Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Approach for Automatic Complex Fractured Networks Parameter Inversion Based on Surrogate Model and Deep Reinforcement Learning

Complex fracture networks are commonly generated in subsurface reservoirs, and the first and primary task is to understand their transport characteristics and properties. Like pumping test interpretation, the well‐testing interpretation is an effective means for transport characteristics analysis and parameter estimations, this work attempts to propose a parameter inversion approach for complex fracture networks based on well‐testing interpretation. Given that non‐unique solutions and computational inefficiencies are obstacles to practical interpretation, especially when complex fractures are existing, a new deep reinforcement learning (DRL) based approach is proposed for automatic curve matching on complex fractured networks well‐testing interpretation. Based on twin delayed deep deterministic policy gradient (TD3) algorithm, the proposed DRL approach is successfully applied to automatic matching of complex fractured networks' well test curves. In addition, to improve the training efficiency, a surrogate model of the well test model based on bidirectional GRU (Bi‐GRU) neural network was established. After episodic training, the agent finally converged to an optimal curve matching policy through interaction with the surrogate model. The results show that the average relative error of the curve's parameter interpretation is 4.34%. Additionally, the results from the case studies show that the proposed DRL approach has a high calculation speed.
The Effects of Long-Term Nitrogen Enrichment on Estuarine Benthic-Pelagic Coupling

Biogeochemical cycling in the marine coastal zone regulates the availability of nitrogen and carbon within soft sediment habitats. However, these pathways are being fundamentally altered by anthropogenic increases in nutrient delivery. Few studies have incorporated long-term enrichment and ecological complexity (in situ experiments), restricting our ability to manage effectively and prevent ecological shifts. This study investigates the influence of sediment nutrient availability (at 3 levels, across 2 seasons) on biogeochemical cycling over a 20-month period in 4 estuaries. Overall, net denitrification rates were highly variable, ranging between 4 and 208 µmol N m−2 h−1. However, no increases were observed with increasing enrichment highlighting the limited capacity for nitrogen removal in response to large increases in bioavailable nitrogen. Additionally, macrofaunal communities and sediment trophic status were shown to have important influences on nitrogen processing. Overall, alterations to ecosystem relationships and the appearance of non-linear responses to increasing nutrient enrichment reveal the vulnerability of estuaries to increasing stressor loads owing to the increased likelihood of reaching a tipping point.
Elemental content allometries and silicon uptake rates of planktonic Rhizaria: Insights into their ecology and role in biogeochemical cycles

The last two decades have shown the importance of Rhizaria in the biogeochemical cycles of carbon and silicon in modern oceans. This eukaryotic supergroup, which includes Radiolaria and Phaeodaria, represents an important part of zooplanktonic carbon biomass and contributes to carbon and silica export. Still, accurate estimations of their carbon biomass are hindered by poor knowledge of their elemental composition, contrasting with well‐established allometric carbon‐to‐volume relationships for smaller protists such as phytoplankton. Here, we directly measured carbon, nitrogen, and biogenic silica content as well as silicon uptake rates of planktonic Rhizaria. We highlight that size can be used as a predictor of elemental content for a broad variety of planktonic Rhizaria ranging from 200 μm to several mm, whereas size is weakly correlated with silicon uptake rates. Our results indicate that the scaling exponent of the carbon‐to‐volume allometry is significantly lower than those for smaller protists, underlining the low carbon strategy of these organisms. Still, we show that carbon and nitrogen densities span over four orders of magnitude, possibly accounting for the differences in depth ranges, nutritional modes and colonial or solitary forms. We estimate Rhizaria sinking speeds by combining carbon, nitrogen, and silica content data and show that great variability exists among the different taxa. Besides giving a better understanding of rhizarian ecology and biogeochemistry, these analyses, at the individual scale, are a first step to subsequent biomass and flux estimations at larger scales.
Anti-Mycobacterial N-(2-Arylethyl)quinolin-3-amines Inspired by Marine Sponge-Derived Alkaloid

The synthesis and evaluation of simplified analogs of marine sponge-derived alkaloid 3-(phenethylamino)demethyl(oxy)aaptamine were performed to develop novel anti-mycobacterial substances. Ring truncation of the tricyclic benzo[de][1,6]-naphthyridine skeleton effectively weakened the cytotoxicity of the natural product, and the resulting AC-ring analog exhibited good anti-mycobacterial activity. A structure–activity relationship (SAR) study, synthesizing and evaluating some analogs, demonstrated the specificity and importance of the N-(2-arylethyl)quinolin-3-amine skeleton as a promising scaffold for anti-mycobacterial lead compounds.
Effect of Pressure on the Direct Energy Gap of LiH

The direct energy gap of LiH has been measured at RT under high pressure up to 48 GPa. The gap, estimated from the peak energy of the n =1 exciton in the reflectance spectra, decreases from 4.94 eV at normal pressure to 4.52 eV at 48 GPa. The pressure coefficient of the gap decreases from 0.5 meV/GPa at 0 GPa to -11 meV/GPa at 48 GPa. No discontinuity of peak energy is observed up to 48 GPa. The insulator to metal transition pressure is estimated to be about 400 GPa.
Fractional Fourier Transform in Time Series Prediction

Several signal processing tools are integrated into machine learning models for performance and computational cost improvements. Fourier transform (FT) and its variants, which are powerful tools for spectral analysis, are employed in the prediction of univariate time series by converting them to sequences in the spectral domain to be processed further by recurrent neural networks (RNNs). This approach increases the prediction performance and reduces training time compared to conventional methods. In this letter, we introduce fractional Fourier transform (FrFT) to time series prediction by RNNs. As a parametric transformation, FrFT allows us to seek and select better-performing transformation domains by providing access to a continuum of domains between time and frequency. This flexibility yields significant improvements in the prediction power of the underlying models without sacrificing computational efficiency. We evaluated our FrFT-based time series prediction approach on synthetic and real-world datasets. Our results show that FrFT gives rise to performance improvements over ordinary FT.
Blind Vector Parameter Estimation for Burst Type CPM Transmissions

Short burst continuous phase modulation transmission is of practical relevance in e.g. frequency hopping systems applied in sensor or tactical networks. The channel conditions can be seen as mutually uncorrelated for each burst due to spectral and or temporal separation of those. Because of this time variant nature, a recurring acquisition of the impairment parameters is required for each burst. In this work, a blind joint estimation of several parameters in a flat fading environment for continuous phase modulation bursts is realized by the expectation maximization algorithm. The main contributions are first the formulation of the expectation and maximization steps to enable the joint computation of the maximum likelihood parameter estimates and second the analysis of the likelihood functions to obtain an optimized initialization for the algorithm. It is shown, that the joint estimator produces unbiased estimates and its performance in terms of the mean squared estimation error achieves the theoretical limits, i.e. the modified Cramér-Rao-Vector-Bound and slightly outperforms a state of the art pilot based estimator. Furthermore, the effective throughput is discussed and bit and frame error rates are compared to each other and to the perfectly synchronized estimator. Its computational complexity is analyzed and efficient computation steps and further approaches are outlined to decrease it.
Spin valves using insulating cobalt ferrite exchange-spring pinning layers

Cobalt ferrite, CoFe2O4, thin films are explored as pinning layers for read sensor applications. High ΔR/R values, 12.8%, and high pinning fields, 1500 Oe, are observed. Unlike other coercivity based pinning layers, the soft properties of the free layer are not compromised. The properties of the cobalt ferrite layers are strongly dependent on the microstructure, which, in turn, depends on the reactive sputtering process.
Impact of Soil Salinity on Soil Dielectric Constant and Soil Moisture Retrieval From Active Microwave Remote Sensing

Soil salinity plays a key role in influencing the soil dielectric constant and soil backscatter coefficient. However, soil moisture (SM) retrieval models constructed based on active microwave data hardly consider soil salinity. Thus, obtaining the SM datasets with various salinity on regional and local scales is difficult. This study aimed to employ theoretical model simulation to investigate the errors of SM retrieval due to not considering the impact of soil salinity. Then, three typical saline soil dielectric constant models were validated and compared based on the experimental measurement datasets. Results show that the WYR saline soil dielectric constant model has excellent performance. The soil salinity mainly affects the imaginary part of the dielectric constant and the effect of salinity on the soil dielectric constant is more significant when the SM has larger values. In addition, in retrieving SM with soil salinity more than 10 g/kg, the retrieval result of SM has an absolute error of 0.04 $\text{m}^{3}/\text{m}^{3}$ and a relative error of 5% when not considering the soil salinity impact. In retrieving SM with soil salinity less than 10 g/kg, the retrieved SM error increased by 2%, and the absolute error increased by 0.01 $\text{m}^{3}/\text{m}^{3}$ as soil salinity increased by 3 g/kg. We believe that The study will give a theoretical reference for establishing the SM retrieval model in saline soil areas using microwave data.
Isolation, Speciation And Antifungal Drug Susceptibility Of Candida Species Isolated From Various Clinical Specimens In A Tertiary Care Hospital

: Background-Over the last 20 years, the rates of fungal infection have increased and Candida has emerged as a major cause of human disease. Candida species cause various clinical infections ranging from mucocutaneous infections to life threatening blood stream infections. Emergence of Candida species resistance is on the rise especially to triazoles and amphotericin B has led to use echinocandins, mostly caspofungin in the management of invasive candidiasis. The aim of the study was to identify the spectrum of Candida species and to determine the susceptibility pattern to antifungal agents. Methods-A total of 150 Candida isolates were isolated over a period of 2 years. Growth of each isolate was evaluated for colony appearance, Gram stain, Germ tube test, morphological appearance by Dalmau technique using cornmeal agar, sugar utilization test, Culture on CHROMagar for species identification was done for species identification. Antifungal susceptibility was performed as recommended by Clinical and Laboratory Standards Institute (CLSI) M44-A2 document. Results-Out of
Capacity Allocation Optimization Framework for Hydrogen Integrated Energy System Considering Hydrogen Trading and Long-Term Hydrogen Storage

As an essential part of the future national energy system, hydrogen energy has the advantages of clean, long-time scale energy storage and good complementary characteristics to electrical power, making it an important player in the low-carbon transformation of energies. However, the long timescale storage characteristics and tradability of hydrogen energy are rarely considered in existing capacity allocation optimization methods for hydrogen integrated energy systems(HIES). Meanwhile, the impact of the physical characteristics of the hydrogen storage equipment is rarely considered in the construction of HIES. Therefore, this paper proposes a new capacity allocation method for HIES in industrial park considering hydrogen trading and long-term hydrogen storage. The proposed capacity allocation optimization method is a bilevel mixed-integer linear programming model, which is solved by the reconfiguration decomposition algorithm. The nonlinear constraint problem due to the physical characteristics of the hydrogen storage device is solved by the Big-M method and the binary method. The proposed method can effectively improve the economy of HIES and reduce the cost of hydrogen production. Meanwhile, the reconstruction decomposition algorithm can effectively solve the bilevel mixed integer programing model. Case studies demonstrate that the proposed method can reduce hydrogen production economics by 28%. Considering hydrogen trading, the total investment and operating cost of HIES is reduced by 25%, while long-term hydrogen storage can reduce the cost of hydrogen production for HIES and reduce the impact of hydrogen trading fluctuations.
Using a Simple Representation of Non‐Structural Carbohydrates Allocation to Predict Wood Growth in Temperate Forests

Wood growth is an important process of carbon sequestration and plant physiology in forest ecosystems. Hence, understanding and predicting wood growth is central to quantifying forest carbon cycling. Current dynamic global vegetation models (DGVMs) are mainly driven by carbon inputs (e.g., Gross Primary Production, GPP). However, observations indicate that wood growth is often independent of carbon flux at the annual scale. Here, we revised a DGVM, FORCCHN2 model, to use the active non‐structural carbohydrates (NSC) pool and slow NSC pool (i.e., represented temporary and long‐term NSC storage, respectively), and to integrate the stored NSC with annual wood growth. For the diameter increments (ΔDBH) and aboveground wood growth of 506 trees in 32 plots of Harvard Forest, we tested the NSC allocation coefficients from 5% to 95%. The predictions reproduced 31.2%–55.1% of individual‐tree ΔDBH and 36.8%–43.0% of the aboveground wood increment. Trees of shade‐intolerant species invested more of their available carbon resources (i.e., NSC storage) into wood growth than shade‐tolerant species. Specifically, the shade‐tolerant trees consumed approximately 32% NSC storage by wood growth at the annual scale, while the shade‐intolerant trees consumed about 90%. Our study provides a simple framework that constructs a direct link between annual wood growth and NSC dynamics. The findings highlight the need for research into NSC storage and allocation in trees, particularly in considering NSC allocation strategies of different tree species.
Tunable Bandpass Filter With Continuously-Tuned Center Frequency And Bandwidth

Abstract This paper presents a bandpass filter with electricallytunable center frequency and bandwidth. The proposed filter employs a centrally-loaded resonator with three varactors. The odd-mode and even-mode resonant frequencies of the resonator can be flexibly controlled by changing the voltage of the corresponding varactors. The adjustability of center frequency is achieved by adjusting both odd- and even-mode resonant frequencies, and the tuning of bandwidth can be achieved by probably adjusting the discrepancy between odd- and evenmode resonant frequencies. To improve the selectivity, the source-load coupling is utilized to generate transmission zeros. The performance of this electronically tuned filter is promising in terms of continuous center frequency and bandwidth tunings. For validation, both theory and experimental results are provided. It is shown that the filter could operate within the frequency range from 525MHz to 655MHz and be applicable to cognitive radio systems.
Teachers’ Social-Emotional Health and Resilience in Covid-19 Crisis: Latvian Sample

Research on social-emotional health and resilience of Latvian teachers was conducted as part of the ERASMUS+ project “Supporting teachers to face the challenge of distance teaching”. The aim of this study is to assess teachers’ social-emotional health and resilience to reveal those areas which require significant support and development in the Covid-19 pandemic situation. In the article, the concepts used in the project – social-emotional health, resilience, covitality will be theoretically analysed to substantiate the structure of the empirical study. Teachers’ mental health was tested using Social-emotional Health Survey– Teachers SEHS-T (Furlong et al., 2017; Furlong et al., 2014; Furlong & Gajdošová, 2018, as mentioned in Lapiņa, 2021) and Resilience Scale RS 25 (Wagnild & Young, 1993; Wagnild, 2009; Wagnild, 2016) with supplementary questions. 636 teachers of general and vocational schools took part in the research. The results identified that positive teachers’ strengths are self-regulation, empathy, and cognitive reappraisal. However, the teachers demonstrated limits in resilience as such, and in some scores of SEHS-T, as in covitality domain Belief in Others, especially in institutional and colleagues’ support, and in Engaged Living – gratitude and zest. The identified weaknesses and limits will be used as a foundation for preparing further intervention activities – a digital psychological support programme for strengthening the teachers’ resilience and mental health in general.
The Intricate Interplay between the ZNF217 Oncogene and Epigenetic Processes Shapes Tumor Progression

Simple Summary The ZNF217 oncogene promotes progression, pluripotency and resistance to therapy in different types of cancer and is associated with increased risk of metastasis. High ZNF217 expression levels are a biomarker of poor prognosis in several cancers. Therapies targeting ZNF217 could be promising in cancer treatment; however, the lack of identification of the full 3D structure of ZNF217 incites investigation of mechanisms of regulation of ZNF217 to delineate alternative candidate therapeutic strategies. In fact, studies show that ZNF217 shapes cancer growth through epigenetic mechanisms that promote tumor progression, including DNA methylation, interactions with noncoding RNAs and epitranscriptome refining, which are summarized in this review. We also discuss how it would be promising to assess noncoding RNAs that regulate ZNF217 expression and function, as well as to investigate the DNA methylation status of ZNF217 locus, as biomarkers of poor prognosis and to monitor therapeutic response to treatment. Abstract The oncogenic transcription factor ZNF217 orchestrates several molecular signaling networks to reprogram integrated circuits governing hallmark capabilities within cancer cells. High levels of ZNF217 expression provide advantages to a specific subset of cancer cells to reprogram tumor progression, drug resistance and cancer cell plasticity. ZNF217 expression level, thus, provides a powerful biomarker of poor prognosis and a predictive biomarker for anticancer therapies. Cancer epigenetic mechanisms are well known to support the acquisition of hallmark characteristics during oncogenesis. However, the complex interactions between ZNF217 and epigenetic processes have been poorly appreciated. Deregulated DNA methylation status at ZNF217 locus or an intricate cross-talk between ZNF217 and noncoding RNA networks could explain aberrant ZNF217 expression levels in a cancer cell context. On the other hand, the ZNF217 protein controls gene expression signatures and molecular signaling for tumor progression by tuning DNA methylation status at key promoters by interfering with noncoding RNAs or by refining the epitranscriptome. Altogether, this review focuses on the recent advances in the understanding of ZNF217 collaboration with epigenetics processes to orchestrate oncogenesis. We also discuss the exciting burgeoning translational medicine and candidate therapeutic strategies emerging from those recent findings connecting ZNF217 to epigenetic deregulation in cancer.
Development of adaptive optics elements for solar telescope

The devices and components of adaptive optical system ANGARA, which is developed for image correction in the Big solar vacuum telescope (BSVT) at Baykal astrophysical observatory are described. It is shown that the use of modernized adaptive system on BSVT not only reduces the turbulent atmospheric distortions of image, but also gives a possibility to improve the telescope developing new methods of solar observations. A high precision Shack-Hartmann wavefront (WF) sensor has been developed on the basis of a low-aperture off-axis diffraction lens array. The device is capable of measuring WF slopes at array sub-apertures of size 640X640 μm with an error not exceeding 4.80 arc.sec. Also the modification of this sensor for adaptive system of solar telescope using extended scenes as tracking objects, such as sunspot, pores, solar granulation and limb, is presented. The software package developed for the proposed WF sensors includes three algorithms of local WF slopes estimation (modified centroids, normalized cross-correlation and fast Fourier-demodulation), as well as three methods of WF reconstruction (modal Zernike polynomials expansion, deformable mirror response functions expansion and phase unwrapping), that can be selected during operation with accordance to the application.
Kombuchas from Green and Black Tea Modulate the Gut Microbiota and Improve the Intestinal Health of Wistar Rats Fed a High-Fat High-Fructose Diet

The Western diet can negatively affect the gut microbiota and is associated with metabolic disorders. Kombucha, a tea fermented by a symbiotic culture of bacteria and yeast (SCOBY), is known for its bioactive properties and has become popular in the last years. In this study, we evaluated the effects of regular kombucha consumption on the gut microbiota and on outcomes related to the intestinal health of Wistar rats fed a high-fat high-fructose diet. After eight weeks receiving a standard diet (AIN-93M) (n = 10) or a high-fat and high-fructose diet (HFHF) (n = 30) to induce metabolic disorders, the animals were subdivided into four groups: AIN-93M (n = 10); HFHF (n = 10); GTK (HFHF + green tea kombucha (n = 10); and BTK (HFHF + black tea kombucha; n = 10) for 10 weeks. Although body composition did not differ among the groups, the HFHF diet was associated with metabolic alterations, and stimulated the growth of gram-negative bacteria such as Proteobacteria and Bacteroides. Kombucha ingestion could somewhat modulate the gut microbiota, attenuating the effects of a Western diet by increasing propionate production and favoring the growth of beneficial bacteria, such as Adlercreutzia in the GTK group. Our results suggest that regular kombucha consumption may be beneficial to intestinal health, which can be mostly attributed to its high content and diversity of phenolic compounds.