Miracle-dz/UniFAR-FTUs huggingface dataset:
A high-fidelity approach towards simulation of pool boiling

A novel numerical approach is developed to simulate the multiscale problem of pool-boiling phase change. The particular focus is to develop a simulation technique that is capable of predicting the heat transfer and hydrodynamic characteristics of nucleate boiling and the transition to critical heat flux on surfaces of arbitrary shape and roughness distribution addressing a critical need to design enhanced boiling heat transfer surfaces. The macro-scale of the phase change and bubble dynamics is addressed through employing off-the-shelf Computational Fluid Dynamics (CFD) methods for interface tracking and interphase mass and energy transfer. The micro-scale of the microlayer, which forms at early stage of bubble nucleation near the wall, is resolved through asymptotic approximation of the thin-film theory which provides a closed-form solution for the distribution of the micro-layer and its influence on the evaporation process. In addition, the sub-grid surface roughness is represented stochastically throug.
Decentralized Secondary Frequency Control of Autonomous Microgrids via Adaptive Robust -Gain Performance

This paper presents an innovative passivity-based <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-gain performance <inline-formula> <tex-math notation="LaTeX">$(L_{2}$ </tex-math></inline-formula>-GP) adaptive robust control method for the design of primary and secondary frequency control in the low-inertia microgrid with lines and the inverter-interfaced generation. First, by exploiting and analyzing the internal structural properties, the Port-controlled Kuramoto-Hamiltonian model with dissipation for the microgrid system is derived. Combining the dynamics of Kuramoto oscillators with energy viewpoints and following the physical features of dynamic energy coordination in the microgrid, an adaptive robust <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-GP decentralized secondary control is developed to guarantee frequency restoration and active power sharing. An adaptive mechanism is adopted to estimate the electrical parameters so that the parameter randomness can be overcome and fast transient response, high performance, and robust stability can be achieved. Via local measurements, the decentralized framework accordingly obviates the need of any communication links for information broadcast or exchange. Finally, the comprehensive case studies are presented to validate satisfactory performance under rapid randomness and flexibility during plug-and-play operations.
3D Shape Partition via Multi-class Spectral Graph Clustering ⋆

This paper proposes a new 3D shape partition algorithm. The main idea of our method is to represent 3D shape as a weighted graph whose nodes denote each face in the shape and the weights on the edges encode the similarities between faces. We then extract a set of geometric features from shape to build an affinity matrix for weighted graph and use multi-class spectral clustering method to find an optimal partition. We compute the generalized eigenvalue problems of the affinity matrix to group the most similar faces into the same semantic part and apply the graph cut method to refine the boundaries between the partition parts. Finally we evaluate our approach on the Princeton shape benchmark. The experiment shows that our method can successfully segment different classes of 3D shapes and can achieve more meaningful segmentation results compared to other leading single shape partition algorithms.
Structural Design of the Substructure of a 10 MW Floating Offshore Wind Turbine System Using Dominant Load Parameters

Fully coupled integrated load analyses (ILAs) to evaluate not only the load response but also the structural integrity are required to design a floating offshore wind turbine, since there has been no firmly established approach for obtaining the structural responses of a FOWT substructure in the time domain. This study aimed to explore if a direct strength analysis (DSA) technique that has been widely used for ships and offshore structures can adequately evaluate the FOWT substructure. In this study, acceleration and nacelle thrust were used for the dominant load parameters for DSA. The turbine thrust corresponding to the 50-year return period was taken from the literature. The acceleration response amplitude operator (RAO) was obtained through frequency response hydrodynamic analysis. The short-term sea states defined by the wave scatter diagram (WSD) of the expected installation area was represented by the JONSWAP wave spectrum. To account for the multi-directionality of the short-crested waves, the 0th order moments of the wave spectrum were corrected. The probabilities of each short-term sea state and each wave incidence angle were applied to derive the long-term acceleration for each return period. DSA cases were generated by combining the long-term acceleration and nacelle thrust to maximize the forces in the surge, sway, and heave directions. Linear spring elements were placed under the three outer columns of the substructure to provide soft constraints for hive, roll, and pitch motions. Nonlinear spring elements with initial tension were placed on the three fairlead chain stoppers (FCSs) to simulate the station-keeping ability of the mooring lines; they provided initial tension in the slacked position and an increased tension in the taut position. The structural strength evaluation of the coarse mesh finite element model with an element size same as the stiffener spacing showed that high stresses exceeding the permissible stresses occurred in the unstable members of the substructure. The high stress areas were re-evaluated using a fine mesh finite element model with an element size of 50 mm × 50 mm. The scope of structural reinforcement was identified from the fine mesh analyses. It was found that the DSA can be properly utilized for the substructure strength assessment of a FOWT.
Visual Navigation of UAVs in Indoor Corridor Environments using Deep Learning

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for various applications, including inspection, surveillance, delivery, and mapping. However, one of the significant challenges in enabling UAVs to perform these tasks is the ability to navigate in indoor environments. Visual navigation, which uses visual information from cameras and other sensors to localize and navigate the UAV, has received considerable attention in recent years. In this paper, we propose a new approach for visual navigation of UAVs in indoor corridor environments using a monocular camera. The approach relies on a novel convolutional neural network (CNN) called Res-Dense-Net, which is based on the ResNet and DenseNet networks. Res-Dense-Net analyzes the images captured by the UAV’s camera and predicts the position and orientation of the UAV relative to the environment. To demonstrate the effectiveness of the proposed approach, experiments were conducted on the NitrUAVCorridorV1 dataset. The proposed approach achieves high accuracy in estimating the position and orientation of the UAV, even in challenging environments with limited visual cues and provides high real-time performance based on visual data from a monocular camera, which can significantly enhance the capabilities of UAVs for various applications.
Examining Charles Darwin’s (Mis)representation within science and history curricula

ABSTRACT Teaching and learning are grounded on age-appropriate, credible curricular resources, which can be formal (i.e. textbooks) and informal (i.e. trade-books). As Charles Darwin’s ideas galvanized biology and racism, this study examined his historical representation within trade-books (e.g. biography, narrative non-fiction, expository, etc.), textbooks (student editions, teacher editions, etc.), and curricular supplements (teacher-facing assessments and lessons; student-facing tests and tasks) published in United States. Through content analysis, I contrasted historians’ understandings of Darwin with history-based trade-books’ (n = 111) and biology-oriented texts’ (n = 132) depictions of Darwin. Misrepresentations abounded. History-based books concealed Darwin’s colonialist past and disregarded—or repeated without qualification and context—the racist ideas within his writing. Biology-based texts largely omitted problematic aspects of Darwin’s past. These 20th- and 21st-century history trade-books and science texts mirrored the patterns of 19th-century American social studies textbooks’ Lost Cause logic and 20th-century science American textbooks’ anti-evolution casuistry. Reviewed texts obscured the racist ideas within Darwin’s words, actions, and inactions, through both omission and commission. Concerns are raised about who determines how historical and scientific content are included, detailed, and omitted within curricular resources published in different countries.
Physical Insights Into the ESD Behavior of Field Plated UHV LDMOS Devices

Field plates are commonly used to extend the breakdown voltage to achieve high voltage devices. But, the role of field plates in the ESD behaviour of these devices is still not addressed. This work provides physical insights into the ESD behaviour of field plated Ultra High Voltage Laterally Diffused Metal Oxide Semiconductor (UHV LDMOS) devices. UHV devices are simulated and compared using 3D TCAD simulations. For the first time, the role of field plates in ESD design aspects is investigated. This paper highlights the importance of each field plate (Gate Field plate (GFp), Source Field plate (SFp) and Drain Field plate (DFp)), exploring their individual roles. It was found that GFp and SFp play a significant role in improving the trigger voltage and holding voltage, while DFp is the sole reason for enhancing the failure threshold. Finally, a 3D device failure study is performed to support the theory drawn from 2D simulations.
Diflunisal Attenuates Virulence Factor Gene Regulation and Phenotypes in Staphylococcus aureus

Virulence factor expression is integral to pathogenicity of Staphylococcus aureus. We previously demonstrated that aspirin, through its major metabolite, salicylic acid (SAL), modulates S. aureus virulence phenotypes in vitro and in vivo. We compared salicylate metabolites and a structural analogue for their ability to modulate S. aureus virulence factor expression and phenotypes: (i) acetylsalicylic acid (ASA, aspirin); (ii) ASA metabolites, salicylic acid (SAL), gentisic acid (GTA) and salicyluric acid (SUA); or (iii) diflunisal (DIF), a SAL structural analogue. None of these compounds altered the growth rate of any strain tested. ASA and its metabolites SAL, GTA and SUA moderately impaired hemolysis and proteolysis phenotypes in multiple S. aureus strain backgrounds and their respective deletion mutants. Only DIF significantly inhibited these virulence phenotypes in all strains. The kinetic profiles of ASA, SAL or DIF on expression of hla (alpha hemolysin), sspA (V8 protease) and their regulators (sigB, sarA, agr (RNAIII)) were assessed in two prototypic strain backgrounds: SH1000 (methicillin-sensitive S. aureus; MSSA) and LAC-USA300 (methicillin-resistant S. aureus; MRSA). DIF induced sigB expression which is coincident with the significant inhibition of RNAIII expression in both strains and precedes significant reductions in hla and sspA expression. The inhibited expression of these genes within 2 h resulted in the durable suppression of hemolysis and proteolysis phenotypes. These results indicate that DIF modulates the expression of key virulence factors in S. aureus via a coordinated impact on their relevant regulons and target effector genes. This strategy may hold opportunities to develop novel antivirulence strategies to address the ongoing challenge of antibiotic-resistant S. aureus.
Identification of Inability States of Rotating Machinery Subsystems Using Industrial IoT and Convolutional Neural Network – Initial Research

Rotating parts can be found in almost all operational equipment in the industry and are of great importance for proper operation. However, reliability theory explains that every industrial system can change its state when failure happens. Predictive maintenance as one of the latest maintenance strategy emerged from the Maintenance 4.0 concept. Nowadays, this concept can include Industrial Internet of Things (IIoT) devices to connect industrial assets thus enable data collection and analysis that can help make better decisions about maintenance activity. Robust data acquisition system is a prerequisite for any modern predictive maintenance task as it provides necessary data for further analysis and health assessment of the industry asset. Fault diagnosis is an important task in the maintenance of industrial rotating subsystems, considering that early state change diagnosis and fault identification can prevent system failure. Vibration analysis in theory and practice is considered a correct technique for early detection of state changes and failure diagnostics of rotating subsystems. The identified technical state should be considered in a context of the ability and different inability states. Therefore, early different inability states identification is the next step in the rotary machinery diagnostics procedure. Most of the existing techniques for fault diagnosis of rotating subsystems that use vibrations involve the step of extracting features from the raw signal. Considering that the features that describe the behavior of the rotary subsystem can differ significantly depending on the type of equipment, such an approach usually requires an expert in the field of signal processing and rotary subsystems who can define the necessary features. Recently, the emergence of machine deep learning and its application in maintenance promises to provide highly efficient fault diagnostics while simultaneously reducing the need for expert knowledge and human labour. This paper presents authors aim to use self-developed IIoT system built as an IIoT accelerometer as the edge device, web API and database with convolutional neural network as deep learning-based data-driven fault diagnosis to detect and identify different inability states of rotating subsystems. Large dataset for two different rotational speed is collected using IIOT system and multiple convolutional neural network models are trained and tested to examine possibility of using IIOT for inability state prediction.
Time-of-Arrival Estimation for Integrated Satellite Navigation and Communication Signals

Global navigation satellite systems (GNSSs) are used in numerous fields, but their vulnerability is a global problem that has yet to be solved. A promising way to effectively address this problem is by integrating navigation into emerging dense nongeosynchronous orbit (NGSO) megaconstellations. To maximize the downlink efficiency for users, an integrated satellite navigation and communication (ISNAC) framework is proposed in this work, in which a necessary number of packetized bursty downlink satellite communication signals are used directly for navigation purposes. Then, the user terminal estimates the time of arrival (TOA) of these ISNAC signals regardless of their coding and modulation type. To this end, a TOA estimation algorithm is proposed. Specifically, the user terminal first constructs a template signal with demodulated data bits to replicate the transmitted ISNAC signal and then calculates the cross-correlation function (CCF) of the template and the received signal for TOA estimation, based on which the TOA is finally estimated. The performance bound of the proposed TOA estimation algorithm is analyzed. Simulations are conducted to corroborate the mathematical analysis and The results show that the proposed algorithm outperforms other TOA estimators for comparsion by approaching the Cramer-Rao lower bound (CRLB) with a much lower computational complexity.
Prediction of pH and total soluble solids content of mango using biresponse multipredictor local polynomial nonparametric regression

: Mango's internal quality can be determined based on its acidity and sweetness in the form of pH and total soluble solids (TSS) content. Research on fruit internal quality prediction based on near-infrared spectroscopy generally uses parametric regression modeling such as linear and partial least square regression. The study proposed biresponse multipredictor local polynomial nonparametric regression to determine mango's internal quality. The study aims to apply the theory of biresponse multipredictor local polynomial nonparametric regression for predicting the mango's internal quality in the form of pH and TSS value. We created R code for estimating nonparametric
Additions to Bambusicolous Fungi of Savoryellaceae from Southwest China

Asexual and sexual morphs of saprobic bambusicolous fungi were collected from freshwater and terrestrial habitats in Sichuan Province, China. Taxonomic identification of these fungi was carried out on the basis of morphological comparison, culture characteristics, and molecular phylogeny. Multi-gene phylogeny based on combined SSU, ITS, LSU, rpb2, and tef1α sequence data was performed to determine their phylogenetic placement, and the result showed that these fungi belong to Savoryellaceae. Morphologically, four asexual morphs are similar to Canalisporium and Dematiosporium, while a sexual morph well-fits to Savoryella. Three new species, Canalisporium sichuanense, Dematiosporium bambusicola, and Savoryella bambusicola are identified and described. Two new records, C. dehongense and D. aquaticum, were recovered from the bamboo hosts in terrestrial and freshwater habitats, respectively. In addition, the nomenclatural confusion of C. dehongense and C. thailandense is discussed.
Application Of Machine Learning Algorithms In Predicting The Heart Disease In Patients

Healthcare services save the life of human beings by making timely effective decisions. The use of data mining tools is crucial for decision making, forecasting, and disease prediction. In this study, data mining algorithms are applied to predict heart disease. The dataset contains 14 attributes such as age, gender, blood pressure, blood fat, etc. These parameters are analyzed to predict the probability of patients prone to heart disease in future. Initially, the relationship between the parameters is analyzed. Following which Naïve Bayes, decision trees and Naïve Bayes with k-means clustering are applied over it for classification and prediction. These algorithms were employed to train the dataset and create a binary classification. The proposed system shows a better prediction of heart disease. The performance measures of the system are measured, and the obtained results illustrate the system can forecast the probability of developing the heart diseases.
Une explication du théorème de Ciesielski-Taylor

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Correlations between ultrasound, tomographic, and intraoperative measurements of the great saphenous vein used as an arterial graft

Abstract Background The great saphenous vein is the major superficial vein of the lower limb, and also the most often used as arterial graft material for lower limb revascularization. Prior knowledge of the quality of the vein can guide choice of therapeutic strategy, avoiding surgery that is doomed to failure. Discrepancies between intraoperative findings of the quality of the great saphenous vein and imaging tests are also frequently observed. Objectives To evaluate the diameter of the great saphenous vein using two imaging methods (Duplex Ultrasound and Computed Tomography) and the gold-standard (intraoperative direct measurement of the vein), comparing the results. Methods Prospective, observational study of data obtained during routine medical procedures performed by the Vascular Surgery team. Results 41 patients were evaluated, with a 12-month follow-up. 27 (65.85%) were male and mean age was 65.37 years. 19 (46.34%) patients had femoropopliteal grafts and 22 (53.66%) had distal grafts. Preoperative saphenous vein internal diameters measured with the patient supine were on average 16.4% smaller on CT and 33.8% smaller on US than the external diameters measured after intraoperative hydrostatic dilatation. There were no statistical differences in measurements when sex, weight, and height were considered. Conclusions Saphenous vein diameters were underestimated by preoperative US and CT scans when compared to intraoperative measurements. Thus, in patients undergoing graft planning for revascularization, the choice of conduit should take this data into consideration, so that use of the saphenous vein is not ruled out unnecessarily during planning.
A Survey of Physical Layer-Aided UAV Security

Low-cost Unmanned Aerial Vehicles (UAVs) and their swarms are increasingly being used and have become an integral part of various industries and aspects of daily life. They are being used in industries such as agriculture, delivery services, construction and to achieve critical tasks such as disaster relief, search and rescue operations, etc. As Unmanned Aerial Vehicles (UAVs) become increasingly pervasive and essential, they are becoming both a target for attack and the very weapon on which such attacks are carried out. For example, UAVs can be hacked and used to perform cyber attacks on targets such as power grids, and communication and military networks. To protect UAVs against cyber-attacks, different techniques both traditional (cryptography-based) and non-traditional (sound-based, video-based, physical layer (PHY) based) have been proposed in the literature. Traditional crypto-based authentication solutions usually involve high computational overhead and require the challenging task of managing secret keys. Recently introduced PHY-based solutions that exploit the inherent randomness of the wireless channels and non-ideal hardware characteristics have witnessed an increased interest from the research community resulting in many publications. Nevertheless, we noticed a lack of an up-to-date survey that keeps track of all the recent research developments in this new area of security research. Thus, the goal of this paper is to bridge the gap in the literature by providing a timely and comprehensive discussion on different techniques and methods proposed in the literature, summarizing their working principles, and highlighting the various strengths and weaknesses. We also discuss different challenges that physical layer security faces and potential future opportunities.
Artificial Intelligence Approaches for Early Detection and Diagnosis of Alzheimer's Disease: A Review

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease common in the elderly. The application of artificial intelligence technology to the early diagnosis of AD can not only improve the accuracy of prediction compared with traditional methods, but also save the complicated manual feature extraction of traditional methods and speed up the diagnosis. This paper reviews various applications of artificial intelligence algorithms in AD diagnosis, including machine learning, convolutional neural network, graph convolutional neural network, cyclic neural network and other mainstream deep learning technologies. The advantages and disadvantages of each approach are discussed, and finally, we discuss limitations and future prospects.