Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Flavonoid Extractives and their Contribution to Color of Dalbergia latifolia Roxb Wood

Dalbergia latifolia wood is a beautiful wood with color of dark purple to dark brown and rich in flavonoid compounds. This study aimed to investigate the presence of flavonoid compounds from D. latifolia wood and their contribution to its color. The D. latifolia wood sample was powdered and extracted with ethanol-toluene (1/2, v/v). The ethanol- toluene extract then was column chromatographed to obtain fraction 1 to fraction 12. Methylation was conducted to each fraction to detect flavonoid compounds by GC-MS. The results of GC-MS analysis showed that ethanol- toluene extract gave Fr- 1- Fr. 3 as soluble flavonoids (34.2%) and Fr. 4- Fr. 12 as insoluble polymers (62.6%). Furthermore, 12 flavonoids i.e latifolin, dalbergiphenol, chalcone, dalbergin, and their derivatives were characterised and these compounds might be significance in contributing to color of D. latifolia wood.
Threats of land use to the global diversity of vascular plants

Land use is a main driver of biodiversity loss worldwide. However, quantifying its effects on global plant diversity remains a challenge due to the limited availability of data on the distributions of vascular plant species and their responses to land use. Here, we estimated the global extinction threat of land use to vascular plant species based on a novel integration of an ecoregion‐level species‐area model and the relative endemism richness of the ecoregions.
The Minimal Estrada Index of Trees with Two Maximum Degree Vertices

Let G be a simple graph with n vertices and let 1; 2; : : : ; n be the eigenvalues of its adjacency matrix. The Estrada index EE of G is the sum of the terms e i . In 2009 Ili c et al. obtained the trees with minimal Estrada index among trees with a given maximum vertex degree. In this paper, we give the trees with minimal Estrada index among the trees of order n with exactly two vertices of maximum degree.
Upper limits on transmitter rate of extragalactic civilizations placed by breakthrough listen observations

The Search for Extra-Terrestrial Intelligence (SETI) has been conducted for over sixty years, yet no technosignatures have been identified. Previous studies have focused on stars in our galaxy, with few searches in the extragalactic Universe despite a larger volume being available. Civilizations capable of harvesting energy from a star or a galaxy are classified as KII or KIII on the Kardashev scale, respectively. Technosignatures from such advanced civilizations would be extremely luminous and detectable by current radio telescopes, even from distant galaxies. To explore the frontier of extragalactic SETI, we investigate the likely prevalence of extragalactic civilizations possessing a radio transmitter, known as the transmitter rate, based on observational results from the Breakthrough Listen (BL) observations. We calculated the transmitter rate by considering the background galaxies in the field of view of target stars in BL observations. We used a statistical method to derive the total mass of stars in those background galaxies from a galaxy stellar mass function. Our statistical method suggests that less than one in hundreds of trillions of extragalactic civilizations within 969 Mpc possess a radio transmitter above 7.7× 1026 W of power, assuming one civilization per one-solar-mass stellar system. Additionally, we cross-matched the BL survey fields with the WISE×SuperCOSMOS Photometric Redshift Catalogue and compared with the statistical method. Our result sets the strictest limits to date on the transmitter rate at such high power levels, emphasizing the high efficiency of searching for radio transmitters in galaxies and the rarity of technologically advanced civilizations in our Universe.
Generating Natural Language From Logic Expressions With Structural Representation

Incorporating logic reasoning with deep neural networks (DNNs) is an important challenge in machine learning. In this article, we study the problem of converting logical expressions into natural language. In particular, given a sequential logic expression, the goal is to generate its corresponding natural sentence. Since the information in a logic expression often has a hierarchical structure, a sequence-to-sequence baseline struggles to capture the full dependencies between words, and hence it often generates incorrect sentences. To alleviate this problem, we propose a model to convert Structural Logic Expressions into Natural Language (SLEtoNL). SLEtoNL converts sequential logic expressions into structural representation and leverages structural encoders to capture the dependencies between nodes. The quantitative and qualitative analyses demonstrate that our proposed method outperforms the seq2seq model, which is based on the sequential representation, and outperforms strong pretrained language models (e.g., T5, BART, GPT3) with a large margin (28.6 in BLEU3) in out-of-distribution evaluation.
Aggregation 2 D-Prediction 1 D-Prediction Irregular Scene Text Recognition Counting Object In Everyday Scenes Regular Scene Text Recognition

In this paper, we propose a novel method, aggregation cross-entropy (ACE), for sequence recognition from a brand new perspective. The ACE loss function exhibits competitive performance to CTC and the attention mechanism, with much quicker implementation (as it involves only four fundamental formulas), faster inference\back-propagation (approximately O(1) in parallel), less storage requirement (no parameter and negligible runtime memory), and convenient employment (by replacing CTC with ACE). Furthermore, the proposed ACE loss function exhibits two noteworthy properties: (1) it can be directly applied for 2D prediction by flattening the 2D prediction into 1D prediction as the input and (2) it requires only characters and their numbers in the sequence annotation for supervision, which allows it to advance beyond sequence recognition, e.g., counting problem. The code is publicly available at https://github.com/summerlvsong/Aggregation-CrossEntropy.
Prevalence and Predictive Factors for Exclusive Breastfeeding at Six Months among Thai Adolescent Mothers

Exclusive breastfeeding (EBF) in the first six months of life is the best and the most frequent choice for infants since it has important benefits for the infants and their mothers. However, the exclusive breastfeeding rate in Thailand remains low, especially among adolescent mothers. This predictive correlation study aimed to investigate factors predicting breastfeeding at six months among 253 Thai adolescent mothers from nine hospitals of the Bangkok Metropolitan Administration. Data were collected by using seven questionnaires: the Personal Characteristics, Pregnancy Intention and Breastfeeding Practice, Perceived Benefits of Breastfeeding, Perceived Barriers to Breastfeeding, Breastfeeding Self-Efficacy, Family Support, Maternity Care Practice, and Digital Technology Literacy Questionnaires. Data were analyzed by using descriptive statistics and logistic regression. The findings revealed that the prevalence of EBF at six months among Thai adolescent mothers was only 17.39%, and predictive factors were occupation (work/study) (p = 0.034), digital technology literacy (p < 0.001), family support (p = 0.021), pregnancy intention (p = 0.001), breastfeeding self-efficacy (p = 0.016), and perceived benefits of breastfeeding (p = 0.004). These factors could, together, predict the EBF rate at six months among Thai adolescent mothers in 42.2% (Nagelkerke R2 = 0.422) of the cases. These findings may help health professionals to develop activities and strategies for promoting exclusive breastfeeding by increasing breastfeeding self-efficacy, perceived benefits of breastfeeding, and family support, as well as improving digital technology skill among Thai adolescent mothers, especially student/employed adolescents who have unintended pregnancies.
Qualifications of Head Teachers and the numbers of Healthcare Personnel available in Public and Private Schools in Owerri Municipal, Imo State, Nigeria

Globally, in spite of the fact that school age children do not suffer from the high mortality of preschool children; there exist significant burden of morbidity among them. School health services are the curative and preventive health services, provided for the promotion of the health of school population to enable them benefit maximally from the school system. The aim of the study was to assess the Qualifications of Head Teachers and the numbers of Healthcare Personnel available in Public and Private Schools in Owerri Municipal, Imo State, Nigeria. A cross sectional descriptive study was carried out to assess school health services in 36 government approved primary (12 public and 24 private) schools within Owerri Municipal LGA. Relevant data was obtained from school head teachers using an evaluation scale and direct observation was done where applicable. The responses were scored using the School Health Programme evaluation scale. Thereafter, a prospective (pre-test – post-test) study was carried out in two public primary schools between May to December 2017 (two academic terms). Within the period (September to December 2017) an intervention was carried out which involved the running of school clinics in two selected public primary schools. School attendances were kept during the pre-intervention and intervention periods using a check off list for monitoring daily attendances and reasons for absenteeism. The overall mean score for School Health Services in Owerri Municipal LGA was 13.14 with the mean scores of 14.42 for private and 10.58 for public schools which were significantly lower than the minimum acceptable score of 19 (p<0.001 respectively). Six (16.7%) schools, all of which were private schools scored up to the minimum acceptable score of 19. The private schools performed better than public schools in practice of School Health Services and their mean difference was statistically significant (p=0.012). School health personnel were available in fourteen (38.9%) schools, out of which one (8.3%) public school had health personnel. School health services in both public and private primary schools within Owerri Municipal LGA is poor and also the availability of health personnel in the scho
Impact of design factors for ESA CCI satellite soil moisture data assimilation over Europe

In this study, soil moisture retrievals of the combined active-passive ESA CCI soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-year study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. (1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. (2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parametrization if the observations are rescaled monthly. (3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.
The Effects of Government Spending in Segmented Labor and Financial Markets

This paper develops a model with high-skilled and low-skilled workers to show the expansionary effects of government spending despite large training costs for new hires. The main idea is that a fiscal stimulus induces changes in the composition of the labor force conditional on the extent of aggregate demand pressure. A period of high aggregate demand pressure is followed by a high value of forgone output as training activity causes production disruption. In this period firms decide to hire more low-skilled workers, who constitute a cheaper part of the labor force. When aggregate demand pressure is diminished, firms switch to hiring more high-skilled workers. However, the current literature considers only high-skilled workers, who tend to increase saving in government bonds to protect against poor employment prospects. In this case, the combination of weak employment prospects and the crowding-out effects of higher lump-sum taxes and government debt on private consumption and capital investment gives rise to recessionary effects. In contrast, this paper provides a model with a more realistic labor and financial market structure and suggests that countercyclical government spending in the form of government consumption and especially government investment can be used to deal with recessions.
Dielectric Properties and Phase Diagram of (Sr1-xBax)2Nb2O7 Single Crystals

Dielectric constants, εb′ and εc′ along the crystallographic b and c axes respectively, were measured in a temperature range of 10 − 700 K on Ba-substituted Sr2Nb2O7 single crystals with different Ba concentrations up to x = 0.32. When x = 0, two dielectric anomalies were observed at TI = 489 K and at TL = 100 K, owing to the incommensurate phase transition and the low-temperature ferroelectric phase transition, respectively. Both TI and TL decrease with Ba-content x at initial slopes of ca. −19 and −37 K/mol%, respectively, and become broad at x = 0.04 and almost disappear at x = 0.08. Furthermore, Ba-substitution induces a weak and broad dielectric maximum in εb' vs. T curve at approximately Tm = 465 K for x > 0.2. Phase diagram of this system was proposed.
Treatment of Enterococcus faecalis Infective Endocarditis: A Continuing Challenge

Today, Enterococcus faecalis is one of the main causes of infective endocarditis in the world, generally affecting an elderly and fragile population, with a high mortality rate. Enterococci are partially resistant to many commonly used antimicrobial agents such as penicillin and ampicillin, as well as high-level resistance to most cephalosporins and sometimes carbapenems, because of low-affinity penicillin-binding proteins, that lead to an unacceptable number of therapeutic failures with monotherapy. For many years, the synergistic combination of penicillins and aminoglycosides has been the cornerstone of treatment, but the emergence of strains with high resistance to aminoglycosides led to the search for new alternatives, like dual beta-lactam therapy. The development of multi-drug resistant strains of Enterococcus faecium is a matter of considerable concern due to its probable spread to E. faecalis and have necessitated the search of new guidelines with the combination of daptomycin, fosfomycin or tigecycline. Some of them have scarce clinical experience and others are still under investigation and will be analyzed in this review. In addition, the need for prolonged treatment (6–8 weeks) to avoid relapses has forced to the consideration of other viable options as outpatient parenteral strategies, long-acting administrations with the new lipoglycopeptides (dalbavancin or oritavancin), and sequential oral treatments, which will also be discussed.
Description of Two Fungal Endophytes Isolated from Fragaria chiloensis subsp. chiloensis f. patagonica: Coniochaeta fragariicola sp. nov. and a New Record of Coniochaeta hansenii

Prospection of the endosphere of the native plant Fragaria chiloensis subsp. chiloensis f. patagonica from the foothills of the Chilean Andes led to the isolation of two strains of the genus Coniochaeta. We addressed the taxonomic placement of these strains based on DNA sequencing data using the ITS and LSU genetic markers, morphological features, and biochemical traits. One of these strains was identified as Coniochaeta hansenii, for which the anamorph and teleomorph states were described. The second strain did not seem to match any of the currently described species of this genus; therefore, we propose the name Coniochaeta fragariicola sp. nov.
Emotional Regulation Underlies Gender Differences in Pathological Eating Behavior Styles of Bariatric Surgery Candidates

Almost a third of bariatric surgery patients present suboptimal weight loss or important weight regain in the first five postoperative years. While the reasons underlying this are not fully understood, it is known that pathological eating styles (such as emotional or binge eating) can thwart efforts to maintain weight loss. However, detailed characterization and understanding of these eating styles have yet to be achieved. In particular, research on gender differences in pathological eating styles and psychiatric symptoms before bariatric surgery is lacking. To characterize gender differences in eating styles and their association with clinical symptoms, we prospectively enrolled 110 bariatric surgery candidates, collecting eating styles and clinical scores. Women displayed a higher frequency of emotional eating as compared to men (x2 = 9.07, p = 0.003), while men showed a higher frequency of quantitative eating behavioral style (x2 = 4.58, p = 0.044). Binge eating style was associated with higher Difficulties in Emotion Regulation Scale (DERS), Hamilton Depression Scale (HAM-D), and Hamilton Anxiety Scale (HAM-A) scores (p < 0.05). Emotional eating style was associated with higher HAM-D and HAM-A scores (p < 0.05). The present findings highlight the importance of understanding the role of gender differences in emotion regulation processes involved in the development and maintenance of pathological eating styles in bariatric surgery candidates. This paves the way to gender- and symptoms-specific interventions on eating behaviors to improve surgery long-term outcomes.
Activated Natural Killer Cell Inoculation Alleviates Fibrotic Liver Pathology in a Carbon Tetrachloride-Induced Liver Cirrhosis Mouse Model

Activated hepatic stellate cells (HSCs) play a detrimental role in liver fibrosis progression. Natural killer (NK) cells are known to selectively recognize abnormal or transformed cells via their receptor activation and induce target cell apoptosis and, therefore, can be used as a potential therapeutic strategy for liver cirrhosis. In this study, we examined the therapeutic effects of NK cells in the carbon tetrachloride (CCl4)-induced liver cirrhosis mouse model. NK cells were isolated from the mouse spleen and expanded in the cytokine-stimulated culture medium. Natural killer group 2, member D (NKG2D)-positive NK cells were significantly increased after a week of expansion in culture. The intravenous injection of NK cells significantly alleviated liver cirrhosis by reducing collagen deposition, HSC marker activation, and macrophage infiltration. For in vivo imaging, NK cells were isolated from codon-optimized luciferase-expressing transgenic mice. Luciferase-expressing NK cells were expanded, activated and administrated to the mouse model to track them. Bioluminescence images showed increased accumulation of the intravenously inoculated NK cells in the cirrhotic liver of the recipient mouse. In addition, we conducted QuantSeq 3′ mRNA sequencing-based transcriptomic analysis. From the transcriptomic analysis, 33 downregulated genes in the extracellular matrix (ECM) and 41 downregulated genes involved in the inflammatory response were observed in the NK cell-treated cirrhotic liver tissues from the 1532 differentially expressed genes (DEGs). This result indicated that the repetitive administration of NK cells alleviated the pathology of liver fibrosis in the CCl4-induced liver cirrhosis mouse model via anti-fibrotic and anti-inflammatory mechanisms. Taken together, our research demonstrated that NK cells could have therapeutic effects in a CCl4-induced liver cirrhosis mouse model. In particular, it was elucidated that extracellular matrix genes and inflammatory response genes, which were mainly affected after NK cell treatment, could be potential targets.
IMPROVED DAMAGE PARAMETERS FOR POST-EARTHQUAKE APPLICATIONS

Various ground shaking and damage parameters are examined for post-earthquake applications. Peak ground motion values, elastic response spectra, spectrum intensity, drift spectrum, inelastic spectra, hysteretic energy spectrum, and two improved damage spectra are examined. The proposed damage spectra will be zero if the response remains elastic, and will be unity when the displacement capacity under monotonic deformation is reached. The damage spectra can be reduced to the special cases of normalized hysteretic energy and displacement ductility spectra. The proposed damage spectra are promising for seismic vulnerability studies and post-earthquake applications of existing facilities, and performance-based design of new structures.
Empirical Bayes estimation in continuous one-parameter exponential families under associated samples

ABSTRACT In this paper, we study the empirical Bayes (EB) estimation in continuous one-parameter exponential families under negatively associated (NA) samples and positively associated (PA) samples. Under certain regularity conditions, it is shown that the convergence rates of proposed EB estimators under NA or PA samples are the same as those of EB estimators under independent observations, which significantly improve the existing results in EB estimation under associated samples.
Energy Management and Optimization of Large-Scale Electric Vehicle Charging on the Grid

The sustainability of a clean energy transition for electric vehicle transportation is clearly affected by increased energy consumption cost, which is associated with large-scale electric vehicles (EVs) charging on a fossil-fuel dependent electricity grid. This places a potential threat on the safe operations and stability of the grid and increases the emissions of greenhouse gases (GHGs) from the power stations that generate the electricity. Furthermore, the uncontrolled large-scale integration of EVs charging on the grid will increase exponentially in the coming years. Because of this, new peaks on the grid will be generated due to the EV charging load variance, and a significant impact on the transformer limit and substation capacity violation will occur. To mitigate the significant impact of the high cost of energy consumption by large-scale EVs charging on the grid, and to reduce the emissions of GHGs, there is a need to provide a multi-level optimization approach that is robust and dynamic to solve the uncontrolled charging problem of large-scale integration of EVs to the grid. This paper investigates the grid energy consumption by EVs and reviews recent applications of EV charging controls and optimization approaches used for the energy management of large-scale EVs charging on the grid. Energy management in this context is not trivial. It implies that the objectives such as load shifting, peak shaving, and minimizing the high cost of electricity consumption with a stable grid operation can be achieved. In the context of this study, EVs charging on the grid includes both battery electric vehicles (BEVs), which have larger battery banks with a longer charging duration and higher energy consumption capacity, and plug-in hybrid electric vehicles (PHEVs) which have smaller battery capacities.