Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping rapeseed in China during 2017-2021 using Sentinel data: an automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC)

ABSTRACT Rapeseed mapping is important for national food security and government regulation of land use. Various methods, including empirical index-based and machine learning-based methods, have been developed to identify rapeseed using remote sensing. Empirical index-based methods commonly employed empirically designed indices to enhance rapeseed’s bright yellow spectral feature during the flowering period, which is straightforward to implement. Unfortunately, the heavy cloud cover in the flowering period of China would lead to serious omission errors; and the required flowering period varies spatially and yearly, which often cannot be acquired accurately. Machine learning-based methods mitigate the reliance on clear observations during the flowering period by inputting all-season imagery to adaptively learn features. However, it is difficult to collect sufficient samples across all of China considering the large intraclass variation in both land cover types of rapeseed and non-rapeseed. This study proposed an automated rapeseed mapping approach integrating rule-based sample generation and a one-class classifier (RSG-OC) to overcome the shortcomings of the two types of methods. First, a set of sample selection rules based on empirical indices of rapeseed were developed to automatically generate samples in cloud-free pixels during the predicted flowering period throughout China. Second, all available features composited based on the rapeseed phenological calendar were used for classification to eliminate the phenology differences in different regions. Third, a specific sample augmentation that removes the observation in the flowering period was employed to improve the generalization to the pixels without cloud-free observation in the flowering period. Finally, to avoid the need for diverse samples of nonrapeseed classes, a typical one-class classifier, positive unlabeled learning implemented by random forest (PUL-RF) trained by the generated samples, was applied to map rapeseed. With the proposed method, China rapeseed was mapped at 20 m resolution during 2017–2021 based on the Google Earth Engine (GEE). Validation on six typical rapeseed planting areas demonstrates that RSG-OC achieves an average accuracy of 94.90%. In comparison, the average accuracy of the other methods ranged from 83.33% to 88.25%, all of which were poorer than the proposed method. Additional experiments show that the performance of RSG-OC was not sensitive to cloud contamination, inaccurate predicted flowering time and the threshold of sample selection rule. These results indicate that the rapeseed maps produced in China are overall reliable and that the proposed method is an effective and robust method for annual rapeseed mapping across China.
Study of four regularization methods for the inverse problem in bioluminescence tomography

As a promising tool for in-vivo molecular imaging of small animals, Bioluminescence Tomography (BLT) aims at the quantitative reconstruction of the bioluminescent source distribution from the detected optical signals on the body surface. Mathematically, BLT is a highly ill-posed inverse problem per se. Most existing works are based on Tikhonov regularization in which the selection of the proper regular parameter is quite difficult. In this paper, two direct regularization methods, truncated singular value decomposition (TSVD) and truncated total least squares (TTLS), as well as two iterative regularization methods, conjugate gradient least squares (CGLS) and least squares QR decomposition (LSQR), are applied to the inverse problem in BLT, with the finite element method solving the diffusion equation. In the numerical simulation, a heterogeneous phantom is designed to compare and evaluate the four methods. The results show that all the four methods can reconstruct the position of bioluminescence sources accurately and are more convenient in the determination of regularization parameter than Tikhonov method. In addition, with a priori knowledge of the source permissible region employed in the reconstruction, the iterative methods are faster than the two direct methods. Among the four methods, LSQR performs quite stably when both model noise and measure noise are considered.
Numerical modelling of coupling mechanisms during freezing in porous materials

Freezing of porous materials is of interest for a wide range of engineering disciplines such as civil‐, geotechnical and environmental engineering as well as food industry. The freezing process is characterized by various couplings between the phase transition and the liquid transport within the pore space, accompanied by considerable deformations. This paper presents a macroscopic three‐phase finite element model of liquid‐infiltrated porous media subjected to freezing. For the description of the associated coupled thermo‐hydro‐mechanical behavior, the governing balance equations of the problem are formulated based upon the constitutive relations derived within the framework of thermo‐poro‐elasticity. The applicability of the model is shown by a simulation example of artificial ground freezing with coupled thermal‐hydro‐mechanical processes. (© 2011 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
A mesh-independence principle for operator equations and their discretizations

The mesh-independence principle asserts that, when Newton’s method is applied to a nonlinear equation between some Banach spaces as well as to some finite-dimensional discretization of that equation, then the behavior of the discretized process is asymptotically the same as that for the original iteration and, as a consequence, the number of steps required by the two processes to converge to within a given tolerance is essentially the same. So far this result has been proved only for certain classes of boundary value problems. In this paper a proof is presented for a general class of operator equations and discretizations. It covers the earlier results and extends them well beyond the cases that have been considered before.
The Impact of Arp2/3 Complex Inhibition on Cytoskeleton Dynamics and Mitochondrial Function during Goat Oocyte Meiosis

Simple Summary The actin-related protein complex 2/3 complex (Arp2/3) is a kind of actin regulatory protein that regulates the nucleation, aggregation and assembly of F-actin. In recent years, there have been reports at home and abroad about the effects of the Arp2/3 complex on oocyte maturation of mammals such as pigs and mice, but few studies are about its effects on goats. In present study we showed that the Arp2/3 complex is necessary for the maintenance of cytoskeletal meiotic dynamics, such as F-actin assembly and spindle organization in goat oocytes. Additionally, these cytoskeletal dynamics are essential for the regulation of mitochondrial distribution and function, including mitochondrial activity and redox homeostasis, which are critical for the meiotic maturation of goat oocytes. Abstract F-actin is of critical importance in oocyte meiotic maturation. Actin assembly and its dynamics are mainly regulated by actin nucleation factors. The actin-related protein complex 2/3 (Arp2/3) is responsible for the organization of F-actin filaments. However, the role of Arp2/3 complex in goat oocytes has not been fully elucidated. Our findings demonstrate that Arp2/3 complex activity is necessary for the maturation of goat oocytes. The Arp2/3 complex-specific inhibitor CK666 impairs the maturation of goat oocytes and alters the genes associated with cumulus expansion, both of which suggest that normal meiosis is affected. Arp2, one of the subunits of the Arp2/3 complex, was found to be mainly accumulated at the oocyte cortex and to co-localize with F-actin during goat oocyte maturation in our results. Thus, we further investigated the cytoskeleton dynamics and found that Arp2/3 complex inhibition disrupts the F-actin assembly and spindle organization. Further analysis revealed that, in addition to direct effects on the cytoskeleton, Arp2/3 complex could also induce ROS accumulation and oxidative stress by disrupting mitochondrial distribution and function, ultimately increasing the rate of early apoptosis in goat oocytes. Our study provides evidence that the Arp2/3 complex is a key regulator of goat oocyte maturation through its regulation of the cytoskeleton dynamics and mitochondrial function.
Numerical Simulation of a Semi-Confined Slot Turbulent Impinging Jet

A Realizable k-ε turbulence model in conjunction with a standard wall function has been applied to the prediction of a fully-developed two-dimensional jet impinging within a semi-confined space. A single geometry with a Reynolds number of 10,000 and a nozzle –to-plate spacing of eight diameters has been considered at different inlet boundary conditions. The numerical results, including the time-averaged velocities and the turbulent intensity, have been compared with the experimental data reported by Yoshida (ref 5). It is found that the trends in the axial velocity, the radial velocity and the turbulent intensity are fairly predicted. The fully-developed boundary condition is generally better than the constant velocity boundary condition. The differences between the numerical and experimental results can be attributed to the turbulence model and the treatment of the low Reynolds number zone near the wall.
Mixing regimes in a cluster of seven maar lakes in tropical monsoon Asia

ABSTRACT The 7 maar lakes of San Pablo are a cluster of small volcanic lakes on Luzon Island, Philippines. These lakes, which are heavily utilized for aquaculture and ecotourism, usually experience fish kills that coincide with the northeast monsoon (NEM). This study explores limnophysical processes, particularly mixing regimes, in the lakes in relation to prevailing monsoons. We monitored monthly vertical and seasonal profiles of water temperature, salinity, conductivity, and dissolved oxygen from October 2016 to December 2018. Three types of mixing regimes were observed among the lakes, which have similar surface areas but different depths: polymixis in the shallowest; warm monomixis in lakes with intermediate depth; and meromixis in the deepest. A boundary between monomixis and meromixis was identified between 36 and 62 m depth. Monthly monitoring showed seasonal mixing occurred exclusively during the NEM (Nov–Apr). We also incorporated meteorological data into the model and performed multiple regression analysis for each lake to determine the best predictor: lake stability, as indicated by the Schmidt stability (ST ). A between-lake comparison showed lake stability was strongly correlated with both air temperature and wind speed, suggesting these 2 meteorological variables are involved in establishing thermal stratification in the lakes during the southwest monsoon. This study provides insights for adaptive lake management and projections of climate impacts on these understudied tropical lake ecosystems in Southeast Asia.
An Effective Potential of Metallic Vanadium and Chromium

Over these two decades, the local density functional approximation (LDA) has been used as an effective potential for various band calculations and has obtained many fruitful results. Recently, however, LDA+ U method came to be used to obtain better results by choosing a suitable value of U . There have been a few versions of the method. In this paper another new version of the LDA+ U method, i.e. U (1.0- ), is proposed and a suitable effective potential of metallic V and Cr is obtained by the value of U = 0.15 Ry= 2 eV.
GMSK Demodulation Combining 1D‐CNN and Bi‐LSTM Network Over Strong Solar Wind Turbulence Channel

To solve the problem of bit error rate (BER) performance degradation over strong solar wind turbulence channel, this paper addresses to Gaussian minimum frequency shift keying (GMSK) demodulation using machine learning. First, by analyzing the scintillation characteristics of the telemetry signal caused by solar wind turbulence during the solar superior conjunction, the K distribution channel model is innovatively established over strong solar wind turbulence channel. Then, the approximate probability density function of the K distribution of the established channel is studied by using Laguerre orthogonal polynomial for convenience. Additionally, the BER performance of GMSK over strong solar wind turbulence channel is analyzed. Second, using a one‐dimensional convolutional neural network (1D‐CNN) and bidirectional long short‐term memory network (Bi‐LSTM), two demodulators for GMSK are proposed. By integrating the 1D‐CNN and LSTM to extract local features and process the dynamic information of GMSK signals, we propose a novel GMSK demodulator over strong solar wind turbulence channel with combining 1D‐CNN and Bi‐LSTM. The simulation results show that, even if the solar wind turbulence has serious effects on the signal at a low signal‐to‐noise ratio, the proposed combining neural network demodulation can obtain better BER performance than the classical Viterbi algorithm, demodulation using only a single 1D‐CNN or Bi‐LSTM. The proposed GMSK demodulator is more suitable for the deep space exploration of the solar system.
Approximation of Light-Ray Deflection Angle and Gravitational Lenses in the Schwarzschild Metric. II. Lensing Magnification in a Binary System

We apply an exact solution of a light deflection angle in the Schwarzschild metric, written with elliptic integrals, to the basic lensing theory. The magnifications for a point and a surface source are calculated and compared to the magnification provided by the usually applied traditional Einstein solution. We propose a simple correction to the Einstein angle 4/b that fixes it closer to the solution written with an elliptic integral. The results are applied to an eclipsing binary system, and the luminosity of a binary system at the secondary eclipse with lensing effects is calculated. Our results show that the radius of the Einstein ring, where the magnification diverges, is slightly underestimated by the Einstein solution. This leads to an error of the overall brightness of a point-mass lens. The effect is pronounced with systems of comparable physical and Einstein radii, especially when the Einstein ring is partially inside the physical radius of the lens object. The difference is not usually large, but in the worst case of a tight double neutron star, the error made in total flux can be on the order of ~10% if the higher order effects are neglected.
Satellite Sampling and the Diurnal Cycle Statistics of Darwin Rainfall Data

Abstract This paper presents an analysis of rainfall data based on the radar echoes collected in the vicinity of Darwin, Australia, during the special observation periods in 1988. The Darwin rainfall data are available in the form of hourly averaged grids of size 141 × 141 with an areal resolution of 2 km × 2 km. The data are available for approximately 19 days in the first subset and for 22 days in the second. Since the rainfall data were taken over both the land and the ocean, separate analyses were performed for land and ocean surfaces; thus, three univariate time series (for land, ocean, and combination) are presented for each set. Time series analysis was performed in both time and frequency domains, and both the correlogram and periodogram showed the presence of a strong diurnal cycle in all the time series. Considerable variations can be seen in the diurnal cycles of these time series. To analyze the effect of the diurnal cycle on the sampling errors, flush visits of idealized satellites were simul.
Secrecy Energy Efficiency Maximization for Multi-User Multi-Eavesdropper Cell-Free Massive MIMO Networks

In this paper, the secrecy energy efficiency (SEE) optimization problem is studied in multi-user multi-eavesdropper cell-free massive multiple-input multiple-output (CF-mMIMO) networks, where a large number of distributed access points (APs) equipped with multiple antennas connect to a central processing unit via backhaul links to communicate with multiple users in the presence of multiple active non-colluding eavesdroppers. In order to theoretically analyze the SEE performance of CF-mMIMO systems, we first provide a lower bound on the ergodic secrecy rate. Meanwhile, a realistic model is used to measure the power consumption of CF-mMIMO networks, which takes into account the radio transmit power of all APs, circuit power consumption, and the power consumed by the backhaul link transmissions. Then, we develop a confidential and energy-efficient design for transmit power allocation subject to individual quality-of-service requirements for the users and total transmit power constraint of all APs. For the reason that the proposed SEE maximization (SEEM) problem is non-convex, we give a two-tier iterative power allocation algorithm, capitalizing on Dinkelbach's method and successive convex approximation approach. Simulation results show that the proposed SEEM algorithm converges fast and outperforms the conventional secrecy rate maximization and energy efficiency maximization strategies in terms of SEE performance.
Orientia tsutsugamushi Infection in Wild Small Mammals in Western Yunnan Province, China

Small mammals can transmit and serve as a reservoir for Orientia tsutsugamushi (Ot) in nature by carrying infected mites. In Yunnan, one of China’s main foci of scrub typhus, etiological evidence and genetic diversity for Ot is limited. A total of 2538 small mammals were captured seasonally from 2015 to 2016 in the three counties of Yunnan, and the spleen or liver tissue was examined for Ot based on 56 kDa nPCR. The overall prevalence of Ot was 1.77%, ranging from 0.26 to 9.09% across different species. The Gilliam strain was found in 35.6% (16/45) of the wild small mammals, followed by the Karp 11.1% (5/45) and TA763 (1/45) strains, the last of which was discovered in western Yunnan for the first time. In Lianghe, Ot infection rates in wild small mammals were higher than in the other two counties. The infection rates of Eothenomys miletus with Ot were highest in the three dominant species. Ot infection rates in wild small mammals were higher in Lianghe (1200–1400 m) and Yulong (2800–3000 m). These findings could provide research clues for further confirmation of scrub typhus foci in western Yunnan or other similar natural environments.
The Use of Artificial Intelligence (AI) in the Radiology Field: What Is the State of Doctor–Patient Communication in Cancer Diagnosis?

Simple Summary Artificial Intelligence (AI) has been increasingly used in radiology to improve diagnostic procedures over the past decades. The application of AI at the time of cancer diagnosis also creates challenges in the way doctors should communicate the use of AI to patients. The present systematic review deals with the patient’s psycho-cognitive perspective on AI and the interpersonal skills between patients and physicians when AI is implemented in cancer diagnosis communication. Evidence from the retrieved studies pointed out that the use of AI in radiology is negatively associated with patient trust in AI and patient-centered communication in cancer disease. Abstract Background: In the past decade, interest in applying Artificial Intelligence (AI) in radiology to improve diagnostic procedures increased. AI has potential benefits spanning all steps of the imaging chain, from the prescription of diagnostic tests to the communication of test reports. The use of AI in the field of radiology also poses challenges in doctor–patient communication at the time of the diagnosis. This systematic review focuses on the patient role and the interpersonal skills between patients and physicians when AI is implemented in cancer diagnosis communication. Methods: A systematic search was conducted on PubMed, Embase, Medline, Scopus, and PsycNet from 1990 to 2021. The search terms were: (“artificial intelligence” or “intelligence machine”) and “communication” “radiology” and “oncology diagnosis”. The PRISMA guidelines were followed. Results: 517 records were identified, and 5 papers met the inclusion criteria and were analyzed. Most of the articles emphasized the success of the technological support of AI in radiology at the expense of patient trust in AI and patient-centered communication in cancer disease. Practical implications and future guidelines were discussed according to the results. Conclusions: AI has proven to be beneficial in helping clinicians with diagnosis. Future research may improve patients’ trust through adequate information about the advantageous use of AI and an increase in medical compliance with adequate training on doctor–patient diagnosis communication.
Bone vascularization: a way to study bone microarchitecture?

Trabecular bone and its microarchitecture are of prime importance for health. Studying vascularization helps to better know the relationship between bone and vascular microarchitecture. This research is an animal study (nine Lewis rats), based on the perfusion of vascularization by a contrast agent (a mixture of 50% barium sulfate with 1.5% of gelatin) before euthanasia. The samples were studied by micro CT at a resolution of 9μm. Softwares were used to show 3D volumes of bone and vessels, to calculate bone and vessels microarchitecture parameters. This study aims to understand simultaneously the bone microarchitecture and its vascular microarchitecture.
Smoothing Observational Data: A Philosophy and Implementation for the Health Sciences

Standard statistical methods (such as regression analysis) presume the data are generated by an identifiable random process, and attempt to model that process in a parsimonious fashion. In contrast, observational data in the health sciences are generated by complex, nonidentified, and largely nonrandom mechanisms, and are analyzed to form inferences on latent structures. Despite this gap between the methods and reality, most observational data analysis comprises application of standard methods, followed by narrative discussion of the problems of entailed by doing so. Alternative approaches employ latent‐structure models that include components for nonidentified mechanisms. Standard methods can still be useful, however, provided their modeling philosophy is modified to encourage preservation of structure, rather than achieving parsimonious description. With this modification they can be viewed as smoothing or filtering methods for separating noise from signal before the task of latent‐structure modeling begins. I here give a detailed justification of this view, and a hierarchical‐modeling implementation that can be carried out with popular software. Concepts are illustrated in the smoothing of a contingency table from an analysis of magnetic fields and childhood leukemia.
Accounting for observation errors in image data assimilation

This paper deals with the assimilation of image-type data. Such kinds of data, such as satellite images, have good properties (dense coverage in space and time), but also one crucial problem for data assimilation: they are affected by spatially correlated errors. Classical approaches in data assimilation assume uncorrelated noise, because the proper description and numerical manipulation of non-diagonal error covariance matrices is complex. This paper proposes a simple way to provide observation error covariance matrices adapted to spatially correlated errors. This is done using various image transformations: multiscale (Wavelets, Fourier, Curvelets), gradients, and gradient orientations. These transformations are described and compared to classical approaches, such as pixel-to-pixel comparison and observation thinning. We provide simple yet effective covariance matrices for each of these transformations, which take into account the observation error correlations and improve the results. The effectiveness of the proposed approach is demonstrated on twin experiments performed on a 2-D shallow-water model.