Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of CICC for KSTAR TF coil system

The KSTAR (Korea Superconducting Tokamak Advanced Research) superconducting magnet system consists of 16 TFs (Toroidal Field) and 14 PFs (Poloidal Field) coils. Internally-cooled cabled superconductors will be used for the magnet system. The magnet systems adopt a superconducting CICC (Cable-In-Conduit Conductor) type conductor. The KSTAR TF CICC uses Nb/sub 3/Sn superconducting cable with Incoloy 908 conduit. For the fabrication of TF 1/spl sim/3 CICC, cables have been fabricated and the cable has a length of 640 m and a diameter of 22.3 mm. A continuous CICC jacketing system is developed for the CICC jacketing and the jacketing system uses the tube-mill process, which consists of forming, welding, sizing and squaring procedures. The cabling and the jacketing process is described. The welding condition of the Incoloy 908 and design specification of CICCs are also discussed. The fabrication results including the geometrical specification and the void fraction will be discussed.
On the Exact Moments of Asymptotic Distributions in an Unstable AR(1) with Dependent Errors

In this paper, the authors derive the exact moments of asymptotic distributions of the OLS estimate and t-statistic in an unstable AR(l) with dependent errors. The authors also study the relationship between the number of lagged dependent variables required for matching the distribution moments in the 'approximately i.i. d. erors' model with those occurring in the 'purely i.i. d. ' model. Copyright 1998 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
Learning skills adjacency representations for optimized reskilling recommendations

Today’s fast changing workplace necessitates constant reskilling of the workforce at both the corporate and national level. Current approaches to reskilling depend on manual logic, which can be time-consuming and expensive due to their dependence on manual labour. In this paper, we propose a scalable machine-learning driven alternative by introducing a method to make reskilling recommendations using word embeddings of skill keywords trained on a corpus of historical job listings and resumes. We achieve this by training dense vector embeddings to represent skill keywords using Word2Vec and fine-tuned BERT models, allowing us to make comparisons between skills. Given an individual’s current skills, this model is leveraged to identify which skills to prioritize for their development based on their target role and to recommend reskilling plans based on the identified skill gap. The proposed framework has the potential to aid both public and private organizations to better direct their educational resources to individuals.
Data Virtual Machines: Simplifying Data Sharing, Exploration & Querying in Big Data Environments

Today’s analytics environments are characterized by a high degree of heterogeneity in terms of data systems, formats and types of analysis. Many occasions call for rapid, ad hoc, on demand construction of a data model that represents (parts of) the data infrastructure of an organization, including ML tasks. This data model is given to data scientists to play with (express reports, build ML models, explore, etc.) We present a novel graph-based conceptual model, the Data Virtual Machine (DVM) representing data (persistent, transient, derived) of an organization. A DVM can be built quickly and agilely, offering schema flexibility. It is amenable to visual interfaces for schema and query management. Dataframing, a frequent querying/preprocessing task in analytics applications, is usually carried out by experienced data engineers employing SQL (in the presence of a relational data warehouse) or Python/R: a procedural approach with all the known drawbacks. Dataframes over DVMs are expressed declaratively - and visually, via a simple and intuitive tool. This way, non-IT experts can be involved in dataframing. In addition, query evaluation takes place within an algebraic framework with all the known benefits. I.e. a DVM enables the delegation of data engineering tasks to simpler users. We have seen analogous cases in the past, e.g. with the introduction of SQL. Finally, a DVM offers a formalism that facilitates data sharing, data portability and a single view of any entity – because a DVM’s node is an attribute and an entity at the same time. In this respect, DVMs can excellently serve as a data virtualization technique, an emerging trend in the industry. We argue that DVMs can have a significant practical impact in today’s big data environments.
Design of Audio based Accident and Crime Detection using Simple Architecture of Neural Network

: The accident and crime happened on the road still increasing nowadays. Those two events were considered as emergency event that need a quick response. In this research, a method to detect accident and crime were proposed. The proposed method uses audio data as input and extracting the Mel spectrogram as the feature, which later be fed to our simple neural network architectures. We classify our dataset into engine_idling, car_crash, and gun_shot classes to represent normal, accident, and crime condition on the road. Our simple CNN architecture obtains accuracy of 95.31% and 93.75% with 200ms and 1000ms segment duration respectively, and our simple RNN architecture obtains 86.67% and 58.67% by using 200ms and 1000ms segment duration respectively. We can conclude that the best simple architecture was performed by CNN architecture with 200ms segment duration.
An Efficient and Secured Energy Management System for Automated Guided Vehicles

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems.
Synchronous Reluctance Motors Torque Ripples Reduction using Feedback Cascaded P-I-I Controller

Synchronous Reluctance Motors (SynRMs) -or Reluctance Synchronous Motors (RSMs)- have recently become an important component of the electric vehicles' (EVs) industry when it used as a variable speed AC drives, due to its excellent features of high ratio of power to weight and torque to current, high efficiency and ruggedness. One of the major SynRMs' problems is the Torque Ripples (TRs). In this paper and to mitigate or minimize these TRs, a feedback cascaded p-I-I controller is used and worked as an Active Ripple Filter. Simulation results showed its effectiveness in TRs minimization.
Skeletal Microstructure in Caribbean Hispanic Women

Hispanic individuals are underrepresented in skeletal research. Bone mineral density (BMD) and fracture data are conflicting. We investigated skeletal health in elderly Caribbean Hispanic (HW), non‐Hispanic white (NHW), and non‐Hispanic black (NHB) women in a population‐based study in New York City. We utilized high‐resolution peripheral quantitative CT (HRpQCT), dual‐energy X‐ray absorptiometry (DXA), and finite element analysis (FEA). Of 442, 48.4% were HW, 21.3% NHW, and 30.3% NHB. Adjusted analyses are shown. Compared to NHW, HW had 8.5% (p < 0.01) lower spine areal BMD (aBMD) and 5.1% lower trabecular bone score (TBS). The frequency of morphometric vertebral fractures did not differ between HW and NHW. By HRpQCT, HW had 2.9% higher cortical (Ct) volumetric BMD (vBMD), 7.9% greater Ct area (Ct.Ar) and 9.4% greater Ct thickness (Ct.Th) at the radius compared to NHW. Results were similar at the tibia but trabecular microstructure tended to be worse. Ultimately, failure load (FL) did not differ between HW and NHW at either site. aBMD was 3.8% to 11.1% lower at the spine, femoral neck, and radius in HW compared to NHB (all p < 0.001) and vertebral fractures were twice as common. Compared to NHB, HW had 7.7% to 10.3% lower Ct.Ar at both the radius and tibia as well as 8.4% lower total vBMD, 6.3% lower trabecular number, and 10.3% lower Ct.Th at the tibia associated with 18.2% and 12.5% lower FL at both sites, respectively. In conclusion, HW had lower spine aBMD and TBS versus NHW women, whereas microstructural differences at the radius and tibia were small and not associated with differences in FL. In contrast, HW had lower aBMD, as well as deteriorated radial and tibial microstructure associated with worse FL compared to NHB women. Our results provide insight into racial/ethnic differences in skeletal health, adding to data that may be used to improve osteoporosis screening and treatment in HW. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Renormalization-group treatment of the critical dynamics of the binary-fluid and gas-liquid transitions

A simplified model for the dynamics of the gas-liquid and binary-fluid transitions is studied with the renormalization group. An exact scaling law is found, connecting the exponents for the diverging transport coefficients to static exponents for arbitrary value of the dimensionality $dl4$. This scaling law had been anticipated by Kadanoff and Swift, and Kawasaki, on the basis of approximate mode-coupling arguments. The "Kawasaki-Stokes" relation between the diffusivity, the shear viscosity, and the correlation length is shown to hold exactly, but with a universal amplitude which differs slightly from its mode-coupling value. Critical exponents for the transport coefficients are evaluated to second order in $\ensuremath{\epsilon}=4\ensuremath{-}d$, and lead to the prediction of a weak divergence of the shear viscosity [$\ensuremath{\eta}(T)\ensuremath{\propto}{(T\ensuremath{-}{T}_{c})}^{\ensuremath{-}0.04}$] in three dimensions. The weakness of this divergence reflects the existence of a small parameter in the theory, which explains the excellent agreement between Kawasaki's evaluation of the Rayleigh linewidth and experiment. Corrections to the simple Kawasaki theory carried out by various authors are reviewed, and a number of suggestions are made for refining these calculations. The simple model studied in this paper is shown to have the same dynamic properties as real fluids, sufficiently close to the critical point.
Methods for optimizing and assessing diagnostic capability, demonstrated for collective Thomson scattering (invited)

Commonly diagnostic systems are sensitive to a wide range of parameters, only a subset of which are parameters of interest. Optimal analysis and assessment of diagnostic capability must take account of the sensitivity to the remaining nuisance parameters which may not be accurately known. New least squares type methods of inference in the presence of uncertain nuisance parameters have been developed, along with methods for assessing diagnostic capability and identifying critical nuisance parameter uncertainties. These methods are of a general nature and suitable for complex and computationally demanding forward models. They are demonstrated by application to collective Thomson scattering for fast ion diagnosis in fusion plasmas.
Lutein Isomers: Preparation, Separation, Structure Elucidation, and Occurrence in 20 Medicinal Plants

Lutein and its cis-isomers occur in a lot of plants, including a variety of flowers. In this study, lutein isomers were produced via iodine-catalyzed isomerization, and four cis-isomers (9Z-, 9′Z-, 13Z-, and 13Z′) were isolated by means of column chromatography and semipreparative HPLC. The structures of the 9′Z- and 13′Z-isomers were elucidated via NMR measurements. These compounds were used as standards for the HPLC-DAD-MS determination of the carotenoid composition of the flowers of 20 plant species, in which lutein and its geometrical isomers are the main components. The flowers showed great variation in their cis- and trans-lutein content, and also in the presence or absence of other carotenoids, such as violaxanthin, neoxanthin, β-cryptoxanthin, and β-carotene. Some of the investigated flowers were found to be rich sources of lutein without zeaxanthin.
Deduplicating Large Volumes of Data from Natural and Legal Entities in the Governmental Field

Record Deduplication (RD) aims to identify instances that represent the same real-world entity in data repositories. In the government environment, the RD process facilitates the identification of irregularities and reduces the consumption of computing resources in data integration tasks. In this context, we propose a scalable, effective and efficient platform, called DedupeGov, for integrating large data repositories (i.e., with large volumes of data, in the order of millions of records) to unify duplicate entities from multiple and different sources. Our experimental results indicate a 21.8% of reduction in the number of records of the original repository with 99% of precision and 95% of recall when identifying duplicate records. In addition, our platform was capable of building more complete records, eliminating at least 32% of records with null attributes. Furthermore, our solution is very efficient and scalable for large volumes of data, deduplicating a repository of almost 400 million records in around one hour, besides being easy to generalize to different types of entity.
Gradual Domain Adaptation with Pseudo-Label Denoising for SAR Target Recognition When Using Only Synthetic Data for Training

Because of the high cost of data acquisition in synthetic aperture radar (SAR) target recognition, the application of synthetic (simulated) SAR data is becoming increasingly popular. Our study explores the problems encountered when training fully on synthetic data and testing on measured (real) data, and the distribution gap between synthetic and measured SAR data affects recognition performance under the circumstances. We propose a gradual domain adaptation recognition framework with pseudo-label denoising to solve this problem. As a warm-up, the feature alignment classification network is trained to learn the domain-invariant feature representation and obtain a relatively satisfactory recognition result. Then, we utilize the self-training method for further improvement. Some pseudo-labeled data are selected to fine-tune the network, narrowing the distribution difference between the training data and test data for each category. However, the pseudo-labels are inevitably noisy, and the wrong ones may deteriorate the classifier’s performance during fine-tuning iterations. Thus, we conduct pseudo-label denoising to eliminate some noisy pseudo-labels and improve the trained classifier’s robustness. We perform pseudo-label denoising based on the image similarity to keep the label consistent between the image and feature domains. We conduct extensive experiments on the newly published SAMPLE dataset, and we design two training scenarios to verify the proposed framework. For Training Scenario I, the framework matches the result of neural architecture searching and achieves 96.46% average accuracy. For Training Scenario II, the framework outperforms the results of other existing methods and achieves 97.36% average accuracy. These results illustrate the superiority of our framework, which can reach state-of-the-art recognition levels with appropriate stability.
Robust absolute stability criteria for uncertain Lur'e systems of neutral type

This paper is concerned with robust absolute stability of uncertain Lur'e systems of neutral type. Some delay‐dependent stability criteria are obtained and formulated in the form of linear matrix inequalities. The criteria cover some existing results as their special cases. Neither model transformation nor bounding technique for cross terms is involved through derivation of the stability criteria. Numerical examples show the effectiveness of the criteria. Copyright © 2007 John Wiley & Sons, Ltd.
Prospect-Net: Top-K Retrieval Problem Using Prospect Theory

In e-Commerce Industry, customers’ purchase decision of an item are usually influenced by the reference price of that item, which is implied within the context of the items (e.g. prices of an item set from search/recommendation) or external environments (e.g. prices from another e-Commerce platform). Despite of the prevalence and influence of the reference price on customers’ behavior, existing works in Information Retrieval domain do not exploit the value of the reference price in ranking problems. In this paper, we propose a list-wise ranking model named "Prospect-Net" by incorporating the prospect theory, which is the theoretical foundation for framing the reference price. We consider the Top-K retrieval task under a product recommendation setting, and demonstrate the effectiveness of Prospect-Net to capture various forms of reference price under different scenarios. Polynomial solutions are proposed to solve the Top-K retrieval problem for some of the cases where the reference price is dependent on the recommended set of items to the user. Both offline e valuation and online experiments are performed on a real-world industrial dataset with significant performance improvement.
DRP1 Inhibition Enhances Venetoclax-Induced Mitochondrial Apoptosis in TP53-Mutated Acute Myeloid Leukemia Cells through BAX/BAK Activation

Simple Summary Poor response to venetoclax in TP53-mutated (TP53mut) acute myeloid leukemia (AML) is a clinical challenge. To develop more effective therapeutic strategies for this subset of AML with dismal prognosis, the molecular mechanism involved in the development of venetoclax resistance needs to be elucidated. To the best of our knowledge, this study is the first to demonstrate that DRP1, a key player in the regulation of mitochondrial fission, is functionally involved in the development of venetoclax resistance in TP53mut AML. Inhibition of DRP1 with Mdivi-1 led to enhanced mitochondria-mediated cell death with the upregulation of BAX/BAK in TP53mut leukemia cell lines and primary leukemic blasts obtained from patients with TP53mut AML. Anti-apoptotic molecules, such as MCL-1 and BCL-xL, were reduced after combination treatment with venetoclax and Mdivi-1. These findings suggest that the combination of venetoclax with pharmacological DRP1 inhibition needs to be clinically evaluated for TP53mut AML. Abstract Although TP53 mutations in acute myeloid leukemia (AML) are associated with poor response to venetoclax, the underlying resistance mechanism remains unclear. Herein, we investigated the functional role of dynamin-related protein 1 (DRP1) in venetoclax sensitivity in AML cells with respect to TP53 mutation status. Effects of DRP1 inhibition on venetoclax-induced cell death were compared in TP53-mutated (THP-1 and Kasumi-1) and TP53 wild-type leukemia cell lines (MOLM-13 and MV4-11), as well as in primary AML cells obtained from patients. Venetoclax induced apoptosis in TP53 wild-type AML cells but had limited effects in TP53-mutated AML cells. DRP1 expression was downregulated in MOLM-13 cells after venetoclax treatment but was unaffected in THP-1 cells. Cotreatment of THP-1 cells with venetoclax and a TP53 activator NSC59984 downregulated DRP1 expression and increased apoptosis. Combination treatment with the DRP1 inhibitor Mdivi-1 and venetoclax significantly increased mitochondria-mediated apoptosis in TP53-mutated AML cells. The combination of Mdivi-1 and venetoclax resulted in noticeable downregulation of MCL-1 and BCL-xL, accompanied by the upregulation of NOXA, PUMA, BAK, and BAX. These findings suggest that DRP1 is functionally associated with venetoclax sensitivity in TP53-mutated AML cells. Targeting DRP1 may represent an effective therapeutic strategy for overcoming venetoclax resistance in TP53-mutated AML.
On the Generation of Discrete Frequency Tones by the Flow around an Aerofoil

Tonal noise, the self-induced discrete frequency noise generated by aerofoils, is investigated. It is heard from an aerofoil placed in streams at low Mach number flows when inclined at a small angle to the stream. It is a piercing whistle, commonly up to 30 dB above the background noise level. Previous authors have attributed tonal noise to a feedback loop consisting of a coupling between laminar boundary-layer instability waves and sound waves propagating in the free stream. Boundary-layer measurements have shown the presence of tonal noise is closely related to the existence of a region of separated flow close to the trailing edge of the aerofoil. An analysis of the linear stability of the boundary-layer flow over the aerofoil is presented. The amplification of the instability waves is shown to be controlled almost entirely by the region of separated flow close to the trailing edge. In light of these new experimental and theoretical results the suitability of the aero-acoustic feedback model is discussed.
Deep learning approach to transformer-based arrhythmia classification using scalogram of single-lead ECG

Arrhythmia is the heartbeat losing its regularity or deviating from its average number. Among the types of arrhythmia is atrial fibrillation (AF) and atrial flutter (AFL), which are considered risk factors for development due to high morbidity and mortality. The early detection of AF/AFL is essential because their effects on the heart or complications appear after a considerable time. Electrocardiography (ECG) is a widely used screening method in primary care because of its low cost and convenience. ECG records the heart's electrical activity for a period of time via electrodes attached to the body. Owing to the development of computing power and interest in big data, attempts at deep learning (DL) have increased. The transformer was proposed by Google in 2017 and has achieved state-of-the-art performance in natural language processing. Various transformer-based models have been applied to various tasks beyond natural language processing and have shown promising prospects. However, there have been few cases of vision transformer (ViT) applications in ECG domain. It was difficult to determine whether ViT had sufficient influence in ECG domain. This study determined whether our extensive ECG dataset could make an AF/AFL diagnosis. We also confirmed whether the recently proposed ViT has AF/AFL diagnostic power.
An Early Soft Internal Short-Circuit Fault Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

The early detection of soft internal short-circuit faults in lithium-ion battery packs is critical to ensuring the safe and reliable operation of electric vehicles. This article proposes a fault diagnosis method that can achieve the detection and assessment of soft internal short-circuit faults for lithium-ion battery packs. Specifically, based on the incremental capacity curve, fault features are extracted from the data, making them easier to identify than small voltage differences. Then, the local outlier factor method is proposed to detect the early soft internal short-circuit fault by calculating the local outlier factor value of each cell within the battery pack. Furthermore, soft short-circuit simulations of a series-connected battery pack under different conditions and various short-circuit resistance values are conducted to generate an internal short-circuit fault data set. Finally, the validity of the proposed fault diagnosis method is verified using simulation and real-world vehicle data. The results show that the proposed method can effectively identify the short-circuit fault of the battery at the early stage, accurately locate the faulty cells in the battery pack, and describe the severity of the fault.