Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of the Variability in Fatty Acids and Carotenoid Profiles: Laying the Ground for Tank Milk Authentication

This study analyzes 174 tank milk samples taken from 89 commercial farms located all along the Cantabrian Coast (Green Spain). Sampling was performed in two periods: autumn 2016 and spring 2017. A survey was carried out for every day of sampling to record the average lactating dairy cow production and its diet composition. For each sample, the fatty acid (FA) profile (49 FA plus its main relationships) and nine fat-soluble antioxidant (FSA) profiles (retinol (vitamin A), α- and Υ-tocopherol (vitamin E), all-trans-β-carotene, 9-cis-β-carotene, 13-cis-β-carotene, lutein, zeaxanthin, and β-cryptoxanthin) were determined. The milk production varied between 7.3 and 45.9 liters per cow per day, highlighting the diversity found among production systems. The milk fat content ranged from 2.64% to 4.38% and the protein content from 2.87% to 3.56%. Regarding the fatty acids profile, the percentage of saturated fat varied between 59.95% and 75.99%. The linolenic acid content fluctuated between 0.21 and 1.31 and rumenic acid ranged from 0.20 to 2.47 (g 100 g−1 total FA). The most important correlations between diet and milk FA were always related to the content of fresh grass and total forage (which is defined by both fresh and conserved forage derived from fresh grass (GCF)) in the diet. The content of vaccenic acid, linolenic acid, total omega-3, rumenic acid, and total CLA isomers showed the highest correlation with the proportion of fresh grass in the diet. The antioxidant contents were also highly variable, although correlations with dietary components were lower. The highest correlations were between total forage content (fresh grass (FG) plus GCF) and lutein, all-trans-β-carotene, and 13-cis-β-carotene. Diets without fresh grass had lower omega-3 content, CLA, vaccenic acid, lutein, all-trans-β-carotene, and 13-cis-β-carotene.
Are government energy technology research, development, and demonstration budgets converging or diverging? Insights from OECD countries

ABSTRACT Accelerating the pace of energy technological innovation is essential to curb global climate change and build a clean energy system. Based on this idea, from the perspective of renewable energy technology gaps across countries, this study presents a thorough empirical analysis of the convergence of energy technology research, development, and demonstration (RD&D) budgets across OECD countries over 1985–2017. In doing so, we apply two-regimes threshold autoregressive approach to account for heterogeneity, nonlinearity, and transition path in energy technology RD&D convergence. One observation is that only a small number of advanced countries namely Canada, Japan, and the U.S. follow a nonlinear process and exhibit partial convergence in energy technology RD&D budgets with Japan as a transition country. Interestingly, our results also provide clear support for the global convergence of energy technology RD&D budgets once accounting for the two regimes jointly. These trends in national energy innovation policies could arguably help Canada, Japan, and the U.S. to drive up their energy RD&D trajectory into the future, suggesting that these countries would be able to achieve a more sustainable energy innovation supply system.
Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy

ABSTRACT In this study, the potential of visible and near infrared spectroscopy was investigated to classify the maturity stage and to predict the quality attributes of pomegranate variety “Ashraf” such as total soluble solids content, pH, and titratable acidity during four distinct maturity stages between 88 and 143 days after full bloom. Principal component analysis was used to distinguish among different maturities. The prediction models of internal quality attributes of the pomegranate were developed by partial least squares regression. The transmission spectra of pomegranate were obtained in the wavelength range from 400 to 1100 nm. In this research several preprocessing methods were utilized including centering, smoothing (Savitzky–Golay algorithm, median filter), normalization (multiplicative scatter correction and standard normal variate) and differentiation (first derivative and second derivative). It concluded that different preprocessing techniques had effects on the classification performance of the model using the principal component analysis method. In general, standard normal variate and multiplicative scatter correction gave better results than the other pretreatments. The correlation coefficients (r), root mean square error of calibration and ratio performance deviation for the calibration models were calculated: r = 0.93, root mean square error of calibration = 0.22 °Brix and ratio performance deviation = 6.4 °Brix for total soluble solids; r = 0.84, root mean square error of calibration = 0.064 and ratio performance deviation = 4.95 for pH; r = 0.94, root mean square error of calibration = 0.25 and ratio performance deviation = 5.35 for titratable acidity.
Q‐band dual‐mode substrate integrated waveguide filter and diplexer with circular cavity

This paper proposes novel dual‐mode substrate integrated waveguide (SIW) filter and diplexer with circular cavities, in which a pair of symmetrical metallic via perturbations is placed at different positions in a single cavity combining different angle of feeding lines to obtain flexible transmission response. So that multiple transmission zeros can be obtained on one or both sides of the passband. Compared with traditional structure, the proposed dual‐mode circular cavity filter and diplexer not only reduce the number of resonators and the volume of filters, but also realize the transmission in higher frequency with the existing machining precision. For verify the structure mentioned above, the dual‐mode SIW filter and diplexer are designed, fabricated, and measured in a standard printed circuit board (PCB) process at Q‐band. The filter is measured at a central frequency(CF) of 44.86 GHz with a 3 dB fractional bandwidth (FBW) of 10.2%, insertion loss (IL) is 1.9 dB, meanwhile the measured diplexer insertion losses (IL) are 2.1 and 2.4 dB in the lower and upper passbands centered at 38.3 and 44.8 GHz with the fractional bandwidths of 10.7% and 10.1%. The isolation is lower than ‐40 dB. The measured results show good agreements with the simulated ones.
Effects of plant extracts and (essential) oil mixture on breast meat quality of Japanese quails (Coturnix coturnix japonica)

SUMMARY The objective of this study is to determine the effects of vegetable extracts and oil mixture, which is based on adding peppermint, thyme and anise to drinking waters, on the quality of breast meat in quails. Three groups of 14 quails (one-day old) with 7 males and 7 females were defined. The quails were fed ad libitum during 35 days with a 22% of crude protein and 12.9 Mj/kg of metabolizable energy diet. Two groups received fresh drinking water supplemented with 1 ml/5 l and 1.5 ml/5 l of plant extracts and oil mixture, respectively. The control group received nonsupplemented drinking water. Several quality characteristics including pH, water-holding capacity, cooking loss, color, malondialdehyde, dry matter, crude ash, ether extract, crude protein, and fatty acids were defined in the breast meats of the quails slaughtered after the trial. No differences among the groups or sexes were defined for pH, water-holding capacity, cooking loss, color, dry matter, crude ash, ether extract, crude protein, and total and index values of fatty acids in the breast meat. At 24 hour the groups that were supplemented in oil mixture had a meat malondialdehyde level lower (P<0.05) than the control group. As a result, adding vegetable extracts and oil mixture to the drinking waters did not affect the breast meat qualities but increased significantly the antioxidant characteristics of the meat. Accordingly, it was concluded that addition of 1ml/5 l and 1.5 ml/5 l vegetable extract and oil mixture to the drinking waters of quails improve the quality of meat.
Evaluation of antioxidant activity of flower and seed oil of Azadirachta indica A. juss

The present study on evaluation of antioxidant activity of the flowers and seed oil of Neem plant Azardirachta indica A. Juss revealed that the ethanolic extract of flowers and seed oil at 200 I¼g/ml producing the highest free radical scavenging activity i.e. 64. 17Â±0.02% and 66. 34Â±0.06% respectively. The Neem oil has the highest amount of total phenol content (132 I¼g/ml) which is responsible for highest percentage of inhibition of DPPH radical. In conclusion Neem flower and seed oil have potential for use in human health which is used as food by common people and in diabetes and Neem seed oil is widely used for variety of diseases and also antioxidant potential for use in different pharmaceutical industries.
Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping.
Temperature-Dependent Fluorescence in Carbon Dots

Carbon dots are cost-effective, environmental friendly, and biocompatible nanoparticles with many potential applications in optoelectronics and biophotonics. Their dual fluorescence bands were observed and could be attributed to core and surface state emission. We also conduct temperature-dependent fluorescence measurements from cryogenic to room temperatures. The dual emission bands exhibit similar temperature dependence. The strong electron–electron interactions and weak electron–phonon interactions could account for the very broad photoluminescence (PL) band even at 77 K. Our experimental results also suggest that carbon dots exhibit similar temperature behavior as metallic quantum dots (nanoclusters) but are different from inorganic semiconductor quantum dots. Here, for the first time, we present the temperature-dependent spectroscopic results to shed some light on the presently unclear fluorescence mechanism.
4D BIM for Construction Logistics Management

This study presents an investigation into the extent to which emerging building information modelling (BIM) can be applied to construction logistics management (CLM). Given the specialist nature of the domains, the study employed an in-depth qualitative interview, whereby six experts were interrogated about their experiences of BIM for CLM. The study found the main applications of BIM on CLM to be the creation of three-dimensional (3D) site layout plans and four-dimensional (4D) coordination of site processes and common user plant, updating the 4D logistics plan as the project progressed and collaboration in BIM-based logistics coordination. Furthermore, there was a consensus amongst interviewees on improvement in site safety, comprehension of logistics information, efficiency on site, and effectiveness of layout planning as the main benefits. Lastly, the lack of training with implications on understanding was one of the main barriers to applying BIM to CLM. The findings from this study have the potential to stimulate the uptake of BIM by construction logistics practitioners. By so doing, the performance construction project delivery can be improved, and waste can be minimised or eliminated.
Strategic Samaritanism: how armed conflict affects aid receipts

ABSTRACT How does armed conflict affect the flow of foreign aid? Aid scholars have frequently argued that assistance is given either to serve donor interests or meet recipient needs and evaluated their theories using a single measure of aid. In this project we introduce the concept of an aid portfolio comprised of different types of assistance that a conflict-affected country might receive: humanitarian, economic, and aid to support governance and the social sector. We expect these components of a recipient country’s aid portfolio to vary according to both its needs and its capacity to absorb them. We call this logic, which assumes donors are motivated in part by considerations of efficacy, ‘strategic Samaritanism. ’ We also expect conflict-affected countries’ needs and capacity to vary in predictable ways, with states that are severely affected by violence in need of more aid but less able to absorb some types of assistance until the fighting is over. Meanwhile, countries heavily affected by refugee flows should both need and be able to absorb more of each type of aid. We test our argument on a dataset of aid flows and violent conflicts in developing countries from 1989–2009 and find considerable supporting evidence for our expectations.
Presepsin as a predictor of severe sepsis in urinary tract infection

Background Recently, presepsin is reported to be a biomarker for early diagnosis of sepsis and evaluation of prognosis in septic patients, but there are few reports about urinary-tract infections. The objective of this study is to evaluate whether presepsin is a recent marker for detecting severe sepsis, and whether it can predict the therapeutic course in UTI when compared with procalcitonin (PCT) and C-reactive protein (CRP), already used markers.Methods From April 2014 to December 2016, a total of 50 patients, who were admitted into Gunma university hospital with urinary-tract infections, were enrolled in this study. Vital signs, presepsin, PCT, CRP, white blood cell (WBC), causative diseases of urinary-tract infections and other data were evaluated at the enrollment, third and fifth days. The patients were divided into two groups; with (n=11) or without (n=39) septic shock at the enrollment day, and with (n=7) or without (n=43) sepsis at the fifth day, respectively. Presepsin was evaluated for systemic inflammatory response syndrome (SIRS) or septic shock. Results Concerning the enrollment day, there was no significant difference of presepsin between SIRS and non-SIRS groups (p=0.276). The median presepsin (pg/mL) was significantly higher in the septic shock group (p<0.001). Multivariate logistic regression analysis showed presepsin (≧ 500 pg/ml) was an independent risk factor associated with septic shock (p=0.007). ROC curve for diagnosing septic shock indicated an area under the curve (AUC) at 0.881 for presepsin (vs. 0.690, 0.583 and 0.527 for PCT, CRP and WBC, respectively). Concerning the 5th day after admission, the median presepsin of the enrollment day was significantly higher in SIRS groups than non-SIRS groups (p=0.006). On the other hand, PCT (≥ 2 ng/ml) of the enrollment day was an independent risk factor associated with SIRS. ROC curve for diagnosing sepsis at the fifth day indicated an AUC at 0.837 for PCT (vs. 0.817, 0.811 and 0.802 for presepsin, CRP and WBC, respectively). Conclusions This study shows that presepsin may be a good marker for diagnosis of severe patients who need vasopressor therapy at the data of admission, and PCT may be a good marker for predicting hard-to-treat cases in UTI.
Visualization of Multi-Dimensional Data - The data-slicer Package

From prehistoric cave-wall paintings to the invention of print and most recently electronic harddisks, human data storage capacity has evolved tremendously. Information/data is of great value and hence associated with innovation and technological progress. This is especially true in analytical disciplines i.e. all sciences ranging from physics to psychology and medicine. In observational sciences, most measurement techniques undergo steady improvements in acquisition time and resolution. As a result the sheer data throughput is continually increasing. Examples of techniques where the typical data output has moved from 1D to 3D in the past few decades are shown in Figure 1.
Word Embeddings for Constructive Comments Classification

Word embeddings are so significant today that it is common to see their application in multiple natural language tasks. Indeed, word embeddings as the first layer of a deep learning model are widely adopted and they can also be found in multiple natural language tasks such as classification of texts and named entity recognition. The focus of this paper is the identification of constructive online comments through the use of dense vector semantics such as word embeddings. We specifically explore two approaches: learning distributed word representations and the use of a pre-trained word embedding model. We evaluate these word embedding methods on a recently created constructive comments corpus comprised of 12,000 annotated news comments, intended to improve the quality of online discussions. The obtained results show how the performance of complicated architectures like recurrent and convolutional neural networks can be matched by a language model based on learning embeddings.
Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network

Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to create fused, cloud-free Sentinel-2-like images for facilitating land surface applications. In this paper, we propose a new data fusion model, the Multi-channels Conditional Generative Adversarial Network (MCcGAN), based on the conditional generative adversarial network, which is able to convert images from Domain A to Domain B. With the model, we were able to generate fused, cloud-free Sentinel-2-like images for a target date by using a pair of reference Sentinel-1/Sentinel-2 images and target-date Sentinel-1 images as inputs. In order to demonstrate the superiority of our method, we also compared it with other state-of-the-art methods using the same data. To make the evaluation more objective and reliable, we calculated the root-mean-square-error (RSME), R2, Kling–Gupta efficiency (KGE), structural similarity index (SSIM), spectral angle mapper (SAM), and peak signal-to-noise ratio (PSNR) of the simulated Sentinel-2 images generated by different methods. The results show that the simulated Sentinel-2 images generated by the MCcGAN have a higher quality and accuracy than those produced via the previous methods.
Modelling Reliability Characteristics of Technical Equipment of Local Area Computer Networks

Technical systems in the modern global world are rapidly evolving and improving. In most cases, these are large-scale multi-level systems and one of the problems that arises in the design process of such systems is to determine their reliability. Accordingly, in the paper, a mathematical model based on the Weibull distribution has been developed for determining a computer network reliability. In order to simplify calculating the reliability characteristics, the system is considered to be a hierarchical one, ramified to level 2, with bypass through the level. The developed model allows us to define the following parameters: the probability distribution of the count of working output elements, the availability function of the system, the duration of the system’s stay in each of its working states, and the duration of the system’s stay in the prescribed availability condition. The accuracy of the developed model is high. It can be used to determine the reliability parameters of the large, hierarchical, ramified systems. The research results of modelling a local area computer network are presented. In particular, we obtained the following best option for connecting workstations: 4 of them are connected to the main hub, and the rest (16) are connected to the second level hub, with a time to failure of 4818 h.
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization

In this paper, we describe our approach to question summarization and multi-answer summarization in the context of the 2021 MEDIQA shared task (Ben Abacha et al., 2021). We propose two kinds of transfer learning for the abstractive summarization of medical questions. First, we train on HealthCareMagic, a large question summarization dataset collected from an online healthcare service platform. Second, we leverage the ability of the BART encoder-decoder architecture to model both generation and classification tasks to train on the task of Recognizing Question Entailment (RQE) in the medical domain. We show that both transfer learning methods combined achieve the highest ROUGE scores. Finally, we cast the question-driven extractive summarization of multiple relevant answer documents as an Answer Sentence Selection (AS2) problem. We show how we can preprocess the MEDIQA-AnS dataset such that it can be trained in an AS2 setting. Our AS2 model is able to generate extractive summaries achieving high ROUGE scores.