Miracle-dz/UniFAR-FTUs huggingface dataset:
The potential of indigenous Paenibacillus ehimensis BS1 for recovering heavy crude oil by biotransformation to light fractions

Microbial Enhanced Oil Recovery (MEOR) is a potential technology for residual heavy oil recovery. Many heavy oil fields in Oman and elsewhere have difficulty in crude oil recovery because it is expensive due to its high viscosity. Indigenous microbes are capable of improving the fluidity of heavy oil, by changing its high viscosity and producing lighter oil fractions. Many spore-forming bacteria were isolated from soil samples collected from oil fields in Oman. Among the isolates, an autochthonous spore-forming bacterium was found to enhance heavy oil recovery, which was identified by 16S rDNA sequencing as Paenibacillus ehimensis BS1. The isolate showed maximum growth at high heavy oil concentrations within four days of incubation. Biotransformation of heavy crude oil to light aliphatic and aromatic compounds and its potential in EOR was analyzed under aerobic and anaerobic reservoir conditions. The isolates were grown aerobically in Bushnell-Haas medium with 1% (w/v) heavy crude oil. The crude oil analyzed by GC-MS showed a significant biotransformation from the ninth day of incubation under aerobic conditions. The total biotransformation of heavy crude oil was 67.1% with 45.9% in aliphatic and 85.3% in aromatic fractions. Core flooding experiments were carried out by injecting the isolates in brine supplemented with Bushnell-Haas medium into Berea sandstone cores and were incubated for twelve days under oil reservoir conditions (50°C). The extra recovered oil was analyzed by GC-MS. The residual oil recovered from core flood experiments ranged between 10–13% compared to the control experiment. The GC-MS analyses of the extra recovered oil showed 38.99% biotransformation of heavy to light oil. The results also indicated the presence of 22.9% extra aliphatic compounds in the residual crude oil recovered compared to that of a control. The most abundant compound in the extra recovered crude oil was identified as 1-bromoeicosane. The investigations showed the potential of P. ehimensis BS1 in MEOR technology by the biotransformation of heavy to lighter crude oil under aerobic and reservoir conditions. Heavy oil recovery and biotransformation to lighter components are of great economic value and a few studies have been done.
3-D surface reconstruction from stereoscopic image sequences

A stereoscopic scene analysis system for 3-D modeling of objects from stereoscopic image sequences is described. A dense map of 3-D surface points is obtained by image correspondence, object segmentation, interpolation, and triangulation. Emphasis is put on the accurate measurement of image correspondences from grey level images. The surface geometry of each scene object is approximated by a triangular wire-frame which stores the surface texture in texture maps. Sequence processing serves to track camera motion and to fuse surfaces from different view points into a consistent 3-D surface model. From the textured 3-D models, highly realistic image sequences from arbitrary view points can be synthesized using computer graphics techniques. <<ETX>>
Experimental Calibration and Validation of a Speed Scaling Simulator

In this paper, we use experimental measurements to calibrate and validate a discrete-event simulator for dynamic speed scaling systems. The experimental implementation work is carried out in an Ubuntu Linux environment using a quad-core 2.3 GHz Intel i7 processor with the Ivy Bridge micro-architecture. Our implementation provides fine-grain user-level control of process execution, and uses the Running-Average Power Limit (RAPL) Machine Specific Registers (MSRs) to track energy usage. Through careful micro-benchmarking experiments, we determine the power consumption for each of the 12 discrete speeds supported by the processor, while also quantifying the costs of context switches and CPU speed changes. Finally, we use our suitably-parameterized speed scaling simulator to evaluate three different CPU speed scaling algorithms from the literature on simple batch workloads. To the best of our knowledge, our paper provides the first direct comparison of these speed scaling strategies with realistic system costs.
Verified implementations of the information card federated identity-management protocol

We describe reference implementations for selected configurations of the user authentication protocol defined by the Information Card Profile V1.0. Our code can interoperate with existing implementations of the roles of the protocol (client, identity provider, and relying party). We derive formal proofs of security properties for our code using an automated theorem prover. Hence, we obtain the most substantial examples of verified implementations of cryptographic protocols to date, and the first for any federated identity-management protocols. Moreover, we present a tool that downloads security policies from services and identity providers and compiles them to a verifiably secure client proxy.
Pharmacokinetic Study on Hyperoside in Beagle's Dogs

Abstract Objective To develop and validate a simple, rapid, sensitive, and reproducible HPLC method for determination of hyperoside in plasma of dogs and for the subsequent pharmacokinetic (PK) study. Methods An accurate and reproducible HPLC-UV method was developed and validated for the determination of hyperoside in plasma of dogs, using kaempferol as internal standard. The plasma samples of dogs following ig administration of hyperoside were analyzed for the detection of quercetin after enzymatic hydrolysis treatment with combined β-glucuronidase and sulphatase. The analytes were separated on a Diamonsil C 18 column (250 mm × 4.6 mm, 5 μm). The mobile phase consisted of methanol-buffer solution (0.1 mol/L NH 4 Ac + 0.3 mmol/L EDTA-Na 2 )-acetic acid (60:40:1) and was delivered at a flow rate of 1 mL/min. The UV detector was set at 370 nm and the column temperature was maintained at 35 °C. The sample injection volume was 20 μL. Data were collected and analyzed using the ANASTAR software. PK parameters were calculated with DAS software (2.0). Results Linear relationships were validated over the range of 0.01–1 μg/mL for hyperoside ( r = 0.9997). The intra- and inter-day precision values for all samples were within 10.0%, and the accuracies of intra- and inter-day assays were within the range of 92.4%–102.4%. The validated method was successfully used to determine the hyperoside concentration in plasma of dogs for up to 12 h, after a single ig administration (25 mg/kg). The mean PK parameters for male and female dogs were as follows: C max (0.18 ± 0.05) and (0.16 ± 0.05) μg/mL, AUC 0- ∞ (0.79 ± 0.34) and (0.86 ± 0.27) μg/(mLh), t 1/2(ka) (0.89 ± 0.41) and (0.88 ± 0.28) h, respectively. Statistical analysis on the PK of hyperoside in male and female groups showed that sex had no significant impact on the PK of hyperoside ( P > 0.05). Conclusion The method is able and sufficient to be used in drug PK studies of hyperoside.
The development of Erlang

This paper describes the development of the programming language Erlang during the period 1985--1997.Erlang is a concurrent programming language designed for programming large-scale distributed soft real-time control applications.The design of Erlang was heavily influenced by ideas from the logic and functional programming communities. Other sources of inspiration came from languages such as Chill and Ada which are used in industry for programming control systems.
Assembly of protein structure from sparse experimental data: An efficient Monte Carlo model

A new, efficient method for the assembly of protein tertiary structure from known, loosely encoded secondary structure restraints and sparse information about exact side chain contacts is proposed and evaluated. The method is based on a new, very simple method for the reduced modeling of protein structure and dynamics, where the protein is described as a lattice chain connecting side chain centers of mass rather than Cαs. The model has implicit built‐in multibody correlations that simulate short‐ and long‐range packing preferences, hydrogen bonding cooperativity and a mean force potential describing hydrophobic interactions. Due to the simplicity of the protein representation and definition of the model force field, the Monte Carlo algorithm is at least an order of magnitude faster than previously published Monte Carlo algorithms for structure assembly. In contrast to existing algorithms, the new method requires a smaller number of tertiary restraints for successful fold assembly; on average, one for every seven residues as compared to one for every four residues. For example, for smaller proteins such as the B domain of protein G, the resulting structures have a coordinate root mean square deviation (cRMSD), which is about 3 Å from the experimental structure; for myoglobin, structures whose backbone cRMSD is 4.3 Å are produced, and for a 247‐residue TIM barrel, the cRMSD of the resulting folds is about 6 Å. As would be expected, increasing the number of tertiary restraints improves the accuracy of the assembled structures. The reliability and robustness of the new method should enable its routine application in model building protocols based on various (very sparse) experimentally derived structural restraints. Proteins 32:475–494, 1998. © 1998 Wiley‐Liss, Inc.
Semantic v.s. Positions: Utilizing Balanced Proximity in Language Model Smoothing for Information Retrieval

Work on information retrieval has shown that language model smoothing leads to more accurate estimation of document models and hence is crucial for achieving good retrieval performance. Several smoothing methods have been proposed in the literature, using either semantic or positional information. In this paper, we propose a unified proximity-based framework to smooth language models, leveraging semantic and positional information simultaneously in combination. The key idea is to project terms to positions where they originally do not exist (i.e., zero count), which is actually a word count propagation process. We achieve this projection through two proximity-based density functions indicating semantic association and positional adjacency. We balance the effects of semantic and positional smoothing, and score a document based on the smoothed language model. Experiments on four standard TREC test collections show that our smoothing model is effective for information retrieval and generally performs better than the state of the art.
Gene expression profiling reveals U1 snRNA regulates cancer gene expression

U1 small nuclear RNA (U1 snRNA), as one of the most abundant noncoding RNA in eukaryotic cells plays an important role in splicing of pre-mRNAs. Compared to other studies which have focused on the primary function of U1 snRNA and the neurodegenerative diseases caused by the abnormalities of U1 snRNA, this study is to investigate how the U1 snRNA over-expression affects the expression of genes on a genome-wide scale. In this study, we built a model of U1 snRNA over-expression in a rat cell line. By comparing the gene expression profiles of U1 snRNA over-expressed cells with those of their controls using the microarray experiments, 916 genes or loci were identified significantly differentially expressed. These 595 up-regulated genes and 321 down-regulated genes were further analyzed using the annotations from the GO terms and the KEGG database. As a result, three of 12 enriched pathways are well-known cancer pathways, while nine of them were associated to cancers in previous studies. The further analysis of 73 genes involved in 12 pathways suggests that U1 snRNA regulates cancer gene expression. The microarray data with ID GSE84304 is available in the NCBI GEO database.
Learning Case-based Knowledge for Disambiguating Chinese Word Segmentation: A Preliminary Study

Just like other NLP applications, a serious problem with Chinese word segmentation lies in the ambiguities involved. Disambiguation methods fall into different categories, e.g., rule-based, statistical-based and example-based approaches, each of which may involve a variety of machine learning techniques. In this paper we report our current progress within the example-based approach, including its framework, example representation and collection, example matching and application. Experimental results show that this effective approach resolves more than 90% of ambiguities found. Hence, if it is integrated effectively with a segmentation method of the precision P > 95%, the resulting segmentation accuracy can reach, theoretically, beyond 99.5%.
Death to the Log-Linearized Consumption Euler Equation! (And Very Poor Health to the Second-Order Approximation)

Abstract This paper shows that standard methods for estimating log-linearized consumption Euler equations using micro data cannot successfully uncover structural parameters like the coefficient of relative risk aversion from a dataset of simulated consumers behaving exactly according to the standard model. Furthermore, consumption growth for the simulated consumers is very highly statistically related to predictable income growth -- and thus standard `excess sensitivity' tests would reject the hypothesis that consumers are behaving according to the model. Results are not much better for the second-order approximation to the Euler equation. The paper concludes that empirical estimation of consumption Euler equations should be abandoned, and discusses some alternative empirical strategies that are not subject to the problems of Euler equation estimation.
Nonparametric Budgeted Stochastic Gradient Descent

One of the most challenging problems in kernel online learning is to bound the model size. Budgeted kernel online learning addresses this issue by bounding the model size to a predefined budget. However, determining an appropriate value for such predefined budget is arduous. In this paper, we propose the Nonparametric Budgeted Stochastic Gradient Descent that allows the model size to automatically grow with data in a principled way. We provide theoretical analysis to show that our framework is guaranteed to converge for a large collection of loss functions (e.g. Hinge, Logistic, L2, L1, and ε-insensitive) which enables the proposed algorithm to perform both classification and regression tasks without hurting the ideal convergence rate O ( 1 T ) of the standard Stochastic Gradient Descent. We validate our algorithm on the real-world datasets to consolidate the theoretical claims.
The Impact of Wind Farms on Property Values: A Geographically Weighted Hedonic Pricing Model

Wind power is the most important renewable energy source in many countries today, characterized by a rapid and extensive diffusion since the 1990s. However, it has also triggered much debate with regard to the impact on landscape and vista. Therefore, siting processes of wind farm projects are often accompanied by massive public protest, because of visual and aural impacts on the surrounding area. These mostly negative consequences might be reflected in property values and house prices. The aim of this paper is to investigate the impacts of wind farms on the surrounding area through property values, by means of a hedonic pricing model, using both a spatial fixed (viewshed) effects (accounting for spatially clustered unobserved influences) and a Geographically Weighted Regression model (accounting for spatial heterogeneity). The analysis is the first of its kind undertaken for a local region in Continental Europe (North Rhine-Westphalia, Germany). Viewsheds are calculated for each property using a digital surface model. Focusing on proximity and visibility effects caused by wind farm sites, we find that proximity, measured by the inverse distance to the nearest wind turbine, indeed causes significant negative impacts on the surrounding property values. Thereby, local statistics reveal varying spatial patterns of the coefficient estimates across and within the city areas and districts. In contrast, no evidence is found for a statistically significant impact of the visibility of the wind farm turbines.
Social Networks , Achievement Motivation , and Corruption : Theory and Evidence

We develop an equilibrium model to analyze the inuence of economic and cultural factors on the level of corruption that can be found in di¤erent countries. We focus our attention on the level of achievement motivation and the importance of social networks for trade in the informal economy, both of which increase the willingness of agents to participate in corrupt activities. These variables together with productivity in the formal economy and the ability of authorities to punish corruption determine the level of corruption that prevails in equilibrium. There are multiple equilibria, and societies may get trapped in a steady state equilibrium of generalized corruption. We test the predictions of the theoretical model using empirical evidence from a variety of sources for a large cross-section of countries and use two-stage least squares methods to asses the direction of causality. Keywords: Corruption; Informal Economy; Rule of Law; Social Values JEL classi cation: A13; D73; K42; O17
Redundancy optimization strategy for hands-on robotic surgery

During hands-on cooperative surgery, the use of a redundant robot allows to address encumbrance issues in the Operating Room (OR), which can occur due to the presence of large medical instrumentation, such as the surgical microscope. This work presents a new Null Space Optimization (NSO) strategy to constraint the position of the manipulator's elbow within predefined range of motions, according to the spatial requirements of the specific procedure, also taking into account the physical joint limits of the robotic assistant. The proposed strategy was applied to the 7 degrees of freedom (dof) lightweight robot LWR4+. The performance of the NSO was compared to two state-of-the-art null space optimization strategies, i.e. damped posture and fixed optimal posture, over a pool of three non-expert users in both strict (20deg) and negligible (100deg) angular encumbrance limitations. The NSO strategy was proved versatile in providing wide elbow mobility together with safe distance from relevant continuity null space boundaries, guaranteeing smooth guidance trajectories. Future works would be performed in order to evaluate the potential feasibility of NSO in a real surgical scenario.
A Reexamination of Diffusion Estimators With Applications to Financial Model Validation

Time-homogeneous diffusion models have been widely used for describing the stochastic dynamics of the underlying economic variables. Recently, Stanton proposed drift and diffusion estimators based on a higher-order approximation scheme and kernel regression method. He claimed that “higher order approximations must outperform lower order approximations” and concluded nonlinearity in the instantaneous return function of short-term interest rates. To examine the impact of higher-order approximations, we develop general and explicit formulas for the asymptotic behavior of both drift and diffusion estimators. We show that these estimators will reduce the numerical approximation errors in asymptotic biases, but their asymptotic variances escalate nearly exponentially with the order of approximation. Simulation studies also confirm our asymptotic results. This variance inflation problem arises not only from nonparametric fitting, but also from parametric fitting. Stanton's work also postulates the interesting question of whether the short-term rate drift is nonlinear. Based on empirical simulation studies, Chapman and Pearson suggested that the nonlinearity might be spurious, due partially to the boundary effect of kernel regression. This prompts us to use the local linear fit based on the first-order approximation, proposed by Fan and Yao, to ameliorate the boundary effect and to construct formal tests of parametric financial models against the nonparametric alternatives. Our simulation results show that the local linear method indeed outperforms the kernel approach. Furthermore, our nonparametric “generalized likelihood ratio tests” are indeed versatile and powerful in detecting nonparametric alternatives. Using this formal testing procedure, we show that the evidence against the linear drift of the short-term interest rates is weak, whereas evidence against a family of popular models for the volatility function is very strong. Application to Standard & Poor 500 data is also illustrated.
Autosomal Dominant Gnathodiaphyseal Dysplasia Maps to Chromosome 11p14.3‐15.1

Gnathodiaphyseal dysplasia (GDD) is a syndrome characterized by bone fragility, sclerosis of tubular bones, and cemento‐osseous lesions of jawbones. Although some cases of this syndrome exist in families with autosomal dominant inheritance, the underlying gene has never been identified. We analyzed a large four‐generation family with GDD by linkage analysis using genomic DNA from nine affected and six nonaffected family members. A genome‐wide search using a set of highly polymorphic microsatellite markers showed evidence for linkage to chromosome 11p14.3‐15.1. Two‐point linkage analysis of microsatellite markers spanning this locus resulted in a maximum logarithm of odds (LOD) score of 2.70 with a recombination fraction (θ) of 0 at D11S1755, D11S1759, and D11S915, and a maximum LOD score of 3.01 at D11S4114 was obtained in multipoint linkage analysis. Haplotype analysis detected no recombination between GDD and six closely linked markers (D11S928, D11S1755, D11S4114, D11S1759, D11S915, and D11S929) and established the candidate interval of 8.7 cM on chromosome 11p for GDD. Although GDD has been considered to be a variation of osteogenesis imperfecta (MIM 166260), our results indicate that this syndrome is a new and distinct disease entity from other systemic bone diseases. Furthermore, these genetic markers are useful for presymptomatic diagnosis of GDD in some families and for identification of the GDD gene.
Isolation of Whole Mononuclear Cells from Peripheral Blood and Cord Blood

Peripheral blood is the primary source of lymphoid cells for investigations of the human immune system. Its use is facilitated by the Ficoll‐Hypaque density gradient centrifugation method described here. It is a simple and rapid method of purifying peripheral blood mononuclear cells (PBMC) that takes advantage of the density differences between mononuclear cells and other elements found in the blood sample. The mononuclear cell sample can be purified from monocytes by adherence or by exposure to L‐leucine methyl ester; methods are described for both procedures. Cord blood and peripheral blood from infants contain immature cells, including nucleated red cells, that can result in significant contamination of the mononuclear cell layer, and removal of these cells requires additional steps that are described. The isolation procedures presented here can also be applied to cell populations derived from tissues.