Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning

Introduction Leaves are important organs for photosynthesis in plants, and the restriction of leaf growth is among the earliest visible effects under abiotic stress such as nutrient deficiency. Rapidly and accurately monitoring plant leaf area is of great importance in understanding plant growth status in modern agricultural production. Method In this paper, an image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed. A total of 1,080 rapeseed leaf image areas from five nutrient amendment treatments were continuously collected using the automatic leaf acquisition device and the commonly used area measurement methods (manual and stretching methods). Results The average error rate of the manual method is 12.12%, the average error rate of the stretching method is 5.63%, and the average error rate of the splint method is 0.65%. The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with the manual and stretching methods, respectively, and had the advantages of speed and automation. Experiments on the effects of the manual method, stretching method, and splinting method on the growth of rapeseed are conducted, and the growth rate of rapeseed leaves under the stretching method treatment is considerably greater than that of the normal treatment rapeseed. Discussion The growth rate of leaves under the splinting method treatment was less than that of the normal rapeseed treatment. The mean intersection over union (mIoU) of the UNet-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed.
Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022)

Mental fatigue is a psychophysical condition with a significant adverse effect on daily life, compromising both physical and mental wellness. We are experiencing challenges in this fast-changing environment, and mental fatigue problems are becoming more prominent. This demands an urgent need to explore an effective and accurate automated system for timely mental fatigue detection. Therefore, we present a systematic review of brain-computer interface (BCI) studies for mental fatigue detection using artificial intelligent (AI) techniques published in Scopus, IEEE Explore, PubMed and Web of Science (WOS) between 2011 and 2022. The Boolean search expression that comprised (((ELECTROENCEPHALOGRAM) AND (BCI)) AND (FATIGUE CLASSIFICATION)) AND (BRAIN-COMPUTER INTERFACE) has been used to select the articles. Through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, we selected 39 out of 562 articles. Our review identified the research gap in employing BCI for mental fatigue intervention through automated neurofeedback. The AI techniques employed to develop EEG-based mental fatigue detection are also discussed. We have presented comprehensive challenges and future recommendations from the gaps identified in discussions. The future direction includes data fusion, hybrid classification models, availability of public datasets, uncertainty, explainability, and hardware implementation strategies.
Semiparametric time series regression modeling with a diverging number of parameters

Variable selection and error structure determination of a partially linear model with time series errors are important issues. In this paper, we investigate the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation penalty for a partially linear model with a divergent number of covariates and finite order autoregressive time series errors. Both consistency and asymptotic normality of the proposed penalized estimators are derived. The oracle property of the resultant estimators is proved. Simulation studies are carried out to assess the finite‐sample performance of the proposed procedure. A real data analysis is made to illustrate the usefulness of the proposed procedure as well.
Adaptive Finite Element Methods for Computing Nonstationary Incompressible Flows

Subject of this work is the development of numerical methods for efficiently solving nonstationary incompressible flow problems. In contrast to stationary flow problems, here errors due to discretization in time and space occur. Furthermore, especially three-dimensional simulations lead to huge computational costs. Thus, adaptive discretization methods have to be used in order to reduce the computational costs while still maintaining a certain accuracy. The main focus of this thesis is the development of an a posteriori error estimator which is computable and able to assess both discretization errors separately. Thereby, the error is measured in an arbitrary quantity of interest (such as the drag-coefficient, for example) because measuring errors in global norms is often of minor importance in practical applications. The basis for this is a finite element discretization in time and space. The techniques presented here also provide local error indicators which are used to adaptively refine the temporal and spatial discretization. A key ingredient in setting up an efficient discretization method is balancing the error contributions due to temporal and spatial discretization. To this end, a quantitative assessment of the individual discretization errors is required. The described methods are validated by several numerical tests. These also include established Navier-Stokes benchmarks as well as a two-phase flow problem with complex three-dimensional geometry.
The annual recurrence risk model for tailored surveillance strategy in 1 cervical cancer patients

52 Purpose Current guidelines for surveillance strategy in cervical cancer are rigid, recommending the 53 same strategy for all survivors. The aim of this study was to develop a robust model allowing for 54 individualised surveillance based on a patient´s risk profile. 55 Methods Data of 4,343 early-stage cervical cancer patients treated between 2007-2016 were obtained 56 from international SCCAN (Surveillance in Cervical CANcer) consortium. Cox proportional hazards 57 model predicting disease-free survival (DFS) was developed and internally validated. Risk score, 58 derived from regression coefficients of the model, stratified the cohort into significantly distinctive 59 risk groups. On its basis, the annual recurrence risk model (ARRM) was calculated. 60 Results Five variables were included in the prognostic model: maximal pathologic tumour diameter, 61 tumour histotype, grade, number of positive pelvic lymph nodes, and lymphovascular space invasion. 62 Five risk groups significantly differing in prognosis were identified: with five-year DFS of 97.5%, 63 94.7%, 85.2%, and 63.3% in increasing risk groups, while two-year DFS in the highest risk group 64 equalled 15.4%. Based on ARRM, the annual recurrence risk in the lowest risk group was below 1% 65 since the beginning of follow-up and declined below 1% at years three, four, and >5 in the medium-66 risk groups. In the whole cohort, 26% of recurrences appeared at the first year of the follow-up, 48% 67 by year two, and 78% by year five.
Contribution of the polarization moments of different rank to the integral CPT signal

In the present work we investigate the relation of the polarization moments having different ranks with the tensor components which form the observable integral CPT signal, in the presence of a stray magnetic field. A numerical experiment with parameters close to the real ones is performed, using a program based on the irreducible tensor operator formalism1. The integral fluorescent signal is calculated for the non-polarized fluorescence at different laser power excitation. Detailed analysis of the numerical solutions for all tensor components which describe population and alignment allows visualizing the dynamics of their behavior in dependence on the experimental geometry and laboratory magnetic field B′. The dependence of population f00, longitudinal f02 and transverse f22 alignment in the presence of transverse magnetic field is investigated. The shape and sign of the resonance change with laser power.
Sparse Large Margin Matrix Factorization

Assume U fixed. Each row of V can be solved for using the Support Vector Machine. Define • I(i, j) number of observed entries in column j with row index ≤ i. • Ĵ(i, j) number of observed entries in row i with column index ≤ j. • I(s) row index of observed entry s ∈ S • J(s) column index of observed entry s ∈ S Let j index a column of Y (and, correspondingly, a row of V ). For all s ∈ S such that J(s) = j, let
Terminal sliding mode observers for uncertain linear systems with matched disturbance

In this paper, a terminal sliding mode observer (TSMO) for systems with general state‐space representation is proposed. The effects of uncertainties and matched disturbances are incorporated in the system dynamics. Compared to conventional sliding mode observers, which are capable of finite time convergence of the measured states’ (outputs) errors and asymptotic convergence of unmeasured states’ errors, the proposed TSMO guarantees finite time convergence of all states. Three appropriate Lyapunov functions are defined for proving robust and finite time convergence of all state estimation errors of the TSMO. In addition, to highlight the effectiveness of the methodology, the developed TSMO is applied to a well‐known realistic wind turbine model. Results confirm that the proposed TSMO estimates all the states in finite time with good accuracy.
Quantum-Inspired Weighted Bilateral Filtering for Despeckling in Ultrasound Images

The ultrasound is a nondestructive technique used widely in the medical field for detection of soft tissues in the human body. But this should be validated by an expert radiologist, since ultrasound images are highly affected by noises. In this paper four methods for denoising the speckle noise are compared and analyzed, namely, diffusion tensors, heavy-tailed Levy’s distribution, quantum inspired bi-lateral filtering and locally adaptive wavelet domain Bayesian processor. Performances of each method were quantified by means of Peak Signal to Noise Ratio (PSNR) and Mean Structural Similarity Index Matrix (MSSIM). It was found that denoising of US images through QWBF has higher PSNR value of 16.75 and MSSIM of 0.88. Hence this method was proved to be more efficient compared to other three methods presented in this paper. Keywords—Diffusion tensors; Heavy-tailed Levy’s distribution; Quantum inspired bi-lateral filtering; Locally adaptive wavelet domain Bayesian processor.
Electric field gradients and phase stability of Zr9Ni11 and Hf9Ni11 intermetallics; perturbed angular correlation (PAC) and first-principles calculations studies

Intermetallic compounds Zr9Ni11 and Hf9Ni11 have been studied by time-differential perturbed angular correlation (TDPAC) spectroscopy. Multiphase components have been found in both these compounds. In Zr9Ni11, the phase Zr9Ni11 is found to be predominant (89%) at room temperature. Besides this, a secondary phase due to Zr8Ni21 is found (11%). From temperature-dependent TDPAC studies, it is found that Zr9Ni11 is unstable at higher temperature (>773 K). At 773 K, Zr9Ni11 partially decomposes to Zr7Ni10 and at 973 K, it is completely decomposed to ZrNi and Zr7Ni10. This compositional phase change from Zr9Ni11 to ZrNi and Zr7Ni10 is not reversible and we do not retrieve Zr9Ni11 at remeasured room temperature. In stoichiometric Hf9Ni11, the phase HfNi is found to be predominant (81%) while the phase due to Hf9Ni11 is found as a minor phase (19%). However, no compositional phase change at higher temperature is found in Hf9Ni11. After heating the sample at 873 K, the same two phases of HfNi and Hf9Ni11 have been observed with almost same fractions as found before heating the sample. Similar values of quadrupole frequencies and asymmetry parameters for both Zr9Ni11 and Hf9Ni11 indicate isostructurality of the two phases. X-ray diffraction (XRD) and transmission electron microscopy (TEM)/selected area electron diffraction (SAED) measurements have been carried out in both Zr9Ni11 and Hf9Ni11 to further characterize these compounds and to determine the phase components in these samples. Phase components found from XRD and TEM/SAED measurements are similar to those observed from PAC measurements. Electric field gradients (EFG) at 181Ta impurity atom have been calculated in both Zr9Ni11 and Hf9Ni11 by density functional theory (DFT) using all electron full potential (linearized) augmented plane wave plus local orbitals (FP-(L)APW+lo) method in order to assign the phase components.
Virtual Non-Contrast Spectral CT in Renal Masses: Is It Time to Discard Conventional Unenhanced Phase?

Dual-layer Dual-Energy CT (dl-DECT) allows one to create virtual non-contrast (VNC) reconstructions from contrast-enhanced CT scans, with a consequent decrease of the radiation dose. This study aims to assess the reliability of VNC for the diagnostic evaluation of renal masses in comparison with true non-contrast (TNC) images. The study cohort included 100 renal masses in 40 patients who underwent dl-DECT between June and December 2021. Attenuation values and standard deviations were assessed through the drawing of regions of interest on TNC and VNC images reconstructed from corticomedullary and nephrographic phases. A Wilcoxon signed-rank test was performed in order to assess equivalence of data and Spearman’s Rho correlation coefficient to evaluate correlations between each parameter. The diagnostic accuracy of VNC was estimated through the performance of receiver operating characteristic (ROC) curve analysis. Differences between attenuation values were, respectively, 74%, 18%, 5% and 3% (TNC-VNCcort), and 74%, 15%, 9% and 2% (TNC-VNCneph). The Wilcoxon signed-rank test demonstrated the equivalence of attenuation values between the TNC and VNC images. The diagnostic performance of VNC images in the depiction of kidney simple cysts remains high compared to TNC (VNCcort-AUC: 0.896; VNCneph-AUC: 0.901, TNC-AUC: 0.903). In conclusion, quantitative analysis of attenuation values showed a strong agreement between VNC and TNC images in the evaluation of renal masses.
Variation across two dimensions: testing the Complexity Principle and the Uniform Information Density Principle on adjectival data 1

This study tests the applicability of the Complexity Principle (Rohdenburg 1996) and the Uniform Information Density Principle (Jaeger 2010) on adjectival data as regards the variation between retaining and omitting the complementizer that in English adjectival complementation constructions. More specifically, the study tests the effect of different factors of potential importance on this variation across extraposed (e.g. It was inevitable (that) he should be nicknamed ‘the Ferret’) and post-predicate clauses (e.g. I'm happy (that) we are married). While both the factors concerned with the Complexity Principle and the Uniform Information Density Principle are found to have an effect on post-predicate clauses, less clear effects are found concerning extraposed clauses. I attribute these findings to the difference between the two constructions in terms of their frequency of co-occurrence with different matrix subject types and with different adjectives.
A Higher Order Hopfield Network for Vector

A higher order version of the Hopfield neural network is presented which will perform a simple vector quantisation or clustering function. This model requires no penalty terms to impose constraints in the Hopfield energy, in contrast to the usual one where the energy involves only terms quadratic in the state vector. The energy function is shown to have no local minima within the unit hypercube of the state vector so the network only converges to valid final states. Optimisation trials show that the network can consistently find optimal clusterings for small, trial problems and near optimal ones for a large data set consisting of the intensity values from a digitised, grey-level image.
mTOR Inhibition via Low-Dose, Pulsed Rapamycin with Intraovarian Condensed Platelet Cytokines: An Individualized Protocol to Recover Diminished Reserve?

No major breakthroughs have entered mainstream clinical fertility practice since egg donation and intracytoplasmic sperm injection decades ago, and oocyte deficits secondary to advanced age continue as the main manifestation of diminished ovarian reserve. In the meantime, several unproven IVF ‘accessories’ have emerged including so-called ovarian rejuvenation which entails placing fresh autologous platelet-rich plasma (PRP) directly into ovarian tissue. Among cellular responses attributed to this intervention are reduced oxidative stress, slowed apoptosis and improved metabolism. Besides having an impact on the existing follicle pool, platelet growth factors might also facilitate de novo oocyte recruitment by specified gene upregulation targeting uncommitted ovarian stem cells. Given that disordered activity at the mechanistic target of rapamycin (mTOR) has been shown to exacerbate or accelerate ovarian aging, PRP-discharged plasma cytokines combined with mTOR suppression by pulsed/cyclic rapamycin represents a novel fusion technique to enhance ovarian function. While beneficial effects have already been observed experimentally in oocytes and embryos with mTOR inhibition alone, this proposal is the first to discuss intraovarian platelet cytokines followed by low-dose, phased rapamycin. For refractory cases, this investigational, tailored approach could amplify or sustain ovarian capacity sufficient to permit retrieval of competent oocytes via distinct but complementary pathways—thus reducing dependency on oocyte donation.
Blockchain for Energy Trading in Energy Communities Using Stochastic and Distributed Model Predictive Control

In this article, a stochastic programming method is used to control a residential microgrid where possible disturbance realizations over a horizon are shaped into a tree structure with a given probability. This tree is used by model predictive controllers within a hierarchical and distributed scheme. The upper layer is characterized by a smart contract deployed in the blockchain, acting as a fully distributed coordinator that performs control tasks and collects and distributes relevant data. The bottom layer consists of individual agents that solve locally a reduced tree-based model predictive control (TBMPC) problem and interact with the smart contract to iteratively optimize the overall problem in a distributed fashion. The performance of the approach is demonstrated through several simulations in which various power-trading configurations are assessed.
Anomalous reflection of visible light by all-dielectric gradient metasurfaces

Plane wave scattering by a planar metasurface composed of two periodically alternating rectangular dielectric rods is considered. A rigorous integral equation methodology is employed for the analysis and the accurate determination of the reflected and transmitted fields. Systematic optimizations with respect to the configuration’s parameters are performed, which reveal that it is possible to obtain significantly enhanced anomalous reflection (with simultaneously suppressed ordinary reflection predicted by Snell’s law) with power varying from 92% to almost 100% of the input one, depending on the color of the incident light. It is shown that these reflection properties are supported by metasurfaces easily realizable with specific low-loss dielectric materials. In this way, several all-dielectric optimal designs are reported that can be used in numerous applications demanding anomalous reflection in the visible range.
100 Years of Attempts to Transform Physics Education

As far back as the late 1800s, U.S. physics teachers expressed many of the same ideas about physics education reform that are advocated today. However, several popular reform efforts eventually failed to have wide impact, despite strong and enthusiastic support within the physics education community. Broad-scale implementation of improved instructional models today may be just as elusive as it has been in the past, and for similar reasons. Although excellent instructional models exist and have been available for decades, effective and scalable plans for transforming practice on a national basis have yet to be developed and implemented. Present-day teachers, education researchers, and policy makers can find much to learn from past efforts, both in their successes and their failures. To this end, we present a brief outline of some key ideas in U.S. physics education during the past 130 years. We address three core questions that are prominent in the literature: (a) Why and how should physics be taught? (b) What physics should be taught? (c) To whom should physics be taught? Related issues include the role of the laboratory and attempts to make physics relevant to everyday life. We provide here only a brief summary of the issues and debates found in primary-source literature; an extensive collection of historical resources on physics education is available at https://sites.google. com/site/physicseducationhistory/home.
Disturbance Feedforward Control for Vibration Suppression in Adaptive Optics of Large Telescopes

With increasing the telescope size the effects of structural vibrations become more dominant and therefore its suppression in Adaptive Optics system is a big challenge. For that reason new control concepts as the observerbased disturbance compensation (LQG control) were developed. Nevertheless, under different environmental conditions these concepts are not sufficient to suppress the vibrations in an acceptable range. When observing with faint natural guide stars the integration time of the wavefront sensor is increased to obtain a better Signalto-Noise ratio. However by reducing the loop speed the bandwidth of the AO system cannot mitigate high frequency vibrations (> 5 Hz). In this paper an additional accelerometer-based disturbance feedforward (DFF) control is proposed to improve the performance for high frequency vibrations. In an AO end-to-end simulation the DFF control is compared with the common concepts. Thereby, it is shown that the Strehl ratio can be increased for faint natural guide stars (> 13 mag) by a factor two up to four.
Resistant multiple sparse canonical correlation

Abstract Canonical correlation analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables can be grouped together, thus better understanding multiple interactions that are otherwise difficult to compute or grasp intuitively. CCA appears to have quite powerful applications to high-throughput data, as we can use it to discover, for example, relationships between gene expression and gene copy number variation. One of the biggest problems of CCA is that the number of variables (often upwards of 10,000) makes biological interpretation of linear combinations nearly impossible. To limit variable output, we have employed a method known as sparse canonical correlation analysis (SCCA), while adding estimation which is resistant to extreme observations or other types of deviant data. In this paper, we have demonstrated the success of resistant estimation in variable selection using SCCA. Additionally, we have used SCCA to find multiple canonical pairs for extended knowledge about the datasets at hand. Again, using resistant estimators provided more accurate estimates than standard estimators in the multiple canonical correlation setting. R code is available and documented at https://github.com/hardin47/rmscca.