Miracle-dz/UniFAR-FTUs huggingface dataset:
Historical perspectives on vegetable oil-based diesel fuels

Biodiesel, defined as monoalkyl esters of vegetable oils or animal fats (Scheme 1), is a promising alternative fuel for use in compression-ignition (diesel) engines and is being produced or used commercially in numerous countries around the world, including the United States, Austria, the Czech Republic, France, Germany, Italy, Malaysia, and Sweden. It is generally known that vegetable oils were tested as diesel fuels well before the energy crises of the 1970s and early 1980s generated renewed interest in alternative fuels, but details on the first uses (until the early 1950s) are often unclear or presented inconsistently in the literature. Valuable insights into the use of vegetable oils and their derivatives as diesel fuels were achieved during that time, only to be rediscovered beginning in the late 1970s. This article will discuss the history of biodiesel or, more generally, that of vegetable oil-based diesel fuels and correlate it with current use. Most literature references used here were found through a Chemical Abstracts search or are listed in a publication summarizing literature prior to 1949 on fuels from agricultural sources (1). After the 1940s, literature on vegetable oil-based diesel fuels is very sparse until the late 1970s. Although discussed later in this article in more detail, two references (2,3) describing the first use of a fuel meeting the current definition of biodiesel are worth close examination.
Low resource point process models for keyword spotting using unsupervised online learning

Point Process Models (PPM) have been widely used for keyword spotting applications. Training these models typically requires a considerable number of keyword examples. In this work, we consider a scenario where very few keyword examples are available for training. The availability of a limited number of training examples results in a PPM with poorly learnt parameters. We propose an unsupervised online learning algorithm that starts from a poor PPM model and updates the PPM parameters using newly detected samples of the keyword in a corpus under consideration and uses the updated model for further keyword detection. We test our algorithm on eight keywords taken from the TIMIT database, the training set of which, on average, has 469 samples of each keyword. With an initial set of only five samples of a keyword (corresponds to ∼ 1% of the total number of samples) followed by the proposed online parameter updating throughout the entire TIMIT train set, the performance on the TIMIT test set using the final model is found to be comparable to that of a PPM trained with all the samples of the respective keyword available from the entire TIMIT train set.
Gaijin : A Bootstrapping , Template-Driven Approach to Example-Based MT

Example-based Machine Translation (EBMT) is a recent approach to MT that offers robustness, scalability and graceful degradation, deriving as it does its competence not from explicit linguistic models of source and target languages, but from the wealth of bilingual corpora that are now available. Gaijin is such a system, employing statistical methods, string-matching, case-based reasoning and template-matching to provide a linguistics-lite EBMT solution. The only linguistics employed by Gaijin is a psycholinguistic constraint—the marker hypothesis—that is minimal, simple to apply, and arguably universal. The scope and current state of Gaijin is described, and some initial evaluation results are reported.
Cod liver oil (n-3 fatty acids) as an non-steroidal anti-inflammatory drug sparing agent in rheumatoid arthritis.

OBJECTIVES Dose-dependant gastrointestinal and cardiovascular side-effects limit the use of NSAIDs in the management of RA. The n-3 essential fatty acids (EFAs) have previously demonstrated some anti-inflammatory and NSAID-sparing properties. The objective of this study was to determine whether cod liver oil supplementation helps reduce daily NSAID requirement of patients with RA. METHODS Dual-centre, double-blind placebo-controlled randomized study of 9 months' duration. Ninety-seven patients with RA were randomized to take either 10 g of cod liver oil containing 2.2 g of n-3 EFAs or air-filled identical placebo capsules. Documentation of NSAID daily requirement, clinical and laboratory parameters of RA disease activity and safety checks were done at 0, 4, 12, 24 and 36 weeks. At 12 weeks, patients were instructed to gradually reduce, and if possible, stop their NSAID intake. Relative reduction of daily NSAID requirement by >30% after 9 months was the primary outcome measure. RESULTS Fifty-eight patients (60%) completed the study. Out of 49 patients 19 (39%) in the cod liver oil group and out of 48 patients 5 (10%) in the placebo group were able to reduce their daily NSAID requirement by >30% (P = 0.002, chi-squared test). No differences between the groups were observed in the clinical parameters of RA disease activity or in the side-effects observed. CONCLUSIONS This study suggests that cod liver oil supplements containing n-3 fatty acids can be used as NSAID-sparing agents in RA patients.
Dosing of U-100 insulin and associated outcomes among Medicare enrollees with type 1 or type 2 diabetes

Objective To examine costs, resource utilization, adherence, and hypoglycemic events among various doses of U-100 insulin regimens among elderly patients (age ≥65 years) diagnosed with diabetes. Methods Truven Health Analytics Medicare databases from January 1, 2008 through December 31, 2011 were utilized. General linear models with a gamma distribution and log link were used to examine costs, while logistic and negative binomial regressions were used to examine resource utilization and hypoglycemic events. Analyses controlled for patient characteristics, pre-period comorbidities, general health, and use of antidiabetic medications as well as index dose of insulin. Results All-cause inpatient, emergency room, and outpatients costs, as well as diabetes-related inpatient costs, were highest among individuals who were treated with an index dose of 10–100 units/day followed by >300 units/day, while drug costs and total costs generally increased as index dosage increased. Resource utilization generally followed the same pattern as costs, with number of office visits increasing as the dose increased and the highest hospital length of stay, number of hospitalizations, number of emergency room visits, and number of diabetes-related hospitalizations were generally highest among those in the lowest and highest index dose cohorts. Compared to patients who initiated with an index dose of 10–100 units/day, all other patients were significantly less likely to achieve an adherence threshold of 80% based upon index dose range, and while those with an index dose of >100–150 units/day were significantly more likely to experience a hypoglycemic event. Conclusion These results suggest that, for elderly individuals with diabetes, there is a higher patient burden among those who receive the lowest and highest insulin doses.
Identification of key locations based on online social network activity

Ubiquitous Internet connectivity enables users to update their Online Social Network profile from any location and at any point in time. These, often geo-tagged, data can be used to provide valuable information to closely located users, both in real time and in aggregated form. However, despite the fact that users publish geo-tagged information, only a small number implicitly reports their base location in their Online Social Network profile. In this paper we present a simple yet effective methodology for identifying a user's key locations, namely her home and work places. We evaluate our methodology with Twitter datasets collected from the country of Netherlands, city of London and Los Angeles county. Furthermore, we combine Twitter and LinkedIn information to construct a work location dataset and evaluate our methodology. Results show that our proposed methodology not only outperforms state-of-the-art methods by at least 30% in terms of accuracy, but also cuts the detection radius at least at half the distance from other methods.
Hand Written Digit Recognition Using Kalman Filter

Different pattern recognition techniques are used in various fields for different applications. Methods based on Radial basis function (RBF) neural networks are found to be very successful in pattern classification problems. Training neural network is in general a challenging nonlinear optimization problem. Several algorithms have been proposed for selection of RBF neural network prototypes and training the network. This paper proposes RBF neural network using kalman filter training method for recognition of hand written digits. The working of the proposed method is tested on hand written digits of different fonts. Experiments revealed that this method is successful in recognizing the digits.
Fine-scale patterns driving dynamic functional connectivity provide meaningful brain parcellations

Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging (fMRl) allows identifying large-scale functional brain networks based on spontaneous activity and their temporal reconfigurations. Due to limited memory and computational resources, these pairwise measures are typically computed for a set of brain regions from a pre-defined brain atlas, which choice is non-trivial and might influence results. Here, we first leverage the availability of dynamic information and new computational methods to retrieve dFC at the finest voxel level in terms of dominant patterns of fluctuations, and, second, we demonstrate that this new representation is informative to derive meaningful brain parcellations that capture both long-range interactions and fine-scale local organization. We analyzed resting-state fMRI of 54 healthy participants from the Human Connectome Project. For each position of a temporal window, we determined eigenvector centrality of the windowed fMRl data at the voxel level. These were then concatenated across time and subjects and clustered into the most representative dominant patterns (RDPs). Each voxel was then labeled according to a binary code indicating positive or negative contribution to each of the RDPs. We obtained a 36-label parcellation displaying long-range interactions with remarkable hemispherical symmetry. By separating contiguous regions, a finer-scale parcellation of 448 areas was also retrieved, showing consistency with known connectivity of cortical/subcortical structures including thalamus. Our contribution bridges the gap between voxel-based approaches and graph theoretical analysis.
morphological evolution ? Are rates of species diversification correlated with rates of

publication. Citations to Advance online articles must include the digital object identifier (DOIs) and date of initial online articles are citable and establish publication priority; they are indexed by PubMed from initial publication. the paper journal (edited, typeset versions may be posted when available prior to final publication). Advance Advance online articles have been peer reviewed and accepted for publication but have not yet appeared in
Genetic Restoration of Plasma ApoE Improves Cognition and Partially Restores Synaptic Defects in ApoE-Deficient Mice

Alzheimer's disease (AD) is the most common form of dementia in individuals over the age of 65 years. The most prevalent genetic risk factor for AD is the ε4 allele of apolipoprotein E (ApoE4), and novel AD treatments that target ApoE are being considered. One unresolved question in ApoE biology is whether ApoE is necessary for healthy brain function. ApoE knock-out (KO) mice have synaptic loss and cognitive dysfunction; however, these findings are complicated by the fact that ApoE knock-out mice have highly elevated plasma lipid levels, which may independently affect brain function. To bypass the effect of ApoE loss on plasma lipids, we generated a novel mouse model that expresses ApoE normally in peripheral tissues, but has severely reduced ApoE in the brain, allowing us to study brain ApoE loss in the context of a normal plasma lipid profile. We found that these brain ApoE knock-out (bEKO) mice had synaptic loss and dysfunction similar to that of ApoE KO mice; however, the bEKO mice did not have the learning and memory impairment observed in ApoE KO mice. Moreover, we found that the memory deficit in the ApoE KO mice was specific to female mice and was fully rescued in female bEKO mice. Furthermore, while the AMPA/NMDA ratio was reduced in ApoE KO mice, it was unchanged in bEKO mice compared with controls. These findings suggest that plasma lipid levels can influence cognition and synaptic function independent of ApoE expression in the brain. SIGNIFICANCE STATEMENT One proposed treatment strategy for Alzheimer's disease (AD) is the reduction of ApoE, whose ε4 isoform is the most common genetic risk factor for the disease. A major concern of this strategy is that an animal model of ApoE deficiency, the ApoE knock-out (KO) mouse, has reduced synapses and cognitive impairment; however, these mice also develop dyslipidemia and severe atherosclerosis. Here, we have shown that genetic restoration of plasma ApoE to wild-type levels normalizes plasma lipids in ApoE KO mice. While this does not rescue synaptic loss, it does completely restore learning and memory in the mice, suggesting that both CNS and plasma ApoE are independent parameters that affect brain health.
Comparing Credal C-Means and Neutrosophic C-Means Clustering Techniques: Comprehensive Study with Application on Road Safety

Clustering techniques are unsupervised learning methods. Data clustering is one of the important methods of data mining. It is a process of finding clusters from data set with the greatest similarity in the same cluster and the greatest divergence between different clusters. Therefore, these are popular for various data mining and pattern recognition activities. However, the performance of these approaches depends on the data distribution. Thus, selecting an appropriate clustering technique for a given data set is a research challenge. In this article, two concepts of clustering will be compared, credal clustering and neutrosophic clustering. These notions are described by the Credal C-means (CCM) and Neutrosophic C-means (NCM) algorithms. These algorithms were compared for clustering accuracy on different cluster structures. Finally, to show the behavior and effectiveness of the two algorithms, real road safety data were tested and their results compared. These data are represented by trajectories (identified by six variables) crossed by vehicles in a bend (Nantes, France).
A METHOD FOR IDENTIFYING THE POSSIBLE CAUSES OF FAILURE IN THE CASE OF SERVICE COMPANIES

The core of our paperwork consists in determining a classification of qualitative and quantitative causes that are influencing service companies’ performance and financial health based on grey systems theory and fuzzy sub-sets. The quantitative causes of firms’ performance are mainly financial causes and can be represented by the financial changes, structural changes and by macroeconomic changes. All of these are objectively measurable. Instead, the qualitative causes are subjectively measurable and in most of the cases they are quantified using some experts’ opinion. For a better representation of reality, we will consider that those values are intervals and not numbers, situated in [0; 1]. In order to reduce the degree of subjectivity, we took advantage of the methods offered by fuzzy systems, mainly in construction of the expertons. Expertons are in fact intervals built using the f-fuzzy sub-set and the opinion of several experts over a certain problem. Furthermore, after constructing the expertons, we use the methods offered by grey systems theory and grey arithmetic to determine the degree of influence of each qualitative and quantitative cause on company’s performance. By classifying the causes and acting on the most important of them, the activity of the analyst can be really improved and the company’s performance will rise. The first part of our paper describes the evolutions of the methods used for establishing the companies’ financial health and some related works regarding the causes that affects it. After that, we focus on the methods offered by grey systems theory, on grey arithmetic, and on the steps that should be done to classify the causes, including also a numerical example. We conclude our paper with some remarks over the role and the utility of the grey systems theory and fuzzy in establishing the service companies’ performance and financial health.
Chi‐squared and Fisher–Irwin tests of two‐by‐two tables with small sample recommendations

Two‐by‐two tables commonly arise in comparative trials and cross‐sectional studies. In medical studies, two‐by‐two tables may have a small sample size due to the rarity of a condition, or to limited resources. Current recommendations on the appropriate statistical test mostly specify the chi‐squared test for tables where the minimum expected number is at least 5 (following Fisher and Cochran), and otherwise the Fisher–Irwin test; but there is disagreement on which versions of the chi‐squared and Fisher–Irwin tests should be used. A further uncertainty is that, according to Cochran, the number 5 was chosen arbitrarily. Computer‐intensive techniques were used in this study to compare seven two‐sided tests of two‐by‐two tables in terms of their Type I errors. The tests were K. Pearson's and Yates's chi‐squared tests and the ‘N−1’ chi‐squared test (first proposed by E. Pearson), together with four versions of the Fisher–Irwin test (including two mid‐P versions). The optimum test policy was found to be analysis by the ‘N−1’ chi‐squared test when the minimum expected number is at least 1, and otherwise, by the Fisher–Irwin test by Irwin's rule (taking the total probability of tables in either tail that are as likely as, or less likely than the one observed). This policy was found to have increased power compared to Cochran's recommendations. Copyright © 2007 John Wiley & Sons, Ltd.
DNA barcode data confirm new species and reveal cryptic diversity in Chilean Smicridea (Smicridea) (Trichoptera:Hydropsychidae)

Abstract Mitochondrial deoxyribonucleic acid (mtDNA) sequence data have been both heralded and scrutinized for their ability or lack thereof to discriminate among species for identification (DNA barcoding) or description (DNA taxonomy). Few studies have systematically examined the ability of mtDNA from the DNA barcode region (658 base pair fragment of the 5′ terminus of the mitochondrial cytochrome c oxidase I gene) to distinguish species based on range-wide sampling of specimens from closely related species. Here we examined the utility of DNA barcode data for delimiting species, associating life stages, and as a potential genetic marker for phylogeographic studies by analyzing a range-wide sample of closely related Chilean representatives of the caddisfly genus Smicridea subgenus Smicridea. Our data revealed the existence of 7 deeply diverged, previously unrecognized lineages and confirmed the existence of 2 new species: Smicridea (S.) patinae, new species and Smicridea (S.) lourditae, new species. Based on our current taxonomic evaluation, we considered the other 5 lineages to be cryptic species. The DNA barcode data proved useful in delimiting species within Chilean Smicridea (Smicridea) and were suitable for life-stage associations. The data also contained sufficient intraspecific variation to make the DNA barcode a candidate locus for widespread application in phylogeographic studies.
Electromagnetic Positioning for Tip Tracking and Shape Sensing of Flexible Robots

Wire-driven flexible robots are efficient devices for minimally invasive surgery, since they can work well in complex and confined environments. However, the real-time positional and shape information of the robot cannot be well estimated, especially when there is an unknown payload or force working on the end effector. In this paper, we propose a novel tip tracking and shape sensing method for wire-driven flexible robots. The proposed method is based on the length of each section of the robot as well as the positional and directional information of the distal end of each section of the robot. For each section, an electromagnetic sensor will be mounted at the distal end to estimate the positional and directional information. A reconstruction algorithm, which is based on a three-order Bézier curve, is carried out utilizing the positional and directional information along with the length information of the section. This method provides the advantage of good tracking results and high shape reconstruction accuracy with limited modification to the robot. Compared with other reconstruction methods, no kinematic model is needed for reconstruction. Therefore, this method works well with an unknown payload that applied at the tip of the robot. The feasibility of the proposed method is verified by simulation and experimental results.
Scalability Performance of AODV , TORA and OLSR with Reference to Variable Network Size

An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any pre-existing network infrastructure. A number of ad hoc routing protocols have been developed during the time, but none of these is able to produce efficient routing of packets in large number of nodes due to their own limitations. Therefore, scalability is an open issue in all routing protocols. In this paper, we presented our observations regarding the scalability comparison of the three MANET routing protocols, Ad hoc On Demand Distance Vector (AODV), Temporally Ordered Routing Protocols (TORA) and Optimized Link State Routing (OLSR) by varying the number of nodes. The simulation is done by using OPNET Modeler 14.5 simulator by taking throughput and network delay as performance metrics. In case of delay AODV and OLSR perform in a similar manner as the number of nodes increases. However, in throughput OLSR outperforms AODV and TORA. Keywords–AODV, MANET, OLSR, Scalability, TORA
Increasing the Photoactivity of N-doped Tio2 Photocatalysts Using Phosphors as Light Carriers

The aim of the work was to increase the photoactivity of N-doped TiO2 photocatalyst, active in visible light region, through the improving of the illumination efficiency. Microradiators like phosphors (namely, ZPS) which absorb an electromagnetic radiation by the external light source (UV) and, with no time delay, emit visible light, opportunely selected to be able to excite N-doped TiO2 photocatalyst. Preliminary investigations were performed on physical mixtures of phosphors and N-doped TiO2, evaluating the decolourization of methylene blue (MB). The simultaneous presence of N-doped TiO2 and light carriers showed an increased decolourization activity, reaching about 100 % with a total TOC removal after 3 h of irradiation. To get core-shell photocatalysts, N-doped TiO2 was supported on ZPS (NTiO2-ZPS) by a modified sol–gel method. The content of N-doped TiO2 was varied in the range 5-50 wt % to find an optimum loading. The photocatalytic activity of NTiO2-ZPS was evaluated for the removal of MB under UV illumination. The performances of NTiO2-ZPS showed an improved efficiency in decolourization process of MB, equal to 100 % after 90 min of irradiation, with a total TOC removal. The influence of several parameters, such as dye type and concentration has been studied.