Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of flexibility and responsiveness on the financial performance in Malaysia’s manufacturing industry during the COVID-19 pandemic

In order to combat the disruption caused by COVID-19, it is essential for the company's operations to implement flexibility and responsiveness in their work. In this study, the researchers will investigate the effect of these two variables on the financial performance of the company. The current study evaluates the data obtained from 215 manufacturing companies in Malaysia. The data is collected and analysed using PLS-SEM in order to provide an in-depth look at the issue. The results show that flexibility and responsiveness are positively related to a company's financial condition. The primary contribution of this study is to provide the management of company with a ground-breaking idea of dealing with a pandemic situation in the current business environment. A company's capacity to survive also depends on planning changes, particularly those that affect supply chain management and production operations. When making tough decisions, managers must be less rigid and more flexible. Demand management, inventory management, supply chain management, and operation management are some of the things that call for flexibility and responsiveness. The business will experience losses if they are unable to meet the demands of the COVID-19 era and maintain business operations. Of course, this will have an impact on the company’s financial performance.
Explainability of deep learning models in medical video analysis: a survey

Deep learning methods have proven to be effective for multiple diagnostic tasks in medicine and have been performing significantly better in comparison to other traditional machine learning methods. However, the black-box nature of deep neural networks has restricted their use in real-world applications, especially in healthcare. Therefore, explainability of the machine learning models, which focuses on providing of the comprehensible explanations of model outputs, may affect the possibility of adoption of such models in clinical use. There are various studies reviewing approaches to explainability in multiple domains. This article provides a review of the current approaches and applications of explainable deep learning for a specific area of medical data analysis—medical video processing tasks. The article introduces the field of explainable AI and summarizes the most important requirements for explainability in medical applications. Subsequently, we provide an overview of existing methods, evaluation metrics and focus more on those that can be applied to analytical tasks involving the processing of video data in the medical domain. Finally we identify some of the open research issues in the analysed area.
NO and GSH Alleviate the Inhibition of Low-Temperature Stress on Cowpea Seedlings

Low-temperature stress in early spring seriously affects the growth and development of cowpea seedlings. To study the alleviative effect of the exogenous substances nitric oxide (NO) and glutathione (GSH) on cowpea (Vigna unguiculata (Linn.) Walp. ) seedlings under 8 °C low-temperature stress, 200 μmol·L−1 NO and 5 mmol·L−1 GSH were sprayed on cowpea seedlings whose second true leaf was about to unfold to enhance the tolerance of cowpea seedlings to low temperature. Spraying NO and GSH can eliminate excess superoxide radicals (O2−) and hydrogen peroxide (H2O2) to varying degrees, reduce the content of malondialdehyde and relative conductivity, delay the degradation of photosynthetic pigments, increase the content of osmotic regulating substances such as soluble sugar, soluble protein, and proline, and improve the activity of antioxidant enzymes such as superoxide dismutase, peroxidase, catalase, ascorbate peroxidase, dehydroascorbate reductase, and monodehydroascorbate reductase. This study revealed that the mixed use of NO and GSH played an important role in alleviating low temperature stress, and the effect of spraying NO alone was better than that of spraying GSH.
Increasing the Homogeneity of CAT's Item‐Exposure Rates by Minimizing or Maximizing Varied Target Functions While Assembling Shadow Tests

A computerized adaptive testing (CAT) algorithm that has the potential to increase the homogeneity of CAT's item-exposure rates without significantly sacrificing the precision of ability estimates was proposed and assessed in the shadow-test (van der Linden & Reese, 1998) CAT context. This CAT algorithm was formed by a combination of maximizing or minimizing varied target functions while assembling shadow tests. There were four target functions to be separately used in the first, second, third, and fourth quarter test of CAT. The elements to be used in the four functions were associated with (a) a random number assigned to each item, (b) the absolute difference between an examinee's current ability estimate and an item difficulty, (c) the absolute difference between an examinee's current ability estimate and an optimum item difficulty, and (d) item information. The results indicated that this combined CAT fully utilized all the items in the pool, reduced the maximum exposure rates, and achieved more homogeneous exposure rates. Moreover, its precision in recovering ability estimates was similar to that of the maximum item-information method. The combined CAT method resulted in the best overall results compared with the other individual CAT item-selection methods. The findings from the combined CAT are encouraging. Future uses are discussed.
Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images

In the world, brain tumor (BT) is considered the major cause of death related to cancer, which requires early and accurate detection for patient survival. In the early detection of BT, computer-aided diagnosis (CAD) plays a significant role, the medical experts receive a second opinion through CAD during image examination. Several researchers proposed different methods based on traditional machine learning (TML) and deep learning (DL). The TML requires hand-crafted features engineering, which is a time-consuming process to select an optimal features extractor and requires domain experts to have enough knowledge of optimal features selection. The DL methods outperform the TML due to the end-to-end automatic, high-level, and robust feature extraction mechanism. In BT classification, the deep learning methods have a great potential to capture local features by convolution operation, but the ability of global features extraction to keep Long-range dependencies is relatively weak. A self-attention mechanism in Vision Transformer (ViT) has the ability to model long-range dependencies which is very important for precise BT classification. Therefore, we employ a hybrid transformer-enhanced convolutional neural network (TECNN)-based model for BT classification, where the CNN is used for local feature extraction and the transformer employs an attention mechanism to extract global features. Experiments are performed on two public datasets that are BraTS 2018 and Figshare. The experimental results of our model using BraTS 2018 and Figshare datasets achieves an average accuracy of 96.75% and 99.10%, respectively. In the experiments, the proposed model outperforms several state-of-the-art methods using BraTS 2018 and Figshare datasets by achieving 3.06% and 1.06% accuracy, respectively.
Long-Term In Situ Conservation Drove Microevolution of Solina d’Abruzzo Wheat on Adaptive, Agronomic and Qualitative Traits

Solina is an example of a bread wheat landrace that has been conserved in situ for centuries in Central Italy. A core collection of Solina lines sampled in areas at different altitudes and climatic conditions was obtained and genotyped. A clustering analysis based on a wide SNP dataset generated from DArTseq analysis outlined the existence of two main groups, which, after Fst analysis, showed polymorphism in genes associated with vernalization and photoperiod response. Starting from the hypothesis that the different pedoclimatic environments in which Solina lines were conserved may have shaped the population, some phenotypic characteristics were studied in the Solina core collection. Growth habit, low-temperature resistance, allelic variations at major loci involved in vernalization response, and sensitivity to photoperiod were evaluated, together with seed morphologies, grain colour, and hardness. The two Solina groups showed different responses to low temperatures and to photoperiod-specific allelic variations as well as the different morphology and technological characteristics of the grain. In conclusion, the long-term in situ conservation of Solina in environments sited at different altitudes has had an impact on the evolution of this landrace which, despite its high genetic diversity, remains clearly identifiable and distinct so as to be included in conservation varieties.
New gender genetic algorithm for solving graph partitioning problems

An efficient genetic algorithm based on gender selection is proposed. In this algorithm, each individual (chromosome) in the population has an additional feature, its gender. Individuals are arranged in descending order according to their fitness values, then a gender is assigned to each chromosome by simply alternating female with male. Crossover, or mating, is only permitted between individuals of opposite genders. The new gender-based genetic algorithm (GGA) is applied to partitioning problems and its performance is compared to the conventional genetic algorithm (GA). Experimental results indicate that GGA significantly outperformed GA in terms of the quality of the optimum solution and the number of generations (convergence).
New Perspectives on AX Monocerotis

AX Moncerotis is a 232d, noneclipsing, interacting binary star that consists of a K giant, a Be-like giant, and large amounts of circumstellar material. The K star is almost certainly a synchronous rotator and is probably in contact with its critical lobe. The Be star was believed to be a rapid rotator based on extremely wide absorption lines, but new spectra show that these lines arise from the circumstellar environment. Hydrogen emission, also circumstellar, is many times stronger than the continuum. Near-ultraviolet light curves exhibit a 0.5 mag dip near phase 0.75, but there is no such variability at longer wavelengths. Gas flow trajectories from the cusp of the K star toward the Be star provide a simple explanation for the photometric and spectroscopic behavior. We may have found a decreasing orbital period, but more data are necessary to confirm this result. We present several models for AX Mon based on (1) new and archival visible photometry, (2) archival ultraviolet spectroscopy, (3) new and archival visible spectroscopy, (4) new visible polarimetry, and (5) new radio photometry. Future observations, including optical interferometry, are proposed.
A Refinement Boosted and Attention Guided Deep FISTA Reconstruction Framework for Compressive Spectral Imaging

Hyperspectral images (HSIs) contain rich spatial and spectral information. A double dispersers coded aperture snapshot spectral imaging (DD-CASSI) system takes advantage of compressive sensing (CS) theory to map 3-D HSI data into a single 2-D measurement. One of the key components of DD-CASSI is to reconstruct high-quality HSI from measurement. Traditional model-based methods use mathematical optimization to reconstruct HSIs according to prior knowledge. Current deep learning-based methods achieve pleasant results. However, fully learned deep learning methods lack interpretability, and model-based deep learning methods cannot achieve pleasant performance. In this article, we propose a novel HSI reconstruction framework na med refinement boosted and attention guided tensor fast iterative shrinkage-thresholding algorithm-Net (ReAttFISTA-Net), which combines model-based deep learning and fully learned deep learning reconstruction strategies. In this framework, we introduce an attention guided fusion mechanism, which enhances spatial–spectral information, refinement subnetwork, and auxiliary loss terms to improve the reconstruction performance. Extensive experimental results show that the proposed reconstruction algorithm outperforms the state-of-the-art algorithms on both simulation and real-world datasets.
Ridge regression energy levels calculation of neutral ytterbium (Z = 70)

In view of the difficulty in calculating the atomic structure parameters of high-Z elements, the Hartree–Fock with relativistic corrections (HFR) theory in combination with the ridge regression (RR) algorithm rather than the Cowan code’s least squares fitting (LSF) method is proposed and applied. By analyzing the energy level structure parameters of the HFR theory and using the fitting experimental energy level extrapolation method, some excited state energy levels of the Yb I (Z = 70) atom including the 4f open shell are calculated. The advantages of the ridge regression algorithm are demonstrated by comparing it with Cowan code’s LSF results. In addition, the results obtained by the new method are compared with the experimental results and other theoretical results to demonstrate the reliability and accuracy of our approach.
Structural condition assessment of offshore wind turbine monopile foundations using vibration monitoring data

The offshore wind farm industry has substantially grown in recent years. There is a substantial number of wind farms in the waters offshore Europe with several others proposed for regions in North America and Asia. A vast majority of the existing offshore wind turbines are supported on monopile foundations. The presence of a monopile in a flowing body of water causes the water to accelerate around the monopile, which can result in seafloor scour. Scour can significantly reduce the soil support thereby reducing the monopile stability and strength. Scour can also reduce the fundamental natural frequency of the turbine structure. If the natural frequency of the support structure approaches the turbine rotor frequency, a resonant condition will develop that can damage the turbine or its support structure. To avoid this situation, a monitoring system consisting of two strategically located bidirectional accelerometers was installed on the towers of four turbines of an existing offshore wind farm. The objective of the monitoring campaign was to correlate frequency measurements with the overall structural performance of the monopile foundations. The developed approach was based on operational modal analysis and system identification techniques using acceleration response data and calibrated computer models. Identified frequencies were correlated to specific structural performance limit states including; vibrating resonance of the tower with the turbine rotor, lack of foundation stability, and yielding of the monopile foundation material. This paper presents the findings of this work, which can assist owners and operators who may benefit from similar structural health monitoring systems.
Costs, quality-adjusted life years, and value-of-information of different thresholds for the initiation of invasive ventilation in hypoxemic respiratory failure

Objective: To estimate costs, quality-adjusted life-years, and the value of undertaking a future randomized controlled trial for different oxygenation thresholds used to initiate invasive ventilation in hypoxemic respiratory failure. Design: Model-based cost-utility estimation with individual-level simulation and value-of-information analysis. Setting: Critical care units. Participants: Adults admitted to critical care receiving non-invasive oxygen. Interventions: We compared four strategies: initiation of invasive ventilation at thresholds of saturation-to-inspired oxygen fraction ratio (SF) < 110, < 98, or < 88, and usual care. Main results: An invasive ventilation initiation threshold of SF < 110, compared to usual care, resulted in more predicted invasive ventilation (62% vs 31%), hospital survival (78.4% vs 75.5%), quality-adjusted life years (QALYs) (8.48 vs 8.34), and lifetime costs (86,700 Canadian dollars (CAD) vs 75,600 CAD). Among the four strategies, threshold SF < 110 had the highest expected net monetary benefit (761,000 CAD), but there was significant uncertainty, because all four strategies had similar probability (range: 23.5% to 27.5%) of having the best net monetary benefit. The expected value to society over the next 10 years of a 400-person randomized trial of oxygenation thresholds was 4.27 billion CAD, and remained high (2.64 billion CAD) in a scenario analysis considering a hypothetical threshold that resulted in less invasive ventilation and similar survival compared to usual care. Conclusion: The preferred threshold to initiate invasive ventilation in hypoxemic respiratory failure is uncertain. It would be highly valuable to society to identify thresholds that, in comparison to usual care, either improve survival or reduce invasive ventilation without reducing survival.
A generalized grid connectivity–based parameterization for subsurface flow model calibration

We develop a novel method of parameterization for spatial hydraulic property characterization to mitigate the challenges associated with the nonlinear inverse problem of subsurface flow model calibration. The parameterization is performed by the projection of the estimable hydraulic property field onto an orthonormal basis derived from the grid connectivity structure. The basis functions represent the modal shapes or harmonics of the grid, are defined by a modal frequency, and converge to special cases of the discrete Fourier series under certain grid geometries and boundary assumptions; therefore, hydraulic property updates are performed in the spectral domain and merge with Fourier analysis in ideal cases. Dependence on the grid alone implies that the basis may characterize any grid geometry, including corner point and unstructured, is model independent, and is constructed off‐line and only once prior to flow data assimilation. We apply the parameterization in an adaptive multiscale model calibration workflow for three subsurface flow models. Several different grid geometries are considered. In each case the prior hydraulic property model is updated using a parameterized multiplier field that is superimposed onto the grid and assigned an initial value of unity at each cell. The special case corresponding to a constant multiplier is always applied through the constant basis function. Higher modes are adaptively employed during minimization of data misfit to resolve multiscale heterogeneity in the geomodel. The parameterization demonstrates selective updating of heterogeneity at locations and spatial scales sensitive to the available data, otherwise leaving the prior model unchanged as desired.
Uncooled infrared-imaging face recognition using kernel-based generalized discriminant analysis

We investigate the application of the kernel-based generalized discriminant analysis (GDA) approach to the recognition of infrared face images collected using a low-resolution uncooled IR camera. Results show that a low-cost, low-resolution IR camera combined with an efficient classifier is a usable tool in uncooled IR face recognition applications, and that best GDA-based recognition performance improves over that obtained with the fisherface approach by 3.96 percentage points, from 94.59% to 98.55%, on the data considered. This study also investigates the effects the number of projection vectors used in the GDA step, the kernel expression, and the specific distance type have on recognition performance. Results show the Mahalanobis angular distance to be the best choice, and that the recognizer computational load may be reduced by decreasing the number of eigenvectors selected in the GDA projection step without significant impact on recognition performance.
Les familles exponentielles sur R2 de fonction-variance V(m)=am⊗m+B(m)+C

Nous explicitons la classe L des familles exponentielles naturelles de R 2 dont la fonction-variance est de la forme V(m)=am⊗m+B(m)+C. Aux affinites pres, il y en a exactement 5 types. Cette classification decoule de celle des endomorphismes de R 2
Flow Control by Dynamic Vane Vortex Generators Based on Piezoceramic Actuators

This paper describes a study of dynamical vane vortex generators in a flow over a flat plate. Fluidic vortex generators are more effective when operated dynamically. Thus, it is the aim of this study to find out whether mechanical vortex generators are also superior under dynamic operating conditions. The motion of the vortex generators is generated by piezoceramic actuators constructed in a bimorph configuration, which consists of a carbon-fiber bar covered with piezoceramic face actuators. The actuators exploit the longitudinal piezoelectric effect (d 33 effect), they are operated in resonance to reach the required displacement and generate a sinusoidal motion of the vortex generators. Vortex generators and actuators were integrated into a flat plate in a low-speed wind tunnel. A stereo particle image velocimetry system was used to record phase-locked flowfields that were analyzed using vortex classification methods. It was found that the transient development of the vortex core position and circulation is very different from that of static vanes. While vortices from static vortex generators are able to survive over a considerable distance, the vortices from dynamically driven ones decay faster. It is argued that the dynamic vortices have a greater ability to reorganize the momentum in the turbulent boundary layer.
A subarcsecond H2O maser shell surrounding a variable star

Radio continuum and H{sub 2}O maser emission have been detected from the semiregular variable W Hya. The continuum emission at radio frequencies probably comes from the outer photosphere of the red giant star. A partial resolution of the stellar disk shows it to be approximately 0.09 arcsec in diameter. The H{sub 2}O maser emission arises from a ring of diameter 0.3 arcsec that is well centered on the star. These results for the first time yield a clear detection of a stellar photosphere at radio frequencies (except for the sun) and a radio measurement of the size of a stellar disk. Also, these are the first observations that precisely locate circumstellar maser emission with respect to the exciting star, allowing a detailed modeling of the maser amplification. The H{sub 2}O masers are unsaturated, amplify spontaneous emission, and are highly beamed. 13 refs.
On the 3D Curvature and Dynamics of the Musca Filament

Filaments are ubiquitous in the interstellar medium, yet their formation and evolution remain the topic of intense debate. In order to obtain a more comprehensive view of the 3D morphology and evolution of the Musca filament, we model the C18O(2-1) emission along the filament crest with several large-scale velocity field structures. This indicates that Musca is well described by a 3D curved cylindrical filament with longitudinal mass inflow to its center unless the filament is a transient structure with a lifetime ≲0.1 Myr. Gravitational longitudinal collapse models of filaments appear unable to explain the observed velocity field. To better understand these kinematics, we further analyze a map of the C18O(2-1) velocity field at the location of SOFIA HAWC+ dust polarization observations that trace the magnetic field in the filament. This unveils an organized magnetic field that is oriented roughly perpendicular to the filament crest. Although the velocity field is also organized, it progressively changes its orientation by more than 90° when laterally crossing the filament crest and thus appears disconnected from the magnetic field in the filament. This strong lateral change of the velocity field over the filament remains unexplained and might be associated with important longitudinal motion that can be associated to the large-scale kinematics along the filament.