Miracle-dz/UniFAR-FTUs huggingface dataset:
Geographic information science: contribution to understanding salt and sodium affected soils in the Senegal River valley

The Senegal River valley and delta (SRVD) are affected by long term climate variability. Indicators of these climatic shifts include a rainfall deficit, warmer temperatures, sea level rise, floods, and drought. These shifts have led to environmental degradation, water deficits, and profound effects on human life and activities in the area. Geographic Information Science (GIScience), including satellite-based remote sensing methods offer several advantages over conventional ground-based methods used to map and monitor salt-affected soil (SAS) features. This study was designed to assess the accuracy of information on soil salinization extracted from Landsat satellite imagery. Would available imagery and GIScience data analysis enable an ability to discriminate natural soil salinization from soil sodication and provide an ability to characterize the SAS trend and pattern over 30 years? A set of Landsat MSS (June 1973 and September 1979), Landsat TM (November 1987, April 1994 and November 1999) and ETM+ (May 2001 and March 2003) images have been used to map and monitor salt impacted soil distribution. Supervised classification, unsupervised classification and post-classification change detection methods were used. Supervised classifications of May 2001 and March 2003 images were made in conjunction field data characterizing soil surface chemical characteristics that included exchange sodium percentage (ESP), cation exchange capacity (CEC) and the electrical conductivity (EC). With this supervised information extraction method, the distribution of three different types of SAS (saline, saline-sodic, and sodic) was mapped with an accuracy of 91.07% for 2001 image and 73.21% for 2003 image. Change detection results confirmed a decreasing trend in non-saline and saline soil and an increase in saline-sodic and sodic soil. All seven Landsat images were subjected to the unsupervised classification method which resulted in maps that separate SAS according to their degree of salinity. The spatial distribution of sodic and saline-sodic soils has a strong relationship with the area of irrigated rice crop management. This study documented that human-induced salinization is progressively replacing natural salinization in the SRVD. These pedologic parameters obtained using GIScience remote sensing techniques can be used as a scientific tool for sustainable management and to assist with the implementation of environmental policy.
Supplementary Material , Integration of incomplete data for mediation analysis

Here we give details on extending our methods to scenarios with additional adjustments for covariates (confounders) X = (X1, . . . , Xk). However, this extension requires that (i) our reference dataset contains complete information on E, X and M and (ii) the conditions outlined in Table 1 of the main paper are satisfied. We assume that given E, M and X, the distribution of Y is in the exponential family,
Influence of ambiguity of reliability indices information on configuration shaping of combined cycle plant

Methodology for accounting of ambiguity of reliability indices information on configuration shaping of combined cycle plant (CCP) is elaborated. Ambiguity of calculated failure density of heat-recovery steam generators (HRSG) will have no impact on configuration shaping of combined cycle cogeneration plant (CCCP), if calculated value is about 10-12 percent.
Conundrums in Event Coreference Resolution: Making Sense of the State of the Art

Despite recent promising results on the application of span-based models for event reference interpretation, there is a lack of understanding of what has been improved. We present an empirical analysis of a state-of-the-art span-based event reference systems with the goal of providing the general NLP audience with a better understanding of the state of the art and reference researchers with directions for future research.
N ° 2009-28 Do food scares explain supplier concentration ? An analysis of EU agri-food imports

This paper documents a decreasing trend in the geographical concentration of EU agro-food imports. Decomposing the concentration indices into intensive and extensive margins components, we find that the decrease in overall concentration indices results from two diverging trends: the pattern of trade diversifies at the extensive margin (EU countries have been sourcing their agri-food products from a wider range of suppliers), while geographical concentration increases at the intensive-margin (EU countries have concentrated their imports on a few major suppliers). This leads to an increasing inequality in market shares between a small group of large suppliers and a majority of small suppliers. We then move on to exploit a database of food alerts at the EU border that had never been exploited before. After coding it into HS8 categories, we regress the incidence of food alerts by product on determinants including exporter dummies as well as HS8 product dummies. Coefficients on product dummies provide unbiased estimates of the intrinsic vulnerability of exported products to food alerts, as measured at the EU border. We incorporate the product risk coefficient as an explanatory variable in a regression of geographical concentration and show that concentration is higher for risky products. § Without implicating them, we thank participants to a seminar at the Paris School of Economics, in particular Anne-Célia Disdier and Lionel Fontagné, for helpful comments on a earlier version.
Brane physics in M-theory

The thesis begins with an introduction to M-theory (at a graduate student's level), starting from perturbative string theory and proceeding to dualities, D-branes and finally Matrix theory. The following chapter treats, in a self-contained way, of general classical p-brane solutions. Black and extremal branes are reviewed, along with their semi-classical thermodynamics. We then focus on intersecting extremal branes, the intersection rules being derived both with and without the explicit use of supersymmetry. The last three chapters comprise more advanced aspects of brane physics, such as the dynamics of open branes, the little theories on the world-volume of branes and how the four dimensional Schwarzschild black hole can be mapped to an extremal configuration of branes, thus allowing for a statistical interpretation of its entropy. The original results were already reported in hep-th/9701042, hep-th/9704190, hep-th/9710027 and hep-th/9801053.
An Eigenanalysis of Angle-Based Deformation Energies

Angle-based energies appear in numerous physics-based simulation models, including thin-shell bending and isotropic elastic strands. We present a generic analysis of these energies that allows us to analytically filter the negative eigenvalues of the second derivative (Hessian), which is critical for stable, implicit time integration. While these energies are usually formulated in terms of angles and positions, we propose an abstract edge stencil that succinctly parameterizes the edge deformation, and allows us to derive generic, closed-form analytical expressions for the energy eigensystems. The resultant eigenvectors have straightforward geometric interpretations. We demonstrate that our method is readily applicable to a variety of 2D and 3D angle-based elastic energies, including both cloth and strands, and is up to 7× faster than numerical eigendecomposition.
The effect of good corporate governance on earnings management in companies that perform IPO

Initial Public Offering (IPO) is one of the motivations of the occurrence of earnings management practices. Earnings management is done by a company to obtain a positive response from the market in order to increase the amount of funds. Good corporate go-vernance (GCG) is considered capable of minimizing the measures of earnings manage-ment because it has a goal to achieve a better and healthier company under the principles owned. This study aims to determine the effect of GCG on earnings management in companies that perform IPO. Samples are taken using purposive sampling method and are acquired as many as 31 companies, which are analyzed using multiple linear regres-sions. The result of this study indicates that management ownership, independent com-missioners, and audit committee have negative and significant relationship with earn-ings management, in which this result is consistent with the research hypothesis. Meanwhile, institutional ownership has positive and significant relationship with earn-ings management, in which this result is not consistent with the research hypothesis.
Minimizing Network and Storage Costs for Consensus with Flexible Erasure Coding

Consensus protocols like Paxos and Raft provide data consistency and fault tolerance for upper-layer distributed services. Log replication in these protocols can be supported by erasure coding, which incurs a lower redundancy ratio than full-copy replication and hence significantly saves network and storage costs for overall performance improvements. However, existing consensus protocols with erasure coding cannot achieve the minimum network and storage costs during log replication. Our observation is that the optimal coding scheme varies with the number of healthy servers in a group, such that the coding scheme with the lowest redundancy ratio in normal cases incurs more network traffic and storage overhead for log replication in the presence of server failures. To this end, we propose FlexRaft, which dynamically adjusts the coding scheme used in Raft based on the server status to always achieve the theoretically minimum redundancy ratio, while maintaining the same liveness as in the original Raft. To address the issue of an inconsistent coding scheme between the leader and its followers, we specify the prerequisite of overwriting a log entry, and also allow the leader and its followers to exactly track the coding scheme used. We further consider how to handle server failures and prove the safety of FlexRaft. We implement a prototype of FlexRaft, atop which we build a distributed key-value store to show its efficacy. Experiments on Alibaba Cloud show that FlexRaft achieves the theoretically minimum network and storage costs in practice, and reduces the commit latency by 44.51% and 19.37% compared with state-of-the-art CRaft and HRaft, respectively.
The effect of US economic policy uncertainty on market risk of international crude oil and the portfolio strategy

The paper investigates the impact of US economic policy uncertainty (EPU) on major crude oil markets. GARCH-tDDC-Copula model is constructed to study the spillover effect of US EPU on the international crude oil market risk, and then the median-CoVaR portfolio model is constructed to discuss the optimal port-folio strategy of crude oil importers when US EPU is in different states. Results show that the spillover effect of US EPU on international crude oil market risk is nonlinear, asymmetric and time-varying. When US EPU increases from the average level to the 0.95 quantile level, the price reduction risk of crude oil market in Brent, Dubai and Western Texas increases by 37.26%, 42.66%, and 39.28% respectively, and the price increase risk increases by 7.22%, 6.64%, and 7.53% respectively. Compared with the median-VaR portfolio strategy and the equal-weight combination portfolio strategy, the advantage of the median-CoVaR model is that it can achieve ‘ targeted ’ management for asset risk under specific conditions. When US EPU peak occurs, crude oil importers can formulate a basket price strategies with dynamic weighted based on the median-CoVaR model, which can better reduce the depreciation risk of crude oil assets. The findings have important implications for importers and investors.
FLORA STRUCTURE IN RURAL PARKS ON EXAMPLE OF SANDOMIERSKA VALLEY

The paper presents analysis of flora structure in rural parks on the example of the Sandomierska Valley. The research was done in 51 rural parks, (landscape historic style). Parks were established in XVIII-XIX centuries (landscape historic style) on oakhornbeam site ver. małopolska. Parks were divided into groups including area and antropic pressure.
Fusion-SUNet: Spatial Layout Consistency for 3D Semantic Segmentation

The paper discusses the need for a reliable and efficient computer vision system to inspect utility networks with minimal human involvement, due to the aging infrastructure of these networks. We propose a deep learning technique, Fusion-Semantic Utility Network (Fusion-SUNet), to classify the dense and irregular point clouds obtained from the airborne laser terrain mapping (ALTM) system used for data collection. The proposed network combines two networks to achieve voxel-based semantic segmentation of the point clouds at multi-resolution with object categories in three dimensions and predict two-dimensional regional labels distinguishing corridor regions from non-corridors. The network imposes spatial layout consistency on the features of the voxel-based 3D network using regional segmentation features. The authors demonstrate the effectiveness of the proposed technique by testing it on 67km2 of utility corridor data with average density of 5pp/m2, achieving significantly better performance compared to the state-of-the-art baseline network, with an F1 score of 93% for pylon class, 99% for ground class, 99% for vegetation class, and 98% for powerline class.
Effects of Glycemic Variability on Regulatory T Cells in Patients with Type 2 Diabetes and Kidney Disease

Purpose To investigate the pathogenesis of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM), we evaluated the effects of short-term glycemic variability (GV) on the profile of T cell subpopulations. Methods A total of 47 T2DM patients with normoalbuminuria, 47 microalbuminuria, and 49 macroalbuminuria were enrolled. The continuous glucose monitoring (CGM) determined the GV of enrolled patients. Flow cytometry was used to determine the proportion of T cell subpopulations. Results The frequency of T helper (Th) 17 and Th1 cells significantly increased while regulatory T cells (Tregs) significantly decreased in the macroalbuminuria group compared to normoalbuminuria and microalbuminuria groups (P < 0.01). The suppressive function of Tregs was significantly lower in the macroalbuminuria group than the normoalbuminuria group (P < 0.05). Compared with the normoalbuminuria group, the mean amplitude of glucose excursions (MAGE) of the macroalbuminuria group was significantly higher (P<0.05). Furthermore, there were negative associations between the proportion of Tregs and MAGE. Conclusions Increased GV could decrease the proportion of Tregs and may impair their function. This may lead to increases in Th1 and Th17 cells, and some inflammatory cytokines, which might contribute to the development and progression of DKD in T2DM.
Comparison of the Effectiveness of Emotion Regulation Method Based On Gross Model and Benson Muscle Relaxation technique on Resilience And Anger In Patients With Coronary Artery Disease

Background: Coronary heart disease which imposes a heavy burden on the health care system worldwide. Psychological factors are effective in the occurrence and persistence of this disease. The aim of this study was to compare the effectiveness of emotion regulation method based on Gross model and muscle relaxation technique on resilience and anger in patients with coronary heart disease. Methods: The research method is quasi-experimental and pre-test-post-test and follow-up with the control group. The statistical population includes all patients with coronary heart disease (CHD) between 30 and 50 years old referred to medical centers and private clinics of cardiologists in Ilam in 2021, of which 60 people were available by random sampling and random distribution to three groups. Were divided. The data collection tool is the use of standard resilience and anger scales. Results: The results of repeated measures analysis of variance showed the effectiveness of Gross model-based emotional regulation training in patient resilience, so that Benson muscle relaxation exercises compared to Gross model-based emotional regulation training increased patients' resilience and effect. It was better. Also, Benson muscle relaxation exercises were effective in reducing patients' anger. However, the results showed the ineffectiveness of Gross model-based emotion regulation training on anger. Conclusions: By using Gross model emotion regulation training and Benson muscle relaxation exercises, we can take steps to provide services to patients with or with a history of heart disease and reduce the severity of the disease.
Understanding the Characteristics of Vertical Structures for Wind Speed Observations via Wind-LIDAR on Jeju Island

Wind observations at multiple levels (40–200 m) have been conducted over a five-year time period (2016–2020) on Jeju Island of South Korea. This study aims to understand the vertical and temporal characteristics of the lower atmosphere. Jeju Island is a region located at mid-latitude and is affected by seasonal wind. The maximum wind speed occurs in the relatively lower altitudes during daytime and is delayed in the relatively higher altitude after sunset in a diurnal cycle. In the summer season, the altitudes appear earlier than in other seasons via the dominant solar radiation effect during daytime, and the altitude after sunset increases up to 160 m. However, the maximum wind speed in the winter season occurs irregularly among altitudes, and it is lower than that in the summer season. This can be attributed to the increase in the mean wind speed in the diurnal cycle caused by the strong northwestern wind in the winter season. These results imply that the relationship between near-surface and higher altitudes is primarily affected by solar radiation and seasonal winds. These results are expected to contribute to site selection criteria for wind farms.
CANShield: Deep-Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal Level

Modern vehicles rely on a fleet of electronic control units (ECUs) connected through controller area network (CAN) buses for critical vehicular control. With the expansion of advanced connectivity features in automobiles and the elevated risks of internal system exposure, the CAN bus is increasingly prone to intrusions and injection attacks. As ordinary injection attacks disrupt the typical timing properties of the CAN data stream, rule-based intrusion detection systems (IDS) can easily detect them. However, advanced attackers can inject false data to the signal/semantic level, while looking innocuous by the pattern/frequency of the CAN messages. The rule-based IDS, as well as the anomaly-based IDS, are built merely on the sequence of CAN messages IDs or just the binary payload data and are less effective in detecting such attacks. Therefore, to detect such intelligent attacks, we propose CANShield, a deep learning-based signal level intrusion detection framework for the CAN bus. CANShield consists of three modules: 1) a data preprocessing module that handles the high-dimensional CAN data stream at the signal level and parses them into time series suitable for a deep learning model; 2) a data analyzer module consisting of multiple deep autoencoder (AE) networks, each analyzing the time-series data from a different temporal scale and granularity; and 3) finally an attack detection module that uses an ensemble method to make the final decision. Evaluation results on two high-fidelity signal-based CAN attack data sets show the high accuracy and responsiveness of CANShield in detecting advanced intrusion attacks.
Une approche milieu poreux pour la modélisation de l'interaction fluide-structure des assemblages combustibles dans un coeur de réacteur à eau pressurisée : simulation et expérimentation

Le dimensionnement au seisme, du cœur d'un reacteur a eau pressurisee, est une preoccupation majeure de l'industrie du nucleaire. Nous proposons, dans ce memoire de these, d'etablir les equations globales, du comportement du cœur, par une approche milieu poreux. Les equations locales, du fluide et de la structure, sont moyennees sur un volume de controle, nous definissons ainsi un fluide equivalent et une structure equivalente, dont les inconnues sont definies sur tout le domaine spatial. Le caractere non lineaire des assemblages combustibles est modelise par une loi de comportement visco-elastique quadratique. Le couplage fluide-structure est pris en compte par une force volumique dont l'expression est issue de formules empiriques des forces fluides s'exercant sur un tube soumis a un ecoulement axial. Les equations ainsi obtenues sont resolues a l'aide d'une methode elements finis. Une validation du modele est proposee sur trois series d'essais. La premiere presente deux assemblages combustibles soumis a un ecoulement axial. L'un des deux assemblages est ecarte de sa position d'equilibre et lâche, tandis que l'autre est laisse au repos. La deuxieme met en œuvre six assemblages en ligne, immerges dans une eau stagnante, poses sur une table vibrante pouvant simuler un seisme. Enfin, la derniere propose neuf assemblages, disposes en un reseau trois par trois, soumis a un ecoulement axial. Le deplacement de l'assemblage central est impose. Les simulations sont en accord avec les experiences ; le modele reproduit l'influence de la vitesse d'ecoulement du fluide sur la dynamique et le couplage des assemblages.
How loneliness influences the city identity of Chinese migrant children: A moderated mediation model

This study explored the mediating role of resilience and the moderating role of social class on the relationship between loneliness and migrant children’s city identity. The participants were 479 Chinese migrant children (females = 52%, mean age = 10.07 years, SD = 0.43 years) from Jiangsu Province. They completed a series of questionnaires, including the Children’s Loneliness Scale, the Resilience Scale, the Migrant Children’s City Identity Questionnaire and the Socioeconomic Status Index. Results from the moderated mediation analysis showed that Chinese migrant children self-reporting higher loneliness had a lower city identity. Resilience mediates the relationship between loneliness and city identity. Social class negatively moderates the influence of resilience on city identity. Compared to those with higher social class, migrant children with lower social class were more resilient in constructing their city identity. These findings might be helpful to social welfare programmes to support immigrant children in their city identity for better integration and adaptation.
SaTransformer: Semantic-aware transformer for breast cancer classification and segmentation

Breast cancer classification and segmentation play an important role in identifying and detecting benign and malignant breast lesions. However, segmentation and classification still face many challenges: 1) The characteristics of cancer itself, such as fuzzy edges, complex backgrounds, and significant changes in size, shape, and intensity distribution make accurate segment and classification challenges. 2) Existing methods ignore the potential relationship between classification and segmentation tasks, due to the classification and segmentation being treated as two separate tasks. To overcome these challenges, in this paper, a novel Semantic‐aware transformer (SaTransformer) for breast cancer classification and segmentation is proposed. Specifically, the SaTransformer enables doing the two takes simultaneously through one unified framework. Unlike existing well‐known methods, the segmentation and classification information are semantically interactive, reinforcing each other during feature representation learning and improving the ability of feature representation learning while consuming less memory and computational complexity. The SaTransformer is validated on two publicly available breast cancer datasets – BUSI and UDIAT. Experimental results and quantitative evaluations (accuracy: 97.97%, precision: 98.20%, DSC: 86.34%) demonstrate that the SaTransformer outperforms other state‐of‐the‐art methods.