Miracle-dz/UniFAR-FTUs huggingface dataset:
A Modified Implicit Z-Bus Method for an Unbalanced Hybrid AC-DC Microgrids

This study proposes a power flow algorithm based on the modified implicit Z-bus method for an unbalanced hybrid AC-DC microgrid (HMGs) operated in islanded mode. In this paper, a 4-wire multi-ground ac microgrid and a bi-polar DC microgrid are coupled through interlinking converters (ICs). Microgrid (MG) elements are represented through sequence component models. In contrast to grid-connected systems, where frequency and voltage are constant, changeable frequency and voltage are employed for power synchronization amongst AC and DC microgrids. In this work, a load flow algorithm for hybrid AC-DC microgrids considering various load models (constant load, voltage, and frequency-dependent load) and distributed generation is developed. The proposed algorithm is validated on 12-bus and 47-bus hybrid microgrid test systems. The obtained result is compared with result of newton rapson-method (NR) and time domain-based simulation modules to show accuracy of the proposed method.
External boundary value problems of steady vibrations in the theory of rigid bodies with a double porosity structure

This paper concerns with the linear 3D theory of rigid solids with a double porosity structure. Basic external boundary value problems (BVPs) of steady vibrations are formulated. The uniqueness and existence theorems for regular (classical) solutions of these BVPs are established. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Improved predictive algorithm of RNA tertiary structure based on GNN

RNA-targeted drugs are expected to push the third wave of industrial revolution in the field of drug development. The premise of the development of RNA-targeted drugs is to determine the three-dimensional spatial structure of the target RNA, but due to the biological characteristics of RNA, it is difficult to determine its structure by experimental means. This paper introduces a graph neural network-based improved predictive algorithm of RNA tertiary structure, IPARTS, which uses the latest macromolecular sampling method FARFAR2 under the Rosetta framework to learn the interatomic forces including non-bonded connected atoms, and realize the spatial structure of RNA is predicted at the atomic level. Our method surpassed other prediction methods, and completed the accurate prediction and efficient modeling of the tertiary structure of RNA. The accuracy of the loss function reached 2.204, which also provided new ideas for the structure prediction of other biological macromolecules.
Research on stock price prediction using TextRank based text summarization technology and sentiment analysis

News sentiment analysis is widely used in stock price forecasting, and the existing research is mostly limited to sentiment mining of news headlines, ignoring the effective information contained in article news. This study introduces extractive text summarization technology into stock price prediction, use Word2Vec and TextRank algorithm to extract effective text information contained in article news, and adopt the emotion comprehensive calculation method based on news headlines and news abstracts, taking into account the effective information of headlines and original texts. Input the comprehensively calculated news sentiment value as sentiment feature into the stock price prediction model LSTM, and propose a stock price prediction framework based on TextRank text summarization techniques and sentiment analysis. Finally, select the stock transaction data of A-share CTG DUTY-FREE for a total of 587 trading days from December 25, 2019 to May 31, 2022 for comparative experiments. The result shows, the sentiment analysis algorithm based on TextRank text summarization technology proposed in this article has the best extraction effect on the sentiment value of news texts, in terms of prediction accuracy, the model is 11.67% higher than the benchmark model on the test set.
Solution of Large Scale Partially Separable Unconstrained Optimization Problems Using Element-by-Element Preconditioners

where each element function fi has a large invariant subspace. Typically, this occurs when fi(x) is only a function of a small subset of the variables x, but may, of course, happen for other reasons (see, for example, [2]). The decomposition (1) is extremely general. Indeed, any sufficiently differentiable function with a sparse Hessian matrix may be expressed in this form [1]. In this paper, we shall be concerned with those partially separable functions for which
A Novel Deep Learning Based Framework for Cardiac Arrest Prediction

Cardiovascular diseases are a major health issue that calls for prompt medical attention. In order to determine the most advantageous methods in this field, numerous techniques and studies have been carried out over the past few years. It should be mentioned that the majority of these cardiovascular illnesses are treatable with earlier detection and prediction. This paper suggests using an automated methodology to predict and classify the likelihood of cardiac arrests in a patient. Due to their collection from various sources, the Electrocardiogram (ECG) signals are initially preprocessed by normalizing them to the [0 1] range. The significant features from these preprocessed signals are then extracted using Mel-Frequency Cepstrum (MFCC), Melspectrogram, MFCC Delta 1 & 2, MFCC Merge File, etc. We also propose a novel feature vector using ensemble of MFCC features. The features are then classified using Artificial Neural Network (ANN), Support Vector Machine (SVM), TPOT and K-Nearest Neighbor (KNN). The proposed vector outperformed all other feature vectors and obtained highest accuracy of 95.8% via ANN. The study is conducted using publicly accessible dataset composed of approximately 52 thousand ECG signals. The suggested strategy is compared with existing techniques and the results indicate the robustness and effectiveness of our approach. Therefore, the proposed methodology can be effectively deployed in a clinical setting to classify ECG data and identify the risks likelihood of cardiac arrests in a patient.
Research on ORB-SLAM2 Based on Dynamic Scenarios

Most visual SLAM systems can achieve ideal mapping results in a static environment. However, there are a large number of moving objects in the real world, which leads to the failure of feature point tracking and other problems in the traditional visual SLAM system. Based on the deficiency of ORB-SLAM2 algorithm in dynamic scenes, this paper introduces two YOLO target detection methods and visual SLAM algorithm in dynamic scenes combined with ORB-SLAM2. YOLO V3 and YOLO V5 models are used to segment and eliminate dynamic feature points, and then effective feature points are used for frame matching and pose estimation. TUM dataset is used for experimental analysis to compare the exactness and robustness of ORB-SLAM2 algorithm and two optimization algorithms in dynamic environment.
Evaluation of matching cost on the ISPRS stereo matching benchmark

In this paper we evaluated several typical matching costs including CENSUS, mutual information (MI) and the normalized cross correlation using the ISPRS Stereo Matching Benchmark datasets for DSM generation by stereo matching. Two kinds of global optimization algorithms including semi-global matching (SGM) and graph cuts (GC) were used as optimization method. We used a sub-pixel method to obtain more accurate MI lookup table and a sub-pixel method was also used when computing cost by MI lookup table. MI itself is sensitive to partial radiation differences. So we used a kind of cost combined MI and CENSUS. After DSM generation, the deviation data between the generated DSM and Lidar was statistics out to compute the mean deviation (Mean), the median deviation (Med), the standard deviation (Stdev), the normalized median absolute deviation (NMAD), the percentage of deviation in tolerance etc., which were used to evaluate the accuracy of DSM generated from different cost.
Değişken Dönüşümlerinin Kelebek Dağılan Artıklar için Değişen Varyanslılığa Etkileri (021301) (1-10)

Weighting or transformations are used for fulfill the assumptions if there is any violation of assumptions in linear regression analysis. Heteroscedasticity is one of the violations these assumptions and butterflydistributed- residuals are particular type of heteroscedastcity. The aim of this study is to examine the effects of transformations variables of regression model for butterfly distributed residuals
Spaceborne laser filamentation for atmospheric remote sensing

A proof‐of‐concept of space‐borne laser filamentation for atmospheric remote sensing is presented. The remote generation of laser filaments from an Earth‐orbiting satellite is shown by numerical simulations to be theoretically possible for a large range of laser parameters. The model includes a realistic representation of the stratified atmosphere and accounts for multi‐species ionization and the dependence of air density upon the molecule type and altitude profile. The remote generation of a white light continuum extending from 350 nm to 1.1 μm within the filament is demonstrated, and hereby proposed as an atmospheric in‐situ light source for monitoring greenhouse gases and pollutants on a global scale by light detection and ranging (lidar) techniques. Scaling laws are also derived for estimating the filament altitude as a function of peak pulse power (3 GW‐3 TW), beam radii (10‐200 cm) and for three different curvatures (300, 390, 500 km) for femtosecond infrared (800 nm) pulses. We find that operating conditions for remote supercontinuum generation are already available with current ground‐based mobile laser technology and within reach of future space laser systems.
Algorithm in Case Generation Based on Similarity Rough Set

A case selection algorithm selects representative cases from a large data set for future case-based reasoning tasks. This paper proposes the SRS algorithm, based on similarity-based rough set theory, which selects a reasonable number of the representative cases while maintaining satisfactory classification accuracy. It also can handle noise and inconsistent data. Experimental result has confirmed the algorithm feasibility and the validity.
Confidence intervals for the ratio of two binomial proportions

Sometimes the ratio of two binomial proportions is the parameter of major interest, for instance as the risk ratio in a two-group cohort study (Fleiss, 1973) or as the likelihood ratio for a diagnostic test procedure (McNeill, Keeler and Adelstein, 1975). Let X and Y be independent binomial variates based on sample sizes m and n and parameters p' and P2, respectively. Let 0 = Pm/P2. We consider the construction of confidence intervals for 0. Santner and Snell (1980) have proposed a nonanalytical procedure which guarantees a lower bound on the confidence coefficient of the 1 a interval for 0. Their algorithm is usable for small m and n but may produce conservative intervals. In ? 2, an analytical method for obtaining approximate confidence intervals for 0 is proposed and compared to the one recommended by Katz et al. (1978).
Feedback predictive control for constrained fuzzy systems with Markovian jumps

In this paper, a feedback model predictive control method is presented to tackle control problems with constrained multivariables for uncertain discrete‐time nonlinear Markovian jump systems. An uncertain Markovian jump fuzzy system (MJFS) is obtained by employing the Takagi‐Sugeno (T‐S) fuzzy model to represent a discrete‐time nonlinear system with norm bounded uncertainties and Markovain jump parameters. To achieve more generality, the transition probabilities of the Markov chain are assumed to be partly unknown and partly accessible. The predictive formulation adopts an on‐line optimization paradigm that utilizes the closed‐loop state feedback controller and is solved using the standard semi‐definite programming (SDP). To reduce the on‐line computational burden, a mode independent control move is calculated at every sampling time based on a stochastic fuzzy Lyapunov function (FLF) and a parallel distributed compensation (PDC) scheme. The robust mean square stability, performance minimization and constraint satisfaction properties are guaranteed under the control move for all admissible uncertainties. A numerical example is given to show the efficiency of the developed approach.
Polynomial Preserving Diffusions and Applications in Finance

This paper provides the mathematical foundation for polynomial preserving diffusions. They play an important role in a growing range of applications in finance, including financial market models for interest rates, credit risk, stochastic volatility, commodities and electricity. Uniqueness of polynomial preserving diffusions is established via moment determinacy in combination with pathwise uniqueness. Existence boils down to a stochastic invariance problem that we solve for semialgebraic state spaces. Examples include the unit ball, the product of the unit cube and nonnegative orthant, and the unit simplex.
Views and Challenges of COVID-19 Vaccination in the Primary Health Care Sector. A Qualitative Study

COVID-19 has affected the global community as it has severely raised population mortality and morbidity rates. Vaccination was seen as a mechanism against the spread of the pandemic. Yet, there are still several reservations about its adoption. Professionals in the field of health care have a crucial frontline role. The present study uses a qualitative research approach to examine Greek health professionals’ views on vaccination acceptance. According to the key findings, health professionals largely accept vaccination. The main reasons cited were scientific knowledge, a sense of obligation to society, and protection from disease. However, there are still numerous restrictions to adhering to it. This is due to the lack of knowledge of certain scientific disciplines or to misinformation, as well as to religious or political convictions. The issue of trust is central to the acceptance of vaccinations. According to our research, the most adequate strategy to enhance immunization and ensure that it is widely accepted is to promote health educational interventions for professionals working in primary care settings.
Improving Film Cooling Efficiency with Lobe-Shaped Cooling Holes: An Investigation with Large-Eddy Simulation

Shaped cooling holes have received considerable attention in recent years due to their potential to improve heat transfer while minimizing pressure drop. In this study, the effects of lobe-shaped cooling holes on film cooling performance and turbulent flow characteristics are investigated using large-eddy simulation (LES). The LES approach was validated by comparing the calculated film cooling effectiveness with experimental data. The results show that well-designed lobe-shaped cooling holes can improve the cooling efficiency by up to 37% and reduce the total pressure drop. Additionally, the cooling jets of lobe-shaped cooling holes have a smaller angle to the wall surface, indicating better wall attachment. However, poorly designed lobe-shaped cooling holes can reduce the cooling efficiency. Overall, this study suggests that lobe-shaped cooling holes are a promising technology for improving gas turbine engine performance.
Frugal innovation beyond emerging countries: the key role of developed countries

Frugal innovation is no longer relevant only for emerging countries or for low-tech artifacts. The interest of countries developed by frugal innovation has been increasing in recent years, since it is an alternative of less use of natural and financial resources, however, it is not clear which is the role of developed countries in the advancement of frugal innovation. In this article, we examine the literature, technical reports and patents to propose some possible paths. Through qualitative methods such as semantic analysis, we argue that research for the modification of the technological matrix of products, processes and organizational arrangements and the development of a better understanding of the frugal entrepreneur may be key roles for developed countries to contribute to the development of frugal innovation.
Synthesis and Behavior of DNA Oligomers Containing the Ambiguous Z-Nucleobase 5-Aminoimidazole-4-carboxamide

5-Amino-1-β-D-ribofuranosylimidazole-4-carboxamide 5′-monophosphate (ZMP) is a central intermediate in de novo purine nucleotide biosynthesis. Its nucleobase moiety, 5-aminoimidazole-4-carboxamide (Z-base), is considered an ambiguous base that can pair with any canonical base owing to the rotatable nature of its 5-carboxamide group. This idea of ambiguous base pairing due to free rotation of the carboxamide has been applied to designing mutagenic antiviral nucleosides, such as ribavirin and T-705. However, the ambiguous base-pairing ability of Z-base has not been elucidated, because the synthesis of Z-base-containing oligomers is problematic. Herein, we propose a practical method for the synthesis of Z-base-containing DNA oligomers based on the ring-opening reaction of an N1-dinitrophenylhypoxanthine (HxaDNP) base. Thermal denaturation studies of the resulting oligomers revealed that the Z-base behaves physiologically as an A-like nucleobase, preferentially forming pairs with T. We tested the behavior of Z-base-containing DNA oligomers in enzyme-catalyzed reactions: in single nucleotide insertion, Klenow fragment DNA polymerase recognized Z-base as an A-like analog and incorporated dTTP as a complementary nucleotide to Z-base in the DNA template; in PCR amplification, Taq DNA polymerase similarly incorporated dTTP as a complementary nucleotide to Z-base. Our findings will contribute to the development of new mutagenic antiviral nucleoside analogs.
Psychological Impact and Women’s Evaluation of the First-Trimester Pre-Eclampsia Screening and Prevention: ASPRE Trial

Objective: This study aims to extend the understanding of the psychological impact of the first-trimester pre-eclampsia (PE) screening on women identified as high risk for preterm PE. We examined the differences between low- vs. high-risk women throughout pregnancy in: symptoms of distress (anxiety, depression, physical and mental health, and worry), health behaviour changes, the experience of pregnancy, and attitudes towards PE screening. Methods: This study was nested within the ASPRE trial. Pregnant women were screened for preterm-PE risk status in the first trimester; the assessments were carried out before the screening, in the second and in the third trimester (n = 155 low-risk women and N = 82 high-risk women in the second trimester). Results: The high-risk-for-PE women exhibited more depressive symptoms compared to the low-risk women in the second but not in the third trimester. No differences were observed between the two groups in other distress symptoms or in the women’s evaluation of their experience of pregnancy. The high-risk group reported greater health behaviour changes compared to the low-risk group, but this was moderated by depression levels. Conclusions: Overall, pregnant women reported positive attitudes towards first-trimester PE screening, despite transient depressive symptoms. This study offers supportive evidence concerning the appropriateness of PE screening in ethical terms.
Emotion Arousal Assessment Based on Multimodal Physiological Signals for Game Users

Emotional arousal, an essential dimension of game users’ experience, plays a crucial role in determining whether a game is successful. Game users’ emotion arousal assessment (GUEA) is of great importance. However, GUEA often faces challenges, such as selecting emotion-inducing games, labeling emotional arousal, and improving accuracy. In this study, the scheme for verifying the effectiveness of emotion-induced games is proposed so that the selected games can induce the target emotions. In addition, the personalized arousal label generation method is developed to reduce the errors caused by individual differences among subjects. Furthermore, to improve the accuracy of GUEA, the Breath Rate Variability (BRV) signal is used as a GUEA indicator along with commonly used physiological signals. Comparative experiments on GUEA based on multimodal physiological signals are conducted. The experimental result shows that the accuracy of GUEA is improved by adding the BRV signal, up to 92%.
Segmentation of mouse tibia on MRI using deep learning U-Net models

We are developing deep learning models for the segmentation of mouse tibia in MRI scans by utilizing three U-Net architectures: Attention, Inception, and basic U-Net, on a data set of 32 mice with 158 MRI scans. The data set was split into training (23 mice, 108 scans), validation (3 mice, 17 scans), and test (6 mice, 33 scans) sets. Two expert annotators (EA1 and EA2) provided manual 3D segmentations of the tibia on the MRI scans. EA1 provided outlines on all MRI scans, which were used as the reference for the training, validation, and testing of U-net models. EA2 provided outlines on the validation and test set, which were used for the assessment of inter-observer reference variability. The model performance was evaluated based on the average Jaccard index (%AJI), average volume intersection ratio (%AVI), average volume error (%AVE), and average Hausdorff distance (AHD, mm). For the test set, the %AJI with reference to EA1 was 83.45 ± 5.11 for the Attention U-Net, 83.05 ± 6.21 for the Inception U-Net, and 83.38 ± 5.36 for the basic U-Net. The %AJI was 80.70 ± 2.91 for EA1 versus EA2 and 79.70 ± 6.28 for Attention U-Net versus EA2. The variability between the U-Net models and EA1 and EA2 references was similar to the variability between EA1 and EA2. All 3 U-Net architectures achieved similar performances with the Attention U-Net performing marginally better.