Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of combination of essential oils on in vitro methane production, volatile fatty acid fractions and feed digestibility with goat rumen flora

Different combination of garlic oil (GO from Allium sativum L.), clove oil (CO from Eugenia spp.) and peppermint oil (P from Mentha piperita L.) as GO+PO (1:1), GO+CO (1:1), CO+PO (1:1) and GO+PO+CO (1:1:1) was evaluated in in vitro gas production test for their effect on total gas production, methane production, dry matter organic matter digestibility and volatile fatty acid fractions. The addition level was low (D1) at 0.25 g/L, medium (D2) at 0.5 g/L, and high (D3) at 1.0 g/L of fermentation medium. Concentrate mixture and bengal gram straw (Cicer arietinum L.) in 40:60 ratio was incubated with incubation medium containing goat rumen liquor for 24 h at 39°C in glass syringes. Three goats fed with concentrate pellet and Bengal gram straw served as donor of the rumen liquor. In vitro fluid pH varied between 6.50 to 6.59. Total gas production (mL/g DM) in control group was 154.07, at lower dose level (0.25 g/L) there was increase in gas production which significantly decreased at highest dose level. There was significant (P<0.05) decrease in the methane production (mL/g DM and mL/g DDM) on GO and PO supplementation together. At low dose level (D1, 0.25g/L) there was no statistically (P>0.05) significant difference in in vitro dry matter and organic matter digestibility with the addition of different EO combinations evaluated as compared to control except PO+ CLO (1:1). At dose level (D2 and D3) GO+PO combination led to a moderate decrease in the in IVDMD and IVOMD along with decrease in in vitro methane production. Fraction of propionic acid was higher on addition of garlic and peppermint oil together. There was relative decrease in the methanogens population with bacteria as housekeeping gene as expressed by RT-PCR amplification with GO+PO addition. Present study concluded that among the EOs combinations in vitro tested, garlic and peppermint oil combination (GO+PO) has better potential in reducing in vitro methane production with lower effect on in vitro digestibility.
Fast Warming of the Surface Ocean Under a Climatological Scenario

We examine various strategies for forcing ocean‐only models, including an atmospheric boundary layer model. This surface forcing allows air‐sea exchanges to affect atmospheric temperature and relative humidity, thus removing the assumption of an infinite atmospheric heat capacity associated with the prescription of these variables. When exposed to climatological winds, the simulated North Atlantic oceanic temperature warms considerably at the surface as compared to a model with full atmospheric variability. This warming is mainly explained by a weakened upper‐ocean vertical mixing in response to the weakly varying climatological winds. Specifying the atmospheric temperatures inhibits this warming but depends on the unrealistic large atmospheric heat capacity. We thus interpret the simulated warmer ocean as a more physically consistent ocean response. We conclude the use of an atmospheric boundary layer model provides many benefits for ocean only modeling, although a “normal” year strategy is required for maintaining high‐frequency winds.
Performances of Post-weaned Pasundan Calves Fed Extra Diets in an Extensive Grazing System

Changing from the pre-weaning to the post-weaning period is a transition from liquid to solid feed accompanied by a dramatic transformation of the GI tract. Â Supplemental solid feeding of grazing post-weaning calves is expected to improve their productivity. Â The study aimed to determine the effect of extra diet on the performances of post-weaned Pasundan calves (15 males and 15 females) that were grazed, using a completely randomized design of 2x3 factorials (two sex factors x three diet factors). Â The diet treatments were that post-weaned Pasundan calves were grazed: without being given extra diets (grazing/control), extra diets without urea-impregnated zeolite (extra diet-1), and extra diets with urea-impregnated zeolite (extra diet-2). Â The parameters were diet consumption (only for extra diets, unmeasured grazing consumption), body weights, and body condition. Â The data were analyzed with GLM Univariate. Â The results showed: The interaction between diet treatment and sex did not affect all the observed parameters. Â Consumption of extra diet-1 (529.37Â±3.06 g DM/head/day) was higher (P0.05) than extra diet-2 consumption (525.17Â±1.96 g DM/head/day), but after a correction of body weight, the result was no different. Â Sex factors did not affect all observed parameters. Â The ADG of post-weaned calves fed extra diet-1 (155.8Â±27.5 g), or extra-2 diet (124.2Â±56.3 g) was higher (P0.05) than the grazing (66.67Â±35.8 g). Â The extra diet-1 produced a higher change in body condition (1.8Â±0.78, P0.05) than extra diet-2 or grazing (1.2Â±0.42). Â In conclusion, feeding extra diet-1 to post-weaned Pasundan calves increased ADG by 234% and improved the body condition by 150% better than calves that were only grazed in an extensive grazing system.
Well-log attributes to map upward-fining and upward-coarsening parasequences

Traditionally, well logs have been analyzed as each well is drilled and completed, allowing for careful human interpretation. Modern resource plays and old conventional plays may have thousands of newly available digital wells in a survey, providing both the opportunity and the need for computer-assisted interpretation workflows. Because seismic attributes provide significant help in the interpretation of the 100s of thousands of traces in a 3D seismic survey, we hypothesize that similar “well log” attributes should be able to help the interpretation of the thousands of wells that fall within a given 3D seismic survey. In this paper, we modify a one-dimensional Kuwahara filter to identify upward fining and upward coarsening trends routinely used in the interpretation of gamma ray logs. We believe that by mapping such quantitative estimates that the interpreter will gain deeper insight into the environment of deposition.
Economic Policy Uncertainty and Corporate Green Innovation

: This paper mainly discusses how economic policy uncertainty affects enterprise green innovation. This paper uses the data of China's A-share listed companies from 2010 to 2021 for empirical research. The results show that economic policy uncertainty positively affects green innovation. In addition, the relationship between economic policy uncertainty and green innovation activities is influenced by ownership concentration. Combined with the theoretical analysis and empirical research, this paper puts forward relevant policy suggestions from the two levels of government and enterprise respectively. On the one hand, the government should introduce policies to encourage enterprises to make green innovations. On the other hand, enterprises should also actively respond to the challenges brought by uncertainty.
Characterization of Temporal and Spatial Variability of Phosphorus Loading to Lake Erie from the Western Basin Using Wavelet Transform Methods

The characterization of temporal and spatial patterns in phosphorus (P) loading in Lake Erie is essential in order to continue monitoring the excessive P condition that comes from the western Lake Erie Basin. This study aims to perform such a characterization using the continuous wavelet transform (CWT) and the discrete wavelet transform (DWT) methods. These wavelet transformations were conducted on streamflow data, TP loads, and soluble reactive phosphorus (SRP) of six stations located near Lake Erie of Northern Ohio. These stations are located near the outlet of Cuyahoga, Grand, Maumee, Vermilion, Raisin, and Sandusky watersheds. Long-term continuous P loading data, in which some dated back to 1970, were used in the analysis. The results obtained from the CWT and DWT approaches were found to complement each other. Streamflow had significant mixed variability at 1, 2, and 4 years. The variability for SRP was limited to 1 and 2 years while the TP variability was only seen at the 1-year scale. It was interesting to find that strong temporal patterns of SRP were observed in most of the watersheds only after the mid-1990s. The CWT wavelet spectra also reflected the land use characteristics of each watershed. For example, the wavelet spectra of surface runoff and TP for the agricultural watersheds (i.e., Raisin, Sandusky, and Maumee Rivers) were similar and characterized by significant variability primarily at the annual scale and at the two to four-year periodicities. The urbanized watershed (i.e., Cuyahoga River) did not show any association between either phosphorus (TP or SRP) with surface runoff and baseflow, which indicates that P in the urbanized watershed was not driven by the flow.
Guide, Safety Net, Project Tester, and More: Investigating the Roles of Facilitators in an AI Summer Camp

: Summer camps have become popular for introducing K-12 learners to computer science (CS) and artificial intelligence (AI) in informal learning environments. Facilitators play crucial roles in guiding and engaging learners in these contexts, but there is limited research on their roles in informal AI learning. This paper examines facilitators’ dialogues with campers in a middle school AI summer camp, identifying eight major facilitator roles. The roles differed depending on group dynamics and project phase. The paper provides empirical grounding to define facilitators’ roles in AI learning and guide the design of professional development for camp facilitators.
Phyllosphere bacterial community dynamics in response to bacterial wildfire disease: succession and interaction patterns

Plants interact with complex microbial communities in which microorganisms play different roles in plant development and health. While certain microorganisms may cause disease, others promote nutrient uptake and resistance to stresses through a variety of mechanisms. Developing plant protection measures requires a deeper comprehension of the factors that influence multitrophic interactions and the organization of phyllospheric communities. High-throughput sequencing was used in this work to investigate the effects of climate variables and bacterial wildfire disease on the bacterial community’s composition and assembly in the phyllosphere of tobacco (Nicotiana tabacum L.). The samples from June (M1), July (M2), August (M3), and September (M4) formed statistically separate clusters. The assembly of the whole bacterial population was mostly influenced by stochastic processes. PICRUSt2 predictions revealed genes enriched in the M3, a period when the plant wildfire disease index reached climax, were associated with the development of the wildfire disease (secretion of virulence factor), the enhanced metabolic capacity and environmental adaption. The M3 and M4 microbial communities have more intricate molecular ecological networks (MENs), bursting with interconnections within a densely networked bacterial population. The relative abundances of plant-beneficial and antagonistic microbes Clostridiales, Bacillales, Lactobacillales, and Sphingobacteriales, showed significant decrease in severally diseased sample (M3) compared to the pre-diseased samples (M1/M2). Following the results of MENs, we further test if the correlating bacterial pairs within the MEN have the possibility to share functional genes and we have unraveled 139 entries of such horizontal gene transfer (HGT) events, highlighting the significance of HGT in shaping the adaptive traits of plant-associated bacteria across the MENs, particularly in relation to host colonization and pathogenicity.
Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station

Accurate understanding of passenger flow distribution is crucial for effective station crowd management. However, due to the complexity and randomness of passenger flow and the unclear spatial-temporal correlation between functional areas within the station, predicting the spatiotemporal distribution dynamics of inflow and future short-term distribution trends is challenging. Emerging deep learning models offer valuable insights for accurately predicting passenger flow distribution. Thus, we propose a deep learning architecture, named “ST-Bi-LSTM,” which combines a bidirectional long short-term memory network with a spatial-temporal attention mechanism. Initially, we outline the methodologies of Bi-LSTM, the DeepWalk-based spatial attention mechanism, and the temporal attention mechanism. The spatial attention mechanism is employed to extract station spatial network topology information and enhance the representation of passenger flow characteristics in highly correlated areas during the forecasting process. Simultaneously, the temporal attention Bi-LSTM is utilized for capturing temporal correlations. The architecture comprises four branches dedicated to station real-time video monitoring data, spatial network topology, function area attributes, and train timetables. Subsequently, leveraging in-station CCTV data, passenger travel behavior data, and train timetables, we apply the architecture to the Tianjin West High-Speed Railway Station. We conduct a comparative analysis of the prediction performance and time complexity of the proposed architecture against existing baseline models, demonstrating superior performance and robustness exhibited by the ST-Bi-LSTM model (achieving a reduction in RMSE of over 10%). This study facilitates the transition of station management from passive response to active prediction of station passenger flow dynamics.
Multiple metamorphic events in the Palaeozoic Mérida Andes basement, Venezuela: insights from U–Pb geochronology and Hf–Nd isotope systematics

ABSTRACT The metamorphic basement of the Mérida Andes in western Venezuela is constituted of paragneiss, orthogneiss, amphibolite, and metagranitoid of the Iglesias Complex. In this paper, U–Pb zircon geochronology, Lu–Hf, and Sm–Nd isotope systematics of these rocks are presented. Biotite-garnet orthogneiss protoliths intruded paragneiss at ~487 Ma, during Barrovian metamorphism (M1) that reached anatexis at ~467 Ma. Paragneiss and orthogneiss were later intruded by mafic to intermediate lower-crust and mantle-derived magmas (now hornblende orthogneiss and amphibolite) in a back-arc setting at ~460–455 Ma. A new compressional setting led to high-grade metamorphism (M2) at ~450 Ma, followed by decompression melting at ~430–420 Ma (M3). Finally, an upper greenschist- to amphibolite-facies overprint in the complex occurred at ~230 Ma (M4). Ordovician and Late Triassic metagranitoids are probably syn-tectonic to metamorphism M2 and M4, respectively. The Iglesias Complex has several similarities to rocks of the same age in other crustal fragments adjacent to northwest Gondwana, such as the Santander Massif in Colombia, and the Maya block and the Acatlán Complex in southern Mexico and Central America. Both, the early Palaeozoic and the early Mesozoic tectonothermal events, are related to active continental arcs broadly recognized as the Famatinian orogeny and the Proto-Andean Permo-Triassic arc, respectively. No exposures of Rodinia-type basement were found, but isotope systematics suggest that such basement is probably buried underneath the Mérida Andes.
Measuring Rice Yield from Space: The Case of Thai Binh Province, Viet Nam

Despite a growing interest in using satellite data to estimate paddy rice yield in Southeast Asia, significant cloud coverage has led to a scarcity of usable optical data for such analysis. In this paper, we study the feasibility of using two alternative sources of satellite data—(i) surface reflectance fusion data which integrates Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images, and (ii) L-band radar backscatter data from the Advanced Land Observing Satellite 2 (ALOS-2) PALSAR-2 sensors—to circumvent the cloud cover problem and estimate yield in Thai Binh Province, Viet Nam during the second growing season of 2015. Our findings indicate that although Landsat– MODIS fusion data are not necessarily beneficial for paddy rice mapping when compared with only using Landsat data, fusion data allows us to estimate the peak value of various vegetation indexes and derive the best empirical relationship between these indexes and yield data from the field. We also find that the L-band radar data not only has a lower performance in paddy rice mapping when compared with optical data, but also contributes little to rice yield estimation.
Impact of Natural Disaster on Health of Children: Empirical Evidence from Nepal’s Earthquake

This study estimates the impact of the 2015 Nepal earthquake on the health of children: weight, height, and body mass index (BMI). It uses two rounds of data related to Nepal Demographic Health Surveys (NDHS) before and after the earthquake, i.e., in 2011 and 2016. The study combines child-related information and household-level data from NDHS with the earthquake data available at the district level to identify the impact of earthquakes on children’s health. By focusing on children between 0 to 5 years of age and using the difference-in- difference framework as an identification strategy, results show that the BMI increased for children living in affected areas. This increase in BMI is primarily driven by an increase in their weight. The study further shows that the BMI of children increases in earthquake-affected areas because of the increased food consumption. The study underscores the importance of being selective in the type of food-aid distributed as well as changing the consumption behavior of children during the post-disaster periods.
Cancer Risk in Patients with Down Syndrome—A Retrospective Cohort Study from Germany

Simple Summary Individuals with Down syndrome (DS) are thought to have a unique tumor profile. This study retrospectively compared patients with DS aged ≥18 years from primary care practices with patients without DS between 2005 and 2021 for cancer incidence after matching for age, sex, average annual visit frequency, and comorbidity. The 5-year cumulative incidence of cancer overall and specific cancers was analyzed using Kaplan–Meier curves and compared using the log-rank test. In addition, univariable Cox regression analysis was performed. A total of 2438 patients with DS and 12,190 patients without DS were included. Regression analysis showed no significant association between DS and subsequent cancer in the overall population. Analyses by cancer type and sex showed a strong but non-significant negative association between DS and breast cancer in women. Our results may provide the basis for future studies to clarify whether and to what extent an adapted screening program needs to be modified for individuals with Down syndrome due to the particular cancer distribution pattern. Abstract Background: Individuals with Down syndrome are thought to have a unique tumor profile. Methods: Using the IQVIA Disease Analyzer database, patients aged ≥18 years diagnosed with Down syndrome in German general practices between 2005 and 2021 were compared with patients without Down syndrome for cancer incidence, adjusting for age, sex, average annual visit frequency, and comorbidity. The 5-year cumulative incidence of cancer overall and specific cancers was analyzed using Kaplan–Meier curves and compared using the log-rank test. In addition, univariable Cox regression analysis was performed. Results: A total of 2438 patients with Down syndrome and 12,190 patients without Down syndrome were included; 3.9% of patients without Down syndrome and 3.1% of patients with Down syndrome were diagnosed with cancer (p = 0.143). Regression analysis showed no significant association between Down syndrome and subsequent cancer in the total population (HR: 0.79; 95% CI: 0.57–1.09), in women (HR: 0.89; 95% CI: 0.56–1.37), or in men (HR: 0.69; 95% CI: 0.43–1.11). Analyses by cancer type and sex showed a strong but not significant negative association between Down syndrome and breast cancer in women (HR: 0.33; 95% CI: 0.12–0.93). Conclusions: Our results could form the basis for future studies to clarify whether and to what extent an adapted screening program needs to be modified for individuals with Down syndrome due to the particular cancer distribution pattern.
Domain based Opinion Lexicon Generation for Sentiment Analysis of the Academic Resources

In this paper, we have design and augment an existing sentiment lexicon dictionary in the context of academic domain to provide academic community members facility to judge the quality of research articles by analyzing whether it has positive sentiments or negative sentiments. We have also developed a system which is able to identify opinionated words and phrases from the documents based on online dictionary such as WordNet. Our system uses SentiWordNet to assign sentiment scores to each word found in the document. Sentiments of words are assigned three sentiment scores: Positivity, Negativity and Objectivity with a word and lies in between the range of [0-1]. Our system provides user friendly interface to enable any member of the community to contribute in opinion lexicon generation by providing sentiment words and their polarity values.
Horn banana peel powder biocoagulants in lowering the turbidity of water

Purpose: In response to contemporary global issues like pollution, global warming, and the loss of natural resources, the circular bioeconomy has been proposed. Using organic waste as a bioagulant facilitates the shift to a circular and sustainable bioeconomy. For homes that use well water that has been excavated, the concentration of chemical coagulants must be taken into consideration. The purpose of this study is to use banana peel powder as a biocoagulant to lower the turbidity level of water from a dug well. Research methodology: The Paired Sample T-Test and the ANOVA test with Welsh correction were used to examine the average change in turbidity 0.000 <0.05 was the ? value obtained. Results: The Post Hoc Games-Howell test findings revealed a noteworthy distinction between the treatment groups. This study concludes that the biocoagulant made of powdered banana horn can lower turbidity to a level that satisfies health standards.
An Argo mixed layer climatology and database

A global climatology and database of mixed layer properties are computed from nearly 1,250,000 Argo profiles. The climatology is calculated with both a hybrid algorithm for detecting the mixed layer depth (MLD) and a standard threshold method. The climatology provides accurate information about the depth, properties, extent, and seasonal patterns of global mixed layers. The individual profile results in the database can be used to construct time series of mixed layer properties in specific regions of interest. The climatology and database are available online at http://mixedlayer.ucsd. edu. The MLDs calculated by the hybrid algorithm are shallower and generally more accurate than those of the threshold method, particularly in regions of deep winter mixed layers; the new climatology differs the most from existing mixed layer climatologies in these regions. Examples are presented from the Labrador and Irminger Seas, the Southern Ocean, and the North Atlantic Ocean near the Gulf Stream. In these regions the threshold method tends to overestimate winter MLDs by approximately 10% compared to the algorithm.