Miracle-dz/UniFAR-FTUs huggingface dataset:
Flood Wave Dynamics in the Transboundary Dniester River Floodplain under Reservoirs Impact

: The present research aims to evaluate the impact of stream reservoirs on the Dniester river ﬂoods wave dynamics. The main approach consisted in the evaluation of the changes that occur in the ﬂood parameters for three periods: natural ﬂow, ﬂow regulated by Dubasary reservoir, ﬂow regulated by the Dniester hydropower complex and Dubasary reservoir. Using the hydrological time series from the stations situated upstream and downstream of the reservoirs, the following were calculated and compared: ﬂood characteristics, peaks of 10, 5, 1, 0.5, and 0.1% probability, and environment ﬂow components such as high-ﬂow pulses, small ﬂoods, large ﬂoods. The results show that high ﬂood protection is speciﬁc to the Dniester hydropower complex, while through the Dubasary reservoir the ﬂood wave passes mainly in transit. Due to the ﬂow regulating impact, small ﬂoods as well as their average peaks and duration were reduced toward the reservoirs’ downstream part. High-ﬂow pulses increased in number after the Dniester hydropower complex construction due to hydropeaking effect; however, their reduction is observed downstream of the Dubasary reservoir. Large ﬂoods increase in number in the upper part but are transformed into small ﬂoods toward the downstream, thus increasing the ﬂood protection capacity.
Microbial communities in freshwater used for hydraulic fracturing are unable to withstand the high temperatures and pressures characteristic of fractured shales

Natural gas is recovered from shale formations by hydraulic fracturing, a process known to create microbial ecosystems in the deep subsurface. Microbial communities that emerge in fractured shales include organisms known to degrade fracturing fluid additives and contribute to corrosion of well infrastructure. In order to limit these negative microbial processes, it is essential to constrain the source of the responsible micro-organisms. Previous studies have identified a number of potential sources, including fracturing fluids and drilling muds, yet these sources remain largely untested. Here, we apply high-pressure experimental approaches to assess whether the microbial community in synthetic fracturing fluid made from freshwater reservoir water can withstand the temperature and pressure conditions of hydraulic fracturing and the fractured shale environment. Using cell enumerations, DNA extraction and culturing, we show that the community can withstand high pressure or high temperature alone, but the combination of both is fatal. These results suggest that initial freshwater-based fracturing fluids are an unlikely source of micro-organisms in fractured shales. These findings indicate that potentially problematic lineages, such as sulfidogenic strains of Halanaerobium that have been found to dominate fractured shale microbial communities, likely derive from other input sources into the downwell environment, such as drilling muds.
The Interaction Effect of Anti-RgpA and Anti-PPAD Antibody Titers: An Indicator for Rheumatoid Arthritis Diagnosis

Porphyromonas gingivalis secretes virulence factors like Arg-gingipains and peptidyl arginine deiminase (PPAD), that are associated with rheumatoid arthritis (RA) pathogenesis. However, there is no information regarding the antibody titers for these bacterial enzymes as systemic indicators or biomarkers in RA. In this cross-sectional study, 255 individuals were evaluated: 143 were diagnosed with RA, and 112 were without RA. Logistic regression models adjusted for age, sex, basal metabolic index, smoking, and periodontitis severity were used to evaluate the association of RA with rheumatoid factor (RF), anti-citrullinated protein antibodies (ACPAs), erythrocyte sedimentation rate, high sensitivity C-reactive protein, anti-RgpA, anti-PPAD, and double positive anti-RgpA/anti-PPAD. It was found that RF (odds ratio [OR] 10.6; 95% confidence interval [CI] 4.4–25), ACPAs (OR 13.7; 95% CI 5.1–35), and anti-RgpA/anti-PPAD double positivity (OR 6.63; 95% CI 1.61–27) were associated with RA diagnoses. Anti-RgpA was also associated with RA (OR 4.09; 95% CI 1.2–13.9). The combination of anti-RgpA/anti-PPAD showed a high specificity of 93.7% and 82.5% PPV in identifying individuals with RA. RgpA antibodies were associated with the periodontal inflammatory index in RA individuals (p < 0.05). The double positivity of the anti-RgpA/anti-PPAD antibodies enhanced the diagnosis of RA. Therefore, RgpA antibodies and anti-RgpA/anti-PPAD may be biomarkers for RA.
Analyzing the Spatiotemporal Characteristics of Extreme Rainfall Using CAPE and GNSS-Derived ZTD Across China

The power-law relationship between precipitable water vapor (PWV)/convective available potential energy (CAPE) and extreme rainfall (ER) has been explored. However, the retrieval of PWV is reliant on the zenith total delay (ZTD) of the Global Navigation Satellite System (GNSS), and errors are introduced when converting ZTD to PWV. In this study, we propose the ZTD, CAPE, and ER (ZCER) model, a comprehensive analysis model that integrates ZCER, for investigating long-term variation in ER intensity and frequency, which expresses their close relationships in a novel approach and expands the application area of GNSS-derived ZTD. Daily ZTD, CAPE, and rainfall data were collected from 219 GNSS stations in China from 2011 to 2020 (ten years). Time- and frequency-domain information encapsulated in the three variables was extracted using wavelet coherence, proving that both ZTD and CAPE contributed to rainfall. The relationships between ZTD/loge(CAPE) and loge(ER) were investigated at the annual, seasonal, and monthly scales. The results revealed that the contribution of ZTD/CAPE to ER varied spatially and temporally. Furthermore, the synergistic contributions of ZTD and CAPE to ER were further investigated. Statistical results showed that CAPE and ZTD complemented each other not only on the geographical scales to ER in China, but also on the seasonal and monthly scales. Moreover, the qualitative relationships between the ZCER frequency were elucidated. Our findings validate the strong links between high ZCER intensity and frequency in China on geographical and temporal scales.
Cooling and quenching of {sup 24}MgH{sup +}(X {sup 1}{Sigma}{sup +}) by {sup 4}He({sup 1}S) in a Coulomb trap: A quantum study of the dynamics

The quantum dynamics of the rotational quenching of the {sup 24}MgH{sup +}(X {sup 1}{Sigma}{sup +}) molecular cation interacting with {sup 4}He({sup 1}S) as a buffer gas, at relative temperatures ranging from 1 K down to millikelvins, is described by accurate close coupling scattering calculations on an ab initio potential energy surface. The efficiency of the process is quantitatively analyzed by employing the ab initio quenching rates and cross sections and effective decay rate values are obtained from simple unimolecular kinetics over a broad range of trap conditions.
Early Exposure to Nature and Willingness to Pay for It: The Value of Tallgrass Prairie Grassland Restoration

Widespread global grassland destruction motivates restoration efforts. However, little research on public preferences exists to inform restoration decisions, and reduced exposure to nature such as grasslands could diminish public willingness to pay (WTP) for it. We conducted a choice experiment to estimate preferences over tallgrass prairie grassland restorations and quantify how those preferences are correlated with childhood experiences. We find that WTP for grassland restoration can be large, especially with recreational opportunities. Further, people who participated in outdoor activities or grew up near grasslands during their childhood place a higher value on grassland restoration than people who did not.
Higher derivative supergravities from superconformal methods

The most promising candidate for a consistent theory of quantum gravity is superstring theory. The low energy effective physics of string theory is described by supergravity. In this thesis we review the superconformal construction of supergravity and focus on the construction of supergravity Lagrangians containing higher derivative terms. In the first part of the thesis we introduce supersymmetry, supergravity and the superconformal method. Particular emphasis is put on the minimal D = 5 and D = 6 settings. We construct off-shell formulations of the pure minimal D = 5 and D = 6 supergravities and explain how 5 dimensional gauged Einstein-Maxwell theories and the 6 dimensional Salam-Sezgin model, which is 6 dimensional gauged Einstein-Maxwell supergravity, can be obtained from superconformal methods. We discuss to which settings the superconformal method is restricted and distinguish between settings with not more than 8 supersymmetries for which off-shell formulations exist and settings with 16 supersymmetries for which only on-shell formulations exist. In the second part we focus on higher derivative deformations of supergravity theories. An overview is given of the different superconformal higher derivative invariants that have been constructed in the literature. As an example we consider the deformation of the Salam-Sezgin model by a supersymmetric completion of the Riemann tensor squared. We explain the difficulties related to the construction of superconformal higher derivative invariants for theories with 16 supersymmetries for which only on-shell formulations exist. We look for new ways of constructing such invariants based on the worldvolume action of D-branes.
Varietal Difference in Seed Germination under Different Levels of Salt Stress in Rice

Background: Salinity, major abiotic stress affects many field crops including rice. Salinity account 20% of cultivated area and may cover 50% irrigated area by the end of 2050 due to climate change. Global food security ensured by developing high yielding rice varieties tolerant to different abiotic stress. Salinity affect both germination and other growth stages of rice. Methods: Present study conducted in Tamil Nadu Agricultural University aim to assess the seed germination of rice under salinity stress using germination and growth parameters. In our study we included 11 rice genotypes with one tolerant check Pokkali and one susceptible check IR 64 recorded germination percentage, mean germination time, germination index, seedling vigour index, shoot length, root length, fresh weight, dry weight, root length to shoot length ratio and stress tolerance index. Result: Two way ANOVA used for comparing means. Two component extracted through principle component analysis which explained a total of 83.54% variance. Component 1 explained 64.96% and component 2 explained 18.57%. Stress tolerance index recorded higher for TRY 3 under 150mM NaCl followed by TRY 2.
Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties

The development and implementation of the methods for designing amorphous metal alloys with desired mechanical properties is one of the most promising areas of modern materials science. Here, the machine learning methods appear to be a suitable complement to empirical methods related to the synthesis and testing of amorphous alloys of various compositions. In the present work, it is proposed a method to determine amorphous metal alloys with mechanical properties closest to those required. More than 50,000 amorphous alloys of different compositions have been considered, and the Young’s modulus E and the yield strength σy have been evaluated for them by the machine learning model trained on the fundamental physical properties of the chemical elements. Statistical treatment of the obtained results reveals that the fundamental physical properties of the chemical element with the largest mass fraction are the most significant factors, whose values correlate with the values of the mechanical properties of the alloys, in which this element is involved. It is shown that the values of the Young’s modulus E and the yield strength σy are higher for amorphous alloys based on Cr, Fe, Co, Ni, Nb, Mo and W formed by the addition of semimetals (e.g., Be, B, Al, Sn), nonmetals (e.g., Si and P) and lanthanides (e.g., La and Gd) than for alloys of other compositions. Increasing the number of components in alloy from 2 to 7 and changing the mass fraction of chemical elements has no significantly impact on the strength characteristics E and σy. Amorphous metal alloys with the most improved mechanical properties have been identified. In particular, such extremely high-strength alloys include Cr80B20 (among binary), Mo60B20W20 (among ternary) and Cr40B20Nb10Pd10Ta10Si10 (among multicomponent).
Metasurface-Assisted Broadband Compact Dual-Polarized Dipole Antenna for RF Energy Harvesting

In this letter, a low-cost dual-polarized broadband dipole antenna is studied for radio frequency (RF) energy harvesting applications. The 2 × 2 antennas are arranged with a rotational symmetry over an artificial magnetic conductor surface to capture the ambient RF energy from both the horizontal and vertical polarizations in a multiple-input–multiple-output environment. The antenna is then integrated with a single-series rectifier circuit network in a series fashion. In the final step, an LED is connected as the common load to demonstrate the process of capturing the RF energy, thereby glowing the LED.
Efficient Local Search With Search Space Smoothing: A Case Study of the Traveling Salesman Problem (TSP)

Local search is very efficient to solve combinatorial optimization problems. Due to the rugged terrain surface of the search space, it often gets stuck at a locally optimum configuration. In this paper, we give a local search method with a search space smoothing technique. It is capable of smoothing the rugged terrain surface of the search space. Any conventional heuristic search algorithm can be used in conjunction with this smoothing method. In a parameter space, by altering the shape of the objective function, the original problem instance is transformed into a series of gradually simplified problem instances with smoother terrain surfaces. Using an existing local search algorithm, an instance with the simplest terrain structure is solved first, the original problem instance with more complicated terrain structure is solved last, and the solutions of the simplified problem instances are used to guide the search of more complicated ones. A case study of using such technique to solve the traveling salesman problem (TSP) is described. We tested this method with numerous randomly generated TSP instances. We found that it has significantly improved the performance of existing heuristic search algorithms. >
Web Interface of NER and RE with BERT for Biomedical Text Mining

The BioBERT Named Entity Recognition (NER) model is a high-performance model designed to identify both known and unknown entities. It surpasses previous NER models utilized by text-mining tools, such as tmTool and ezTag, in effectively discovering novel entities. In previous studies, the Biomedical Entity Recognition and Multi-Type Normalization Tool (BERN) employed this model to identify words that represent specific names, discern the type of the word, and implement it on a web page to offer NER service. However, we aimed to offer a web service that includes Relation Extraction (RE), a task determining the relation between entity pairs within a sentence. First, just like BERN, we fine-tuned the BioBERT NER model within the biomedical domain to recognize new entities. We identified two categories: diseases and genes/proteins. Additionally, we fine-tuned the BioBERT RE model to determine the presence or absence of a relation between the identified gene–disease entity pairs. The NER and RE results are displayed on a web page using the Django web framework. NER results are presented in distinct colors, and RE results are visualized as graphs in NetworkX and Cytoscape, allowing users to interact with the graphs.
Anatomical, Histological, and Morphometrical Investigations of the Auditory Ossicles in Chlorocebus aethiops sabaeus from Saint Kitts Island

Simple Summary The present paper explores a series of macromorphological, micromorphological, and morphometrical aspects of the auditory ossicles of the green monkey. The particular aspects of the gross morphologies of the three ossicles are described in detail, comparing data with those on other primates and non-primates, underlining the most important features. This study adds some histological insights (never before described to the present date) on all three adult auditory ossicles and supplies a series of metrical data (raw and other complex interpretations of data) to complete the knowledge of the functional anatomy of the lever system in ear ossicles. Abstract Otological studies rely on a lot of data drawn from animal studies. A lot of pathological or evolutionary questions may find answers in studies on primates, providing insights into the morphological, pathological, and physiological aspects of systematic biological studies. Our study on auditory ossicles moves from a pure morphological (macroscopic and microscopic) investigation of auditory ossicles to the morphometrical evaluation of several individuals as well as to some interpretative data regarding some functional aspects drawn from these investigations. Particularities from this perspective blend with metric data and point toward comparative elements that might also serve as an important reference in further morphologic and comparative studies.
Invariant rigid geometric structures and expanding maps

Abstract In the first part of this paper, we consider several natural problems about locally homogeneous rigid geometric structures. In particular, we formulate a notion of topological completeness which is adapted to the study of global rigidity of chaotic dynamical systems. In the second part of the paper, we prove the following result: let φ be a C∞ expanding map of a closed manifold. If φ preserves a topologically complete C∞ rigid geometric structure, then φ is C∞ conjugate to an expanding infra-nilendomorphism.
The Model Theory of Algebraically Closed Fields

Model theory can express properties of algebraic subsets of complex n-space. The constructible subsets are precisely the rst order de nable subsets, and varieties correspond to maximal consistent collections of formulas, called types. Moreover, the topological dimension of a constructible set is equal to the Morley rank of the formula which de nes it.
Toward Stabilizable Large-Scale Boolean Networks by Controlling the Minimal Set of Nodes

This article studies structural controllers and distributed pinning controllers for the global stabilization of Boolean networks (BNs) by integrating the information on their network structures. The main contribution is that several computationally efficient procedures are presented to reduce the number of controlled nodes and to determine a minimal set of controlled nodes without using the brute-force searches. The primary objective is to identify a minimal set of nodes that need to be controlled in the structural controllers for the strong structural stabilization of BNs when network structures is available yet nodal dynamics are unknown. To this end, a theorem shows this minimum controlled node problem can be addressed by seeking the minimum feedback vertex set of network structure. The subsequent part concentrates on designing distributed pinning controllers that merely rely on the node-to-node information exchanges for the global stabilization of BNs with the full knowledge of the nodal dynamics. Several sufficient conditions are provided by utilizing the irreducibility and activation–inhibition network structures to reduce the conservatism. Notably, we claim that, for regulatory BNs without positive cycles, the minimal set of pinned nodes can be determined with a linear amount of time subject to the total number of logical operators in the nodal dynamics. Finally, the effectiveness of these results are validated by three case studies.
Hyperparameter tuning of GDBT models for prediction of heart disease

According to World Health Organization, heart disease has remained the leading cause of death for the last twenty years.Patients with heart disease will have palpitations, chest pain, dyspnea and other characteristics. How to diagnose heart disease based on these characteristics is an important research topic for doctors and scientists. Classifying heart disease with machine learning is known as a reliable and accurate heart disease detection technique. In this study, we aim to find a good model for predicting heart disease. To find the best model, we compare three GBDT(Gradient Boosting Decision Tree) models i.e., XGBoost, LightGBM and CatBoost to identifywhether a person has heart disease or not. We compare the three models tuning the hyperparameters to improve heart disease predicting. To compare results, we create three base models with default hyperparameters. The findings demonstrate that the CatBoost with hyperparamer tuning model as the the most appropriate heart disease prediction model with accuarcy of 90%. Results indicated that tuning the hyperparameter improved modeling results.
Chromium Complexes Supported by Salen-Type Ligands for the Synthesis of Polyesters, Polycarbonates, and Their Copolymers through Chemoselective Catalysis

Salen, Salan, and Salalen chromium (III) chloride complexes have been investigated as catalysts for the ring-opening copolymerization reactions of cyclohexene oxide (CHO) with CO2 and of phthalic anhydride (PA) with limonene oxide (LO) or cyclohexene oxide (CHO). In the production of polycarbonates, the more flexible skeleton of salalen and salan ancillary ligands favors high activity. Differently, in the copolymerization of phthalic anhydride with the epoxides, the salen complex showed the best performance. Diblock polycarbonate-polyester copolymers were selectively obtained by one-pot procedures from mixtures of CO2, cyclohexene oxide, and phthalic anhydride with all complexes. In addition, all chromium complexes were revealed to be very active in the chemical depolymerization of polycyclohexene carbonate producing cyclohexene oxide with high selectivity, thus offering the opportunity to close the loop on the life of these materials.
Isolation and Cultivation of Human Gut Microorganisms: A Review

Microbial resources from the human gut may find use in various applications, such as empirical research on the microbiome, the development of probiotic products, and bacteriotherapy. Due to the development of “culturomics”, the number of pure bacterial cultures obtained from the human gut has significantly increased since 2012. However, there is still a considerable number of human gut microbes to be isolated and cultured. Thus, to improve the efficiency of obtaining microbial resources from the human gut, some constraints of the current methods, such as labor burden, culture condition, and microbial targetability, still need to be optimized. Here, we overview the general knowledge and recent development of culturomics for human gut microorganisms. Furthermore, we discuss the optimization of several parts of culturomics including sample collection, sample processing, isolation, and cultivation, which may improve the current strategies.