Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Decadal Land Cover Changes in Mining Regions based on Satellite Remotely Sensed Imagery: A Case Study of the Stone Mining Area in Luoyuan County, SE China

Abstract Mining regions often undergo abrupt and extensive land coverchanges that pose serious environmental and social impacts.In this study, decadal land cover changes in stone mining areas of Luoyuan County, southeastern China from 2001 to2010 were examined based on multi-source satellite remote sensing imageries including ALOS, SPOT2, and Landsat-7.Object-oriented classifications combined with decision tree and retrospective approaches are employed to extract land cover and change information for the ten-year period. The study results show that the stone mining area nearly quadrupled over the ten-year period. It is found that the digging area accounts for only 14.3 percent of the stone mining region. However, mine dumps and tailings occupy the majority of the region, a remarkable characteristic distinct from other mining regions. Reclaimed land in the mined region is very limited.An evident increase in the extent of urbanized land cover is also observed in the study area for the last decade.
Hydrological Control of Greenhouse Gas Fluxes in a Sierra Nevada Subalpine Meadow

Abstract Alpine and subalpine meadows are often hotspots of water availability and biodiversity in montane landscapes, but we know little about whether these attributes also make meadows hotspots of greenhouse gas (GHG) emission. Furthermore, many of these meadows will likely become drier during the growing season in the future because of less precipitation, earlier timing of snowmelt, and increased evapotranspiration associated with climatic warming. To evaluate the potential effects of soil drying on GHG emission, we studied a soil moisture gradient in a Sierra Nevada subalpine meadow in California. Our objectives were: (1) to assess the strength of hydrological control for soil carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes both earlier and later in the growing season; and (2) to quantify the contribution of CH4 and N2O to net GHG emission. The replicated gradient spanned 50 m, from the wet middle to dry edge of the meadow, and soil volumetric water content was measured 0 to 12 cm deep. Fluxes of CO2, CH4, and N2O were measured using static chambers at 10 m intervals across the gradient. We found that the wet side of the gradient was not a CH4 or N2O source on either sampling date. Net CH4 emission from soil was rare and CH4 uptake was prevalent, particularly on the dry side of the gradient. Soil N2O fluxes shifted from net uptake at the middle of the meadow to net emission at the edge, but only earlier in the growing season. Of the three GHGs, CO2 fluxes showed the most temporal variation but surprisingly varied little across the hydrological gradient. Other environmental factors—including plant species richness and soil carbon concentration—appeared more important than soil moisture in explaining CO2 fluxes. Therefore, the strength of near-surface hydrological control increased in the following order: CO2 < N2O < CH4. Our results suggest that non-CO2 greenhouse gases will need proper accounting during the snow-free season in order to more accurately predict the effects of future soil drying on GHG emissions in heterogeneous montane landscapes.
CANCER RATIO-BASED DIAGNOSTIC TOOL IN IDENTIFYING MALIGNANT PLEURAL EFFUSION: SENSITIVITY, SPECIFICITY AND CLINICAL INSIGHTS

Background: Malignant pleural effusion (MPE) is a common cause of exudative lymphocytic pleural effusion. Although pleural fluid evaluation is routinely performed, cytology or histopathology remains the gold standard for MPE diagnosis. The cancer ratio (CR), calculated by comparing serum LDH to pleural fluid ADA levels, has shown promise in diagnosing MPE. However, no studies have investigated its utility in the Thai population, which has a high tuberculosis prevalence. Objectives: This study aimed to evaluate the diagnostic accuracy of the CR in MPE diagnosis, compare clinical and pleural fluid parameters between MPE and nonMPE cases, determine the appropriate CR cut-off for the Thai population and develop a prediction score for prediagnosing MPE. Results: Between July 2021 and December 2022, patients presenting exudative lymphocytic pleural effusion were included in the study. Demographics, symptoms, radiographic findings and pleural fluid parameters were collected and cytology/histopathology served as the reference test. CR performance was assessed using receiver operating characteristic curves, and a prediction score was developed using multivariable logistic regression analysis. Among 122 patients, 46.7% received a diagnosis of MPE. The CR exhibited a sensitivity of 87.7% and specificity of 72.3% (AUC 0.83) with a cut-off level >10. Patients with MPE showed longer symptom duration, lower fever and massive pleural effusion, which were more common in MPE than nonMPE cases. A prediction score incorporating symptom duration, fever history, effusion amount and CR demonstrated superior diagnostic performance for MPE (AUC 0.94) compared with the CR alone. Conclusion: The CR can effectively differentiate MPE from nonMPE among patients with exudative lymphocytic pleural effusion. A cut-off level >10 is recommended for diagnosing MPE in the Thai population. Combining clinical, radiologic and CR data may aid in prediagnosing MPE; however, further research is needed for validation.
High Altitude Venus Operational Concept (HAVOC): Proofs of Concept

The atmosphere of Venus is an exciting destination for both further scientific study and future human exploration. A recent internal NASA study of a High Altitude Venus Operational Concept (HAVOC) led to the development of an evolutionary program for the exploration of Venus, with focus on the mission architecture and vehicle concept for a 30-day crewed mission into Venus's atmosphere at 50 kilometers. Key technical challenges for the mission include performing the aerocapture maneuvers at Venus and Earth, inserting and inflating the airship at Venus during the entry sequence, and protecting the solar panels and structure from the sulfuric acid in the atmosphere. Two proofs of concept were identified that would aid in addressing some of the key technical challenges. To mitigate the threat posed by the sulfuric acid ambient in the atmosphere of Venus, a material was needed that could protect the systems while being lightweight and not inhibiting the performance of the solar panels. The first proof of concept identified candidate materials and evaluated them, finding FEP-Teflon (Fluorinated Ethylene Propylene-Teflon) to maintain 90 percent transmittance to relevant spectra even after 30 days of immersion in concentrated sulfuric acid. The second proof of concept developed and verified a packaging algorithm for the airship envelope to inform the entry, descent, and inflation analysis.
Transcranial magnetic stimulation and ketamine: implications for combined treatment in depression

Drug-resistant mental disorders, particularly treatment-resistant depression, pose a significant medical and social problem. To address this challenge, modern psychiatry is constantly exploring the use of novel treatment methods, including biological treatments, such as transcranial magnetic stimulation (TMS), and novel rapid-acting antidepressants, such as ketamine. While both TMS and ketamine demonstrate high effectiveness in reducing the severity of depressive symptoms, some patients still do not achieve the desired improvement. Recent literature suggests that combining these two methods may yield even stronger and longer-lasting results. This review aims to consolidate knowledge in this area and elucidate the potential mechanisms of action underlying the increased efficacy of combined treatment, which would provide a foundation for the development and optimization of future treatment protocols.
Analysis of Scenarios to Adapt to Climate Change Impacts in the Rio Conchos Basin

This investigation assesses some water management scenarios to adapt to and mitigate the impacts of climate change on the water resources of the Rio Conchos Basin, a main tributary of the bi-national Rio Grande Basin. The water problems in the basin are characterized by recurrent long drought periods, increasing water demands, and low efficiencies; consequently, significant deficits are experienced, not only in the water system, but also for water delivery to the USMexico 1944 treaty. Future climate change will exacerbate the intense competition for water resources on both sites of the border. First, an assessment was made of climate change impacts on water resources of the basin under IPCC emission scenarios A2 and A1B for the period 20402099. Second, simulation and analysis was done of water management scenarios that have the potential to mitigate the effects of climate change. Because a reduction in river discharge is expected, these scenarios envision an increase in groundwater use, especially in drought periods, and a decrease in water demands and an improvement in water use efficiency. Under climate change conditions, results show that reliability and resiliency of the water system will tend to decrease; consequently, the vulnerability of the system increases over the time. Furthermore, the proposed scenarios indicate some adaptation measures that could make the system more reliable and less vulnerable in meeting water demands for irrigation and municipal uses.
Ka-Band Polarimetric Doppler Radar for Profiling of Low Level Clouds at Mumbai During Monsoon Season

Cloud microphysical study is one of the important physical processes that impact predictability of the NWP models. Qualitative observations of these cloud microphysical properties can be obtained from Cloud radar. In this paper the technical, hardware specifications, system design & development and initial results of the prototype ka-band cloud radar developed by SAMEER Mumbai are presented.
Artiﬁcial Immunology for Collective Adaptive Systems Design and Implementation

Distributed autonomous systems consisting of large numbers of components with no central control point need to be able to dynamically adapt their control mechanisms in order to deal with an unpredictable and changing environment. Existing frameworks for engineering self-adaptive systems fail to account for the need to incorporate self-expression , i.e. the capability of a system to dynamically adapt its coordination pattern during run-time. Although the beneﬁts of incorporating self-expression are well-known, there is currently no principled means of enabling this during system design. We propose a conceptual framework for principled design of systems that exhibit self-expression, based on inspiration from the natural immune system. The framework is described as a set of design principles and customisable algorithms, and then instantiated in three case-studies, including two from robotics and one from artiﬁcial chemistry. We show that it enables self-expression in each case, resulting in systems that are able to adapt their choice of coordination pattern during run time to optimise functional and non-functional goals as well as to discover novel patterns and architectures. Categories and Subject Descriptors
Transcriptome analysis reveals the mechanism for blue-light–induced biosynthesis of delphinidin derivatives in harvested purple pepper fruit

Anthocyanins are the main pigments that affect the color and quality of purple-fruited sweet pepper (Capsicum annuum). Our previous study indicated that blue light can induce anthocyanin accumulation in purple pepper. In view of its underlying mechanism that is unclear, here, anthocyanin content was determined, and transcriptome analysis was performed on pepper fruits harvested from different light treatments. As a result, among the identified anthocyanin metabolites, the levels of delphinidin (Dp) glycosides, including Dp-3-O-rhamnoside, Dp-3-O-rutinoside, and Dp-3-O-glucoside, were highly accumulated in blue-light–treated fruit, which are mainly responsible for the appearance color of purple pepper. Correlation between anthocyanin content and transcriptomic analysis indicated a total of 1,619 upregulated genes were found, of which six structural and 12 transcription factor (TF) genes were involved in the anthocyanin biosynthetic pathway. Structural gene, for instance, CaUFGT as well as TFs such as CaMYC2-like and CaERF113, which were highly expressed under blue light and presented similar expression patterns consistent with Dp glycoside accumulation, may be candidate genes for anthocyanin synthesis in response to blue-light signal.
Inventory Of Natural Emissions In Switzerland

Biogenic species are emitted mainly from vegetation (volatile organic compounds, e.g. isoprene, monoterpenes) and from soils (nitrogen compounds). The emission rates per unit area from forests are known to be correlated to leaf temperature, photosynthetically active radiation and biomass density leading to space and time dependent emissions. In this study, a biogenic emission inventory for Switzerland was established, the grid size being 5 km x 5 km. The spatial vegetation distribution was taken from the Swiss land-use inventory. Hourly values of the air temperature and the solar radiation above the canopy were calculated using their dependence on altitude. For forests, these quantities were converted to the corresponding values within the canopy using a forest canopy model. The hourly biogenic emission inventory was prepared for three consecutive days in summer 1993 as well as for monthly averages over ten years.
Social Network Analysis as a Driver of Continuous Improvement

Social network analysis (SNA) is a research method that, when applied to improvement science, can help leaders understand the strength of relationships within their organization. The COVID-19 pandemic has had a lasting impact on organizational norms, and it has interrupted relationship building efforts. This paper documents a case study of the Kentucky Department of Education (KDE), which deployed SNA techniques to strategically identify areas of growth within its network and design intentional, targeted solutions to improve the network health. As organizations emerge from the pandemic environment and begin to plan continuous improvement efforts, they would be well served to examine the impact of the pandemic on their level of connectedness. The broader impact and generalizability of the case study as well as considerations for replication are also discussed.
Discontinuous Galerkin frequency domain forward modelling for the inversion of electric permittivity in the 2D case

We have recently developed a discontinuous Galerkin frequency domain modelling algorithm for the solution of the 2D transverse magnetic Maxwell equations. This method is formulated on an unstructured triangular discretization of the computational domain and makes use of a high order polynomial interpolation of the electromagnetic field components within each triangular element. The discontinuous nature of the approximation naturally allows for a local definition of the interpolation order that is, in combination with a possibly non‐conforming local refinement of the mesh, a key ingredient for obtaining a flexible and accurate discretization method. Moreover, heterogeneity of the propagation media is easily dealt with by assuming element‐wise values of the electromagnetic parameters. In this paper, we propose the use of this discontinuous Galerkin frequency domain method as the forward modelling algorithm for solving the inverse problem for the electric permittivity in the 2D case. The inversion process is based on a gradient minimization technique developed by Pratt for seismological applications. Preliminary numerical results are presented for the imaging of a simplified subsurface model with the aim of assessing the performances of the proposed inversion methodology with regards to the number of frequencies, the number of recorded data and the number of sources.
Assessing the importance of hypsometry for catchment soil erosion: A case study of the Yanze watershed, Rwanda

Implementing a watershed erosion control programme requires resource-intensive and time-consuming preliminary studies to prioritize such interventions and to focus on those sub-catchments where they are most likely to yield the most effective results. In this study, we explore and document the effectiveness of using hypsometric analysis as a method to prioritize erosion control measures and apply it to the Yanze watershed located in central Rwanda. Based on a 30m-resolution DEM of the watershed and using ArcGIS and R software, we made estimates of hypsometric integral values and calculated soil loss estimates through RUSLE modelling and by using data from different sources, namely the Rwanda Meteorological Agency (rainfall data), ISRIC (soil data), and Sentinel-2 images (land cover maps). The hypsometric integral values of the Yanze sub-catchments were high, ranging from 0.5 to 0.936. This, combined with the overall convex upward hypsometric curves, indicates that the Yanze watershed is still at a youthful stage in its erosional cycle. The results of the RUSLE model showed that the average potential soil loss in the Yanze watershed is 55.63 tonnes.ha-1.year-1, which is comparable to the national average estimated at 62 tonnes.ha-1.year-1. The correlation analysis that we conducted between the hypsometric integral values of the Yanze sub-catchments and their respective mean soil loss values revealed no correlation between the two variables. From the results of this study, we conclude that in watersheds where lithology affects soil erosion significantly, morphology can indeed indicate the potential for erosion. However, we further concluded that future studies to characterize erosion potential using morphometry should employ additional morphometric parameters in the regression model.
Bumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas

ABSTRACT As momentum and interest build to leverage new forms of user-generated content that contains geographical information, classical issues of data quality remain significant research challenges. In this article we explore issues of representativeness for one form of user-generated content, geotagged photographs in US urban centers. Generalized linear models were developed to associate photograph distribution with underlying socioeconomic descriptors at the city-scale, and examine intra-city variation in relation to income inequality. We conclude our analyses with a detailed examination of Dallas, Seattle, and New Orleans. Our findings add to the growing volume of evidence outlining uneven representativeness in user-generated data, and our approach contributes to the stock of methods available to investigate geographic variations in representativeness. In addition to city-scale variables relating to distribution of user-generated content, variability remains at localized scales that demand an individual and contextual understanding of their form and nature. The findings demonstrate that careful analysis of representativeness at both macro and micro scales can simultaneously provide important insights into the processes giving rise to user-generated data sets and potentially shed light on their embedded biases and suitability as inputs to analysis.
Biomass Productivity-Based Mapping of Global Land Degradation Hotspots

Land degradation affects negatively the livelihoods and food security of global population. There have been recurring efforts by the international community to identify the global extent and severity of land degradation. Using the long-term trend of biomass productivity as a proxy of land degradation at global scale, we identify the degradation hotspots in the world across major land cover types. We correct factors confounding the relationship between the remotely sensed vegetation index and land-based biomass productivity, including the effects of inter-annual rainfall variation, atmospheric fertilization and intensive use of chemical fertilizers. Our findings show that land degradation hotpots cover about 29 % of global land area and are happening in all agro-ecologies and land cover types. This figure does not include all areas of degraded lands, it refers to areas where land degradation is most acute and requires priority actions in both in-depth research and management measures to combat land degradation. About 3.2 billion people reside in these degrading areas. However, the number of people affected by land degradation is likely to be higher as more people depend on the continuous flow of ecosystem goods and services from these affected areas. Land improvement has occurred in about 2.7 % of global land area during the last three decades, suggesting that with appropriate actions land degradation trend could be reversed. We also identify concrete aspects in which these results should be interpreted with cautions, the limitations of this work and the key areas for future research.
Title: Identifying Significant Genetic Regulatory Networks in the Prostate Cancer from Microarray Data Based on Transcription Factor Analysis and Conditional Independency Summary for Revision Identifying Significant Genetic Regulatory Networks in the Prostate Cancer from Microarray Data Based on Tra

We appreciate the comments from the associate editor and all reviewers in this paper. We have made necessary experiments and modifications to cope with all the comments. The basic notations for different fonts are: Bold face fonts are from reviewers' original comments. Italic face fonts are the modified/added texts in the paper. Plain fonts are our answers to reviewers' comments. Q1: There is a mistake in interpretation of missing value estimation (Section3.1). The formula in step 1 of KNN algorithm should not include the missing value in A while all N samples are calculated. Second, it should be pointed out that only genes without any missing values can be used to impute missing value in gene A, otherwise it's impossible to calculate Euclidean distance by that formula. A1: Thanks for your comments for the mistake in interpretation of missing value estimation. In the step 1 of KNN algorithm, while we impute only one missing values of gene A in microarray data, we should not calculate Euclidean distance with this missing value and modify the formula in Equation(1) that is not included the missing value in A. We have modified. A2: In formula of Euclidean distance, we pointed out that only genes in the experiments without missing values can be used to impute in Equation(1). If genes with missing values in the experiment, we ignore this experiment for calculating Euclidean distance formula. Because we ignore the genes with more than 20% of missing values, there are not many missing data in the microarray data. On P.7 from line 14-22 The steps of KNN algorithm are listed as follows: 1) We consider gene A with the missing value in experiment t and calculate the Euclidean distance between Gene A and other genes without missing values in other t-1 experiment. Suppose (p 1 , p 2 , p t-1 , p t+1 , …, p N) and (q 1 , q 2 , q t-1 , q t+1 ,…, q N) are the expression values of the gene A and other genes in other N-1 experiments. The Euclidean distance between the two gene expressions is as follows: () () () () () () n i i i t t t t q p q p q p q p q p q p 1 2 2 N N 2 1 1 2 1 1 2 2 2 2 1 1 . If genes with …