use domain specific knowledge data of the driving environment, such as data changing rules of various driving maneuvers as well as the temporal features over time. We use class functions for seven driving maneuver types and convert those into binary feature vector to use with the LSTM models. We present a comparative analysis of the per class accuracy of the proposed semi-supervised and supervised models with and without using domain-specific knowledge, where the models with the domain specific knowledge outperform. Our proposed semi-supervised and supervised models are compared with the other existing approaches, where our models trained with the domain specific knowledge provided better performance. We also compared the per class accuracy for both the supervised and semi-supervised models, where all the maneuver class accuracy for supervised model was above 98% and semi-supervised model was above 95%. Although the supervised model outperforms the semi-supervised model, the semi-supervised model would be more beneficial in applications where the labeled driving maneuvers data are hard to capture or insufficient.
Data Sanitization of SRAM by Thermal Distortion

Thermal distortion of a memory state is demonstrated for data sanitization of static random access memory (SRAM). Data stored in SRAM are believed to be deleted when power is turned off. However, it has been reported that the data can remain for a certain time even in powered- OFF SRAM, especially in a low-temperature environment. When a cold boot attack is attempted at low temperature, the stored data in SRAM are secured by thermal distortion at induced high temperature. For an experimental demonstration of data sanitization, heat is intentionally applied to a commercial SRAM chip, which is connected with a Raspberry pi board to write and read data. Supportive circuit and thermal simulations are performed to analyze electrical and thermal characteristics during heat treatment.
A Mediated Multi-RNN Hybrid System for Prediction of Stock Prices

A multi-recurrent neural network (RNN) hybrid system made up of three RNNs is introduced to predict the stock prices for 10 different companies (five selected from the Dow Jones Industrial Average and five from the Standard and Poor’s 500.) The daily historical data used to train and test the system are collected for the period of October 15, 2013 to March 5, 2019. For each company, the system provides two separate predictions of the daily stock price by using (1) historical stock prices and (2) historical trends along with the historical daily net changes in stock price. The two predictions are mediated to select one as the final output of the hybrid system. For each company, the accuracy of the system was tested for the prediction of the most recent 98 consecutive days using the forecast accuracy measure of the Mean Squared Error (MSR). The results revealed that for every company the difference between the predicted and actual stock price is not statistically different from zero, which is the ideal (error-free) forecast.
Differentially Private Publication of Multi-Party Sequential Data

Given a set of local sequential datasets held by multiple parties, we study the problem of publishing a synthetic dataset that preserves approximate sequentiality information of the integrated dataset while satisfying differential privacy for each local dataset. The existing solutions for publishing differentially private sequential data in the centralized setting mostly adopt tree-based approaches. Such approaches rely on different tree structures that encode sequential data’s statistical information. The construction of a tree structure is normally done by recursively splitting nodes whose noisy scores (e.g., entropy or count) are larger than a given threshold. However, extending similar ideas to the multi-party setting is challenging. First, the comparison between noisy scores and a given threshold needs to be done in a distributed manner without letting the parties know the noisy scores, while satisfying differential privacy for each local dataset. Second, in the multi-party setting the large number of node splitting decisions incurs prohibitive computation costs. In addressing the above challenges, we present DPST, a distributed prediction suffix tree construction solution. In DPST, we first introduce a novel node splitting decision method that calculates the comparison result under encryption with substantially improved efficiency. Then we present a novel batch-based tree construction approach to reduce the computation costs. In order to achieve high parallel performance without incurring any extra communication cost, we introduce the conjunction and slide methods to ensure that each batch contains a stable number of carefully arranged decision tasks. Extensive experiments on real datasets demonstrate that our DPST solution offers desirable data utility with low computation and communication costs.
A Probabilistic Methodology to Quantify the Impacts of Cold Weather on Electric Vehicle Demand: A Case Study in the U.K.

The wide adoption of electric vehicles (EVs) is required for deep decarbonisation of transportation sector. The lithium-ion batteries that power EVs are temperature sensitive and electric ranges reduce under cold weather conditions due to excessive need for heating the battery and the driver’s cabin. Therefore, some drivers may need to charge more often during the same day. In this paper, probabilistic modelling and simulation of large-collections of EV charging are presented and the impacts on power generation portfolio is investigated for the case of the UK. Particularly, the extra energy and power demands are quantified across different regions. Extensive simulations reveal that cold weather necessities an additional 630 MW of peak power when compared to optimal ambient temperatures. Simulation results further show that the hourly carbon intensity of the power grids could increase by 25% during cold days with low renewable generation. The problems pertinent to temperature effects on EV charging require greater attention as EVs are becoming the main mode of transport in the next decade.
EVMAS: An Energy-aware Virtual Machine Allocation Scheme in Fog Centers

Due to the rapid growth of the Industrial IoT (IIoT), social media and digitization, and wireless communication technology in various sectors, data volume is increasing rapidly. Cloud computing is an emerging solution with fog computing assistance for handling and processing a huge volume of data. It is a soar of means to improve the quality of services provided to users through cloud computing, which has been more overwhelmed by the massive data flow. Transmitting all the data to the cloud and getting it back from the cloud causes high latency and requires high network bandwidth. In the IIoT applications, there is enough energy needed in the fog layer, a promising area to be handled by the cloud service providers. An important factor that contributes to the energy consumption in fog servers is VM allocation. This paper proposes EVMAS, i.e., an energy-aware VM allocation scheme based on Lagrange’s undetermined multiplier with the Hessian matrix, which improves the Quality of Service (QoS) in parallel with service level agreement (SLA). The suggested technique outperforms in comparison to the other state of the algorithm.
Ranking Desired Tuples by Database Exploration

Database exploration – the problem of finding and ranking desired tuples – is important for data discovery and analysis. Precisely specifying SQL queries is not always feasible in practice, such as "finding and ranking off-road cars based on a combination of Price, Make, Model, Age, and Mileage. " – not only due to the query complexity (e.g., which may have many if-then-else, and, or and not logic), but also because the user typically does not have the knowledge of all data instances.We propose DExPlorer, a system for interactive database exploration. DExPlorer offers a simple and user-friendly interface which allows to: (1) confirm whether a tuple is desired or not, and (2) decide whether a tuple is more preferred than another. Behind the scenes, we jointly use multiple ML models to learn from the above two types of user feedback. Moreover, in order to effectively involve users, we carefully select the set of tuples for which we need to solicit feedback. Therefore, we devise question selection algorithms that consider not only the estimated benefit of each tuple, but also the possible partial orders between any two suggested tuples. Experiments on real-world datasets show that DExPlorer is more effective than existing approaches.
D ec 2 01 2 M 5 / D 4 brane partition function on a circle bundle

We consider Abelian M5 brane on a six-manifold which we take as a circle bundle over a five-manifold M . We compute the zero mode part of the M5 brane partition function using Chern-Simons theory and Hamiltonian formulation respectively and find an agreement. We also show that the D4 brane on M shares exactly the same zero mode partition function again using the Hamiltonian formulation. For the oscillator modes we find that KK modes associated with the circle compactification are missing from the D4 brane. By making an infinitesimal noncommutative deformation we have instanton threshold bound states. We explicitly compute the instanton partition function up to instanton charge three, and show a perfect match with a corresponding contribution inside the M5 brane partition function, thus providing a very strong supporting evidence that D4 brane is identical with M5 brane which extends beyond the BPS sector. We comment on the modular properties of the M5 brane partition function when compactified on T 2 times a four-manifold. We briefly discuss a case of a singular fibration. dsbak@uos.ac. kr agbrev@gmail.com
Design of a Fast Discrete Realtime Filters Based on Analog Models

In this paper, a complete design of discrete low-pass, high-pass, band-pass, band-stop filters based on an analog model is performed. This approach combines the advantages of designing continuous-time filters, which are real electrical circuits, whit digital filters in the form of a program running in an infinite loop. Unlike the standard approach of a filter design directly in the discrete-time, in this case, the filter is designed as an analog and subsequently is transformed into a digital form while maintaining all the properties.
Capabilities of the IO-Link Master Device Inegration Through the Web Server

The article is focused on the integration of IO-Link device, specifically IO-Link Master device in the context of global data diagnostics at a selected automated workplace. It provides experimental verification of the configuration process and capabilities at the IO-Link Master device (based on the specific provider type). Subsequently, it will compare the results depending on the others manufacturers with aim to the sufficient reliability of configuration and usability of its individual functionalities. We can state that the IO-Link Master device can be standardly monitored via a web server and the corresponding IP address. The monitoring range depends on the IO-Link port configuration, device type and provider, of course. In addition, the presented procedure was tested at the sensors and actuators that are the part of the automated FMS 500 system to further monitoring of predictive maintenance.
On-The-Fly Stochastic Codebook Re-generation for Sources with Memory

This paper proposes a generalized stochastic mechanism for codebook generation in lossy coding settings for sources with memory. Earlier work has shown that the rate-distortion bound can be asymptotically achieved for discrete memoryless sources by a "natural type selection" (NTS) algorithm. In iteration n, the distribution that is most likely to produce the types of a sequence of K codewords of finite length ℓ that "d-match" a respective sequence of K source words of length ℓ, (i.e., which satisfy the distortion constraint), is used to regenerate the codebook for iteration n+1. The resulting sequence of codebook generating distributions converges to the optimal distribution Q* that achieves the rate-distortion bound for the memoryless source, asymptotically in ℓ, K, and n. This work generalizes the NTS algorithm to account for sources with memory. The algorithm encodes mℓ-length source words consisting of ℓ vectors (or super-symbols) of length m. We show that for finite m and ℓ, the sequence of codebook reproduction distributions Q0,m,ℓ, Q1,m,ℓ, …(each computed after observing a sequence of K d-match events) converges to the optimal achievable distribution $Q_{m,\ell }^{\ast}$ (within a set of achievable distributions determined by m and ℓ), asymptotically in K and n. It is further shown that $Q_{m,\ell }^{\ast}$ converges to the optimal reproduction distribution Q∗ that achieves the rate-distortion bound for sources with memory, asymptotically in m and ℓ.
Impact of Channel Engineering on 16nm, 18nm & 20nm Doping-less DG MOSFET

Double Gate MOSFET is a promising candidate for future low power applications with low leakage current and increased gate control over the channel electric field. Double gate MOSFET (DG-MOSFET) are designed as symmetric and asymmetric architectures with different gate overlap or underlap regions to achieve better subthreshold performance. The channel analysis on double-gate MOSFET shows that low channel doping with equal dopant atoms as in source and drain is a suitable choice to keep leakage current smaller and a suitable threshold voltage change. Subthreshold performance parameters are considerably improved with the concept of low doping junction-less double-gate MOSFET. High-K dielectric material (HfO2) with high work function gate contact material as platinum is also a suitable choice to achieve low leakage current. Channel doping is helpful within the context of leakage power reduction, thus in optimizing the device parameter the channel doping can contribute the necessary role. The proposed DG-MOSFET is designed and analyzed on a Visual TCAD 2D and 3D device simulator.
First Success of Cancer Gene Data Analysis of 169 Microarrays for Medical Diagnosis

To the best of our knowledge, our research work reported in this manuscript is the first high-dimensional gene data analysis of 169 microarrays. We completed the new theory of discriminant analysis and developed Revised IP-OLDF (RIP) based on a minimum NM (MNM). RIP discriminated six microarrays as an applied research theme and showed six MNMs were zero. The Matryoshka feature selection method (Method2) split six microarrays into many small Matryoshkas (SMs). After 2019, RIP and Method2 confirm these facts by 163 microarrays. Although many researchers studied this theme from 1995, they did not know LSD-discrimination and failed in this theme.