Miracle-dz/UniFAR-FTUs huggingface dataset:
Alternative Missing Data Techniques to Grade Point Average: Imputing Unavailable Grades

In this article, grade point average (GPA) is considered a missing data technique for unavailable grades in school grade records. In Study 1, theoretical and empirical differences between GPA and seven alternative missing grade techniques were considered. These seven techniques are subject mean substitution, corrected subject mean, subject correlation substitution, regression imputation, expectation maximization algorithm imputation and two multiple imputation methods-stochastic regression imputation and data augmentation. , The missing grade techniques differ greatly. Data augmentation and stochastic regression imputation appear to be superior as missing grade techniques. In Study 2, the completed grade records (observed and imputed values) were used in two prediction analyses of academic achievement. One analysis was based on unweighed grades, the other on weighed grades. In both analyses, alternative missing grade methods produced better and more consistent predictions. It is concluded that some alternative missing grade methods are superior to GPA.
Characterization of a Lignite Ash from the METC Gasifier III. Correlations of Leaching Behavior and Mineralogy

Lignite gasification ash from the Morgantown Energy Technology Center (METC) gasifier was subiected to two short-term leaching treatments. The cnncentrations of regulated elements in the EPA EP procedure leachate from the bulk METC ash did not exceed the “EP Trigger Limits. ” A modification of this procedure that uses a basic synthetic groundwater instead of the acid EP extractant was also performed on the bulk ash and its eleven size fractions. Water equilibria modeling was used to explain the concentrations of major elements in solution. Corcentrations of minor and trace elements in solution after leaching with synthetic groundwater were also below “EP Trigger Limits. ”
Occupational and non‐occupational allergic contact dermatitis from beryllium

There are various references to sensitization to beryllium in the literature. Since introducing a patch testing series for patients with suspected sensitization to metals, we have found 3 cases of sensitization to beryllium, of these 3 cases, we regard the first 2 as having relevant sensitization Beryllium chloride (l%. pet.) was positive in 3 patients and negative in 150 controls.
Differential electroabsorption spectra of InGaN/GaN QWs with indium surface segregation

In this work, we investigate the influence of the indium surface segregation on the differential absorption observed for different inverse biases applied to the structure. Obtained results evidence that the indium surface segregation leads to more sharp dependence of the absorption on the applied bias for a wide frequency range above the red edge. This feature can be used for experimental measurements of the segregation lengths applying optical spectroscopy.
Hierarchical feature clustering for content-based retrieval in medical image databases

In this paper we describe the construction of hierarchical feature clustering and show how to overcome general problems of region growing algorithms such as seed point selection and processing order. Access to medical knowledge inherent in medical image databases requires content-based descriptions to allow non-textual retrieval, e.g., for comparison, statistical inquiries, or education. Due to varying medical context and questions, data structures for image description must provide all visually perceivable regions and their topological relationships, which poses one of the major problems for content extraction. In medical applications main criteria for segmenting images are local features such as texture, shape, intensity extrema, or gray values. For this new approach, these features are computed pixel-based and neighboring pixels are merged if the Euclidean distance of corresponding feature vectors is below a threshold. Thus, the planar adjacency of clusters representing connected image partitions is preserved. A cluster hierarchy is obtained by iterating and recording the adjacency merging. The resulting inclusion and neighborhood relations of the regions form a hierarchical region adjacency graph. This graph represents a multiscale image decomposition and therefore an extensive content description. It is examined with respect to application in daily routine by testing invariance against transformation, run time behavior, and visual quality For retrieval purposes, a graph can be matched with graphs of other images, where the quality of the matching describes the similarity of the images.
Path planning with force-based foothold adaptation and virtual model control for torque controlled quadruped robots

We present a framework for quadrupedal locomotion over highly challenging terrain where the choice of appropriate footholds is crucial for the success of the behaviour. We use a path planning approach which shares many similarities with the results of the DARPA Learning Locomotion challenge and extend it to allow more flexibility and increased robustness. During execution we incorporate an on-line force-based foothold adaptation mechanism that updates the planned motion according to the perceived state of the environment. This way we exploit the active compliance of our system to smoothly interact with the environment, even when this is inaccurately perceived or dynamically changing, and update the planned path on-the-fly. In tandem we use a virtual model controller that provides the feed-forward torques that allow increased accuracy together with highly compliant behaviour on an otherwise naturally very stiff robotic system. We leverage the full set of benefits that a high performance torque controlled quadruped robot can provide and demonstrate the flexibility and robustness of our approach on a set of experimental trials of increasing difficulty.
Randomized, controlled trial of the effect of e-feedback on knowledge about radiotherapy of breast cancer patients in Finland.

The growing number of women with breast cancer and their unmet knowledge expectations of radiotherapy pose a challenge to develop effective electronic patient education. Development efforts should be focused on e-feedback on knowledge because of its positive effects. In this study, we evaluated how an e-feedback knowledge intervention (e-Re-Know) before first radiotherapy improves breast cancer patients' knowledge of radiotherapy and identified the associations with patients' characteristics. Women with breast cancer (n = 128) were randomized prior to the radiotherapy period either to the intervention group or control group. The outcome measured three months after the radiotherapy period was knowledge level of radiotherapy. The increase in knowledge level was higher in the intervention group after adjustment for baseline knowledge level, and a significantly higher increase in knowledge level was seen in one subdomain, side-effect self-care, compared to the control group. The results of this study indicate that the e-Re-Know can be used in patient education to support empowerment. Future research should target new applications of e-Re-Know available on the Internet for those interested in the subject.
Handling Big Data Efficiently by Using Map Reduce Technique

Extremely large amount of data is being captured by today's organizations and is continue to increase. It becomes computationally inefficient to analyze such huge data. Researchers has addressed problem in discovering knowledge from these continuously growing large data sets. Quantity of available raw data has been increasing at a very high rate. The precious information is concealed in large databases. Data mining has become an interesting area to extract the embedded precious information from them. For many years it has been found its root in all kinds of application areas. Thus, gave evolution to many data mining methods which started to get applied in several real life fields. But not all the methods possess the capability to deal with and handle the huge collection of data. In recent years, numbers of computation and data intensive scientific data analyses are established. To perform the large scale data mining analyses so as to meet the scalability and performance requirements of big data, several efficient parallel and concurrent algorithms got applied. A lot of parallel algorithms are put into action using different parallelization techniques, such as-threads, MPI, MapReduce etc. Which yield different performance and usability characteristics. The MPI model works efficiently in computing rigorous problems but it is a complicated task to bring this model into the practical use. There is currently considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. It is inspired by functional programming which allows expressing distributed computations on massive amounts of data. It is designed for large-scale data processing as it allows to run on clusters of commodity hardware. A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. In this paper, we are going to work around MapReduce, its advantages, disadvantages and how it can be used in integration with other technology.
Big Data architecture for IT incident management

IT incident management aims to restore normal service quality and availability of IT systems from interruptions. IT incidents often have complicated causes aggregated from an IT environment composed of thousands of interdependent components. Incident diagnosis then requires collecting and analyzing a large scale of data regarding these components, often, in real time to find suspect causes. It is extremely difficult to fulfill this requirement using traditional techniques. In this paper, we propose a new analysis architecture using Big Data techniques. This architecture leverages stream computing and MapReduce techniques to analyze data from various data sources, uses NoSQL databases to store incident-related documents and their relationships, and further utilizes other analytical techniques to examine the documents for root causes and failure prediction. We demonstrate this approach using a real-world example and present evaluation results from a recent pilot study.
Parallelizing H.264 Motion Estimation Algorithm using CUDA

Motion estimation is currently the most computationally intensive portion of the H.264 encoding process. Previous attempts to parallelize this algorithm in CUDA 1 showed sizable speedups but sacrificed quality by ignoring the motion vector prediction (MVp). We demonstrate the viability of a hierarchical (pyramid) motion estimation algorithm in CUDA. This solution addresses the MVp problem while still taking advantage of the CUDA framework.
Budget impact of rosuvastatin initiation in high-risk hyperlipidemic patients from a US managed care perspective

Abstract Introduction: Statins reduce low-density lipoprotein cholesterol (LDL-C) levels, which, when elevated, represent a significant risk factor for cardiovascular (CV) disease. Hyperlipidemic patients at risk of CV events initiated on simvastatin or atorvastatin may be less likely to meet LDL-C goals (defined in National Cholesterol Education Program guidelines) and more likely to experience CV events than patients initiated on rosuvastatin. A 3-year budget impact model was developed to estimate the clinical impact and cost to a US managed care organization (MCO) with 1 million members of initiating high-risk hyperlipidemic patients on rosuvastatin rather than simvastatin or atorvastatin. Methods: A total of 1000 adult patients were assumed to initiate statins. The average baseline LDL-C level was 189 mg/dL. In scenario 1, all patients were initiated on simvastatin or atorvastatin and titrated to a higher dose, or switched to atorvastatin (if initiated on simvastatin) or rosuvastatin; in scenario 2, 50% of the 520 high-risk patients were initiated on rosuvastatin. Drug acquisition and administration costs were considered. Product labeling, clinical trial results, national prescription claims data, and published literature were used to populate the model. Results: Over 3 years, 75 additional patients reached their LDL-C goal in scenario 2, compared with scenario 1 (633 vs 558, respectively), at an increased cost of $240,628 ($1,415,516 vs $1,174,888, respectively). The additional per member per month (PMPM) cost of scenario 2 was $0.007. Limitations: This analysis assumed that statin efficacy is the same in real life as in trials, and used titration and switching patterns not based on patients’ goal attainment. However, sensitivity and scenario analyses showed that the model was less sensitive to these parameters than to cost-related parameters. Conclusions: Initiating high-risk hyperlipidemic patients on rosuvastatin may increase the number of patients reaching LDL-C goal at a relatively modest increase in PMPM cost to an MCO.
Multi-Fingered Robotic Grasping: A Primer

Author(s): Carpin, Stefano; Liu, Shuo; Falco, Joe; Wyk, Karl Van | Abstract: This technical report presents an introduction to different aspects of multi-fingered robot grasping. After having introduced relevant mathematical background for modeling, form and force closure are discussed. Next, we present an overview of various grasp planning algorithms with the objective of illustrating different approaches to solve this problem. Finally, we discuss grasp performance benchmarking.
The definition, recognition, and interpretation of convergent evolution, and two new measures for quantifying and assessing the significance of convergence

Convergent evolution is an important phenomenon in the history of life. Despite this, there is no common definition of convergence used by biologists. Instead, several conceptually different definitions are employed. The primary dichotomy is between pattern‐based definitions, where independently evolved similarity is sufficient for convergence, and process‐based definitions, where convergence requires a certain process to produce this similarity. The unacknowledged diversity of definitions can lead to problems in evolutionary research. Process‐based definitions may bias researchers away from studying or recognizing other sources of independently evolved similarity, or lead researchers to interpret convergent patterns as necessarily caused by a given process. Thus, pattern‐based definitions are recommended. Existing measures of convergence are reviewed, and two new measures are developed. Both are pattern based and conceptually minimal, quantifying nothing but independently evolved similarity. One quantifies the amount of phenotypic distance between two lineages that is closed by subsequent evolution; the other simply counts the number of lineages entering a region of phenotypic space. The behavior of these measures is explored in simulations; both show acceptable Type I and Type II error. The study of convergent evolution will be facilitated if researchers are explicit about working definitions of convergence and adopt a standard toolbox of convergence measures.
Design and monitoring of multi‐arm multi‐stage clinical trials

Two‐arm group sequential designs have been widely used for over 40 years, especially for studies with mortality endpoints. The natural generalization of such designs to trials with multiple treatment arms and a common control (MAMS designs) has, however, been implemented rarely. While the statistical methodology for this extension is clear, the main limitation has been an efficient way to perform the computations. Past efforts were hampered by algorithms that were computationally explosive. With the increasing interest in adaptive designs, platform designs, and other innovative designs that involve multiple comparisons over multiple stages, the importance of MAMS designs is growing rapidly. This article provides break‐through algorithms that can compute MAMS boundaries rapidly thereby making such designs practical. For designs with efficacy‐only boundaries the computational effort increases linearly with number of arms and number of stages. For designs with both efficacy and futility boundaries the computational effort doubles with successive increases in number of stages.
A new approach for generating bispecific antibodies based on a common light chain format and the stable architecture of human immunoglobulin G1

Bispecific antibodies combine two different antigen-binding sites in a single molecule, enabling more specific targeting, novel mechanisms of action, and higher clinical efficacies. Although they have the potential to outperform conventional monoclonal antibodies, many bispecific antibodies have issues regarding production, stability, and pharmacokinetic properties. Here, we describe a new approach for generating bispecific antibodies using a common light chain format and exploiting the stable architecture of human immunoglobulin G1. We used iterative experimental validation and computational modeling to identify multiple Fc variant pairs that drive efficient heterodimerization of the antibody heavy chains. Accelerated stability studies enabled selection of one Fc variant pair dubbed “DEKK” consisting of substitutions L351D and L368E in one heavy chain combined with L351K and T366K in the other. Solving the crystal structure of the DEKK Fc region at a resolution of 2.3 Å enabled detailed analysis of the interactions inducing CH3 interface heterodimerization. Local shifts in the IgG backbone accommodate the introduction of lysine side chains that form stabilizing salt-bridge interactions with substituted and native residues in the opposite chain. Overall, the CH3 domain adapted to these shifts at the interface, yielding a stable Fc conformation very similar to that in wild-type IgG. Using the DEKK format, we generated the bispecific antibody MCLA-128, targeting human EGF receptors 2 and 3. MCLA-128 could be readily produced and purified at industrial scale with a standard mammalian cell culture platform and a routine purification protocol. Long-term accelerated stability assays confirmed that MCLA-128 is highly stable and has excellent biophysical characteristics.
Life assessment methodologies incoroporating shot peening process effects: mechanistic consideration of residual stresses and strain hardening Part 2 – approaches to fatigue lifing after shot peening

Abstract Shot peening is a well known process applied to components in order to improve their fatigue resistance. In recent years, there has been an increasing interest in including the effects of the shot peening process in life assessment models since this would allow a reduction in conservatism compared to those in current application. Part 1 of this review dealt with the effects of the shot peening process (surface roughening, strain hardening and compressive residual stresses) and the resulting effect on component fatigue life. This part of the review considers how this effect on component fatigue life can be incorporated into life assessment modelling approaches with discussion of the relative merits of each approach. The paper concludes with a flow chart demonstrating a possible route for the inclusion of shot peening effects within industrial component life assessment frameworks.
An Incremental Architecture for the Semantic Annotation of Dialogue Corpora with High-Level Structures. A case of study for the MEDIA corpus.

The semantic annotation of dialogue corpora permits building efficient language understanding applications for supporting enjoyable and effective human-machine interactions. Nevertheless, the annotation process could be costly, time-consuming and complicated, particularly the more expressive is the semantic formalism. In this work, we propose a bootstrapping architecture for the semantic annotation of dialogue corpora with rich structures, based on Dependency Syntax and Frame Semantics.
Parameteric coding of speech signals

In this paper an FS1015 LPC coder has been designed using Matlab to produce intelligible speech. This paper focuses on the different methods of implementation and compared to determine, which gives the best performance. The coder has been tested on Hindi. Subjective mean opinion scores have been used to measure the speech quality based on the score given by the listeners.
Improving WFST-based G2P Conversion with Alignment Constraints and RNNLM N-best Rescoring

This work introduces a modified WFST-based multiple to multiple EM-driven alignment algorithm for Graphemeto-Phoneme (G2P) conversion, and preliminary experimental results applying a Recurrent Neural Network Language Model (RNNLM) as an N-best rescoring mechanism for G2P conversion. The alignment algorithm leverages the WFST framework and introduces several simple structural constraints which yield a small but consistent improvement in Word Accuracy (WA) on a selection of standard baselines. The RNNLM rescoring further extends these gains and achieves state-of-the-art performance on four standard G2P datasets. The system is also shown to be significantly faster than existing solutions. Finally, the complete WFST-based G2P framework is provided as an open-source toolkit.
Influence of Different Restorative Techniques on the Strength of Endodontically Treated Weakened Roots

Objective. Comparing effect of different restoration techniques on fracture resistance of compromised roots. Methods. Crowns of 100 single-rooted teeth were sectioned and 10 roots were kept as negative control group (Group 1). Remaining roots were instrumented and divided into one and positive control group of 10 samples (Group 2) and 4 experimental groups of 20 samples each. Group 3: roots were obturated with gutta-percha; Group 4: roots were restored with gutta-percha, composite, and glass fiber post; Group 5: roots were obturated with Resilon; Group 6: Roots were restored with Resilon, composite, and glass fiber post. Roots were weakened before obturation in groups 2, 3, and 5 and after obturation in groups 4 and 6. Fracture strengths were measured using Dartec testing machine and fracture load was recorded in kilo-Newton. Statistical analysis was done using ANOVA and Tukeys test. Results. The fractures resistance of restored roots was significantly higher in groups 4, 5, and 6 than in Groups 2 and 3. There were no significant differences between groups 1, 4, 5, and 6. Conclusions. Restoration of weakened roots with Resilon or bonding an intermediate composite resin to coronal radicular dentin and to glass fiber post increased their fracture resistance.
Quantum One-Sided Exact Error Algorithms

We define a complexity class for randomized algorithms with one-sided error that is exactly equal to a constant (unlike the usual definitions, in which the error is only bounded above or below by a constant). We show that the corresponding quantum classes (one each for a different error probability) are in fact all equivalent to each other and to EQP, the quantum analogue of P. The technique used is a form of quantum amplitude amplification.