Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data

Abstract Optical remote sensing is the most widely used method for obtaining forest biomass information. This research investigated the potential of using topographical and high-resolution optical data from Quickbird for measurement of black locust plantation aboveground biomass (AGB) grown in the hill-gully region of the Loess Plateau. Three different processing techniques, including spectral vegetation indices (SVIs), texture, and topography were evaluated, both individually and combined. Simple linear regression and stepwise multiple-linear regression models were developed to describe the relationship between image parameters obtained using these approaches and field measurements. SVI and topography-based approaches did not yield reliable AGB estimates, accounting for at best 23 and 19% of the observed variation in AGB. Texture-based methods were better, explaining up to 70% of the observed variation. A combination of SVIs, texture, and topography yielded an even better R 2 value of 0.74 with the lowest root mean square error ( 17.21 t / ha ) and bias ( − 1.85 t / ha ). The results suggest that texture information from high-resolution optical data was more effective than SVIs and topography to estimate AGB. The performance of AGB estimation can be improved by adding SVIs and topography results to texture data; the best results can be obtained using a combination of these three data types.
Increasing spatial resolution by using an image sequence

In this paper we present two methods for increasing the spatial resolution of images using image sequences where all frames contain the same static scene with unknown shifts. Because of the subpixel shifts, aliased frequencies appear in a slightly different way in all images, making it possibly to reconstruct frequencies above the Nyqvist frequency, thus improving the resolution. To this end, we estimate parameters in the affine transform relating the images to each other from the sequence. To show the applicability of the algorithms, many experiments have been carried out mainly using image sequences captured by a TV-camera and not only using synthetic image sequences. The results from one TV-camera sequence are presented in this report. Measurements of PSF and MTF have been carried out and the results show that we can increase the spatial resolution by almost a factor of two. This technique can be used for target identification/recognition as well as for visualization. The second method (interpolation) is possible to implement in real time.
Gendered Laws and Women's Financial Inclusion

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Kinematic Modeling of a Serial–Parallel Forging Manipulator with Application to Heavy-Duty Manipulations#

Heavy-duty forging manipulators are essential handling equipments for integrated open-die forging plants. This article presents a forging manipulator with serial–parallel hybrid mechanism and develops its closed-form kinematic solutions (inverse and direct) based on the detailed geometric description of the mechanism. The kinematic Jacobian matrix is also derived analytically. Case studies for kinematical simulation, workspace, and kinetostatic analysis are conducted throughout the developing process. The closed-form kinematic solution provides the foundation of simulation and control design, and the results of case studies demonstrate the special features of the proposed mechanism in load capacity and compliant manipulation ability for heavy tasks in automatic open-die forging processes.
A combinatorial method for the vanishing of the Poisson brackets of an integrable Lotka–Volterra system

The combinatorial method is useful to obtain conserved quantities for some nonlinear integrable systems, as an alternative to the Lax representation method. Here we extend the combinatorial method and introduce an elementary geometry to show the vanishing of the Poisson brackets of the Hamiltonian structure for a Lotka–Volterra system of competing species. We associate a set of points on a circle with a set of species of the Lotka–Volterra system, where the dominance relations between points are given by the dominance relations between the species. We associate each term of the conserved quantities with a subset of points on the circle, which simplifies to show the vanishing of the Poisson brackets.
Multimodal Fake News Analysis Based on Image–Text Similarity

With the fast and extensive development of computer vision techniques, multimodal analyses are utilized more frequently for online fake news detection. To better understand the image–text relationship and its role in fake news detection, in this article, we proposed and evaluated four image–text similarities, namely, textual similarity, semantic similarity, contextual similarity, and post-training similarity. The textual and semantic similarities indicate the original image–text similarities in terms of the text information and image caption information. The contextual similarity reflects the image–text similarity in the format of meaningful named entities. The post-training similarity demonstrates how image–text similarity involves before and after a fake news detection model is trained. By evaluating the proposed similarity measurements on three real-world datasets, we find that fake news image–text similarity is higher than real news image–text similarity in most of the cases. Furthermore, the comparison of models’ performance further validates the significance of visual information in online fake news detection. These findings may be considered as the fundamental logic to explain the original purpose of fake news creation and can be used as influential features for improving models’ performance in the future.
Chronic Disease Prediction Using the Common Data Model: Development Study

Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement. This study aimed to develop high-performance prediction models for 4 chronic diseases using the common data model (CDM) and machine learning and to confirm the possibility for the extension of the proposed models. In this study, 4 major chronic diseases—namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease—were selected, and a model for predicting their occurrence within 10 years was developed. For model development, the Atlas analysis tool was used to define the chronic disease to be predicted, and data were extracted from the CDM according to the defined conditions. A model for predicting each disease was built with 4 algorithms verified in previous studies, and the performance was compared after applying a grid search. For the prediction of each disease, we applied 4 algorithms (logistic regression, gradient boosting, random forest, and extreme gradient boosting), and all models show greater than 80% accuracy. As compared to the optimized model’s performance, extreme gradient boosting presented the highest predictive performance for the 4 diseases (diabetes, hypertension, hyperlipidemia, and cardiovascular disease) with 80% or greater and from 0.84 to 0.93 in area under the curve standards. This study demonstrates the possibility for the preemptive management of chronic diseases by predicting the occurrence of chronic diseases using the CDM and machine learning. With these models, the risk of developing major chronic diseases within 10 years can be demonstrated by identifying health risk factors using our chronic disease prediction machine learning model developed with the real-world data–based CDM and National Health Insurance Corporation examination data that individuals can easily obtain.
The Ginzburg interval in soft-mode phase transitions: consequences of the rigid unit mode picture

In soft-mode structural phase transitions the Ginzburg temperature interval in which fluctuations and the interactions between them become important is often observed to be small on the scale of the transition temperature. We consider the size of the Ginzburg interval (GI) in framework and 'cogwheel' structures using the concept of 'rigid unit modes'. Such materials, as well as being very displacive, i.e. close to the soft-mode limit, have an extremely anisotropic phonon spectrum. Modelling these two properties with a suitable effective Hamiltonian for the degrees of freedom driving the transition we find that the GI can range from very small to large, depending on the balance between displaciveness and anisotropy. For the two perovskites SrTiO3 and LaAlO3 and the 'cogwheel' structure K2SeO4, we obtain values of the model parameters describing displaciveness and anisotropy from experimentally measured phonon dispersions and find, for the size of the GI, quantitative agreement with experiment. We also estimate typical values for the model parameters and the size of the GI for framework silicates, using quartz and cristobalite as examples. Finally, we use computer simulations to confirm the results of our theoretical analysis over a wider range of model parameters.
Oxidized Low-Density Lipoproteins Trigger Hepatocellular Oxidative Stress with the Formation of Cholesteryl Ester Hydroperoxide-Enriched Lipid Droplets

Oxidized low-density lipoproteins (oxLDLs) induce oxidative stress in the liver tissue, leading to hepatic steatosis, inflammation, and fibrosis. Precise information on the role of oxLDL in this process is needed to establish strategies for the prevention and management of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Here, we report the effects of native LDL (nLDL) and oxLDL on lipid metabolism, lipid droplet formation, and gene expression in a human liver-derived C3A cell line. The results showed that nLDL induced lipid droplets enriched with cholesteryl ester (CE) and promoted triglyceride hydrolysis and inhibited oxidative degeneration of CE in association with the altered expression of LIPE, FASN, SCD1, ATGL, and CAT genes. In contrast, oxLDL showed a striking increase in lipid droplets enriched with CE hydroperoxides (CE-OOH) in association with the altered expression of SREBP1, FASN, and DGAT1. Phosphatidylcholine (PC)-OOH/PC was increased in oxLDL-supplemented cells as compared with other groups, suggesting that oxidative stress increased hepatocellular damage. Thus, intracellular lipid droplets enriched with CE-OOH appear to play a crucial role in NAFLD and NASH, triggered by oxLDL. We propose oxLDL as a novel therapeutic target and candidate biomarker for NAFLD and NASH.
Cascade Control of Grid-Connected NPC Converters via Sliding Mode Technique

This paper proposes a two-stage control strategy including an adaptive sliding mode control (ASMC) and a nonlinear high-gain observer (HGO) for the three-level neutral-point-clamped (NPC) converter in dc microgrids. In the outer loop, an ASMC is utilized to regulate the dc-link voltage. By introducing an adapted gain, the tradeoff between the chattering and dynamic performance of the classic sliding mode control is overcome, thus the transient response of the NPC converter can be further enhanced without increasing the chattering. In addition, a nonlinear HGO is cooperated with the ASMC method to reject exogenous disturbance for the NPC converter, which not only assures a high disturbance rejection capacity but also accommodates the inevitable measurement noise in the actual applications. For the inner loop, in consideration of the parameter uncertainty, a high-gain observer-based second-order sliding mode (SOSM) control is adopted to assure the steady state performance and the robustness of the NPC converter. Finally, a set of comparative experimental results confirm the effectiveness and superiority of the proposed control scheme implemented in the NPC converter. Additionally, the robustness under parameter variation of the proposed method is also validated.
3D interpolation of irregular data with a POCS algorithm

Seismic surveys generally have irregular areas where data cannot be acquired. These data should often be interpolated. A projection onto convex sets (POCS) algorithm using Fourier transforms allows interpolation of irregularly populated grids of seismic data with a simple iterative method that produces high-quality results. The original 2D image restoration method, the Gerchberg-Saxton algorithm, is extended easily to higher dimensions, and the 3D version of the process used here produces much better interpolations than typical 2D methods. The only parameter that makes a substantial difference in the results is the number of iterations used, and this number can be overestimated without degrading the quality of the results. This simplicity is a significant advantage because it relieves the user of extensive parameter testing. Although the cost of the algorithm is several times the cost of typical 2D methods, the method is easily parallelized and still completely practical.
Piriformis Syndrome in Pre-monsoon, Monsoon, and Winter: An Observational Pilot Study

Introduction: Piriformis syndrome (PS) is a rare focal soft tissue rheumatic disease. Due to heavy rural work, we questioned whether PS was more prevalent in the rainy monsoon than in other seasons. In this pilot research, we studied the pattern of PS, the frequency of PS over the seasons, and whether there were typical preceding events. Methods: In this time-series descriptive study, PS cases diagnosed in a community-based clinic between January 2018 and December 2019 were enrolled. PS was diagnosed by clinical features and a 50% immediate pain relief from ultrasonogram-guided lidocaine (2%) injection in the piriformis muscle (PM). PS mimics were excluded. Results: A total of 38 PS cases (11 males) were enrolled consecutively. In 2018, during dry winter (November-February), pre-monsoon (March-May), and rainy monsoon (June-October), nine, seven, and one PS cases were diagnosed, respectively; in 2019, the numbers were three, eight, and seven, respectively. Thus, over two years, 12 PS patients were diagnosed in dry winter, 15 in pre-monsoon, and eight in rainy monsoon. There was no correlation with the type of preceding events. There were no differences in the pattern of PS between the seasons. Conclusions: In this pilot study, over two years more new PS cases were observed in the pre-monsoon and dry winter than in the rainy season; this was not supporting our research question. There was no association with specific preceding events.
Conspecific nest attendance behaviour of Common Eider (Somateria mollissima) in response to Polar Bear (Ursus maritimus) foraging activity: error or intent?

Common Eider (Somateria mollissima) is a colonial nesting sea duck with extremely high nest attendance rates. Although individuals take few recess breaks away from their nest to feed or preen, previous research has shown that some female eiders in dense nesting assemblages engage in conspecific nest attendance, spending short amounts of time incubating nests of other females. However, to the best of our knowledge, most observations of these behaviours occur during regular recess events, as opposed to instances where females flush from their nest in response to a foraging predator. Using drone videography on East Bay Island, northern Hudson Bay, Nunavut, Canada, we observed conspecific nest attendance behaviours in 11 eiders that flushed in response to a foraging Polar Bear (Ursus maritimus). Of the 11 birds attending to other nests, only two predation events were observed at the focal bird’s nest (i.e., two attenders’ own nests were predated). Of the nine nests that were attended to, we also only observed two predation events. Motivations behind these behaviours are unclear, but conspecific nest attendance may serve as a type of distraction display, whereby activity at another female’s nest leads the predator away from the focal bird’s nest. However, given that, on East Bay Island, eiders are known to nest in proximity to kin, distraction displays at nests of related individuals would incur fitness costs. General confusion on nest location or the concealment of closely related eggs are more likely explanations for these behaviours.
Cold water immersion after a soccer match: Does the placebo effect occur?

Although cold water immersion (CWI) is one of the most widely used post-exercise strategies to accelerate recovery processes, the benefits of CWI may be associated with placebo effects. This study aimed to compare the effects of CWI and placebo interventions on time course of recovery after the Loughborough Intermittent Shuttle Test (LIST). In a randomized, counterbalanced, crossover study, twelve semi-professional soccer players (age 21.1 ± 2.2 years, body mass 72.4 ± 5.9 kg, height 174.9 ± 4.6 cm, V ˙ O2max 56.1 ± 2.3 mL/min/kg) completed the LIST followed by CWI (15 min at 11°C), placebo (recovery Pla beverage), and passive recovery (Rest) over three different weeks. Creatine kinase (CK), C-reactive protein (CRP), uric acid (UA), delayed onset muscle soreness (DOMS), squat jump (SJ), countermovement jump (CMJ), 10-m sprint (10 mS), 20-m sprint (20 mS) and repeated sprint ability (RSA) were assessed at baseline and 24 and 48 h after the LIST. Compared to baseline, CK concentration was higher at 24 h in all conditions (p < 0.01), while CRP was higher at 24 h only in CWI and Rest conditions (p < 0.01). UA was higher for Rest condition at 24 and 48 h compared to Pla and CWI conditions (p < 0.001). DOMS score was higher for Rest condition at 24 h compared to CWI and Pla conditions (p = 0.001), and only to Pla condition at 48 h (p = 0.017). SJ and CMJ performances decreased significantly after the LIST in Rest condition (24 h: −7.24%, p = 0.001 and −5.45%, p = 0.003 respectively; 48 h: −9.19%, p < 0.001 and −5.70% p = 0.002 respectively) but not in CWI and Pla conditions. 10 mS and RSA performance were lower for Pla at 24 h compared to CWI and Rest conditions (p < 0.05), while no significant change was observed for 20 mS time. These data suggests that CWI and Pla intervention were more effective than the Rest conditions in recovery kinetics of muscle damage markers and physical performance. Furthermore, the effectiveness of CWI would be explained, at least in part, by the placebo effect.
Screening constants for relativistic wave functions

Abstract Formulae for the mean radius and mean square radius of a relativistic Coulomb wave function are obtained. Screening constants for the energy, mean radius and mean square radius are defined relative to non-relativistic wave functions and the results of numerical calculations given. It is shown that if the screening constants so determined are added to the screening constants due to the presence of other electrons as found by the s.c. f. method, good agreement is obtained in a case where both effects have been considered together. The value of solving the relativistic wave equation in a Thomas-Fermi field is also shown.
Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality

The sanitary quality of seed is essential in agriculture. This is because pathogenic fungi compromise seed physiological quality and prevent the formation of plants in the field, which causes losses to farmers. Multispectral images technologies coupled with machine learning algorithms can optimize the identification of healthy peanut seeds, greatly improving the sanitary quality. The objective was to verify whether multispectral images technologies and artificial intelligence tools are effective for discriminating pathogenic fungi in tropical peanut seeds. For this purpose, dry peanut seeds infected by fungi (A. flavus, A. niger, Penicillium sp., and Rhizopus sp.) were used to acquire images at different wavelengths (365 to 970 nm). Multispectral markers of peanut seed health quality were found. The incubation period of 216 h was the one that most contributed to discriminating healthy seeds from those containing fungi through multispectral images. Texture (Percent Run), color (CIELab L*) and reflectance (490 nm) were highly effective in discriminating the sanitary quality of peanut seeds. Machine learning algorithms (LDA, MLP, RF, and SVM) demonstrated high accuracy in autonomous detection of seed health status (90 to 100%). Thus, multispectral images coupled with machine learning algorithms are effective for screening peanut seeds with superior sanitary quality.
Palliative care and end of life management in patients with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic disease with an unknown etiology that causes deterioration of the structure of the lung parenchyma, resulting in a severe and progressive decline in respiratory function and early mortality. IPF is essentially an incurable disease, with a mean overall survival of 5 years in approximately 20% of patients without treatment. The combination of a poor prognosis, uncertainty about the disease's progression, and the severity of symptoms has a significant impact on the quality of life of patients and their families. New antifibrotic drugs have been shown to slow disease progression, but their impact on health-related quality of life (HRQoL) has to be proven yet. To date, studies have shown that palliative care can improve symptom management, HRQoL, and end-of-life care (EoL) in patients with IPF, reducing critical events, hospitalization, and health costs. As a result, it is essential for proper health planning and patient management to establish palliative care early and in conjunction with other therapies, beginning with the initial diagnosis of the disease.
Minimal forbidden minor characterization of planar partial 3-trees and application to circuit layout

All the minimal forbidden minors for planar partial 3-trees are derived K-trees and partial K-trees, theorems on partial K-trees, and theorems on partial 3-trees are discussed. Graph-theoretic definitions are discussed, and application to circuit layout is analyzed. <<ETX>>
The wealth of shared resources: Improving molecular taxonomy using eDNA and public databases

Public databases such as the NCBI's GenBank have been used as repositories for genomic studies for more than 30 years. In this time, our understanding of the natural world, and especially the genomic world, has expanded vastly, and the size of these databases represent this genomic revolution. Databases like GenBank now help populate many molecular studies, supplementing a researcher's newly gathered data with publicly available sequences. Despite this, older sequence records, particularly those from understudied taxa, are frequently not updated in line with this burgeoning understanding, and this means that analyses that leverage this public data – from BLAST through to phylogenetic analyses – cannot do so with the full force of its collective understanding. This is particularly true for environmental DNA (eDNA) records, where older sequence records may identify sequences only to the phylum level, limiting their use in many studies. Here, with a case study of tardigrade 18S sequences, the family identities of 630 sequences, previously only identified to the phylum level, were established using 501 family, genus and species level 18S sequences, effectively doubling the depth and taxonomic resolution of tardigrade 18S sequences in GenBank.