Miracle-dz/UniFAR-FTUs huggingface dataset:
Back Electromotive Force-Based Discrete-Time Rotor Position Bias Compensation Method for Permanent Magnet Synchronous Motors

In permanent magnet synchronous motor (PMSM) drives, the rotor position signal is critical. However, the failure of the mechanical position sensor and the position tracking error of the resolver-to-digital conversion causes position signal biases. These position biases significantly deteriorate motor drive performance, whereas there are few effective methods to handle both biases simultaneously. To uniformly and accurately compensate for these biases, a back electromotive force (back-EMF)-based discrete-time rotor position bias compensation method for PMSMs is proposed, which includes improvements in compensation accuracy and algorithm execution efficiency. In the proposed scheme, an adaptive disturbance observer with multiparameter estimation is designed to estimate the back-EMF. The proposed multiparameter estimation is designed based on the decomposed voltage vector of the current regulator. Compared with widely used recursive-least-square methods, the proposal greatly reduces the computational burden. Then, according to the rotational mode of the back-EMF in the discrete synchronous reference frame, an exactly discretized back-EMF model is established. Based on this model, a discrete-time compensation method for rotor position bias is proposed. Compared with the continuous-time back-EMF model-based method, the proposal can suppress the estimated error between actual and estimated bias, which rises with increasing motor speed. Finally, the experiments are carried out to verify the effectiveness of the proposed method.
Superconducting magnetic bearings with bulks and 2G HTS stacks: comparison between simulations using H and A-V formulations with measurements

A-V and H are two of the most widespread formulations applied in the literature to calculate current distribution in high-temperature superconductors (HTSs). Both formulations can successfully solve problems related to large-scale HTS applications, but the way to implement the calculations is different. In recent years, several authors have chosen the H formulation to solve problems related to HTS applications. This choice can probably be attributed to the easy implementation of the H formulation with the aid of commercial finite element method (FEM) software, producing precise results and performing fast calculations. In a previous work, we proposed the use of the H formulation to solve superconducting magnetic bearing (SMB) problems. However, most of the SMB simulations presented in the literature are solved using the A-V formulation implemented with the finite difference method (FDM). Which of these two techniques is more suitable for superconducting magnetic bearing applications? This paper aims to answer this question. In order to do so, an experimental rig was developed to test SMBs using YBCO bulks or stacks of coated conductors. The simulated levitation force results from the A-V formulation using FDM and from the H formulation using FEM were compared with the experimental data. In general, the calculation time and the results error obtained with both formulations are comparable. It is worth mentioning that the main contribution of this paper is to present improvements to reduce the A-V formulation computational time and details of how to implement it using FDM in any platform. For this reason, most of this work is about the A-V formulation, while the H formulation is just presented for comparison.
Robust H state feedback control methods for bilinear systems

Robust H∞ state feedback control methods for continuous-time bilinear systems with an additive disturbance input are presented. The robust H∞ control for bilinear systems requires a solution of the state dependent algebraic Riccati equation (SDARE). However, the proposed robust H∞ control techniques based on the successive approximation method solve the SDARE by converting the bilinear system into the time varying linear system and solving a sequence of algebraic Riccati and Lyapunov equations, respectively. Numerical examples are considered to verify the efficiency of the proposed algorithms.
The Effect of Technostress on the Motivation to Teach Online in Higher Education Before and During the COVID-19 Pandemic: Perceptions of Business Faculty

This study investigated the relationships among technostress creators (techno-complexity, techno-insecurity, techno-invasion, techno-overload, and techno-uncertainty) on the motivation to teach online using the Motivation to Teach Online – Faculty Version scale. Data were collected from faculty members of the Management and Organizational Behavior Teaching Society (MOBTS), a member of the AACSB Business Education Alliance, and the American Society of Business and Behavioral Sciences (ASBBS), an interdisciplinary professional organization comprised of faculty teaching in accounting, finance, management, marketing, organizational behavior, and computer information systems early 2020 (N = 307). The findings indicated that techno-stressed faculty are less motivated to teach online. Techno-insecurity and techno-overload subconstructs were statistically significant pre-pandemic. Techno-insecurity was statistically significant during the pandemic. Gender and years of teaching online were not observed among faculty to modify online teaching motivations. Results are presented to supply institutions of higher learning with evidence to support faculty with online learning undertakings while leveraging opportunities to manage enrollment.
Neuroprotective Effects of Tryptanthrin-6-Oxime in a Rat Model of Transient Focal Cerebral Ischemia

The activation of c-Jun N-terminal kinase (JNK) plays an important role in stroke outcomes. Tryptanthrin-6-oxime (TRYP-Ox) is reported to have high affinity for JNK and anti-inflammatory activity and may be of interest as a promising neuroprotective agent. The aim of this study was to investigate the neuroprotective effects of TRYP-Ox in a rat model of transient focal cerebral ischemia (FCI), which involved intraluminal occlusion of the left middle cerebral artery (MCA) for 1 h. Animals in the experimental group were administered intraperitoneal injections of TRYP-Ox 30 min before reperfusion and 23 and 47 h after FCI. Neurological status was assessed 4, 24, and 48 h following FCI onset. Treatment with 5 and 10 mg/kg of TRYP-Ox decreased mean scores of neurological deficits by 35–49 and 46–67% at 24 and 48 h, respectively. At these doses, TRYP-Ox decreased the infarction size by 28–31% at 48 h after FCI. TRYP-Ox (10 mg/kg) reduced the content of interleukin (IL) 1β and tumor necrosis factor (TNF) in the ischemic core area of the MCA region by 33% and 38%, respectively, and attenuated cerebral edema by 11% in the left hemisphere, which was affected by infarction, and by 6% in the right, contralateral hemisphere 24 h after FCI. TRYP-Ox reduced c-Jun phosphorylation in the MCA pool at 1 h after reperfusion. TRYP-Ox was predicted to have high blood–brain barrier permeability using various calculated descriptors and binary classification trees. Indeed, reactive oxidant production was significantly lower in the brain homogenates from rats treated with TRYP-Ox versus that in control animals. Our data suggest that the neuroprotective activity of TRYP-Ox may be due to the ability of this compound to inhibit JNK and exhibit anti-inflammatory and antioxidant activity. Thus, TRYP-Ox may be considered a promising neuroprotective agent that potentially could be used for the development of new treatment strategies in cerebral ischemia.
Magnetic Resonance Image Super-Resolution via Multi-Resolution Learning

: High-resolution magnetic resonance images are of great significance for medical diagnosis. A convolutional neural network with multi-resolution learning is proposed for magnetic resonance image (MR) superresolution. The network is an improved deep residual network, which involves residual units for feature extraction, a deconvolution layer for multi-resolution up-sampling, and a multi-resolution learning layer. The proposed network performs the super-resolution task in the low-resolution space, which can accelerate the network. Multiresolution upsampling is put forward to integrate multiple residual unit information and to accelerate the network. Multi-resolution learning can adaptively determine the contributions of these upsampled high-dimensional feature maps to high-resolution MR image reconstruction. Experiment results indicate that the proposed method can achieve a good super-resolution reconstruction performance for magnetic resonance images, which is superior to the state-of-the-art deep learning methods.
Tiling the Integers

A set tiles the integers if and only if the integers can be writ ten as a disjoint union of translates of that set. Counterexamples based on finite Abelian groups sho w that Fuglede conjecture is false in high dimensions. A solution for the Fuglede conjecture in Z or all the groupsZN would provide a solution for the Fuglede conjecture in R. Focusing on tiles in dimension one, we will concentrate on the analysis of tiles in the finite groups ZN . Based on the CovenMeyerowitz conjecture, it has been proved that if any spectral set in Z satisfies the the Coven-Meyerowitz properties, then every spectral set inR is a tile. We will present some of the main results related to i n eger tiles and give a self-contained description of the theory with detailed pr oofs.
Body Image and Lifestyle Behaviors in High School Adolescents

This secondary data analysis study aimed to examine the associations between 1) body size perception (BSP) and body size dissatisfaction (BSD) and 2) lifestyle behaviors and BSP and BSD in adolescents. The study pooled cross-sectional data from two studies (n = 301) performed in adolescents. Weight and height were measured, while lifestyle behaviors and perceived actual and desired body size variables were self-reported. Linear regression analysis assessed the contribution of sex and zBMI to BSP and BSD scores. Pearson’s correlation explored associations between BSP and BSD. Cohen’s effect sizes compared satisfied and dissatisfied adolescents within the underestimators subgroup. A positive association between BSP and BSD scores was observed among girls living with normal-weight and overweight/obesity only (r = 0.26; p ≤ 0.001 and r = 0.38; p < 0.05, respectively). Underestimators who were satisfied with their body size showed a moderate effect size for a lower zBMI, a small effect size for lower screen time, and higher sleep duration compared to dissatisfied underestimators. Underestimation was associated with more body size satisfaction in adolescent girls with normal weight and overweight/obesity, suggesting a protective effect of underestimation. These findings support the hypothesis that body size satisfaction and underestimation in adolescents is associated with healthier lifestyle behaviors.
Options Evaluation Using Monte Carlo Simulation

The present paper evaluates derivative products as options, using Monte Carlo simulation for the support-asset. The Monte Carlo method is one of the most valuable and used methods in modern finance and with great applicability in the pricing of options. The support-asset used in our developments is the shares of Banca Transilvania SA. The Monte Carlo simulation is used by us to create scenarios on the random evolution of the support-asset, and the price of the option is determined using the Feynman-Kac theorem. We also consider that the price of the support-asset follows a stochastic process with a lognormal distribution.
ALC FOR CLA: TOWARDS DESCRIPTION LOGIC ON CONCEPT LATTICES

In this paper, we start with a motivation of the study of modal and/or description logics with values in concept lattices. Then we give a brief survey of approaches to lattice-valued modal and/or description logics. After that we study some methods of context symmetrization, because in our approach the description logic on concept lattices is defined for symmetric contexts only. We conclude with a list of problems related to the comparison of different lattice-valued modal and/or description logics, different variants of context symmetrization and resulting description logics, decidability and axiomatization of these logics.
Characterization of Two AGAMOUS-like Genes and Their Promoters from the Cymbidium faberi (Orchidaceae)

Arabidopsis AGAMOUS (AG) play roles in determining stamens’ and carpels’ identities, floral meristem determinacy, and repression of the A-function. Gynostemium fused by stamens and carpels is a characteristic reproductive structure in orchid flowers, which shows a considerable difference from the reproductive organs of eudicots and other monocot species. The molecular basis of orchid gynostemium development remains largely unknown. Here, we report the identification and functional characterization of two AG-like genes, CyfaAG1 and CyfaAG2, and their promoters from C. faberi. Both CyfaAG1 and CyfaAG2 are highly expressed in the anther cap, gynostemium, and ovary. Ectopic expression of CyfaAG1 and CyfaAG2 promotes early flowering of wild-type Arabidopsis. Moreover, ectopic expression of CyfaAG1 completely rescues floral defects in the Arabidopsis ag-1 mutant, while ectopic expression of CyfaAG2 only completes filament and carpel development. Our findings suggest that CyfaAG1 acts as an evolutionarily conserved C-function gene in determining reproductive organ identity and mediating floral meristem determinacy. CyfaAG2 redundantly mediates the C-function in floral meristem determinacy and gynostemium development. Our results provided more details to understand how the C-class function has been partitioned in orchids, and the roles of two AG orthologs in regulating gynostemium development in C. faberi.
Ulam-Hyers-Stability for nonlinear fractional neutral differential equations

We discuss Ulam-Hyers stability, Ulam-Hyers-Rassias stability and Generalized Ulam-Hyers-Rassias stability for a class of nonlinear fractional functional differential equations with delay involving Caputo fractional derivative by using Picard operator. An example is also given to show the applicability of our results.
High BECN1 Expression Negatively Correlates with BCL2 Expression and Predicts Better Prognosis in Diffuse Large B-Cell Lymphoma: Role of Autophagy

Diffuse large B-cell lymphoma (DLBCL) is characterized by high molecular and clinical heterogeneity. Autophagy, a lysosome-driven catabolic process devoted to macromolecular turnover, is fundamental in maintaining normal hematopoietic stem cells and progenitors homeostasis, and its dysregulation plays a critical role in the initiation and progression of hematological malignancies. One main regulator of autophagy is BECLIN-1, which may interact alternatively with either BCL-2, thus allowing apoptosis, or PI3KC3, thus promoting autophagy. The altered expression of BCL2 and BECN1 correlates with lymphoma outcomes, but whether this is associated with dysregulated cross-talk between autophagy and apoptosis remains to be elucidated. Analysis of the TCGA database revealed that BCL2 and BECN1 mRNA expression were inversely correlated in DLBCL patients. In representative DLBCL cell lines exposed to doxorubicin, the cells highly expressing BCL-2 were resistant, while the ones highly expressing BECLIN-1 were sensitive, and this correlated with low and high autophagy flux, respectively. Venetoclax targeting of BCL-2 increased while the spautin-1-mediated inhibition of BECLIN-1-dependent autophagy reversed doxorubicin sensitivity in the former and in the latter, respectively. By interrogating the TCGA DLBCL dataset, we found that BCL2 and BECN1 acted as negative and positive prognostic markers for DLBCL, respectively. The differentially expressed gene analysis in the respective cohorts revealed that BCL2 positively correlated with oncogenic pathways (e.g., glucose transport, HIF1A signaling, JAK-STAT signaling, PI3K-AKT-mTOR pathway) and negatively correlated with autophagy-related transcripts, while BECN1 showed the opposite trend. Notably, patients with high BECN1 expression displayed longer survival. Our data reveal, for the first time, that the modulation of BECLIN-1-dependent autophagy influences the prognosis of DLBCL patients and provide a mechanistic explanation supporting the therapeutic use of drugs that, by stimulating autophagy, can sensitize lymphoma cells to chemotherapy.
Digital staining facilitates biomedical microscopy

Traditional staining of biological specimens for microscopic imaging entails time-consuming, laborious, and costly procedures, in addition to producing inconsistent labeling and causing irreversible sample damage. In recent years, computational “virtual” staining using deep learning techniques has evolved into a robust and comprehensive application for streamlining the staining process without typical histochemical staining-related drawbacks. Such virtual staining techniques can also be combined with neural networks designed to correct various microscopy aberrations, such as out-of-focus or motion blur artifacts, and improve upon diffracted-limited resolution. Here, we highlight how such methods lead to a host of new opportunities that can significantly improve both sample preparation and imaging in biomedical microscopy.
Dietary choline, but not L-carnitine, increases circulating lipid and lipoprotein concentrations, without affecting body composition, energy expenditure or respiratory quotient in lean and obese male cats during weight maintenance

Introduction Due to the involvement in one-carbon metabolism and lipid mobilization, choline and L-carnitine supplementation have been recommended to minimize hepatic lipid accumulation and support fat oxidation, respectively. This study investigated the lipotropic benefits of choline or L-carnitine supplementation in lean and obese cats maintaining body weight (BW). Methods Lean [n = 9; body condition score (BCS): 4–5/9] and obese (n = 9; BCS: 8–9/9) adult male neutered colony cats were used in a replicated 3 x 3 complete Latin square design. Treatments included choline (378 mg/kg BW0.67), L-carnitine (200 mg/kg BW) and control (no supplement). Treatments were supplemented to the food for 6 weeks each, with a 2-week washout between treatments. Cats were fed once daily to maintenance energy requirements, and BW and BCS were assessed weekly. Fasted blood collection, indirect calorimetry, and dual-energy X-ray absorptiometry occurred at the end of each treatment period. Serum was analyzed for cholesterol (CHOL), high-density lipoprotein CHOL (HDL-C), triglycerides (TAG), non-esterified fatty acids (NEFA), glucose, creatinine (CREAT), urea, alkaline phosphatase (ALP) and alanine aminotransferase (ALT). Very low-density lipoprotein CHOL (VLDL) and low-density lipoprotein CHOL (LDL-C) were calculated. Data were analyzed using proc GLIMMIX, with group and period as random effects, and treatment, body condition, and their interaction as fixed effects, followed by a Tukey's post-hoc test when significance occurred. Results Cats supplemented choline had lower food intake (P = 0.025). Treatment did not change BW, BCS and body composition (P > 0.05). Obese cats had greater ALP, TAG, and VLDL, and lower HDL-C compared to lean cats (P < 0.05). Choline resulted in greater CHOL, HDL-C, LDL-C and ALT (P < 0.05). L-carnitine resulted in lower CREAT (P = 0.010). Following the post-hoc test, differences between treatment means were not present for ALP (P = 0.042). No differences were found for glucose, urea or NEFA (P > 0.05). Obese cats had a lower fed respiratory quotient (RQ), regardless of treatment (P = 0.045). Treatment did not affect fed or fasted RQ and energy expenditure (P > 0.05). Discussion Choline appeared to increase circulating lipid and lipoprotein concentrations regardless of body condition, likely through enhanced lipid mobilization and hepatic elimination. Neither dietary choline or L-carnitine altered body composition or energy metabolism in the lean or obese cats, as compared to control.
Application of Transfer Learning to Convolutional Neural Network Models for Mango Leaf Disease Recognition

It is vital to first determine whether or not plant illnesses are there, and then to make steps to restrict the spread of those diseases in order to maximize both the quality and quantity of the harvest. First, it is important to determine whether or not plant illnesses are present. There are a number of advantages to mechanizing plant diseases, one of which is reducing the amount of time spent manually examining crops in a big agricultural area that produces a significant amount of mango. As a result of the fact that leaves are in charge of the majority of a plant’s nutrition absorption, it is very important to diagnose leaf diseases in a timely and precise manner. In this particular research, we classified and identified the several illnesses that may be harmful to mango leaf by using CNN. We employ multiple CNN models that have been trained via transfer learning in order to increase the quality of the results obtained from the training set. These CNN models include DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception. Acquiring pictures, segmenting those images, and deriving features from them are all stages that are included in the process of sickness diagnosis. The collection contains approximately a thousand photographs, all of which depict either healthy mango leaves or mango leaves affected by illness. According to the findings of our investigation into the overall performance matrices, the DenseNet201 model earned the highest level of accuracy (98.00%) compared to all of the other models.
An Improved HAF-Based Nonlinear Correction Method of FMCW Radar With CEEMD

The frequency modulation continuous-wave (FMCW) radar has been widely concerned for its compact system structure, low hardware cost, and excellent imaging resolution. However, an FMCW radar suffers from the nonlinearity commonly, which leads to the deterioration of the imaging quality. A challenging problem is that the nonlinear reference beat signal contains multiple components with closely spaced and approximately equal amplitude, which has not been researched in the existing literature to the best of our knowledge. To deal with this trouble, a software-based correction method is proposed by utilizing the complementary ensemble empirical mode decomposition (CEEMD). First, several intrinsic model functions (IMFs) are generated by decomposing the beat signal obtained from a reference target by the CEEMD method. Then, an identification method is proposed to find the reference IMF from the IMFs by the average period method, and a reference delay estimation method based on windowed fast Fourier transform is proposed. Finally, the high-order ambiguity function (HAF) is employed to estimate the polynomial coefficients of the reference IMF, which are utilized to compensate the nonlinear beat signal by a time resampling method. The effectiveness and superiority are verified by numerical simulation and real data. Compared with the state-of-the-art nonlinear correction algorithms, we obtain more outstanding radar imaging quality by adopting the proposed method.