Miracle-dz/UniFAR-FTUs huggingface dataset:
An exploratory study: Customer experience in Government's mobile application

The programmer designed the mobile application with a friendly version. Its application is planned for effortless usage and is easy to use, called User experience (UX). The mobile government application aimed to launch to facilitate people for their daily consumption and transaction. However, some functions and interfaces should be developed and customized to the specific user. The government's mobile application is suggested to explore the Customer experience (CX) to ease and convince people to take advantage of the government mobile application. Using a qualitative and exploratory approach, the interview was conducted with the customer experience to explore the perception and feedback of government applications. A semi-structured interview with 17 participants was conducted to examine the customer experience. These findings provide important implications and input for the government mobile application and user experience development.
How Digital Transformation Improve Government Performance: The Mediating Role of Partnering Agility

With the rapid development of information and communication technologies, digital transformation has already been underway in various fields around the world. In order to construct digital government and promote the modernization process of national management system and management capability, governments of different Countries have started the digital transition process. Government digital transformation has fascinated many experts and scholars, while there is little literature on the relations between digital transformation and government performance. Combining dynamic capability theory and cooperative agility variables, the analysis framework and corresponding structural equation model were constructed. An empirical study on the impact of digital transformation on government performance was made using systematic review and questionnaire survey materials. The results show that sensing capability, seizing capability, transformation capability and partnering agility have significant positive effects on government performance. Moreover, partnering agility plays a mediating role in the interactions of sensing capability, transformation capability and government performance variables. This paper enriches the research achievements in the field of government digital transformation and provides both theoretical and practical supports for the improvement of government performance.
Improving COVID-19 Predictions with Multimodal Neural Networks

In 2020, COVID-19 underscored the vulnerabilities of the healthcare system. Supply lines and hospital capacities presented unrealized complications for dealing with the virus's sudden outbreak. Being able to anticipate an influx of COVID-19 patients would allow for targeted responses by healthcare providers and policy makers. In this work we present a method for predicting COVID-19 case counts on a county-level granularity using neural networks. Utilizing multimodal network architectures, incorporating convolutional and LSTM layers, we were able to outperform a feed forward estimation of COVID-19 case counts. We test our network's performance on a variety of historical and geographical inputs, utilizing static anthropogeographic inputs, and through retraining the network on a span of historical positions. Our results indicate the models well are suited to forecast the spread of COVID-19 on a county-level basis, but our model is best utilized for targeted local applications preventing its overall portability on a national scale.
Understanding the Optical Degradation of 845 nm Micro-Transfer-Printed VCSILs for Photonic Integrated Circuits

For the first time we investigate the optical degradation of vertical-cavity silicon-integrated lasers VCSILs) designed for operation at 845 nm in photonic integrated circuits (PICs). The study is based on the combined electro-optical characterization of VCSIL, submitted to constant-current stress tests at different current levels. The original results obtained within the manuscript indicate that degradation is related to the diffusion of impurities. Remarkably, depending on the region through which these impurities are migrating, the diffusion process affects device characteristics in different ways. During Phase 1 (Ph1), compensating impurities originating from the metal-semiconductor contact cross the top DBR, thus degrading mirror reflectivity, which is rarely observed in the literature, and leading to an increase in the threshold current of the device. As the impurities start reaching the active region we observe the onset of Phase 2 (Ph2), during which both threshold current and sub-threshold slope worsen, due to the increase of the Shockley-Read-Hall recombination rate. This phase is also characterized by a measurable increase in series resistance, which is ascribed to a change in the resistance of the oxide aperture. The identification of the root cause of physical degradation represents a fundamental step for future lifetime improvement of these novel optical sources, which are set to replace conventional solid-state sources in the $0.85 \mu \text{m}$ communication window.
The Influence of Social Media and EWOM on Generation Z's Trust and Purchase Intentions at Coffee Shops

Generation Z has a lifestyle of enjoying daily activities at a coffee shop. This phenomenon affects the lifestyle of Generation Z. Several indicators can influence Generation Z's purchasing intentions (PI) for Coffee Shops, including social media usage (SMU) and electronic word of mouth (EWOM) can raise trust (TR) and influence PI to gain customer loyalty (CL). This study is to find out about PI and CL using non-probability sampling involving 150 coffee shop consumers from generation Z. This research uses a structural equation model (SEM) with SmartPLS software 3. This research provides proof that SMU does not influence PI in generation Z because the p-value of SMU on PI is 0.173 where the result is > 0.05 and is not by standardized measurement values, but for generation Z SMU is more significant in increasing consumer CON with a p-value of 0.010 which is by the standard measuring value criteria. The results of this research provide proof that generation Z can raise CL that is created because of the PI which is influenced by indicators other than SMU, that is EWOM.
Demonstration of β-Ga2O3 Heterojunction Gate Field-Effect Rectifier

In this work, a <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> field-effect rectifier (FER) with a p-NiO<sub><italic>x</italic></sub> gate aimed at low conduction loss, low leakage current, and capability of on-chip integration has been demonstrated. The proposed FER exhibits a low turn-on voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {on}}{)}$ </tex-math></inline-formula> of 0.85 V comparable to Schottky barrier diodes (SBDs). The p-NiO<sub><italic>x</italic></sub> in FER offers an additional conduction path at high forward bias, which enhances the forward current. Compared with the lateral heterojunction diode (HJD) and SBD on the same substrate, the FER shows the best tradeoff between ON-state and OFF-state power dissipation, including lower <inline-formula> <tex-math notation="LaTeX">${V}_{\text {on}}$ </tex-math></inline-formula> than HJD (~41% of HJD) and lower leakage current than SBD (four orders of magnitude lower). The merits of this structure also extend to the high-temperature operation regime of the device. Good rectification characteristics of FER are observed at elevated temperatures. The proposed FER with low <inline-formula> <tex-math notation="LaTeX">${V}_{\text {on}}$ </tex-math></inline-formula>, high-voltage blocking capability and process compatibility with field-effect transistors (FETs) has the potential of being used as a low-power loss rectifier in future <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> power integrated circuits (ICs).
Classification of False Data Injection Attacks in Smart Grid using Multi Label Deep Learning

The traditional electricity structure is displaced by a new physical structure brought about by the smart grid, which is brought about by the creative use of information that leads to the building of a new structure. When applied to a sector of the economy like this one, the dissemination of bad information has the potential to have substantial repercussions. The conventional residual methods that are used in fake data identification will never be in a position to identify accurate FDI attacks. The linearized power system model DC has served as the foundation for the vast bulk of the research that has been conducted on the subject of the detection of FDI attacks. The AC model is not the source of any of the assaults that are uncovered by this study. In addition, the overwhelming majority of the currently available DIA identification algorithms focus on FDIA, despite the fact that significant injection site data cannot be gathered. In light of the ongoing advancements in deep learning, we present a Deep Learning based Locational Detection approach as a means of continually identifying the various parts of FDIA. Solver Gap Happiness is a False Data Detector (FDD) that was developed in the realm of software engineering. It makes use of a Recurrent Neural Network (RNN). The FDD has been around long enough to be able to spot false information. The subsequent RNN is employed here as a multi-label classifier in order to analyse the irregularity and co-occurrence dependence of power flow computations which may have been caused by potential assaults. Due to the fact that they are "model-free," the suggested design does not include any prior statistical assumptions. In addition to this, it is "cost-accommodating," since it does not change the existing FDD structure and the identification method only takes a few microseconds on a typical home computer. Through our extensive work in the IEEE 14, 30, 57, and 118 bus systems, we have shown that ANN-MLP, SVM-RBF, CNN and RNN are capable of performing locational detection in a variety of environments with varying levels of noise and attack. In addition to this, the multi-name classification approach that was applied manages to effectively increase the accuracy of the presence identification.
Toward Accurate and Efficient Road Extraction by Leveraging the Characteristics of Road Shapes

Automatically extracting roads from very high-resolution (VHR) remote sensing images is of great importance in a wide range of remote sensing applications. However, complex shapes of roads (i.e., long, geometrically deformed, and thin) always affected the extraction accuracy, which is one of the challenges of road extraction. Based on the insight into road shape characteristics, we propose a novel road shape-aware network (RSANet) to achieve efficient and accurate road extraction. First, we introduce the efficient strip transformer module (ESTM) to efficiently capture the global context to model the long-distance dependence required by long roads. Second, we design a geometric deformation estimation module (GDEM) to adaptively extract the context from the shape deformation caused by shooting roads from different perspectives. Third, we provide a simple but effective road edge focal loss (REF loss) to make the network focus on optimizing the pixels around the road to alleviate the unbalanced distribution of foreground and background pixels caused by the roads being too thin. Finally, we conduct extensive evaluations on public datasets to verify the effectiveness of RSANet and each of the proposed components. Experiments validate that our RSANet outperforms state-of-the-art methods for road extraction in remote sensing images.
Analysis of Keller Cones for RF Imaging

Imaging still objects with the received signal power of off-the-shelf WiFi transceivers is considerably challenging. The interaction of object edges with the incoming wave, dictated by the Geometrical Theory of Diffraction and the resulting Keller cones, presents new possibilities for imaging with WiFi via edge tracing. In this paper, we are interested in bringing a comprehensive understanding to the impact of several different parameters on the Keller cones and the corresponding edge-based imaging, thereby developing a foundation for a methodical imaging system design. More specifically, we consider the impact of parameters such as curvature of a soft edge, edge orientation, distance to the receiver grid, transmitter location, and other parameters on edge-based WiFi imaging, via both analysis and extensive experimentation. We finally show that Keller cones can be used for imaging objects that lack visibly-sharp edges, as long as the curvature of the edge is small enough, by imaging a number of such daily objects.
Performance Evaluation of X-Ray Nanowire Photoconductor Detector

This article describes the use of the SILVACO TCAD device simulator to inspect the electrical performance of a proposed nanowire (NW) X-ray detector. The objective of this work is to obtain a better physical understanding of the behavior of the detector, specifically as a photoconductor $(\mathrm{n}^{+}-\mathrm{n}-\mathrm{n}^{+})$ type, under different conditions such as flux density, biasing voltage, NW diameter, active region length and temperature. When the length is changed from 1.8 $\mu \mathrm{m}$ to 1.0 $\mu \mathrm{m}$, the output conductance is changed from 4 $\mu \text{ohm}^{-1}$ to 14 $\mu \text{ohm}^{-1}$. The simulation results predict the current produced by incident X-ray beams and the gain, while taking into account the relationship between the diameter and energy gap of InP nanowires. The simulation results show that the overall gain is in the range from 103 to 105 when the flux changes from $2\times 10^{9}$ to $6.2\times 10^{7}$ Ph/s. The study also models a single semiconductor nanowire and considers the effects of trapping charge and photodoping on the behavior of the nanowire. Additionally, PENELOPE is used to indicate the charge distribution in the InP nanowire material. The simulation results are validated against high-precision measurements, and decent agreement is obtained between the SILVACO TCAD simulation results and experimental results for various beam energies and conditions. This research study provides valuable insights into the functioning of nanowire X-ray detectors.
A Novel Combined DenseNet and Gated Recurrent Unit Approach to Detect Energy Thefts in Smart Grids

Due to the illegal use of electricity, non-technical losses are exponentially increasing in electricity distribution systems day by day. With the debut of smart meters in the smart grid, new electricity theft attacks are welcomed. The investigation of abnormal electricity consumption patterns helps in detecting electricity thieves. Moreover, existing methods have poor electricity theft detection (ETD) accuracy due to imbalanced datasets provided by the utilities. They have also failed to capture both periodicity and non-periodicity of 1-D daily electricity usage data. We primarily propose a novel sampling technique to balance the dataset, named as random oversampling using both classes (ROBC). This technique performs oversampling using both the theft and normal classes. With this technique, the problem of low accuracy has been resolved. We also propose a unique ETD model using densenet-fully convolutional network (DenseNet-FCN) and gated recurrent unit (GRU) with a light gradient boosting machine (LightGBM), known as DenseNet-GRU-LightGBM, to address the above mentioned concerns. DenseNet-FCN module extracts periodic and non-periodic patterns from 2-D electricity consumption data in a precise way. Whereas, GRU module captures as well as memorizes features from 1-D consumption data. Afterwards, LightGBM module is used as an ensemble classifier to give final ETD results. As a result, the proposed model has excellent ETD results. Comprehensive simulations indicate that the proposed scheme outperforms other existing methods regarding ETD.
Suppression performance of reciprocal filtering in DVB-T based passive radar

Passive radars exploit illuminators of opportunity, such as radio or television transmitters, to detect moving objects. The broadcast signals, however, are not optimized for radar applications. With conventional radar signal processing, the digital video broadcast - terrestrial (DVB-T) signal has several ambiguities and a noise floor with a high level due to random sidelobes. However, if crafted by specialized signal processing methods, it is possible to remove the ambiguities and reduce the random sidelobes that cause the elevated noise floor. We analyze the reciprocal filtering method that has been shown to improve the ambiguity surface of the DVB-T signal. However, simulations showed that when targets were present at long range, the range-Doppler floor increased, which subsequently masked weak targets. We conduct a real measurement where several synthetic targets are introduced at ranges that should cause an increase in the range-Doppler floor. The experimental results show that the increase in the range-Doppler floor under worst case conditions is considerably lower than the case that was simulated. This implies that reciprocal filtering is more relevant for DVB-T based passive radar, than reported previously.
Neural Network Architectures for Integrated Circuits

This paper presents the architectural design and implementation of neural networks (NNs) for integrated circuit design. The architecture consists of the adder, multiplier, and rectified linear unit (ReLU) blocks. Three architectures (i) single-in single-out (SISO), (ii) multiple-in single-out (MISO), and (iii) multiple-in multiple-out (MIMO) are developed. In NNs, weight values are required, fed from a manually generated memory. The weight values were prepared by training the NNs model on software. Finally, the SISO, MISO, and MIMO neural-networks were taped out.
Equivalent Circuit Model and Parameter Identification of Lithium-Sulfur Battery

To mitigate the intricacies and theoretical vagueness involved in constructing an electrochemical model for lithium-sulfur batteries, as well as the arduousness of parameter identification, which inadequately reflects the battery's dynamic characteristics, this study developed an equivalent circuit model comprising a first-order and a second-order RC circuit based on the impedance spectrum features of lithium-sulfur batteries at different charge levels. Additionally, we established an experimental setup for gathering data for lithium-sulfur battery parameter identification and verified the efficacy and precision of both models and real-time identification methods through simulation and experimental findings. Results evinced that both equivalent circuit models performed proficiently with respect to constant current discharge of lithium-sulfur batteries, with the second-order RC model error amounting to 1% or lower.
The Erdős–Hajnal hypergraph Ramsey problem

Given integers 2 ≤ t ≤ k+1 ≤ n, let gk(t, n) be the minimum N such that every red/blue coloring of the k-subsets of {1, . , N} yields either a (k + 1)-set containing t red k-subsets, or an n-set with all of its k-subsets blue. Erdős and Hajnal proved in 1972 that for fixed 2 ≤ t ≤ k, there are positive constants c1 and c2 such that 21 < gk(t, n) < twrt−1(n 2), where twrt−1 is a tower of 2’s of height t−2. They conjectured that the tower growth rate in the upper bound is correct. Despite decades of work on closely related and special cases of this problem by many researchers, there have been no improvements of the lower bound for 2 < t < k. Here we settle the Erdős-Hajnal conjecture in almost all cases in a strong form, by determining the correct tower growth rate, and in half of the cases we also determine the correct power of n within the tower. Specifically, we prove that if 2 < t < k − 1 and k − t is even, then gk(t, n) = twrt−1(n k−t+1+o(1)). Similar results are proved for k − t odd.
An Efficient and Reliable User Access Protocol for Internet of Drones

The Internet of Drones (IoD) has blown up the interest of academia and industry due to their deployment in military and civil fields. In the IoD environment, drones collect and transmit real-time data to the users through an insecure wireless medium. A secure and efficient authentication protocol is required to ensure that only authorized drones communicate and send data to authentic users. For this purpose, Zhang et al. introduced an authentication protocol for the IoD environment and asserted that it could hold out against many known attacks. However, after cautious scrutiny, this paper manifests that many shortcomings still exist in their protocol design. This article introduces an enhanced, lightweight, and secure protocol for the IoD environment to mitigate the drawbacks of Zhang et al.’s protocol. The performance comparison depicts that the proposed scheme provides enhanced security with minimal computation cost. This rise in communication cost is legitimate as the proposed protocol renders better protection than the preceding protocols. The formal automated analysis is carried out via ProVerif to prove that the proposed scheme withstands numerous attacks.
Nonlinear Control of Five-Phase PMSG Wind Turbine connected to Vienna Type Rectifier Using SVPWM

In this proposed research, we aim to design a non-linear control for a five-phase Permanent Magnet Synchronous Generator (PMSG) connected to a Vienna rectifier based on Space Vector Pulse Width Modulation (SVPWM) control. The five-phase (PMSG) is a more intricate machine compared to the traditional three-phase PMSG, which necessitates a more sophisticated mathematical model and presents a challenging control problem. By connecting the wind turbine to a Vienna rectifier, the system can achieve better performance in terms of power quality and efficiency. The aim of this research is to design a nonlinear controller using the Integral Sliding Mode Control (ISM C) strategy and the SVPWM technique with an artificial neural network optimizer to enhance the system's performance and achieve a multi-objective strategy.
Parkinson Detection using VOT-MFCC Combination and Fully-Connected Deep Neural Network (FC- DNN) Classifier

Vocal cord disorders are considered to be the leading cause of Parkinson's disease (PD) for many people. For this. , speech impairments are among the first indicators of this disease. In this paper. , a new method is suggested to differentiate people with Parkinso's disease from healthy people using the Diadochokinesis (DDK) test. A voice characteristic composed of Mel-Frequency Cepstral coefficients (MFCC) combined with Voice Onset Time (VOT) called VOT-MFCC is integrated into the proposed model in order to benefit further from the improvements provided by MFCC and thus improve performance. This model is built from VOT-MFCC as a feature and a Fully-Connected Deep Neural Network (FC-DNN) as a classifier. The experimental tests were carried out on a Spanish database and the results showed the superiority of the combination of VOT-MFCC with FC-DNN in terms of evaluated performances (Precision=98%., accuracy=98%., Recall=97%., Fl=97.5., and MCC=0.97) compared to those obtained using only MFCC or VOT with the same classifier or with other classifiers.