Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial Discharge Diagnostics of High Voltage Instrument Transformers – Case study

This paper presents a complete measurement procedure to detect partial discharge (PD) in instrument transformer under on-site scenario. The objective of this paper is to illustrate how to utilize the commonly available methods to improve the accuracy, reliability and effectiveness of PD diagnostics regarding the high voltage instrument transformers. Three PD measurement methods are used to detect and confirm the defect in the high voltage current transformer (CT) under normal exploitation. In the first step, a directional UHF antenna is used to screen the device under test and to find the possible source of PD signals. In the second step, acoustic sensor is used to confirm the presence of PD in the object pointed in the previous step. These two steps can be performed under normal working conditions of the tested object, as they are completely noninvasive. In the last step, electric method is used to finally confirm the presence of PD and to assess its intensity.
Type Inference via Constraint Abduction for EADTs

Type inference for Hindley/Milner and variants is well understood as a constraint solving problem. Recent extensions to Hindley/Milner with user-definable primitive constraints, typ e annotations and an extended form of algebraic data types (loosely referred to as EADTs) force us to go beyond this approach to inference. Examples include generalized algebraic data types (GADTs) and multi-parameter type classes (MPTCs). In this paper we show how to perform type inference for EADTs using constraint abduction, a solving method to infer missi ng hypotheses. But type inference for EADTs is very hard, we give example programs with an infinite number of maximal types. By ru ling out “non-intuitive” solutions of the constraint abduct ion process we show how we can construct a complete and decidable type inference approach for GADTs and MPTCs. Further for MPTCs we can show that the inferred “intuitive” type is principal. Ou r results allow us to establish links to previous works and point out new directions in how to perform type inference for EADTs.
• MULTIPLICATIVE ATOM-BOND CONNECTIVITY AND MULTIPLICATIVE GEOMETRIC-ARITHMETIC INDICES OF CHEMICAL STRUCTURES IN DRUGS

I n Medical Science, the multiplicative connectivity indices are used in the analysis of drug molecular structures which are helpful for medical scientists and pharmaceutical scientists to find out the chemical, biological characteristics and medical information of drugs. They are also used in the nanoscience engineering applications. In this paper, the first multiplicative atom bond connectivity and multiplicative geometric-arithmetic indices of some important nanostar dendrimers which appeared in nanoscience. These results have a wide application prospect in medical, nanoscience and pharmaceutical engineering.
Ice Sleeves on Overhead Power Lines: a Deep Learning Approach for Semantic Segmentation

The wet-snow accretion on power grid cables and its detachment due to external phenomena represent a serious threat to the safety and continuity of the electricity grid service provisioning. As external phenomena, for instance, we can include temperature increases, wind and gravity. This paper presents a deep learning solution, based on a U-Net hybrid architecture, to recognize the formation of ice sleeves on overhead power lines. The semantic segmentation model has been trained on a set of images collected at the called Wetsnow Ice Laboratory Detection (WILD) experimental station. The aim of this work is to remove the currently-involved manual effort in studying the images coming from WILD. Starting from the content of this paper, the inference coming from the semantic segmentation model will be enriched in the future by further information about the dimensions of the area involved and the accretion over time. Such an approach will support operators while monitoring and characterizing wet-snow accretion on conductors cables.
Atomistic simulation analysis of plasma surface activation in wafer-to-wafer oxide fusion bonding

Using a variety of modeling techniques in different scales, we gained insights of surface chemical reactions and built the possible bonding mechanism during the surface activation and hydration processes by combining with the experimental results. Experimentally, we measured the bonding strength of the thermal silicon dioxide (TOX) with surface activation process, $N_{2}$ and $o_{2}$ plasma under the same plasma conditions. The results showed that the bonding strength with the $N_{2}$ treatment is higher than that with the $o_{2}$ treatment. Using the quantum chemistry calculation, we built the mechanism explaining how O/N-radicals modify the surface of $SiO_{2}$ to increase the bond density. 0 atoms binding with H, N and C in the $SiO_{2}$ films to create OH, CH20, CO, HNO and nitic oxides (NO) groups. NO-groups are hydrolyzed to form additional surface OH during the deionized water rinsing step, increasing the density of Si-O-Si formation and total bonding energy. Atomistic molecular dynamic simulation also confirmed how nitrogen atoms introduce silanol and siloxane group to the surface during the surface activation treatment and the hydration reaction. We also studied the population effects of N atom on bonding formation, indicating more N atoms exist will lead to better bonding. We extended our work to explain why bare-Silicon (Si) film with same surface activation method diminishes the bonding strength unlike Si02 wafer. During a deionized water rinse, the hydrophobicity of bare-Si created a condensed water on modified bare-Si surface regardless of a surface activation. This generated condensed water would cause the slip of wafer during a bonding process and lead a weak bonding strength. We believe this study would be beneficial to develop the process to enhance a bonding energy of wafer- to-wafer bonding.
REVISTA DE CONTABILIDAD SPANISH ACCOUNTING REVIEW

Studies addressing the impact of board capabilities on CSR reporting are scarce. The aim of this research is to provide further evidence of the impact that certain board capabilities, such as board specific skills, board tenure and board cultural diversity have on CSR disclosure. Additionally, the moderating impact of CEO power on the association between these three board competences and CSR reporting is examined. The paper draws on resource dependence theory and agency theory, which are highly relevant in analysing how board competence influences CSR disclosure. The findings show that board specific skills, board tenure and board cultural diversity have a positive effect on the disclosure of CSR information. Moreover, our evidence also shows that CEOs with greater power can negatively moderate the positive effect of the three board competences considered on CSR reporting. ©2022 ASEPUC. Published by EDITUM Universidad de Murcia. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Códigos JEL: DO2 M14 O3 Palabras clave: Habilidades específicas del consejo Permanencia en el consejo Diversidad de la cultura del consejo Divulgación de la RSE Poder del CEO Las competencias del consejo de administración y los informes de RSC: el papel moderador del poder del CEO
“We Can Do Better”: University Leaders Speak to Tribal-University Relationships

Wielding degrees of influence within educational organizations, university leaders are critical in determining how institutions enact their espoused missions and support severely marginalized campus communities. How do universities address and improve educational outcomes for the most severely underrepresented communities? This article presents emergent findings from an illustrative multiple-case study that examined the relationships between two public universities and local American Indian nations in California. As a preliminary step in understanding the present state of “tribal-university relationships,” I present findings on university leaders’ perceptions and knowledge regarding American Indians broadly and relationships with local Native nations specifically. Using tribal critical race theory as an analytical framework, I posit how colonization, federal recognition, and educational practices affect curricular, political, and economic relationships.
EM algorithm-based likelihood estimation for a generalized Gompertz regression model in presence of survival data with long-term survivors: an application to uterine cervical cancer data

ABSTRACT In this paper we develop a regression model for survival data in the presence of long-term survivors based on the generalized Gompertz distribution introduced by El-Gohary et al. [The generalized Gompertz distribution. Appl Math Model. 2013;37:13–24] in a defective version. This model includes as special case the Gompertz cure rate model proposed by Gieser et al. [Modelling cure rates using the Gompertz model with covariate information. Stat Med. 1998;17:831–839]. Next, an expectation maximization algorithm is then developed for determining the maximum likelihood estimates (MLEs) of the parameters of the model. In addition, we discuss the construction of confidence intervals for the parameters using the asymptotic distributions of the MLEs and the parametric bootstrap method, and assess their performance through a Monte Carlo simulation study. Finally, the proposed methodology was applied to a database on uterine cervical cancer.
Finite Difference Time Domain Implementation of Leader Development Models for Lightning Studies

The prediction of the behavior of long air gaps and insulators when stressed by fast-front overvoltages of non-standard waveshape is crucial for lightning studies. This can be achieved in a more accurate manner by using a leader development model. This work presents a Finite Difference Time Domain (FDTD) implementation of leader development models for lightning studies with emphasis on power lines. The proposed FDTD implementation technique is validated against an ATP-EMTP implementation as well as against literature data. Using the FDTD implementation, the minimum lightning current causing backflashover of a 150 kV single-circuit power line and the backflashover rate, BFR, of the line are estimated both as affected by the power-frequency tower ground resistance and the AC operating voltage of the line. The BFR of the evaluated line is higher when equipped with arcing horns and when the leader predischarge current is not simulated.
Deepfake audio detection with vision transformer based method

In addition to easy access to audio on the Internet, developments in deep learning methods have made it possible to produce deep fake audio. Deep fake audio spoofing is carried out with the aim of producing audio files in the content by cloning the voice of the speaker that is planned to be changed as if he said something he did not say. This forgery method, created using artificial intelligence approaches, poses a great threat, especially to speaker verification systems. This study proposes a new method to detect whether the audio is spoofed or original. Spectrogram images are obtained with the Constant-Q Transform approach then they are classified with the vision transformer network. The system is trained with a public dataset named ASVSpoof 2019 and the comparative performance analysis is done on this dataset.
EWareNet: Emotion Aware Human Intent Prediction and Adaptive Spatial Profile Fusion for Social Robot Navigation

We present EWareNet, a novel intent-aware social robot navigation algorithm among pedestrians. Our approach predicts the trajectory-based pedestrian intent from historical gaits, which is then used for intent-guided navigation taking into account social and proxemic constraints. To predict pedestrian intent, we propose a transformer-based model that works on a commodity RGB-D camera mounted onto a moving robot. Our intent prediction routine is integrated into a mapless navigation scheme and makes no assumptions about the environment of pedestrian motion. Our navigation scheme consists of a novel obstacle profile representation methodology that is dynamically adjusted based on the pedestrian pose, intent, and emotion. The navigation scheme is based on a reinforcement learning algorithm that takes into consideration human intent and robot's impact on human intent, in addition to the environmental configuration. We outperform current state-of-art algorithms for intent prediction from 3D gaits.
Detection of a Novel Object-Detection-Based Cheat Tool for First-Person Shooter Games Using Machine Learning

Detection of novel game cheating tools is critical for ensuring fair online play. Such cheating tools are visual-based and effectively avoid detection because they do not change the data of game software. With the development and popularity of artificial intelligence technology, it has become easier for individuals to develop cheating tools, such as a new cheating tool for first-person shooter games that searches for characters on the game screen and automatically targets them. Therefore, in this study, a new cheat detection method is proposed using machine learning. The proposed method can be used to detect new cheating tools based on object detection.
On the CIM Modelling Approach for the Frequency Estimation of a Power System Restoration Process

This paper aims at presenting and discussing modeling and simulation issues regarding the bottom-up power system restoration process. In particular, it compares different modelling approaches for the main regulation sets of the black-start generator focusing on the frequency behaviour. The regulation sets proposed by the Common Information Models (CIMs) library are compared with the actual ones installed on real power plants. The results are thus compared with on-field recordings performed during a real power system restoration test. These results highlight how, for systems which present a low power frequency characteristic, i.e. an islanded system during the restoration process, the adoption of the CIMs seems to be not always correct to estimate the frequency transient: in some cases, the real model has to be adopted.
Enabling the Department of Defense's Future to Test and Evaluate Artificial Intelligence Enabled Systems

While of great potential benefit, Artificial Intelligence (AI) presents new challenges and exacerbates some existing ones for the Department of Defense (DoD) Test and Evaluation (T&E) community. T&E professionals will need to work to ensure that AI-enabled systems' (AIES) complex and variable nature can be sufficiently characterized by the boundaries of acceptable performance. To help the DoD understand and prepare for the challenges of T&E of AIES, we convened a group of AI adoption, AI development, and policy experts to develop a future vision of T&E with respect to AI. The result is a vision of T&E that incorporates the unique requirements for AIES, encompassing policy changes, user engagement approaches, measures and metrics, data, infrastructure, and cybersecurity. This future vision is accomplishable by identifying focus for efforts across DoD, academia, Federally Funded Research and Development Centers (FFRDCs) and industry to provide processes, policy/standards, tools, data, and infrastructure. Thus, we can assure a more feasible future for the T&E of AIES.
AlGaN/GaN metal oxide semiconductor heterostructure field effect transistor

We report on the AlGaN/GaN metal oxide semiconductor heterostructure field effect transistor (MOS-HFET) and present the results of the comparative studies of this device and a base line AlGaN/GaN heterostructure field effect transistor (HFET). For a 5-/spl mu/ source-to-drain opening, the maximum current was close to 600 mA/mm for both devices. The gate leakage current for the MOS-HFET was more than six orders of magnitude smaller than for the HFET.
Rectifying curves and geodesics on a cone in the Euclidean 3-space

A twisted curve in the Euclidean 3-space $\mathbb E^3$ is called a rectifying curve if its position vector field always lie in its rectifying plane. In this article we study geodesics on an arbitrary cone in $\mathbb E^3$, not necessary a circular one, via rectifying curves. Our main result states that a curve on a cone in $\mathbb E^3$ is a geodesic if and only if it is either a rectifying curve or an open portion of a ruling. As an application we show that the only planar geodesics in a cone in $\mathbb E^3$ are portions of rulings.
First-Order Quantum Correction in Coherent State Expectation Value of Loop-Quantum-Gravity Hamiltonian: Overview and Results

Given the Loop-Quantum-Gravity (LQG) non-graph-changing Hamiltonian $\widehat{H[N]}$, the coherent state expectation value $\langle\widehat{H[N]}\rangle$ admits an semiclassical expansion in $\ell^2_{\rm p}$. In this paper, we compute explicitly the expansion of $\langle\widehat{H[N]}\rangle$ on the cubic graph to the linear order in $\ell^2_{\rm p}$, when the coherent state is peaked at the homogeneous and isotropic data of cosmology. In our computation, a powerful algorithm is developed to overcome the complexity in computing $\langle \widehat{H[N]} \rangle$. In particular, some key innovations in our algorithm substantially reduce the computational complexity in the Lorentzian part of $\langle\widehat{H[N]}\rangle$. Moreover, the algorithm developed in the present work makes it possible to compute the expectation value of arbitrary monomial of holonomies and fluxes on one edge up to arbitrary order of $\ell_{\rm p}^2$.
II. Transport of Nearly Incompressible Magnetohydrodynamic Turbulence from 1 to 75 au

The thermal plasma beta in the solar wind and the solar corona is of the order of and . Zank et al. developed 2D and slab turbulence transport model equations of the order of and using nearly incompressible (NI) theory. We solve the Zank et al. NI MHD coupled turbulence transport equations for the inhomogeneous solar wind from 1 to 75 au, and compare the numerical solutions to Voyager 2 observations. We find that (1) the 2D turbulent energies are larger than the slab energies throughout the heliosphere; (2) the 2D turbulent energies decrease more slowly than the slab turbulent energies within ∼4 au, while the slab energies increase and the 2D energies flatten in the outer heliosphere; (3) the 2D normalized cross-helicity decreases faster than the slab normalized cross-helicity within ∼4 au; (4) the 2D normalized residual energy is more magnetically dominated than the slab; (5) the variance of density fluctuations decreases more rapidly than within ∼10 au, and more slowly in the outer heliosphere; and (6) the observed variance in magnetic field fluctuations as a function of the thermal plasma beta is described by the two-component turbulence transport model. In summary, the NI MHD two-component Zank et al. turbulence transport model captures the behavior of the forward, backward, and total energies in the fluctuating Elsässer variables, the variance in the magnetic field, kinetic energy, and density fluctuations, the cross-helicities and residual energies, the thermal temperature and plasma beta, and the various correlation lengths.
Watermarking 3D triangle meshes for authentication and integrity

Most watermarking schemes for 3D meshes arise from the CAD community. The analysis of their watermarking performances are generally not easily tractable. In this paper, we describe a framework for fragile watermarking of 3D triangle meshes that enables accurate assessment of its performances. Experiments show the relevance of the method towards authentication and integrity of heritage data purposes. One often argues that authentication may be performed using pure cryptographic primitives. We use watermarking because we want to allow some non malicious transformations of the mesh. Moreover, our technique enables visual inspection of the mesh for integrity assessment. Our method is based on alteration of geometrical invariants along a special traversal of the mesh connectivity graph. We proove that our traversal takes linear time for maximizing the number of sites that can store a watermark bit. We deduce several watermarking properties of our scheme, including the minimum watermark segment (MWS), the security (in bits) of the embedding, the class of robustness and the probability of false alarm. To our knowledge, such an accurate characterization of a watermarking scheme for 3D triangle meshes is not trivial for other similar methods.
Coffee Price Prediction: An Application of CNN-BLSTM Neural Networks

Coffee is one of the world's most popular beverages, and its production and demand have been steadily increasing in recent years. In 2020/21, worldwide coffee output hit 174.5 million bags, according to the International Coffee Organization. coffee year, which is a 1.9% increase from the previous year. The demand for coffee is driven by various factors, including changing consumer preferences, economic conditions, and demographic trends. In particular, the growing popularity of specialty coffee and the increasing consumption of coffee in emerging economies have contributed to the growth in demand. However, the coffee market has also faced challenges such as climate change, which can affect coffee production by altering the growing conditions, and the COVID-19 pandemic, which has disrupted supply chains and caused fluctuations in prices.In terms of regional When it comes to coffee output, Brazil leads the globe, followed by Vietnam, Colombia, and Indonesia. These countries collectively account for more than 60% of global coffee production. The United States, Germany, and Japan are the largest importers of coffee.Overall, coffee continues to be an important commodity in the global market, with a significant impact on the economies of producing countries and the daily routines of consumers around the world.In this article, we propose a fresh method of coffee price prediction using the The BLSTM (bidirectional long short-term memory) and CNN (convolutional neural networks) models.We start by collecting historical coffee price data from publicly available sources and preprocess it using feature engineering techniques. The The collected data was then split into training and validation sets and testing sets and feed it into the proposed CNN-BLSTM model.The CNN extraction by using layers the relevant features from the input data and reduce its dimensionality, while the BLSTM layers learn temporal dependencies in the data and capture long-term patterns. The outputs from the BLSTM layers are then fed into fully connected layers, which output the final price prediction.We Use measures like MSE, RMSE, and MAE to measure how far off you are from your target assess how well our suggested model performs (MAE)in both the test and validation data. Our According to the obtained CNN-BLSTM model outperforms several other state-of-the-art machine learning models, including traditional time-series models, on the same dataset.Overall, our approach demonstrates the effectiveness of combining CNN and BLSTM models for coffee price prediction and can be extended to other related forecasting problems.