Miracle-dz/UniFAR-FTUs huggingface dataset:
Feedback control for unsteady flow and its application to the stochastic Burgers equation

Mathematical methods of control theory are applied to the problem of control of fluid flow with the long-range objective of developing effective methods for the control of turbulent flows. The procedure of how to cast the problem of controlling turbulence into a problem in optimal control theory is presented using model problems through the formalism and language of control theory. Then we present a suboptimal control and feedback procedure for general stationary and time-dependent problems using methods of calculus of variations through the adjoint state and gradient algorithms. This suboptimal feedback control procedure is applied to the stochastic Burgers equation. Two types of controls are investigated: distributed and boundary controls. The control inputs are the momentum forcing for the distributed control and the boundary velocity for the boundary control. Costs to be minimized are defined as the sum of the mean-square velocity gradient inside the domain for the distributed control or the square velocity gradient at the wall for the boundary control; and in both cases a term was added to account for the implementation cost. Several cases of both controls have been numerically simulated to investigate the performances of the control algorithm. Most cases considered show significant reductions of the costs. Another version of the feedback procedure more effective for practical implementation has been considered and implemented, and the application of this algorithm also shows significant reductions of the costs. Finally, dependence of the control algorithm on the time-discretization method is discussed.
CHALLENGES FOR ECONOMETRIC MODEL SELECTION

Standard econometric model selection methods are based on four conceptual errors: parametric vision, the assumption of a true data generating process, evaluation based on fit, and ignoring the impact of model uncertainty on inference. Instead, econometric model selection methods should be based on a semiparametric vision, models should be viewed as approximations, models should be evaluated based on their purpose, and model uncertainty should be incorporated into inference methods. These problems have been examined individually but not jointly, and my view is that future research into econometric model selection should attempt to address all four issues.This research was supported by the National Science Foundation. I thank Peter Phillips and a referee for helpful comments that greatly improved the arguments and exposition.
MLDA-Unet:Multi Level Dual Attention Unet for Polyp Segmentation

In recent years, the attention mechanism has received much attention and application in medical image segmentation. Moreover, many studies have obtained specific results by applying different attention mechanisms in various medical image segmentation tasks. However, many studies have applied the attention mechanism to the segmentation model without further discussion. In this paper, we focus on applying the dual attention mechanism and propose a model called MLDA-Unet, which applies the dual attention mechanism block to multiple layers of Unet. Its performance is 9.15%, 6.09%, and 9.47% higher than the normal DA-Unet, Unet, and Unet++, where DA Unet means that a dual attention mechanism block is used at the bottom of Unet. We first review some studies that apply attention to the medical field, and then we discuss dual attention by setting up rich control group experiments. Finally, we analyze and discuss the results, proving that the multi-level DA-Unet can better focus on target features, which could improve the model's performance.
25-Hydroxycholecalciferol Inhibits Cell Growth and Induces Apoptosis in SiHa Cervical Cells via Autocrine Vitamin D Metabolism

Preclinical studies show that the anticancer actions of vitamin D metabolites are mediated by apoptosis, inhibition of cell proliferation and induction of cell cycle arrest. Cervical cancer cells express an autocrine vitamin D metabolising system (VDMS) comprised of a vitamin D receptor, vitamin D catabolic enzyme (CYP24A1), and the activating enzyme of 25-hydroxycholecalciferol (25(OH)D3), CYP27B1. We assessed the anticancer effects of 25(OH)D3 at clinically relevant concentrations on a cervical squamous cell cancer cell line, SiHa. We evaluated cell health parameters (cell count, viability, and cell cycle), cell death modes (apoptosis, autophagic-dependent death, and necrosis by flow cytometry and transmission electron microscopy), and autocrine VDMS gene and protein expression by qPCR and Western blot, respectively. Our study demonstrates that physiological and supraphysiological doses of 25(OH)D3 inhibit cell growth and viability and induce biochemical and morphological apoptosis in SiHa cells. These growth effects are mediated by alteration in the VDMS gene and protein expression, with prominent negative feedback at supraphysiological treatment dose. These data identify promising therapeutic potential of 25(OH)D3 in cervical cancer, which warrants further clinical translational investigations.
Third-Order Finite-Difference Schemes on Icosahedral-Type Grids on the Sphere

Abstract A practical method is proposed to achieve high-order finite-difference schemes on grids that are quasi-homogeneous on the sphere. A family of grids is used that are characterized by the parameter NP, which can take on values of 3, 4, and 5, etc. The parameter NP is the number of grid patches meeting at the Poles. For NP = 3 the cube sphere grid is obtained and for NP = 5 the icosahedron is obtained. While the grid construction method is valid for all values of NP, the tests performed in this paper concern only the case NP = 5 (i.e., the icosahedron). For each of the rhomboidal patches, the grid is created by connecting points on opposing sides of the rhomboid by great circles. This offers the possibility to obtain derivatives for a line of grid points along a great circle in the classical way. Therefore, it becomes possible to use well-known spatial discretizations from limited-area models. Local models can be transferred to the sphere with rather limited effort. The method was tested using the f.
Antimicrobial and Antibiofilm Potential of Thymus vulgaris and Cymbopogon flexuosus Essential Oils against Pure and Mixed Cultures of Foodborne Bacteria

The spread of pathogenic and food spoilage microorganisms through the food chain still faces major mitigation challenges, despite modern advances. Although multiple cleaning and disinfection procedures are available for microbial load reduction in food-related settings, microbes can still remain on surfaces, equipment, or machinery, especially if they have the ability to form biofilms. The present study assessed the biofilm-forming properties of pure and mixed cultures of foodborne and spoilage bacteria (Listeria monocytogenes, Enterococcus faecalis, Aeromonas hydrophila, Brochothrix thermosphacta), using polystyrene and stainless steel contact surfaces. Subsequently, the antimicrobial and antibiofilm properties of Thymus vulgaris and Cymbopogon flexuosus essential oils—EOs—were evaluated against these bacteria. Moreover, in silico prediction of the absorption and toxicity values of the EOs’ major constituents was also performed, perceiving the putative application in food-related settings. Overall, biofilm formation was observed for all microbes under study, at different temperatures and both contact surfaces. In polystyrene, at 25 °C, when comparing pure with mixed cultures, the combination Listeria–Aeromonas achieved the highest biofilm biomass. Moreover, at 4 °C, increased biofilm formation was detected in stainless steel. Regarding thyme, this EO showed promising antimicrobial features (especially against A. hydrophila, with a MIC of 0.60 µg/µL) and antibiofilm abilities (MBEC of 110.79 µg/µL against L. monocytogenes, a major concern in food settings). As for lemongrass EO, the highest antimicrobial activity, with a MIC of 0.49 µg/µL, was also observed against L. monocytogenes. Overall, despite promising results, the in situ effectiveness of these essential oils, alone or in combination with other antimicrobial compounds, should be further explored.
Fault Diagnosis of Planetary Gearbox Using Modal Multiscale Spectral Features and 1-D CNN

The single-scale fault features of original signal from wind turbine planetary gearboxes cannot describe fault information comprehensively; instead, it generally leads to low fault diagnosis accuracy. Therefore, a fault feature extraction method is proposed for wind turbine planetary gearboxes based on the modal multiscale spectral features (MMSF) and 1-D CNN. In the feature extraction, the variational modal decomposition (VMD) is applied to gearbox vibration signal, and the intrinsic mode functions (IMFs) are obtained. These IMFs are subjected to the coarse-grained processing in the time scale. The fast Fourier transform (FFT) is performed on these IMFs, and the multiscale spectral features (MSF) of IMFs’ spectrum are extracted. Furthermore, the MMSFs are normalized and placed in the designed classifier based on 1D-CNN for training, the diagnosis modal is obtained to identify the different fault. The gearbox fault data is collected from the Wind Turbine Drivetrain Diagnostics Simulator. The experimental results show that the proposed method can improve the accuracy of gearbox fault diagnosis and has good practical value to monitor the health condition of the rotating machinery equipment.
Intrusion Detection for Internet of things Using Self-supervised Pre-training on GRU

Intrusion detection is crucial in the Internet of Things (IoT) due to the scarcity of computing resources and the variability of the network environment when it is used in smart buildings, smart factories and other scenarios. Current intrusion detection solutions are mostly applicable to a single environment (or a single dataset) and require large amounts of labeled data for supervised training methods, which are not applicable to variable IoT network environments. In this paper, we investigate the recognition effect of GRU networks on the packet sequences made up of IoT network packets and suggest a self-supervised pre-training method based on GRU networks. In order to improve the effectiveness of supervised training in the following fine-tuning phase, the unlabeled data is utilized in the pre-training of this method to find sequence data correlations in the data flow and deeper representations of the data. The proposed model is evaluated on datasets such as TON-IoT, and it is observed that our method can significantly increase the model detection accuracy.
Constrained variance design for bilinear stochastic continuous systems

Recently, a theory for the control of covariance has been introduced for linear continuous systems. The theory was motivated mainly by the fact that performance requirements of many engineering control problems are naturally described in terms of the root-mean-squared (RMS) values of the system states or outputs. However, the primary purpose of this paper is to deal with the theory of covariance control for a general class of bilinear stochastic continuous systems. Moreover, on the basis of the theory of the state covariance assignment, this paper will address a methodology which deals more directly with these RMS values.
On the Expressions of Estimability in Testing General Linear Hypotheses

In testing a general linear hypothesis of the form K ′β ∈ 𝒞( W ′) under a general linear model, an equivalent hypothesis involving only estimable parametric functions is provided, and then an explicit test statistic in terms of the model matrices is given. The corresponding results are expanded to the case of a general linear model with a restriction and are illustrated by an example.
Foetal Intrapartum Compromise at Term: Could COVID-19 Infection Be Involved? A Case Report

The impact of the SARS-CoV-2 infection on pregnancy has been studied and many reports have been published, mainly focussing on complications and in utero transmission with neonatal consequences. Although the effects of other viruses on foetuses are well known, the impact of maternal COVID-19 during pregnancy is not completely understood. We report a case of acute foetal intrapartum hypoxia without other risk factors than maternal COVID-19 disease 2 weeks previous to birth at term. Placental histological changes suggested that the viral infection could have been the culprit for the unfavourable outcome during labour. The neonate was promptly delivered by Caesarean section. Neonatal intensive care was started, including therapeutic hypothermia. The procedure was successful, the evolution of the neonate was favourable, and she was discharged after 10 days. Follow-up at 2 months of life indicated a normal neurological development but a drop in head growth. The case raises the idea that pregnancies with even mild COVID-19 symptoms may represent the cause of neonate compromise in a low-risk pregnancy. An important follow-up in the neonatal period and infancy is required to identify and treat any subsequent conditions. Further long-term studies are necessary to identify a cause–effect relationship between COVID-19 pregnancies and the whole spectrum of neonatal and infant consequences.
A GM-TCN based Hybrid Prediction Method for Ash Fouling Prediction in Coal-fired Power Plant Boiler

In response to the problem that ash deposition in boilers has a great impact on production efficiency as well as production safety, a grey level theory prediction model based on time convolution networks (GM-TCN) is proposed to predict the degree of ash in boiler heated areas by using a cleaning factor to assess the ash deposition condition on the heated surfaces. The accuracy of the prediction model is improved by utilizing the features of grey level theory that can predict the developmental changes in system behavior, combined with the high-precision approximation capability of the time-convolutional network. The modelling prediction of the ash level in the heated area of boilers during the production of coal-fired power stations is investigated. Experiments demonstrate that the model can predict the cleanliness factor with high accuracy, and the predicted values are closest to the true values compared to the other two models, verifying the accuracy and reliability of the model.
Mean Teacher-Based Cross-Domain Activity Recognition Using WiFi Signals

WiFi channel state information (CSI)-based activity recognition has initiated a great many studies because of wide availability and privacy protection. However, general recognition approaches still struggle to generalize beyond the source domain of training data, i.e., well-trained models might not be suitable to target data with unseen subjects or environments. Existing solutions, such as few-shot learning-based and data augmentation-based approaches, either require a few labeled target samples which is difficult to be collected, especially, for old target users, or inappropriately treat augmented samples with different amounts of noise. To overcome these limitations, we propose a Mean Teacher-based cross-domain human activity recognition framework using WiFi CSI, WiTeacher. In this framework, to address the shift between source and target domains, we built a label smoothing-based classification loss, where the input data are the target-like samples generated by StyleGAN, and corresponding label values are dynamically adjusted by our designed adaptive label smoothing method. To enhance the model robustness, we devise a sample relation-based consistency regularization term to keep the distances of the two samples with and without perturbations invariant, which can exploit the relationships between samples to improve recognition performance. The experiments illustrate that WiTeacher achieves obvious gains without requiring any annotation data from the target domain.
Fusion of Multiple Models for Improving Gross Primary Production Estimation With Eddy Covariance Data Based on Machine Learning

Terrestrial gross primary production (GPP) represents the magnitude of CO2 uptake through vegetation photosynthesis, and is a key variable for carbon cycles between the biosphere and atmosphere. Light use efficiency (LUE) models have been widely used to estimate GPP for its physiological mechanisms and availability of data acquisition and implementation, yet each individual GPP model has exhibited large uncertainties due to input errors and model structure, and further studies of systematic validation, comparison, and fusion of those models with eddy covariance (EC) site data across diverse ecosystem types are still needed in order to further improve GPP estimation. We here compared and fused five GPP models (VPM, EC‐LUE, GOL‐PEM, CHJ, and C‐Fix) across eight ecosystems based on FLUXNET2015 data set using the ensemble methods of Bayesian Model Averaging (BMA), Support Vector Machine (SVM), and Random Forest (RF) separately. Our results showed that for individual models, EC‐LUE gave a better performance to capture interannual variability of GPP than other models, followed by VPM and GLO‐PEM, while CHJ and C‐Fix were more limited in their estimation performance. We found RF and SVM were superior to BMA on merging individual models at various plant functional types (PFTs) and at the scale of individual sites. On the basis of individual models, the fusion methods of BMA, SVM, and RF were examined by a five‐fold cross validation for each ecosystem type, and each method successfully improved the average accuracy of estimation by 8%, 18%, and 19%, respectively.
The allopolyploid origin(s) and diversification of New Caledonian Grevillea (Proteaceae)

ABSTRACT The assembly of island plant communities is the result of a number of processes: immigration (dispersal), speciation, and extinction. Using four plastid genes and one low-copy nuclear gene, we investigated the origin of the New Caledonian Grevillea (Proteaceae), an otherwise largely Australian genus. In the combined plastid analysis, the species form two distinct clades, the exul (four species) and gillivrayi groups (six species), within group 3 of Grevillea. All New Caledonian Grevillea display two distinct copies of PHYA, one copy in group 3, and another in group 4. Previously published chromosome counts for G. meisneri and two new genome size estimates of G. gillivrayi and G. rubiginosa suggest that these plants are tetraploids. Altogether, the current data available suggest that New Caledonian Grevillea are allotetraploids, resulting from one or two hybridisation events between two or three distinct parents. A possible scenario is that modern Grevillea has descended from a hybrid swarm that formed 9–13 Mya between multiple immigrants (now extinct) that reached the island. Grevillea can be added to the list of island plant radiations with an early history of hybridisation and polyploidy. The relative importance, location and timing of these two mechanisms remain to be elucidated. The two groups of New Caledonian Grevillea may be easily distinguished by their inflorescences, flowers and fruits, with the gillivrayi group displaying greater ecological and morphological diversity. The seed characteristics of New Caledonian Grevillea are consistent with their pioneer behavior.
Sensitivity of Remotely Sensed Effective Radius of Cloud Droplets to Changes in LOWTRAN Version

Abstract A method of retrieving effective radius and optical depth of stratocumulus from narrowband near-infrared radiances was recently presented by Rawlins and Foot. Their retrieval scheme used LOWTRAN 5 to calculate atmospheric corrections, and the results showed that the remotely retrieved effective radii were significantly larger than those measured in situ with a PMS Forward Scattering Spectrometer Probe. LOWTRAN 7 has recently been developed and is used in this work for all atmospheric corrections. In this note, the major differences between LOWTRAN 5 and LOWTRAN 7 at near-infrared wavelengths are discussed, and it is shown that the retrieved effective radii using LOWTRAN 7, for two flights during the FIRE87 campaign, are in much better agreement with the in situ measurements.
A Survey of the Transcriptomic Resources in Durum Wheat: Stress Responses, Data Integration and Exploitation

Durum wheat (Triticum turgidum subsp. durum (Desf.) Husn. ) is an allotetraploid cereal crop of worldwide importance, given its use for making pasta, couscous, and bulgur. Under climate change scenarios, abiotic (e.g., high and low temperatures, salinity, drought) and biotic (mainly exemplified by fungal pathogens) stresses represent a significant limit for durum cultivation because they can severely affect yield and grain quality. The advent of next-generation sequencing technologies has brought a huge development in transcriptomic resources with many relevant datasets now available for durum wheat, at various anatomical levels, also focusing on phenological phases and environmental conditions. In this review, we cover all the transcriptomic resources generated on durum wheat to date and focus on the corresponding scientific insights gained into abiotic and biotic stress responses. We describe relevant databases, tools and approaches, including connections with other “omics” that could assist data integration for candidate gene discovery for bio-agronomical traits. The biological knowledge summarized here will ultimately help in accelerating durum wheat breeding.
Reinterpretation of the Dimensions of the McBane Empathy Scale (1995) through Neuroscience

Empathy has been measured in several fields, such as psychology, marketing and sales force, which is why several scales have been developed. The contradictory effects of empathy on sales performance are partly attributed to the conceptualisation of empathy as a one-dimensional, bi-dimensional or multi-dimensional construct. This research aims to contribute to this debate by drawing on a review of the cognitive neuroscience literature and the contributions of the biomarketing paradigm to explain the neurochemical and neuroanatomical processes of empathy. we will focus our attention on the study of the scale of empathy proposed by McBane (1995). This neurological corroboration leads us to consider that empathy is composed of both the affective and cognitive dimensions and that emotional contagion is not a third dimension of empathy, but constitutes the phylogenetic basis of affective empathy. These neurochemical and neuroanatomical corroborations fundamentally reconfirm the number of dimensions proposed by McBane (1995) and constitute a solid theoretical groundwork that paves the way for future empirical research.
On complexity measures

Abstract We have shown in this paper that we inevitably come to a rather general concept of complexity also in the so‐called exact sciences, such as physics or chemistry. Basing on the dualities of order and disorder or confirmation and novelty, two types of complexity measures are distinguished: the classical complexity measures (CCM), which are measures of randomness, and the alternative complexity measures (ACM), which relate highest complexity to a mixing of novelty and confirmation. We have presented typical measures of each class, such as algorithmic complexity or e‐complexity and have discussed how they characterize different kinds of dynamical and structural features. Finally, this concept has been applied for testing a model of the reversals of the Earth's magnetic field.