Miracle-dz/UniFAR-FTUs huggingface dataset:
RDNRnet: A Reconstruction Solution of NDVI Based on SAR and Optical Images by Residual-in-Residual Dense Blocks

The reconstruction of the normalized difference vegetation index (NDVI) is a crucial prerequisite for numerous spatiotemporal continuous studies. To address the limitations posed by satellite temporal resolution and challenging atmospheric conditions, the combination of synthetic aperture radar (SAR) and optical images from diverse sources has proven to be effective and widely employed. In this study, we employ the spatial-temporal Savitzky-Golay (STSG) algorithm to rectify MODIS NDVI maps and eliminate interruptions caused by noise. Random forest (RF) and gradient boosting decision trees (GBDTs) serve as a dual filter to select SAR indices with the highest impact on NDVI reconstruction, ensuring that the chosen indices encapsulate the most valuable information. Subsequently, we conducted a series of ablation experiments and developed a deep learning network named residual-in-residual dense block (RRDB) NDVI reconstruction net (RDNRnet). This network effectively mitigates the impacts of MODIS coarse resolution and speckle noises in SAR data. We also evaluate the network performance in reconstructing NDVI across all seasons and land cover types. Our findings highlight that the modified dual-polarimetric SAR vegetation index and the standard deviation (STD) of the vertical-vertical (VV) band are the most crucial SAR indices. The predictions for summer exhibit the highest performance, with a coefficient of determination ( $R^{2}$ ) reaching 0.9757. Optimal performances by land cover type are observed in forests, paddy fields, and dry farming fields, all with $R^{2}$ values exceeding 0.9580. Our adaptive NDVI reconstruction solution demonstrates robust performance across different data availability scenarios, effectively catering to all seasons and land cover types.
Tipping Points of Space Debris in Low Earth Orbit

Global services like navigation, communication, and Earth observation have increased dramatically in the 21st century due to advances in outer space industries. But as orbits become increasingly crowded with both satellites and inevitable space debris pollution, continued operations become endangered by the heightened risks of debris collisions in orbit. Kessler Syndrome is the term for when a critical threshold of orbiting debris triggers a runaway positive feedback loop of debris collisions, creating debris congestion that can render orbits unusable. As this potential tipping point becomes more widely recognized, there have been renewed calls for debris mitigation and removal. Here, we combine complex systems and social-ecological systems approaches to study how these efforts may affect space debris accumulation and the likelihood of reaching Kessler Syndrome. Specifically, we model how debris levels are affected by future launch rates, cleanup activities, and collisions between extant debris. We contextualize and interpret our dynamic model within a discussion of existing space debris governance and other social, economic, and geopolitical factors that may influence effective collective management of the orbital commons. In line with previous studies, our model finds that debris congestion may be reached in less than 200 years, though a holistic management strategy combining removal and mitigation actions can avoid such outcomes while continuing space activities. Moreover, although active debris removal may be particularly effective, the current lack of market and governance support may impede its implementation. Research into these critical dynamics and the multi-faceted variables that influence debris outcomes can support policymakers in curating impactful governance strategies and realistic transition pathways to sustaining debris-free orbits. Overall, our study is useful for communicating about space debris sustainability in policy and education settings by providing an exploration of policy portfolio options supported by a simple and clear social-ecological modeling approach.
Torus-like solutions for the Landau-de Gennes model. Part II: Topology of $\mathbb{S}^1$-equivariant minimizers

We study energy minimization of a continuum Landau-de Gennes energy functional for nematic liquid crystals, in three-dimensional axisymmetric domains and in a restricted class of $\mathbb{S}^1$-equivariant (i.e., axially symmetric) configurations. We assume smooth and nonvanishing $\mathbb{S}^1$-equivariant (e.g. homeotropic) Dirichlet boundary conditions and a physically relevant norm constraint (Lyuksyutov constraint) in the interior. Relying on results in \cite{DMP1} in the nonsymmetric setting, we prove partial regularity of minimizers away from a possible finite set of interior singularities lying on the symmetry axis. For a suitable class of domains and boundary data we show that for smooth minimizers (torus solutions) the level sets of the signed biaxiality are generically finite union of tori of revolution. Concerning nonsmooth minimizers (split solutions), we characterize their asymptotic behavior around any singular point in terms of explicit $\mathbb{S}^1$-equivariant harmonic maps into $\mathbb{S}^4$, whence the generic level sets of the signed biaxiality contains invariant topological spheres. Finally, in the model case of a nematic droplet, we provide existence of torus solutions, at least when the boundary data are suitable uniaxial deformations of the radial anchoring, and existence of split solutions for boundary data which are suitable linearly full harmonic spheres.
Glioblastoma with high O6-methyl-guanine DNA methyltransferase expression are more immunologically active than tumors with low MGMT expression

Background Glioblastoma (GBM) is a highly lethal brain tumor. The effectiveness of temozolomide (TMZ) treatment in GBM is linked to the methylation status of O6-methyl-guanine DNA methyltransferase (MGMT) promoter. Patients with unmethylated MGMT promoter have limited treatment options available. Consequently, there is a pressing need for alternative therapeutic strategies for such patients. Methods Data, including transcriptomic and clinical information, as well as information on MGMT promoter methylation status in primary GBM, were obtained from The Cancer Genome Atlas (TCGA) (n=121) and Chinese Glioma Genome Atlas (CGGA) (n=83) datasets. Samples were categorized into high and low MGMT expression groups, MGMT-high (MGMT-H) and MGMT-low (MGMT-L) tumors. A comprehensive transcriptome analysis was conducted to explore the tumor-immune microenvironment. Furthermore, we integrated transcriptome data from 13 GBM patients operated at our institution with findings from tumor-infiltrating lymphocyte (TIL) cultures, specifically investigating their response to autologous tumors. Results Gene signatures associated with various immune cells, including CD8 T cells, helper T cells, B cells, and macrophages, were noted in MGMT-H tumors. Pathway analysis confirmed the enrichment of immune cell-related pathways. Additionally, biological processes involved in the activation of monocytes and lymphocytes were observed in MGMT-H tumors. Furthermore, TIL culture experiments showed a greater presence of tumor-reactive T cells in MGMT-H tumors compared to MGMT-L tumors. These findings suggest that MGMT-H tumors has a potential for enhanced immune response against tumors mediated by CD8 T cells. Conclusion Our study provides novel insights into the immune cell composition of MGMT-H tumors, which is characterized by the infiltration of type 1 helper T cells and activated B cells, and also the presence of tumor-reactive T cells evidenced by TIL culture. These findings contribute to a better understanding of the immune response in MGMT-H tumors, emphasizing their potential for immunotherapy. Further studies are warranted to investigate on the mechanisms of MGMT expression and antitumor immunity.
Numerical Study on the Torsional and Lateral Vibrations of Double Universal Joint Driveline System

Utilizing a universal joint can lead to significant vibration within a driveline system. This study presents a model for analyzing the torsional and lateral vibrations of a driveline connected by a double universal joint. The governing equations of motion are derived, and the Runge-Kutta method computes steady-state responses across a spectrum of input rotational speeds. The focus is to examine the effect of system parameters, including static angular misalignment, load torque, and lateral stiffness. Relative amplification is used to analyze the effects of parameters on system vibration. Results indicated that the second-order component of input rotational speed induced by the universal joint was the factor that caused the vibrations. For the considered system, static angular misalignment significantly impacts both the torsional and lateral vibrations. Increasing the angular misalignment from 15° to 30° results in a threefold increase in lateral vibration amplification, while torsional vibration amplification is increased by nearly two times. The effect of load torque is almost linearly proportional to torsional vibration but is nonlinear to lateral vibration. Thus, lateral vibration is significantly impacted compared to torsional vibration for higher load torque. Changing the stiffness leads to a modification of the natural frequency. Increasing the lateral stiffness shifts the critical speed to a higher speed range, resulting in reduced lateral vibration amplitude. It is demonstrated that a slight fluctuation in angular misalignment due to lateral vibration will not affect the torsional vibration even if both vibrations are coupled. The findings may enhance understanding of how changing system parameters affects vibration.
Influence of Deccan volcanism/synrift magmatism on the crust–mantle structure and its implications for the seismogenesis of earthquakes occurring in the Kachchh rift zone

Abstract The present study focuses on the delineation of the crust–mantle structure underlying the Kachchh rift zone (KRZ), by modelling P-wave receiver functions (P-RFs) and P-wave teleseismic tomography. Our RF study delineates marked crustal and lithospheric thinning below the central KRZ relative to the unrifted surrounding regions. This thinning model receives further support from crust-corrected normalized P-residuals, which suggest dominant negative residuals associated with the central KRZ. Teleseismic tomography using these P-residuals reveals low velocity to a depth of 170 km below the central KRZ, while there are positive residuals associated with the surrounding unrifted zones. Such a complex heterogeneous crust–mantle structure, which could be related to K/T boundary Deccan mantle plume activity and rift-related magmatism, might play a crucial role in seismogenesis of lower crustal earthquakes that have occurred in the KRZ since 2001. Inverted crust–mantle models obtained from P-RFs suggest a low shear velocity zone extending from 70–110 to 170–220 km depth beneath the central KRZ. This receives further support from the presence of low P-wave velocity down to 170 km modelled using teleseismic tomography. This low Vp and Vs zone in the upper mantle could be explained by the presence of trapped melts related to Deccan volcanism at 65 Ma or older rift-related magmatism.
Modelling Unconventional Gas Traps: Elmworth Field, Alberta, Canada

The Elmworth Field is the largest gas field of the Western Canada sedimentary Deep Basin. Gas found in stratigraphic traps lies downdip from water, with no known permeability barrier between. A two dimensional quantitative analysis of the basin was undertaken in order to simulate such unconventional entrapment. The modelling shows how it is possible to accumulate gas in the low permeability sand reservoirs of the Lower Cretaceous. It appears that the Elmworth basin is a system in dynamical evolution, where gas loss due to migration is partly compensated for by contemporary gas generation. While the on-going gas generation is highly influenced by the recent paleoheat flow, the early migration has been controlled by cementation which, by lowering the permeability, reduces the gas loss with time, so that significant gas trapping can be obtained today. This work presents a set of conditions leading to such unconventional gas accumulations and discusses the parameters influencing the total amount of gas accumulated at present day; characteristics of the sediments, such as porosity and permeability behavior through time, cementation parameters, amount of sediments removed during the Paleocene erosion, or heat flow variation with time, are also bracketed in order that gas accumulations predicted are in agreement with those observed today. The inherent underpressure due to gas saturated formations is also considered.
Alteration of MX-80 bentonite backfill material by high-pH cementitious fluids under lithostatic conditions – an experimental approach using core infiltration techniques

Abstract We characterize and quantify processes at a cement–bentonite interface spatially and temporally during a long-term core infiltration experiment. A young ordinary Portland cement pore fluid (K+–Na+–OH−: pH 13.4) was infiltrated into a MX-80 bentonite core with an initial saturated density of 1920 kg m−3 that shifted to 1890–1930 kg m−3 after 761 days. A hydrostatic external pressure of 4.1 MPa and an infiltration pressure of 2.1 MPa were applied in a triaxial-type apparatus. A decrease in hydraulic conductivity from approximately 2.2×10−13 to approximately 4.2×10−15 m s−1 was observed passing from advection-dominated flow to a diffusion-dominated regime. Sulphate replaced chloride in the outflow during the high-pH infiltration period controlled by the dissolution of gypsum, the uptake of K+ by ion exchange, and complex mineral reactions occurred near the inlet. X-ray computed tomography (CT) scans performed repeatedly during the experiment tracked a progressing hemispherical reaction plume in the first millimetres of the bentonite, revealing a zone of bulk density increase. This zone consisted of two distinct, but overlapping, zones of Mg- and Ca-enrichment related to precipitation of saponite and calcite. The experiment attested an effective chemical buffering capacity for bentonite, a progressing coupled hydraulic–chemical sealing process and also the preservation of the physical integrity of the interface region in this set-up with a total pressure boundary condition on the core sample.
Reanalysis and validation of the transcriptional pleural fluid signature in pleural tuberculosis

Introduction Pleural tuberculosis (PlTB), the most common site of extrapulmonary TB, is characterized by a paucibacillary nature and a compartmentalized inflammatory response in the pleural cavity, both of which make diagnosis and management extremely challenging. Although transcriptional signatures for pulmonary TB have already been described, data obtained by using this approach for extrapulmonary tuberculosis and, specifically, for pleural tuberculosis are scarce and heterogeneous. In the present study, a set of candidate genes previously described in pulmonary TB was evaluated to identify and validate a transcriptional signature in clinical samples from a Brazilian cohort of PlTB patients and those with other exudative causes of pleural effusion. Methods As a first step, target genes were selected by a random forest algorithm with recursive feature elimination (RFE) from public microarray datasets. Then, peripheral blood (PB) and pleural fluid (PF) samples from recruited patients presenting exudative pleural effusion were collected during the thoracentesis procedure. Transcriptional analysis of the selected top 10 genes was performed by quantitative RT-PCR (RT-qPCR). Results Reanalysis of the public datasets identified a set of candidate genes (CARD17, BHLHE40, FCGR1A, BATF2, STAT1, BTN3A1, ANKRD22, C1QB, GBP2, and SEPTIN4) that demonstrated a global accuracy of 89.5% in discriminating pulmonary TB cases from other respiratory diseases. Our validation cohort consisted of PlTB (n = 35) patients and non-TB (n = 34) ones. The gene expressions of CARD17, GBP2, and C1QB in PF at diagnosis were significantly different between the two (PlTB and non-TB) groups (p < 0.0001). It was observed that the gene expressions of CARD17 and GBP2 were higher in PlTB PF than in non-TB patients. C1QB showed the opposite behavior, being higher in the non-TB PF. After anti-TB therapy, however, GBP2 gene expression was significantly reduced in PlTB patients (p < 0.001). Finally, the accuracy of the three above-cited highlighted genes in the PF was analyzed, showing AUCs of 91%, 90%, and 85%, respectively. GBP2 was above 80% (sensitivity = 0.89/specificity = 0.81), and CARD17 showed significant specificity (Se = 0.69/Sp = 0.95) in its capacity to discriminate the groups. Conclusion CARD17, GBP2, and C1QB showed promise in discriminating PlTB from other causes of exudative pleural effusion by providing accurate diagnoses, thus accelerating the initiation of anti-TB therapy.
GENDER AND OTHER SIGNIFICANT FACTORS CAUSING DISPARITIES IN SENIOR HIGH SCHOOL STUDENTS’ MATHEMATICS PERFORMANCE

Research findings on gender and other student related, teacher related and school related factors affecting students’ performance in mathematics are still debatable. With the recent trend of poor performance in mathematics recorded in both district and national performance statistics in the Assin North District, this present study examined gender factor and other significant factors causing disparities in mathematics performance among high school students. A mathematics achievement test and questionnaires were employed to collect data from a representative sample of 500 final-year students from three public senior high schools in the Assin North District, Ghana. Data were analysed descriptively and quantitatively using independent t-test and probit regression. Results show that male students did better than female students in the mathematics achievement test. The differences were statistically significant at . 05 significance level. Aside gender, self-assurance and self-regard were identified as significant student related factors affecting the mathematics performance among senior high school students in the Assin North District. Teacher subject matter knowledge, teacher methods and teacher-student interaction were also significant teacher related factors affecting performance in mathematics. Finally, teacher motivation and school environment were identified as significant school related factors affecting mathematics performance among the senior high school. Other factors such as students’ socioeconomic background and teaching resources had effect on students’ performance but they were not statistically significant. The study recommends that senior high school mathematics teachers should employ gender responsive pedagogies in their teaching practices. It is also recommended that professional learning communities should also be formed at school levels to enable mathematics teachers improve upon their knowledge, motivation and teaching styles.
Influence of Sub-Debris Thawing on Ablation and Runoff of the Djankuat Glacier in the Caucasus

Superficial moraines grew in size during the entire 32-year-long period of direct monitoring of water and ice balance of the Djankuat Glacier in the Caucasus. The total area of debris cover on the glacier increased from 0.104 km 2 (3% of the entire glacier surface) in 1968 to 0.266 km 2 (8% of the glacier) in 1996. Such rapid dynamics of moraine formation greatly influences the ablation rate and distorts fields of mass-balance components. Sub-debris thawing can be calculated by means of a model, which describes the role of debris cover for the thermal properties of a glacier. Its meltwater equivalent depends mainly on debris thickness. In 1983 and 1994 the debris cover was repeatedly mapped over the whole glacier portion that was covered with morainic material. Sub-moraine ablation increases (vs. pure ice surface) under the thin, less than ca. 7-8 cm, debris layer, whereas the thicker debris cover reduces the liquid runoff due to its shielding effect. Zones differing due to their hydrological effect are depicted on the glacier map and the degree of debris influence on ablation is estimated quantitatively. As a whole runoff from debris-covered parts of the Djankuat Glacier has diminished due to the dominant shielding effect. Variation of the terminus is also shown to be dependent on the evolution of superficial moraine.
Innovation in Russia: The Territorial Dimension

abstract The debate on Russia’s innovation performance has paid little attention to the role of geography. This article addresses this gap by integrating an evolutionary dimension in an augmented regional knowledge production function framework to examine the territorial dynamics of knowledge creation in Russia. The empirical analysis identifies a strong link between regional research and development (R&D) expenditure and patenting performance. However, R&D appears inadequately connected to regional human capital. Conversely, multinational enterprises (MNEs) play a fundamental role as global knowledge pipelines. The incorporation of historic variables reveals that the Russian case is a striking example of long-term path dependency in regional patterns of knowledge generation. Endowment with Soviet-founded science cities remains a strong predictor of current patenting. However, current innovation drivers and policies also concur to enhance (or hinder) innovation performance in all regions. The alignment of regional innovation efforts, exposure to localized knowledge flows and injections of foreign knowledge channeled by MNEs make path renewal and path creation possible, opening new windows of locational opportunity.
DG-GLNet: A Graph Convolutional Network for prediction of time sequence with topological structure

The prediction of time series data with topological structure has garnered significant attention in the field of sequence data analysis. Previous studies have only considered capturing the global space and time dependence, with little focus on the complex nonlinear dependence between global and local. As a solution, we propose the DG-GLNet model, a time series data prediction method based on a graph convolutional neural network with topology structure. This model combines multilevel discrete wavelet decomposition (MDWT), graph convolutional neural network (GCN), multi-head attention feedforward neural network module, and the global-local network model (GLNet) in this paper. Firstly, MDWT decomposes the original time series data into multistage time-frequency time-sequence subsequences with different time scales to capture local features of the sequences. Secondly, GCN learns complex topologies to capture spatial dependencies of data. Adopt a filter-based two-channel GLNet to learn the changes between global and local sequences to capture the time dependence between data more accurately. A multi-head attention feedforward neural network module captures the dependency between time series data observed at different times. Finally, the output of GLNet is connected to the dense connection layer for prediction. We conduct a large number of experiments on real datasets, and the results show that the proposed DG-GLNet model has better accuracy and training speed than traditional mainstream models.