Miracle-dz/UniFAR-FTUs huggingface dataset:
THERMAL IMAGE ANALYSIS

The equipment of thermal stations and heating networks, which is subject to corrosion and is often far from new, loses its original characteristics over time. Such processes, potentially leading to heat losses and coolant leaks, can lead to significant financial and environmental consequences. Infrared thermography is one of the effective problemsolving methods that helps to detect defects and reduce risks. This article discusses some details of the current project for the analysis of thermal images using convolutional neural networks.
KBO Astrometry Using Small Telescopes

CCD images of Makemake (2005 FY9), the second largest Kuiper Belt Object (KBO), were taken with a 14-inch telescope during the summer of 2008. The position of Makemake was found by comparing its position on the frames to UCAC2 stars in the frame. Observations were limited by the tracking capabilities of the telescopes and atmospheric limitations. The average right ascension offset from the ephemeris was found to be 0.0134 0.3394 arcseconds, and the average declination offset was found to be -0.3855 0.4634 arcseconds. It is possible to do KBO astrometry with small telescopes, however several hundred frames are required to equal the quality of observations on larger telescopes.
The Hidden Logic of Sudoku

"The Hidden Logic of Sudoku" provides the first systematic perspective of the logical foundations and of the symmetries of the popular game. These are fully exploited to define new resolution rules, new graphical representations and an ordering of the rules consistent with their complexity. The classical pattern of xy-chains has been extended into a homogeneous set of chain rules that, when added to a few elementary rules, suffices to solve almost any puzzle. These rules are illustrated with a hundred puzzles with their full resolution paths. A large collection of puzzles has been processed by Artificial Intelligence (AI) techniques, leading to a precise evaluation of the efficiency of each rule. This pedagogical book is intended for both Sudoku players (who will discover many new facets of the game and new rules - all set in a uniform conceptual framework based on patterns) and teachers or students of Logic or AI (who will appreciate the strict logical foundations).
Core radiative collapse characterisation and integrated modelling in WEST plasmas

In the full tungsten environment of WEST, during its first phase of operation, around 25% of the pulses exhibited a rapid central electron temperature collapse. In its first phase, WEST plasmas were mostly heated by lower hybrid current drive (LHCD) and ion cyclotron resonance heating (ICRH). In this publication, the collapsing pulses are analysed to understand the key actuators at play. Experimentally, an initial slow reduction of central electron temperature due to a density increase is observed, while the central tungsten profile is flat and constant in time. Then, radiative collapse occurs: the core tungsten profile peaks rapidly, at the same time, the central hard x-ray channel measurement decreases indicating a change in core LHCD absorption. Integrated modelling is used to explore the causality chain. To capture the collapse speed, both, tungsten core peaking and reduction of central LHCD absorption are required. When central LHCD power absorption is reduced, core electron and ion temperature profiles flatten which reduces the tungsten neoclassical thermal screening and leads to the observed core tungsten accumulation.
Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization's functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents' relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users' queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.
Stochastic Differential Games. Theory and Applications

Introduction, Survey and Background Material. - Stochastic Linear Pursuit-Evasion Game. - Two Person Zero-Sum Differential Games-General Case. - Formal Solutions for Some Classes of Stochastic Linear Pursuit. - N-Person Noncooperative Differential Games. - Weak Convergence in Two Player Stochastic Differential Games. - Weak Convergence in Many Player Games. - Some Numerical Methods. - Applications to Finance.
A Robust Monte-Carlo-Based Deep Learning Strategy for Virtual Network Embedding

Network slicing is one of the building blocks in Zero Touch Networks. It mainly consists in a dynamic deployment of services in a substrate network. However, the Virtual Network Embedding (VNE) algorithms used generally follow a static mechanism, which results in sub-optimal embedding strategies and less robust decisions. Some reinforcement learning algorithms have been conceived for a dynamic decision, while being time-costly. In this paper, we propose a combination of deep Q-Network and a Monte Carlo (MC) approach. The idea is to learn, using DQN, a distribution of the placement solution, on which a MC-based search technique is applied. This improves the solution space exploration, and achieves a faster convergence of the placement decision, and thus a safer learning. The obtained results show that DQN with only 8 MC iterations achieves up to 44% improvement compared with a baseline First-Fit strategy, and up to 15% compared to a MC strategy.
A Dual Attention Neural Network for Airborne LiDAR Point Cloud Semantic Segmentation

With the development of airborne light detection and ranging (LiDAR) technology, it has become a common and efficient way to collect large-scale 3-D spatial information. However, efficient and automatic semantic segmentation of LiDAR data, in the form of 3-D point clouds, remains a persistent challenge. To address this, a dual attention neural network (DA-Net) is proposed, consisting of two different blocks, namely, augmented edge representation (AER) and elevation attentive pooling (EAP). First, the AER can adaptively represent local orientation and position, thereby effectively enhancing geometric information. Second, the captured local features of centroid points are utilized to further encode discriminative features using the EAP with the learned attention scores. Finally, a location homogeneity (LH) module is devised to explore the long-range relationship in an encoder–decoder network. Benefiting from the dual attention module, geometric information hidden in unorganized point clouds can be effectively propagated. Besides, the LH forces the network to pay attention to the semantic consistency of elevated objects, which facilitates both point- and object-level point cloud semantic segmentation for scene understanding. A benchmark dataset is used to assess the proposed method, which achieves an overall accuracy of 85.98% and an average F1 score of 72.31%. In addition, comparisons with other latest deep learning methods on the 2019 Data Fusion Contest dataset further demonstrate the robustness and generalization ability of the proposed method.
Tri-band cross-coupling bandpass filter with rectangular defected ground structure array

Abstract A tri-band cross-coupling bandpass filter (BPF) with rectangular defected ground structure (DGS) array is proposed in this paper. Cross-coupling paths comprised of five resonators are employed to achieve general Chebyshev filtering frequency responses. Besides changing gaps among resonators, a novel rectangular DGS array is proposed to exactly meet coupling coefficients for specific demands for different passband in multi-band BPF. The centre frequency of the tri-band filter that is selected at 2.4, 3.4, 5.5 GHz are for LTE, Bluetooth, WiMAX and WLAN. The 3 dB bandwidth of the three passbands are 5, 3.23 and 3.45%, respectively, meanwhile, more transmission zeros are generated, leading to enhanced selectivity and high out of band isolation. The proposed tri-band filter is completed with a few simple calculations and several brief processes. Measured results of the fabricated filter demonstrate the theory prediction and proposed method well.
Effects of sex and weighted vest load arrangements on lower biomechanics and jump height during countermovement jump

Weighted vest (WV) use has been explored as a modifier of jumping and landing performance in athletes, but it is unclear whether performance is modified with different WV loading arrangements. The purposes of this study were to a) examine the effects of different external load arrangements on vertical jump height and lower-extremity biomechanics during a countermovement jump and b) understand the effects on men versus women. A scaled musculoskeletal gait model in OpenSim was used with sagittal plane inverse kinematics procedures for 24 participants (75.71 ± 18.88 Kg; 1.71 ± 0.09 m) equally divided between men and women performing jump-landing in four weighted vest loading conditions (back-loaded, front-loaded, split-loaded, unloaded). Mixed-model factorial analyses of variance (α=0.05) and effect sizes (ES) were used to identify and quantify differences between sexes and loading conditions. Regardless of loading conditions, men showed greater jump height (p<0.001, ES=2.22) and greater hip (p<0.001, ES=1.59), and knee (p=0.026, ES=0.90) moments. No significant difference in the hip (p=0.478, ES=0.30) or knee (p=0.580, ES=0.23) angular displacement was observed between men and women. Without considering sex, the unloaded condition showed greater jump height (p<0.001, ES=0.4), hip displacement (p=0.006, ES=0.34), and hip (p=0.019, ES=0.36), and knee (p=0.004, ES=0.48) moments when compared to the back-loaded condition. Jump height (p=0.04, ES=0.1) and hip moments (p=0.028, ES=0.36) were also greater for the split-loaded compared to the back-loaded condition. Both the unloaded and split-loaded conditions showed greater jump height (p<0.001, ES=0.4; p<0.001, ES=0.3) and hip moments (p<0.001, ES=0.55; p=0.003, ES=0.35) compared with the front-loaded condition. A significantly greater magnitude of the hip displacement was detected for the split loaded condition compared to the front-loaded condition (p<0.001, ES=0.19). These results indicate that different external loading arrangements significantly affect the biomechanical performance output and differences in the load accommodation strategies between men and women during the period between the weighting and propulsion phases of jumping.
Significantly enhanced critical current density in Ag-sheathed (Bi,Pb)2Sr2Ca2Cu3Ox composite conductors prepared by overpressure processing in final heat treatment

Overpressure (OP) processing otherwise fully treated, commercial Ag-sheathed multifilament (Bi,Pb)2Sr2Ca2Cu3Ox (2223) composite conductors increased the critical current density Jc (0.1 T, 77 K) by 37% to 30.8 kA/cm2 and the self-field Jc (SF, 77 K) to 69.6 kA/cm2. These improvements were obtained on full-size high current conductors such that critical current Ic (0.1 T, 77 K) reached 80.6 A and Ic (SF, 77 K) 181.7 A, even though there was a very strong self-field suppression of Ic. Estimated values for the non-self-field-limited Ic and Jc (0 T, 77 K) reached 235 A and 90 kA/cm2. Scanning electron microscopy and superconducting quantum inference device measurement revealed that OP processing effectively suppressed cracks, porosity, and the volume fraction of the Bi2Sr2Ca1Cu2Oy (2212) phase, which are all major current-limiting mechanisms in present 2223 conductors.
Noradrenaline and Movement Initiation Disorders in Parkinson’s Disease: A Pharmacological Functional MRI Study with Clonidine

Slowness of movement initiation is a cardinal motor feature of Parkinson’s disease (PD) and is not fully reverted by current dopaminergic treatments. This trouble could be due to the dysfunction of executive processes and, in particular, of inhibitory control of response initiation, a function possibly associated with the noradrenergic (NA) system. The implication of NA in the network supporting proactive inhibition remains to be elucidated using pharmacological protocols. For that purpose, we administered 150 μg of clonidine to 15 healthy subjects and 12 parkinsonian patients in a double-blind, randomized, placebo-controlled design. Proactive inhibition was assessed by means of a Go/noGo task, while pre-stimulus brain activity was measured by event-related functional MRI. Acute reduction in noradrenergic transmission induced by clonidine enhanced difficulties initiating movements reflected by an increase in omission errors and modulated the activity of the anterior node of the proactive inhibitory network (dorsomedial prefrontal and anterior cingulate cortices) in PD patients. We conclude that NA contributes to movement initiation by acting on proactive inhibitory control via the α2-adrenoceptor. We suggest that targeting noradrenergic dysfunction may represent a new treatment approach in some of the movement initiation disorders seen in Parkinson’s disease.
The effect of digital transformation on employment in Egypt: An applied study using ARDL model

The current study aims to measure the effect of digital transformation on employment in Egypt, in addition to developing some proposed measures to sustain the digital vision in the New Republic to reduce unemployment. So, this study tries to contribute to this discussion, which attracted the attention of the two researchers to the problem of this research. EViews 12 Software was used as this study depended on the autoregressive distributed lag methodology, which gives the best results in the long run, and is considered one of the most suitable models with the sample size used in this research. The sample size consisted of 18 observations extended from 2003 to 2020. As well as, the Vector Auto‐Regression model was used to test the relationship between variables in order to test hypotheses as it is characterized by dealing with the variables entering the system as correlated internal variables. The study results showed that the unemployment variable did not have a causal effect on the independent variables, while the education variable and the Gross domestic product variable had a causal and positive effect on the independent variables that express the transformation digital where the significance value was less than 5%.
Design of Ultra Low Power 7.2 GHz True Single Phase Clock CMOS 2/3 Prescaler244 µW

: In this paper a high operating frequency and power efficient TSPC prescaler layout is proposed and compared with the existing TSPC and E-TSPC prescalers on the basis of operating frequency and power consumption. The maximum operating frequency of the proposedTSPC prescaler is 7.2 GHz which is 10% higher than other TSPC based prescalers and 7% than E-TSPC based prescalers with average power consumption of 307 µW at 1.8 V supply voltage. This High Frequency is achieved by reducing propagation delay in PMOS and NMOS at different stages. The power consumption in divide by 2 and divide by 3 mode is 3% to 10% better than all other prescalers and four times better than those prescalers which can be operated higher than 6.5 GHz frequency.This prescaler consumes 348µW in divide by 2 mode and 244µW in divide by 3 mode.
Analysis and Verification of Leakage Inductance Calculation in DAB Converters Based on High-Frequency Toroidal Transformers under Different Design Scenarios

High-frequency transformers are becoming an essential component in the integration of power resources that rely on power electronic converters; their efficiency and performance are influenced by parasitic characteristics in the interface. In this article, the design of a high-frequency toroidal transformer has been explained in detail using the ANSYS Maxwell platform. Various parameters, such as leakage inductance, magnetic flux density, magnetic field strength and uniform magnetic flux line are analyzed using Finite element analysis. High-frequency transformers using a toroidal core with different winding configurations are examined and all parameters obtained through simulation are validated by an analytical approach. Analysis of each design is based on its leakage inductances, which will aid in the appropriate selection of transformers as a function of their operating frequency. This analysis is expected to guide designers to optimize the high-frequency transformer parameters based on practical applications. The optimized parameters are then applied for a dual active bridge (DAB) converter within MATLAB/Simulink to verify the design process. A prototype has been built to validate the simulation and design procedure. The results obtained from both simulation and experiments are compared and show great correlation.
Evaluation Metrics for Overlapping Community Detection

Networks have provided a representation for a wide range of real systems, including communication flow, money transfer or biological systems, to mention just a few. Communities represent fundamental structures for understanding the organization of real-world networks. Uncovering coherent groups in these networks is the goal of community detection. A community is a mesoscopic structure with nodes heavily connected within their groups by comparison to the nodes in other groups. Communities might also overlap as they may share one or multiple nodes. Evaluating the results of a community detection algorithm is an equally important task. This paper introduces metrics for evaluating overlapping community detection. The idea of introducing new metrics comes from the lack of efficiency and adequacy of state-of-the-art metrics for overlapping communities. The new metrics are tested both on simulated data and standard datasets and are compared with existing metrics.
A Mechanism-Based Semisupervised Online pH Estimation Approach for a Leaching Process

Online pH estimation is essential in the operational optimization of a leaching process. However, delayed and low-rate artificial testing is adopted instead of online pH meter, due to the hostile production environment and heavy maintenance workload. This article proposes an online pH estimation approach based on a mechanism-based semisupervised long short-term memory (MSSLSTM) network. A kinetic reaction model and an accumulative acid-material ratio model incorporate the reaction mechanism in the machine learning framework. It is built to describe the reaction state qualitatively and quantitatively. The artificial testing result of the last sampling instant is held and imported into the network to realize semisupervised learning, which is consistent with the multirate condition in practice. The verification results demonstrate the accuracy and stability of the estimation model, which systematically learns from the reaction state features, low-rate artificial pH testing results, and high-rate online measured basic variables.
The 55 Cancri System: Fundamental Stellar Parameters, Habitable Zone Planet, and Super-Earth Diameter

The bright star 55 Cancri is known to host five planets, including a transiting super-Earth. We use the CHARA Array to directly determine the following of 55 Cnc's stellar astrophysical parameters: $R=0.943 \pm 0.010 R_{\odot}$, $T_{\rm EFF} = 5196 \pm 24$ K. Planet 55 Cnc f ($M \sin i = 0.155 M_{Jupiter}$) spends the majority of the duration of its elliptical orbit in the circumstellar habitable zone (0.67--1.32 AU) where, with moderate greenhouse heating, it could harbor liquid water. Our determination of 55 Cancri's stellar radius allows for a model-independent calculation of the physical diameter of the transiting super-Earth 55 Cnc e ($\simeq 2.1 R_{\earth}$), which, depending on the assumed literature value of planetary mass, implies a bulk density of 0.76 $\rho_{\earth}$ or 1.07 $\rho_{\earth}$.
Design of Efficient AI Accelerator Building Blocks in Quantum-Dot Cellular Automata (QCA)

Digital circuit design technologies based on Quantum-Dot Cellular Automata (QCA) have many advantages over CMOS, such as higher intrinsic switching speed up to Terahertz, lower power consumption, smaller circuit footprint, and higher throughput due to compatibility of the inherent signal propagation scheme with pipelining. Hence, QCA is a perfect candidate to provide a circuit design framework for applications such as Artificial Intelligence (AI) accelerators, where real-time energy-efficient performance needs to be delivered at low cost. A novel QCA design approach based on optimal mix of Majority and NAND-NOR-INVERTER (NNI) gates with USE (Universal, Scalable, Efficient) clocking scheme, has been investigated in this work for latency and energy consumption improvements to fundamental building blocks in AI-accelerators, including multipliers, adders, accumulators and SRAMs. The common $4\times 4$ Vedic multiplier has been redesigned using the proposed approach, and simulated to yield 62.8% reduction in cell count, 82.2% reduction in area, and 71.2% reduction in latency. 83% reduction in cell count, 94.5% reduction in area, and 94.6% reduction in latency was simulated for the proposed 8-bit PIPO register. The proposed SRAM cell design is estimated to have similar improvement figures to those achieved by the sub-blocks, such as the D-Latch, which has been simulated to exhibit 44.4% reduction in cell count, 50% reduction in both area and latency, and 73% reduction in energy dissipation. The contributions from this work can be directly applied to low cost, high throughput, energy efficient AI-accelerators that can potentially deliver orders of magnitude better energy-delay characteristics than their CMOS counterparts, and significantly better energy-delay characteristics than state-of-the-art QCA implementations.