Miracle-dz/UniFAR-FTUs huggingface dataset:
Grão de soja tostado como suplemento para vacas Holândes X Gir em pastagem de Brachiaria brizantha cv. Marandu

Objetivou-se avaliar o efeito da inclusao de quantidades crescentes de grao de soja tostado (GST) em substituicao ao farelo de soja sobre parâmetros da fermentacao, cinetica de degradacao ruminal da materia seca (MS), materia orgânica (MO) e fibra insoluvel em detergente neutro (FDN) da forragem, e sobre a taxa de passagem da fase liquida no rumen de vacas Holandes x Gir em pastagem de Brachiaria brizantha cv. Marandu. Foi utilizado delineamento Quadrado Latino 4 x 4, utilizando-se vacas fistuladas no rumen recebendo 6 kg/vaca/dia de concentrado contendo 0; 1,3; 2,6 e 3,9 kg de GST (base materia natural - MN), que consistiram os tratamentos experimentais. Para a avaliacao dos parâmetros ruminais adotou-se o esquema de parcelas sub-divididas, com os tratamentos alocados nas parcelas e os tempos de amostragem (0; 2; 4; 6; 8; 10; 12 e 24 h apos o fornecimento da primeira parcela do concentrado) nas sub-parcelas. Para o estudo da degradabilidade ruminal, foi utilizada extrusa de capim-braquiaria (12,6% de MS; 10,8% de proteina bruta - PB e 69,7% de FDN) coletada de vaca fistulada no esofago e mantida na mesma pastagem utilizada pelos animais experimentais. Houve efeito (P 0,05) da adicao de GST na dieta sobre o pH ruminal. Nao houve efeito (P>0,05) do nivel de GST sobre os parâmetros de degradacao ruminal da MS, MO e FDN da extrusa de capim-braquiaria. As faixas de valores observadas para taxa de degradacao e degradabilidades potencial e efetiva da extrusa de capim-braquiaria, nos quatro tratamentos, foram, respectivamente, 3,41 a 3,92%/h, 85,4 a 86,4%; e 49,8 a 51,4%. Nao houve efeito (P>0,05) da inclusao de GST sobre a taxa de passagem da fase liquida, taxa de reciclagem, taxa de fluxo e tempo de reciclagem, porem houve aumento linear (P 0,05) para nenhum dos parâmetros ruminais. A inclusao de ate 3,9 kg/vaca/dia de GST no concentrado nao provocou efeito tanto nos parâmetros da fermentacao quanto na cinetica de degradacao no rumen.
Bangla Handwritten Digit Recognition Approach with an Ensemble of Deep Residual Networks

This work presents an Xception ensemble network based Bangla handwritten digit classification scheme. Bangla handwritten digits are challenging to recognize due to some strong similar features between different classes. In this study, heavy augmentation has been used in the training set along with dropout in the model to avoid overfitting. Competitive performance has been achieved with optimized number of model parameters. An ensemble of three Xception networks was evaluated on a hidden test set where it showed promising performance of 96.69% accuracy, F1 score of 97.14%.
Comparison of Several Prediction Equations Using Skinfold Thickness for Estimating Percentage Body Fat in 12-15-year-old Indonesian Children

Background and Objective: Gold standard measures of percentage body fat (%BF) have more obstacles in epidemiological studies. Skinfold prediction equations are among the viable methods widely used to estimate %BF. This study aimed to compare several skinfold equations estimating %BF against the %BF obtained from bioimpedance analysis (BIA) in Indonesian children aged 12-15 years. Materials and Methods: We invited 610 children (290 boys, 320 girls) aged 12-15 years from junior high schools in Yogyakarta Province. Weight, height and skinfold thickness at the triceps, biceps, subscapular and suprailiac skinfolds were taken according to the protocols of the International Society for the Advancement of Kinanthropometry (ISAK). The percentage of body fat was measured with BIA and estimated using several available skinfold equations. Statistical analyses using paired tests, linear regressions and Bland and Altman plots were performed. Results: The skinfold equations of Durnin and Rahaman and Wickramasinghe significantly overestimated %BF (p<0.001) in boys and girls, while the equation of Watanabe et al. underestimated the %BF (p<0.001). The equation of Slaughter et al. gave the lowest bias but it slightly overestimated %BF in boys (-2.02±5.22% difference; p<0.001) and underestimated %BF in girls (0.78±4.48% difference; p<0.001). All equations showed high correlation with %BF obtained from BIA (r = 0.85-0.87; p<0.001) sand acceptable range of limits of agreement (LOA; 2.6-5.1%), with the lowest being from Slaughter et al. Conclusion: All selected equations were highly correlated with %BF obtained from BIA and within the acceptable LOA. The skinfold equation of Slaughter et al. showed the most applicability in predicting %BF in our sample population.
Predicting Bitcoin: a robust model for predicting Bitcoin price directions based on network influencers

The ability to predict financial markets has tremendous potential to limit exposure to risk and provide better assurances of annualized gains. In this thesis, a model for predicting the future daily price of Bitcoin is proposed and evaluated in comparison to that of a purely random model. Bitcoin is a novel digital currency that relies on cryptography instead of a central authority to verify transactions. Without a central authority, Bitcoin requires a complete list of all transactions to be made public so that they can be verified by all users. This unique feature of Bitcoin, where all transactions are public, is exploited by the model to predict the future price directions based on the actions of Bitcoin users. The daily activity of themarkets, aggregate network features, and the actions ofmajor network influencers are all used as features for the predictive model. Where major network influencers are defined as users that accumulate a disproportionate amount of wealth within the Bitcoin network compared to others. The information about the actions of all Bitcoin users are extracted from the blockchain and stored in a relational database for ease of use. Two metrics were created to identify the major network influencers based on the history of their actions recorded on the blockchain. The first metric, based on the concept of an h-index, often used in academia to rank authors by their citations, is used to rank users by the amount of wealth they accumulate monthly. The second metric is based on the optimization of multiple objectives, the maximum increase in wealth with the least amount of activity using Pareto optimization. All of the major network influencers identified were then used as features, in combination with aggregate network features, and market data, to test and evaluate several predictive models. The models created were based on non-linear equations, support vector machines, decision trees, and XGBoost; all evaluated and compared using the same data. The XGBoost model consistently proved to be much more accurate than all other models and was used for the final set of experiments. The XGBoost model was compared to that of a purely random Monte Carlo model using the entire history of data for the period of 2013–2016. The first set of experiments were conducted using various sizes of training and testing data, in each case the XGBoost model had an accuracy 20% greater than that of the Monte Carlo model. For the final experiments, the model was tested in a realistic scenario, predicting the price direction for each future day, while also being re-trained using the results of each new day. The XGBoost model achieved a much better performance in comparison to the Monte Carlo model, which had approximately 50% accuracy, whereas the XGBoost model had 70%–79% accuracy.
Economic Analysis of Cellulosic Ethanol Production from Sugarcane Bagasse Using a Sequential Deacetylation, Hot Water and Disk-Refining Pretreatment

A new process for conversion of sugarcane bagasse to ethanol was analyzed for production costs and energy consumption using experimental results. The process includes a sequential three-stage deacetylation, hot water, and disk-refining pretreatment and a commercial glucose-xylose fermenting S. cerevisiae strain. The simultaneous saccharification and co-fermentation (SScF) step used was investigated at two solids loadings: 10% and 16% w/w. Additionally, a sensitivity analysis was conducted for the major operating parameters. The minimum ethanol selling price (MESP) varied between $4.91and $4.52/gal ethanol. The higher SScF solids loading (16%) reduced the total operating, utilities, and production costs by 9.5%, 15.6%, and 5.6%, respectively. Other important factors in determining selling price were costs for fermentation medium and enzymes (e.g. cellulases). Hence, these findings support operating at high solids and producing enzymes onsite as strategies to minimize MESP.
IN-VITRO ANTIBACTERIAL ACTIVITY OF LACTOBACILLI METABOLITES LOADED HYDROGEL FORMULATIONS AGAINST PSEUDOMONAS AERUGINOSA

Pseudomonas aeruginosa is an opportunistic pathogen that may cause serious infections, ranging from localized skin infections to life-threatening systemic diseases. It is one of the resistant bacteria which lead to failure in the treatment. New treatment strategies are urgently needed for the treatment of infections caused by resistant bacteria. Researchers are interested in Lactobacillus species because of their antimicrobial effective metabolites. The aim of this study was comparing the antibacterial activity of lactobacilli metabolites loaded chitosan and Pluronic F127 hydrogel formulations. Metabolites loaded hydrogel formulations were prepared by adding 20 µl of supernatant into the polymer solutions. The antimicrobial activities of two hydrogel formulations were investigated against P. aeruginosa using Agar Spot Method. Metabolites loaded chitosan hydrogel formulations were found to be effective against P. aeruginosa. No antibacterial activity was observed for metabolites loaded Pluronic F127 hydrogel formulations. When we compare these two hydrogel formulations, we observed that hydrogel formulation prepared by using chitosan released effective metabolites but Pluronic F127 hydrogel formulation couldn’t release antibacterial effective metabolites. The antibacterial effect of metabolites loaded chitosan hydrogel formulation suggests that this formulation could be used as an alternative treatment option in P. aeruginosa skin infections. However, further investigations are needed to develop a pharmaceutical product.
Theoretical studies of protein-ligand binding

Understanding how drugs work is of great importance, since it can facilitate drug discovery, both time- and costwise. At the same time, it is important to have methods that can help predict how well does a potential drug molecule bind to its target. Computational methods can in many ways contribute to drug design process. In this thesis, we employ different computational approaches to study the binding of various ligands to galectin-3 protein, which is an excellent model system and an interesting therapeutic target. We study the effects of solvationthermodynamics, protein and ligand conformational entropy, as well as specific protein–ligand interactions. Our results indicate that accurate modelling of protein–ligand binding requires careful consideration of solvation and protein ligand conformational entropy, since they contribute significantly to protein-ligand binding free energies. We also compared different methods used to study the water structure and thermodynamics in the protein–ligand binding site, where we showed that solvent-exposure of the binding site may dictate the choice of the method. Moreover, we participated in the D3R and SAMPL6 blind challenges, where we tested the performance of different methods used to estimate binding affinities. We have showed that the predictions of relative binding affinities improve if displaced water molecules are included in the free-energy perturbation calculations and if the ligand is treated by quantum mechanical methods. (Less)
Effect of Ovarian Status and Follicular Size on Grade and Maturation In Vitro of Bovine Oocytes in Relation with Follicular Fluid Composition.

The aim of this study was to investigate the effect of corpus luteum (CLs), follicular size on grade and in vitro maturation of bovine oocytes, in relation with and follicular fluid composition. Ovaries were obtained from abattoirs and classified according to bearing CLs (CL) or non-bearing CLs (CL), all visible follicles were measured according to their diameters into small follicles (SF≤8 mm) and large follicles (LF>8 mm). The follicular fluids (FF) and oocytes were aspirated from SF and LF in CL+ and CLovaries. Oocytes were classified into good, fair and poor quality. Only good quality oocytes were in vitro matured in TCM-199 medium. The concentration of estradiol-17β (E2), progesterone (P4), testosterone and lactic acid in FF were estimated. Results showed that CL ovaries showed significantly (P<0.001) lower oocyte recovery rate as compared to CL ovaries. Also, recovery rate of oocytes was significantly (P<0.001) higher for LF than SF. Proportion of good and fair oocytes derived from CL ovaries were significantly (P<0.001) higher, while that of poor oocytes were significantly (P<0.001) lower when oocytes were recovered from CL ovaries as compared to CL ones. Also, proportion of good and fair oocytes were significantly (P<0.001) higher, while that of poor oocytes were significantly (P<0.001) lower when recovered from LF than from SF. Maturation rate was significantly (P<0.001) higher when oocytes were recovered from CL ovaries as compared to CL ones or from LF as compared to SF. Concentration of P4 and lactic acid was significantly (P<0.001) higher, while that of testosterone and E2 was significantly (P<0.001) lower in FF collected from CL than from CL ovaries. However, concentration of P4 and testosterone was significantly (P<0.001) higher, while lactic acid concentration was significantly (P<0.001) lower in FF collected from LF than from SF. Concentration of E2 was not affected significantly by follicular size. Effect of interaction between ovarian status and follicular size was significant (P<0.01) only on concentration of E2 and P4. In conclusion, presence of CLs and reduced follicular size showed negative effects recovery rate, quality and in vitro maturation of bovine oocytes. However, improving quality and in vitro maturation was associated with increasing P4 and testosterone concentrations.
Effectiveness of a computer-based helicopter trainer for initial hover training

Today, simulators are achieving levels of complexity and cost that are comparable to those of the aircraft they should replace. For this reason, questions have been raised, in both the technical and training communities, on the required level of simulation 1delity for effective pilot training. Computer Based Trainers (CBTs) are not currently considered in regulatory standards, because it has not been proven yet whether they can replace or complement actual 2ight training hours. The aim of this paper is to better understand to what extent the low-level hover skills developed on a CBT are effectively transferred to a more realistic simulation environment. To achieve this goal, a quasi-Transfer-of-Training (qToT) experiment with task-naive participants was performed in the CyberMotion Simulator (CMS) at the Max Planck Institute for Biological Cybernetics. Twenty-four subjects, divided in two groups, were trained to perform the hover maneuver controlling an identi1ed model of a Robinson R44 civil light helicopter. The 1rst group (the “experimental” group) was trained in a CBT and then transferred to the realistic setting in the CMS. The second group (the “control” group) received the entire training in the CMS. At the end of the experiment, the two groups were found to show comparable performance. This suggests that, even for the training of low-level 2ying skills, CBTs may be a valid alternative to high 1delity simulators, if supported by a suitable training program.
CoUSBi : A Structured and Visualized Legal Corpus of US State Bills

This paper reports on an approach to automatically transform semi-structured and public databases of US state-level legislative bills into a structured, legal corpus, namely the Corpus of US Bills (CoUSBi). Our work has resulted in a methodology and a corpus that makes this data usable for natural language processing applications. It thus also lays important groundwork for work in the social sciences, particularly in the fields of political science and economics where there is a growing interest in the relationship between legislative policy-making and economic behavior. Against the backdrop of eventually contributing to a Legal Knowledge Graph, the paper shows that the corpus we provide already fulfills the requirements to be connected to other resources: We automatically extract correspondences between individual state bills and model bills from independent organizations, generating interesting insights into the legislative process. We furthermore use NEREx, a Visual Analytics framework, that allows us to capture important content of the bills at a glance.
Predicting ${X}$ -Sensitivity of Circuit-Inputs on Test-Coverage: A Machine-Learning Approach

Digital circuits are often prone to suffer from uncertain timing, inadequate sensor feedback, limited controllability of past states or inability of initializing memory-banks, and erroneous behavior of analog-to-digital converters, which may produce an unknown (<inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>) logic value at various circuit nodes. Additionally, many design bugs that are identified during the post-silicon validation phase manifest themselves as <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-values. The presence of such <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-sources on certain primary or secondary inputs of a logic circuit may cause loss of fault-coverage of a test set, which, in turn, may impact its reliability and robustness. In this paper, we provide a mechanism for predicting the sensitivity of <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-sources in terms of loss of fault-coverage, on the basis of learning only a few structural features of the circuit that are easy to extract from the netlist. We show that the <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-sources can be graded satisfactorily according to their sensitivity using support vector regression, thereby obviating the need for costly explicit simulation. Experimental results on several benchmark circuits demonstrate the efficacy, speed, and accuracy of prediction.
Production of a map of greenhouse gas emissions and energy use from Australian agriculture

The agriculture and forestry sector accounts for approximately 24% of total greenhouse gas (GHG) emissions in Australia. Over the years researchers have produced new knowledge about agricultural GHG emissions and energy use patterns and opportunities to decrease them, albeit in small-scale studies or large-scale ones with coarse resolution. Linking the multiple, diverse and rich datasets around agricultural production in Australia into one dataset that allows for an estimate of GHG emissions and energy use related to agriculture at a national scale and high resolution has not been done before. The approach we describe here is based upon a link between operational data sourced from gross margin (GM) handbooks and life cycle assessment (LCA) process data. We have collected and processed these datasets to produce a comprehensive database of typical agricultural operations covering 72 commodities grown in 42 regions across Australia. We have also created a system that estimates the GHG emission and energy use patterns of the aforementioned commodities using the best available LCA process data. To capture GHG emissions of non-domestically produced fertiliser, we queried the United Nations Commodity Trade Statistics Database (COMTRADE) to analyse the fertiliser and pesticide import patterns for Australia between 2000-2010. This analysis determined the average country energy-mix for fertiliser and pesticide manufacturing and allowed linking the associated GHG emissions to Australian agricultural production. Finally we spatialised agricultural operational data, emissions and energy use at the national scale using the latest Australian Land Use Map (2005/06). Our findings suggest that in 2005/06 greenhouse gas emissions related to Australian agricultural production equate to a total of 95.8 Mt CO-e using 75.7 GWh of energy. According to our results 29.4% of these emissions come from sources that were categorised as non-agricultural (e.g. industrial processes or energy use) in the Australian National Greenhouse Gas Inventory (NGGI) 2006. We find that the provision of transparently modeled GHG emissions and having them linked to a spatially explicit component helps identifying new opportunities for emission reduction and facilitates an assessment of their effects. For example, our findings suggest production of ethanol from corn stover and sugarcane bagasse could have avoided 4.37 Mt CO-e emissions (4.56% of total) without affecting food production.
Business Model Generation: A handbook for visionaries, game changers and challengers

Business Model Generation is a handbook for visionaries, game changers, and challengers striving to defy outmoded business models and design tomorrow's enterprises. If your organization needs to adapt to harsh new realities, but you don't yet have a strategy that will get you out in front of your competitors, you need Business Model Generation. Co-created by 470 "Business Model Canvas" practitioners from 45 countries, the book features a beautiful, highly visual, 4-color design that takes powerful strategic ideas and tools, and makes them easy to implement in your organization. It explains the most common Business Model patterns, based on concepts from leading business thinkers, and helps you reinterpret them for your own context. You will learn how to systematically understand, design, and implement a game-changing business model--or analyze and renovate an old one. Along the way, you'll understand at a much deeper level your customers, distribution channels, partners, revenue streams, costs, and your core value proposition. Business Model Generation features practical innovation techniques used today by leading consultants and companies worldwide, including 3M, Ericsson, Capgemini, Deloitte, and others. Designed for doers, it is for those ready to abandon outmoded thinking and embrace new models of value creation: for executives, consultants, entrepreneurs, and leaders of all organizations. If you're ready to change the rules, you belong to "the business model generation! "