Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Attitude in Mediating the Influence of Perceived Usefulness on Intention to Use Nagari Mobile Banking

The growth of the banking industry is increasing rapidly, this has resulted in increasingly intense competition between these banks. One of them is using mobile banking services. This Mobile Banking service is to answer the needs of society in the digital era to carry out a number of financial transactions for customers who want convenience. Where the use of technology in banking refers to the theory of technology acceptance model (Technology Acceptance Model or TAM) which is a model of acceptance of information technology systems that will be used by customers in the form of using mobile banking applications. This study aims to determine the role of attitude in mediating the effect of perceived usefulness on intention to use Nagari Mobile Banking (Case Study on Customers of Bank Nagari Main Branch). The population is all customers using Nagari Mobile Banking banking services at Bank Nagari Main Branch with a total sample of 100 people. The sampling technique used purposive sampling method. Data analysis using SEM-Partial Least Square (PLS). The results of the research are as follows, perceived usefulness has a positive and significant effect on customer attitudes in using Nagari Mobile Banking, perceived usefulness has a positive and significant effect on intention to use Nagari Mobile Banking, customer attitude has a significa nt positive effect on intention to use Nagari Mobile Banking, and Perceived usefulness has a significant positive effect on the intention to use Nagari Mobile Banking which is mediated by the customer's attitude.
Integrating Dropout Regularization Technique at Different Layers to Improve the Performance of Neural Networks

—In many facial expression recognition models it is necessary to prevent overfitting to check no units (neurons) depend on each other. Therefore, dropout regularization can be applied to ignore few nodes randomly while processing the remaining neurons. Hence, dropout helps dealing with overfitting and predicts the desired results with more accuracy at different layers of the neural network like ‘visible’, ‘hidden’ and ‘convolutional’ layers. In neural networks there are layers like dense, fully connected, convolutional and recurrent (LSTM-long short term memory). It is possible to embed the dropout layer with any of these layers. Model drops the units randomly from the neural network, meaning model removes its connection from other units. Many researchers found dropout regularization a most powerful technique in machine learning and deep learning. Dropping few units (neurons) randomly and processing the remaining units can be considered in two phases like forward and backward pass (stages). Once the model drops few units randomly and select ‘n’ from the remaining units it is obvious that weight of the units could change during processing. It must be noted that updated weight doesn’t reflect on the dropped units. Dropping and stepping-in few units seem to be very good process as those units which step-in will represent the network. It is assumed to have maximum chance for the stepped-in units to have less dependency and model gives better results with higher accuracy.
Photovoltaic defect detection method for marine pasture based on deep learning

As a large-scale marine fishery facility, marine ranch plays an important role. At the same time, in the current context of energy conservation and environmental protection, solar energy has the characteristics of clean, environmental protection, high efficiency and easy access, so it undertakes the important task of energy consumption control and energy conservation and environmental protection. However, under the influence of human and natural factors, solar panels inevitably have cracks, shielding and other defects. If it cannot be found and maintained in time, it will seriously affect the power supply efficiency and energy consumption control. Therefore, it is extremely critical to accurately detect the defects of the photovoltaic system in the marine pasture. Aiming at the problems of high false detection rate, unbalanced detection speed and accuracy in the original photovoltaic defect detection method, this paper proposes an improved method based on YOLOv5s. This method embeds CA (Coord Attention) attention mechanism in the backbone network, and uses BiFPN (Bidirectional Feature Pyramid Network) to replace the original PANet to improve the feature fusion ability. The final experimental results show that the precision of the proposed method is 1.2% higher than that of the original algorithm, and the amount of parameters and computation are reduced by 25.3% and 16.5% respectively, and the reasoning speed is the same. Therefore, this method achieves the balance between speed and accuracy, and the reduction of model size also provides the possibility for its further deployment.
A Bivariate Ordered Probit Model with Truncation: Helmet Use and Motorcycle Injuries

I consider an ordered probit model in which some of the observations in one of the categories are missing. The model can be estimated because of a second variable with a similar truncation scheme. The second variable has the effect of increasing the number of categories quadratically while only increasing the number of parameters linearly. This model arises in the analysis of the effectiveness of helmets in reducing the severity of head and neck injuries in motorcycle accidents. The problem is that accidents in which the rider did not receive a head or neck injury were observed only if the rider received a body injury, and vice versa. The results show that helmets are effective in reducing the severity of the worst head or neck injury but, because the worst overall injury that a rider receives is often a body injury, their effect on the severity of the worst overall injury is smaller.
Shape sensitivity analysis for incompressible fluid using SPH projection method

An analytical shape design sensitivity analysis (DSA) method for timedependent incompressible Newtonian fluid is developed using the direct differentiation approach [1]. Governing equations for the incompressible fluid are the continuity equation and the equations of motion. In conventional smoothed particle hydrodynamics (SPH) method to simulate incompressible flows, the pressure is approximately obtained from the state of equation using artificially small sound speed. The incompressibility can not be satisfied strictly and the time steps for the integration of governing equations should be very small. Thus, unstable responses are observed around the fluid boundary, which have significantly adverse impacts on the computation of shape design sensitivity. To alleviate this difficulty in the SPH method, a pressure projection is employed to enforce the incompressibility [2]. Without imposing incompressibility, velocity field is firstly computed by integrating the equations of motion at an intermediate step. Then, the incompressibility is enforced in this intermediate velocity field by solving a pressure Poisson equation derived from an approximate pressure projection. Even though the additional pressure Poisson equation should be solved, comparing with the conventional SPH method, total computational cost for the SPH projection method is not expensive because larger time steps can be used. The computational cost for the sensitivity analysis is also trivial comparing with that for the response analysis since the factorized system matrix for the pressure Poisson equation is readily available through the response analysis and can be directly utilized in the computation of analytical sensitivity. For instance, consider the two-dimensional vortex spin-down simulation shown in Figure 1-(a), in which the circles denote the velocity streamlines. A uniform time step of 0.005 is used for the SPH projection method and 0.001 for the conventional SPH method, which is based on the CFL stability constraints [2]. To compare the computational efficiency of the methods, the simulations are performed until the time step t = 5. The position and velocity vectors of particles at the time step t = 5 using the SPH projection method are illustrated in Figure 1-(b), where no unstable particles around the boundary are observed. The computing cost required for the SPH projection method is 830.07 seconds whereas for the conventional SPH method 1099.69 seconds.
High-Intensity Interval Cycling and Running Yield a Similar Myokine and Osteokine Response in Young Adult Females

Background: The differential responses of the myokine irisin, in combination with changes in markers and regulators of bone remodeling to high-intensity interval exercise of high and low impact, were examined in 18 young adult females (22.5 ± 2.7 years). Methods: Participants performed two high-intensity interval exercise trials in random order: running on a treadmill and cycling on a cycle ergometer. Trials consisted of eight 1 min running or cycling intervals at ≥ 90% of maximal heart rate, separated by 1 min passive recovery intervals. Blood samples were collected at rest (pre-exercise) and 5 min, 1 h, and 24 h following each exercise trial. Irisin, osteocalcin, sclerostin, osteoprotegerin (OPG), receptor activator nuclear factor kappa-β ligand (RANKL), and parathyroid hormone (PTH) were analyzed in serum, with post-exercise concentrations being corrected for exercise-induced changes in plasma volume. Results: Irisin was elevated 24 h post-exercise compared to its resting values in both trials (20%, p < 0.05) and was higher after cycling compared to running (exercise mode effect, p < 0.05) with no interaction. Osteocalcin, sclerostin, PTH, and RANKL increased from pre- to 5 min post-exercise (18%, 37%, 83%, and 33%, respectively, p < 0.05), returning to baseline levels in 1 h, with no trial or interaction effects. OPG showed a time effect (p < 0.05), reflecting an overall increase at 5 min and 1 h post-exercise, which was not significant after the Bonferroni adjustment. Conclusions: In young adult females, high-intensity interval exercise induced an immediate response in markers and regulators of bone remodeling and a later response in irisin concentrations, which was independent of the gravitational impact.
Influences of the Pacific Decadal Oscillation on the East Asian Summer Monsoon in non‐ENSO years

The impact of different phases of the Pacific Decadal Oscillation (PDO) on East Asia summer monsoon and related precipitation in non‐ENSO (El Niño‐Southern Oscillation) years is examined in this article. Associated with the large‐scale sea surface temperature and circulation change, anomalous summer (JJA mean) precipitation in East China shows tripolar pattern, with increased (decreased) precipitation in the Yangtze‐River Valley (North and South China) in positive PDO phase without ENSO. Besides, the western Pacific subtropical high exhibits expansion and westward shift in positive PDO phase along with significantly different sub‐seasonal variation.
GGRA: a grouped gossip-based reputation aggregation algorithm

An important issue in P2P networks is the existence of malicious nodes that decreases the performance of such networks. Reputation system in which nodes are ranked based on their behavior, is one of the proposed solutions to detect and isolate malicious (low ranked) nodes. Gossip Trust is an interesting previously proposed algorithm for reputation aggregation in P2P networks based on the concept of gossip. Despite its important contribution, this algorithm has deficiencies especially with high number of nodes that leads to high execution time and low accuracy in the results. In this paper, a grouped Gossip based Reputation Aggregation (GGRA) algorithm is proposed. In GGRA, Gossip Trust is executed in each group between group members and between groups instead of executing in the whole network. Due to the reduction in the number of nodes and using strongly connected graph instead of a weakly one, gossip algorithm in GGRA is executed quickly. With grouping, not only reputation aggregation is expected to be more scalable, but also because of the decrement in the number of errors of the gossiped communication, the results get more accurate. The evaluation of the proposed algorithm and its comparison with Gossip Trust confirms the expected results.
Numerical Simulations of an Observed Gravity Current and Gravity Waves in an Environment Characterized by Complex Stratification and Shear

Abstract Numerical simulations of a gravity current in an environment characterized by complex stratification and vertical wind shear have been performed using a nonhydrostatic, two-dimensional, dry, primitive-equation model. Data from one of the most complete documentations to date of gravity waves associated with a gravity current, presented in an earlier study, are used both to prescribe the gravity current's environment and for validation of the simulated gravity current and its associated gravity waves. These comparisons indicate that the gravity current observed by a Doppler wind profiler and sodars was well simulated in terms of depth, density contrast, and propagation speed and that the model produced a variety of gravity waves similar in many ways to these observed. Because uncertainties remained concerning the gravity wave generation mechanisms derived from the observations (e.g., wavelengths were not observed), the validated simulations are used to test these tentative hypotheses. The simulatio.
Achieving near perfect classification for functional data

Summary. We show that, in functional data classification problems, perfect asymptotic classification is often possible, making use of the intrinsic very high dimensional nature of functional data. This performance is often achieved by linear methods, which are optimal in important cases. These results point to a marked contrast between classification for functional data and its counterpart in conventional multivariate analysis, where the dimension is kept fixed as the sample size diverges. In the latter setting, linear methods can sometimes be quite inefficient, and there are no prospects for asymptotically perfect classification, except in pathological cases where, for example, a variance vanishes. By way of contrast, in finite samples of functional data, good performance can be achieved by truncated versions of linear methods. Truncation can be implemented by partial least squares or projection onto a finite number of principal components, using, in both cases, cross‐validation to determine the truncation point. We establish consistency of the cross‐validation procedure.
Application of laser mass spectrometry for trace analysis of plutonium and technetium / Anwendung der Lasermassenspektrometrie zur Spurenanalytik von Plutonium und Technetium.

Resonance ionization mass spectrometry (RIMS) is a highly selective and sensitive method for trace analysis of radiotoxic elements in environmental, biological or technical samples. By multiple resonant laser excitation and ionization of the element under investigation, an extremely high element selectivity is achieved. Additionally, isotope selectivity is obtained by subsequent mass analysis in a magnetic, time-of-flight or quadrupole mass spectrometer which serves for efficient background suppression as well. High sensitivity results from the large optical cross sections in the photo-ionization process and the high detection efficiency for ions. The outstanding element selectivity of RIMS permits a simplification of the chemical separation method. RIMS has been used for ultra-trace analysis of long-lived radioisotopes of Pu and Tc in various samples. The detection limit determined experimentally is of the order of 10 6 atoms per sample. For some of the most important long-lived radioisotopes, this value is distinctly below the radiometric detection limit.
Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C

This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.
The Manifestation of Spatial Wideband Effect in Circular Array: From Beam Split to Beam Defocus

Millimeter-wave (mmWave) and terahertz (THz) communications with hybrid precoding architectures have been regarded as energy-efficient solutions to fulfill the vision of high-speed transmissions for 6G communications. Benefiting from the advantages of providing a wide scan range and flat array gain, the uniform circular array (UCA) has attracted much attention. However, the growing bandwidth of mmWave and THz communications require frequency-independent phase shifts, which can not be perfectly realized through frequency-independent phase shifters (PSs) in classical hybrid precoding architectures. This mismatch causes the beam defocus effect in UCA wideband communications, where the high-gain beams could not form at non-central frequencies. In this paper, we first investigate the characteristics of the beam defocus effect distinguishing itself from the beam split effect in uniform linear array (ULA) systems. The beam pattern of UCA in both frequency domain and angular domain is analyzed, characterizing the beamforming loss caused by the beam defocus effect. Then, the delay-phase-precoding (DPP) architecture which leverages the true-time-delay (TTD) devices to generate frequency-dependent phase shifts is employed to mitigate the beam defocus effect. Finally, performance analysis and extensive simulation results are provided to evaluate the effectiveness of the DPP architecture in UCA systems.
Estimation of symbol time offset using cyclic prefix in OFDM system

In this paper synchronization techniques using cyclic prefix (CP) are analyzed to remove the influence of symbol time offset (STO) for correct synchronization in Orthogonal Frequency Division Multiplex (OFDM) system. For correct detection of STO using CP two techniques are used. The first one is based on finding the maximum correlation between two blocks and the second one on finding the similarity between two blocks which is maximized when the difference between them is minimized. Two cases are observed. The first one uses only additive white Gaussian noise (AWGN), while the second also uses the channel impulse response (CIR) of Rayleigh channel. When channel effect is added both performances of estimation by correlation and difference deteriorate if sufficient level of signal to noise (SNR) and proper length of CP are not provided.
Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective

Blockchain enables peer-to-peer transactions in cyberspace without a trusted third party. The rapid growth of Ethereum and smart contract blockchains generally calls for well-designed Transaction Fee Mechanisms (TFMs) to allocate limited storage and computation resources. However, existing research on TFMs must consider the waiting time for transactions, which is essential for computer security and economic efficiency. Integrating data from the Ethereum blockchain and memory pool (mempool), we explore how two types of events affect transaction latency. First, we apply regression discontinuity design (RDD) to study the causal inference of the Merge, the most recent significant upgrade of Ethereum. Our results show that the Merge significantly reduces the long waiting time, network loads, and market congestion. In addition, we verify our results' robustness by inspecting other compounding factors, such as censorship and unobserved delays of transactions via private changes. Second, examining three major protocol changes during the merge, we identify block interval shortening as the most plausible cause for our empirical results. Furthermore, in a mathematical model, we show block interval as a unique mechanism design choice for EIP1559 TFM to achieve better security and efficiency, generally applicable to the market congestion caused by demand surges. Finally, we apply time series analysis to research the interaction of Non-Fungible token (NFT) drops and market congestion using Facebook Prophet, an open-source algorithm for generating time-series models. Our study identified NFT drops as a unique source of market congestion -- holiday effects -- beyond trend and season effects. Finally, we envision three future research directions of TFM.
IMAP: Intrinsically Motivated Adversarial Policy

Reinforcement learning agents are susceptible to evasion attacks during deployment. In single-agent environments, these attacks can occur through imperceptible perturbations injected into the inputs of the victim policy network. In multi-agent environments, an attacker can manipulate an adversarial opponent to influence the victim policy's observations indirectly. While adversarial policies offer a promising technique to craft such attacks, current methods are either sample-inefficient due to poor exploration strategies or require extra surrogate model training under the black-box assumption. To address these challenges, in this paper, we propose Intrinsically Motivated Adversarial Policy (IMAP) for efficient black-box adversarial policy learning in both single- and multi-agent environments. We formulate four types of adversarial intrinsic regularizers -- maximizing the adversarial state coverage, policy coverage, risk, or divergence -- to discover potential vulnerabilities of the victim policy in a principled way. We also present a novel Bias-Reduction (BR) method to boost IMAP further. Our experiments validate the effectiveness of the four types of adversarial intrinsic regularizers and BR in enhancing black-box adversarial policy learning across a variety of environments. Our IMAP successfully evades two types of defense methods, adversarial training and robust regularizer, decreasing the performance of the state-of-the-art robust WocaR-PPO agents by 34%-54% across four single-agent tasks. IMAP also achieves a state-of-the-art attacking success rate of 83.91% in the multi-agent game YouShallNotPass.
Study of novel properties of graphene-ZnO heterojunction interface using density functional theory

Studies of the structural, electronic, and optical characteristics of the interfaces between graphene and ZnO polar surfaces is carried out using first-principles simulations. At the interface, a strong van der Waals force is present, and because of the different work functions of graphene and ZnO, charge transfer takes place. Graphene's superior conductivity is not impacted by its interaction with ZnO, since its Dirac point is unaffected despite its adsorption on ZnO. In hybrid systems, excited electrons with energies between 0 and 3 eV (above Fermi energy) are primarily accumulated on graphene. The calculations offer a theoretical justification for the successful operation of graphene / ZnO hybrid materials as photocatalysts and solar cells. ZnO semiconductor is found to be a suitable material with modest band gap, ($\sim$ 3 eV), having high transparency in visible region and a high optical conductivity.