Miracle-dz/UniFAR-FTUs huggingface dataset:
Biologic monitoring and surveillance results for the department of veterans affairs' depleted uranium cohort: Lessons learned from sustained exposure over two decades.

BACKGROUND A small group of Gulf War I veterans wounded in depleted uranium (DU) friendly fire incidents have been monitored in a clinical surveillance program at the Veterans Affairs Medical Center, Baltimore since 1994. METHODS An in-patient clinical surveillance protocol was performed on 35 members of the cohort, including exposure monitoring for total and isotopic uranium concentrations in urine and a comprehensive assessment of health outcomes. RESULTS Although urine U concentrations continue to be elevated in this group, illustrating on-going in situ mobilization of U from embedded fragments, no consistent U-related health effects have been observed. CONCLUSIONS Now more than 20 years since first exposure to DU, an aging cohort of military veterans continues to show no U-related health effects in known target organs of U toxicity. As tissue concentrations continue to accrue with exposure duration, critical tissue-specific U concentration thresholds may be reached, thus recommending on-going surveillance of this veteran cohort.
DIFFERENCES IN LEARNING OUTCOMES FOR THE ONLINE AND F2F VERSIONS OF “AN INTRODUCTION TO SHAKESPEARE”

The same course in both an online and on-campus environment makes for an extended experimental comparison of learning outcomes, while controlling for two important variables: the instructor and the content of the course Students learn course content through four kinds of encounters—alone, one-to-one, one-to many, and many-to many.The online version of “Introduction to Shakespeare” course has consistently better learning outcomes than the on-campus version, as a result of the compelling nature of the one-to-one communication mode online and the textual nature of the many-to-many and one-to-many modes online. Text-based communication in the online class reinforces the skills pertinent to a literature class. Other crucial factors are online pedagogy and the self-selection. Ultimately, the differences between the online and F2F classrooms may be less crucial to learning outcomes than the degree to which the course design, regardless of technological environment, develops and supports students’ abilities to practice adult learning styles.
Downlink Channel Estimation in Cellular Systems with Antenna Arrays at Base Stations Using Channel Probing with Feedback

In mobile communication systems with multisensor antennas at base stations, downlink channel estimation plays a key role because accurate channel estimates are needed for transmit beamforming. One efficient approach to this problem is channel probing with feedback. In this method, the base station array transmits probing (training) signals. The channel is then estimated from feedback reports provided by the users. This paper studies the performance of the channel probing method with feedback using a multisensor base station antenna array and single-sensor users. The least squares (LS), linear minimum mean square error (LMMSE), and a new scaled LS (SLS) approaches to the channel estimation are studied. Optimal choice of probing signals is investigated for each of these techniques and their channel estimation performances are analyzed. In the case of multiple LS channel estimates, the best linear unbiased estimation (BLUE) scheme for their linear combining is developed and studied.
Mining closed sequential patterns using genetic algorithm

Closed sequential pattern mining is an important data mining task because it produces more compact result set and it is more efficient than sequential pattern mining. In general closed sequential patterns are generated from large data sets by applying algorithms like CloSpan and BIDE which require more execution time to compute all the closed sequential patterns. By using genetic algorithms we can reduce the execution time. The advantage of using a genetic algorithm in finding the closed sequential patterns is that it performs global search and it has less time complexity compared to other algorithms.This paper proposes a novel genetic algorithm G-CSPM to find closed sequential patterns. To improve the performance, we develop an effective fitness function and a pruning method. Our algorithm is the first method that utilizes genetic approach for closed sequential pattern mining. The results indicate that the proposed algorithm G-CSPM outperforms CloSpan.
Extracting Knowledge about Users' Activities from Raw Workstation Contents

A long-standing goal of AI is the development of intelligent workstation-based personal agents to assist users in their daily lives. A key impediment to this goal is the unrealistic cost of developing and maintaining a detailed knowledge base describing the user's different activities, and which people, meetings, emails, etc. are affiliated with each such activity. This paper presents a clustering approach to automatically acquiring such a knowledge base by analyzing the raw contents of the workstation, including emails, contact person names, and online calendar meetings. Our approach analyzes the distribution of email words, the social network of email senders and recipients, and the results of Google Desktop Search queried with text from online calendar entries and person contact names. For each cluster it constructs, the program outputs a frame-based representation of the conesponding user activity. This paper describes our approach and experimentally assesses its perfonnance over the workstations of three different users.
Progress in understanding the diagnostic and pathogenic role of autoantibodies associated with systemic sclerosis

Purpose of reviewAt the time of diagnosis, systemic sclerosis (SSc) is often well established with significant irreversible tissue and organ damage. Definitions of ‘early SSc’ have been proposed, which include the presence of SSc-associated autoantibodies. In addition, functional autoantibodies that are believed to be involved in SSc pathogenesis need to be considered. In this review, recent advances in the diagnostic utility and pathogenic role of autoantibodies in early SSc are summarized. Moreover, we propose a clinical care pathway illustrating how autoantibody testing along with key clinical features can be used to make an earlier diagnosis of SSc. Recent findingsRecent evidence has helped to develop a clearer understanding of the natural history, early clinical features, and autoantibodies that are predictors of SSc. The role of functional autoantibodies is leading to innovative approaches to evidence-based interventions and therapies that are based on mechanisms of disease. SummaryDespite substantial advances, the high morbidity and mortality that currently characterizes SSc can largely be attributed to a delay in diagnosis, gaps in our understanding of the role of autoantibodies in early disease, and limited effective therapeutic options. An early and accurate diagnosis of SSc and use of autoantibody testing embedded in evidence-based clinical care pathways will help improve SSc-associated clinical outcomes and healthcare expenditures.
Reactive Objects and Functional Programming

The construction of robust distributed and interactive software is still a challenging task, despite the recent popularity-increase for languages that take advanced programming concepts into the mainstream. Several problematic areas can be identified: most languages require the reactivity of a system to be manually upheld by careful avoidance of blocking operations; mathematical values often need to be encoded in terms of stateful objects or vice versa; concurrency is particularly tricky in conjunction with encapsulated software components; and static type safety is often compromised because of the lack of simultaneous support for both subtyping and polymorphism . This thesis presents a programming language, O'Haskell , that has been consciously designed with these considerations in mind. O'Haskell is defined by conservatively extending the purely functional language Haskell with the following features: A central structuring mechanism based on reactive objects , which unify the notions of objects and concurrent processes. Reactive objects are asynchronous, state-encapsulating servers whose purpose is to react to input messages; they cannot actively block execution or selectively filter their sources of input. A monadic layer of object-based computational effects, which clearly separates stateful objects from stateless values. Apart from higher-order functions and recursive data structures, the latter notion also includes first-class commands, object templates, and methods. A safe, polymorphic type system with declared record and datatype subtyping, supported by a powerful partial type inference algorithm. It is claimed that these features make O'Haskell especially well-adapted for the task of modern software construction. The thesis presents O'Haskell from both a practical and a theoretical perspective. The practical contributions are a full implementation of the language, a number of non-trivial programming examples that illustrate the merits of reactive objects in a realistic context, and several reusable programming methods for such applications as graphical user interfaces, embedded controllers, and network protocols. The theoretical results serve to substantiate the informal claim made regarding usability, and include a provably sound polymorphic subtyping system, soundness and partial completeness for the inference algorithm, a formal dynamic semantics, and a result that characterizes the conservative extension of a purely functional language with state and concurrency.
Comparative Analysis of Data Mining Techniques on Educational Dataset

mining is a relatively young and interdisciplinary field of computer science. It is a process that attempts to discover new patterns in large data sets. Different types of mining algorithms have been proposed by different researchers in recent years. A single algorithm may not be applied to all applications due to difficulty for suitable data types of the algorithm. Therefore the selection of a correct data mining algorithm depends on not only the goal of an application, but also on the compatibility of the data set. The aim of this paper is how to use suitable data mining algorithms on educational dataset. This paper focuses on comparative analysis of various data mining techniques and algorithms.
Peer-Assisted Video Streaming With RTMFP Flash Player: A Measurement Study on PPTV

Real-time media flow protocol (RTMFP) is a protocol developed by Adobe for multimedia delivery under both client–server and peer-to-peer (P2P) paradigms. Currently, major Internet video service providers, such as PPTV and iQIYI, have already built their Web-based video streaming systems with RTMFP. In such a system, a user only needs to install a Flash Player plug-in on his Web browser, and can stream videos in a peer-assisted way. Despite its wide usage, RTMFP has received little attention from the measurement community. In this paper, we select PPTV as an example and study the RTMFP video streaming technology with a measurement approach. We reveal the architecture of PPTV’s RTMFP streaming system and show that, compared with proprietary P2P networks, the RTMFP network has a different content distribution policy, and exhibits different features on peers’ streaming behaviors, potential system bottleneck, and network dynamics. We also study RTMFP’s video transmission and find that the protocol’s selective retransmission scheme can effectively overcome packet losses and improve the video playback quality; however, the TCP-like congestion control mechanism of RTMFP does not lead to fairness between RTMFP and Transmission Control Protocol (TCP) traffics, due to the mismatch between the inherited pull-based video segment distribution model of the P2P streaming application and the protocol’s built-in congestion control mechanism. This paper provides insights into the RTMFP-based video streaming technology and is helpful for people to construct better peer-assisted video systems with RTMFP.
On the characterization of VBR MPEG streams

We present a comprehensive model for variable-bit-rate MPEG video streams. This model captures the bit-rate variations at multiple time scales. Long-term variations are captured by incorporating scene changes, which are most noticeable in the fluctuations of I frames. The size of an I frame is modeled by the sum of two random components: a scene-related component and an AR(2) component that accounts for the fluctuations within a scene. Two random processes of i.i. d. rvs are used to model the sizes of P and B frames, respectively. The complete model is then obtained by intermixing the three sub-models according to a given GOP pattern. It is shown that the composite model exhibits long-range dependence (LRD) in the sense that its autocorrelation function is non-summable. The LRD behavior is caused by the repetitive GOP pattern which induces periodic cross-correlations between different types of frames. Using standard statistical methods, we successfully fit our model to several empirical video traces. We then study the queueing performance for video traffic at a statistical multiplexer. The results show that the model is sufficiently accurate in predicting the queueing performance for real video streams.
Gait Modeling for Human Identification

Human gait is a kind of dynamic biometrical feature which is complex and difficult to imitate, it is unique and more secure than static features such as password, fingerprint and facial feature. Analyzing people walking patterns, their "step-prints", can lead to the recognition of personal identity. In this paper, we propose to design, build, calibrate, analyze, and use wearable intelligent shoes; then focus on classifying the wearers into authorized ones and unauthorized ones by modeling their individual gait performance. Firstly the intelligent shoes for collecting and modeling human gait to measure an unprecedented number of parameters relevant to gait are presented. Then we introduce cascade neural networks with node-decoupled extended Kalman filtering (CNN-NDEKF) from the paper by Nechyba and Xu (1997) to apply for modeling and classifier generation. Finally, the experimental results of learning algorithms and comparison are described and verify that the proposed method is valid and useful for human identification.
Aggravation of respiratory disturbances by the use of an occlusal splint in apneic patients: a pilot study.

PURPOSE This pilot study was designed to test the hypothesis that the use of a single oral splint may aggravate respiratory disturbance in sleep apneic patients. MATERIALS AND METHODS A group of 10 patients with a history of snoring and a recording night confirming a diagnosis of sleep apnea were included. Patients were then invited to spend 2 nights in the sleep laboratory: night 2 to establish baseline data (baseline night) and night 3, 1 week later, to assess the influence of an occlusal maxillary splint on sleep (splint night). The following variables were analyzed under blind conditions: total sleep time, sleep efficiency and number of awakenings, microarousals, apnea-hypopnea index per hour of sleep (AHI), respiratory disturbances index per hour of sleep (RDI), and percentage of sleeping time with snoring. RESULTS No statistically significant difference in AHI was noted between baseline and splint nights. However, four patients experienced an aggravation in apnea diagnosis category on the night they used the splint. The AHI was increased by more than 50% in 5 of the 10 patients. The RDI showed a 30% increase from baseline to splint nights. The percentage of sleeping time with snoring also increased by 40% with the splint. CONCLUSION This open study suggested that the use of an occlusal splint is associated with a risk of aggravation of respiratory disturbances. It may therefore be relevant for clinicians to question patients about snoring and sleep apnea when recommending an occlusal splint.
Synthesising Filled Pauses: Representation and Datamixing

Filled pauses occur frequently in spontaneous human speech, yet modern text-to-speech synthesis systems rarely model these disﬂuencies overtly, and consequently they do not output convincing synthetic ﬁlled pauses. This paper presents a text-to-speech system that is speciﬁcally designed to model these particular disﬂuencies more efffectively. A preparatory investigation shows that a synthetic voice trained exclusively on spontaneous speech is perceived to be inferior in quality to a voice trained entirely on read speech, even though the latter does not handle ﬁlled pauses well. This motivates an investigation into the phonetic representation of ﬁlled pauses which show that, in a preference test, the use of a distinct phone for ﬁlled pauses is preferred over the standard /V/ phone and the alternative /@/ phone. In addition, we present a variety of data-mixing techniques to combine the strengths of standard synthesis systems trained on read speech corpora with the supplementary advantages offered by systems trained on spontaneous speech. In a MUSHRA-style test, it is found that the best overall quality is obtained by combining the two types of corpora using a source marking technique. Speciﬁcally, general speech is synthesised with a standard mark, while ﬁlled pauses are synthesised with a spontaneous mark, which has the added beneﬁt of also producing ﬁlled pauses that are comparatively well synthesised.
Mastery learning in der Ausbildung von Industriekaufleuten

Mastery Learning stellt ein Konzept dar, das unter diesem Titel, aber auch unter solchen wie "alle Schuler schaffen es" oder "zielerreichendes Lernen" in den 60er und 70er Jahren . heftig diskutiert wurde. In dem Beitrag wird zunachst die aktuelle Auseinandersetzung um dieses Konzept vorgestellt. Daran anschliesend wird gezeigt, aus welchen Grunden es sich anbietet, angesichts spezifischer Probleme der kaufmannischen Erstausbildung (hier von Industriekaufleuten) Grundideen des Mastery Learning wieder aufzugreifen und dabei zu versuchen, seine Nachteile zu vermeiden. Als ein Mittel der Wahl wird der Einsatz komplexer Lehr-Lern-Arrangements angesehen. Nach einer Darstellung ausgewahlter Ergebnisse zum Erfolg des gewahlten Ansatzes wird ein Ausblick auf weitere, fur notwendig erachtete Entwicklungs- und Forschungsaufgaben gegeben. (DIPF/Orig.) The idea behind the introduction of the mastery-learning concept is to enable all students to achieve the targets set in their learning. The controversial outlines given in the pertinent literature concede quite good chances to the realization of the mastery-learning approach, but, at the same time, they stress the organizational problems of its successful implementation. Among these figure time- management and, above all, the control of the both quantitative and qualitative assignment of learning objects (as representatives of learning targets and contents) to the students. In the project briefly presented here, a solution of these problems was sought by the use of a complex teaching-learning arrangement developed and implemented from a constructivistic perspective. First results show that it might thus be possible to successfully realize these ideas stemming from reform pedagogics. However, they need to be evaluated on the basis of experimental studies, including analyses of their long-term effects. (DIPF/Orig.)
Polynomial Estimation of Time-Varying Multipath Gains With Intercarrier Interference Mitigation in OFDM Systems

In this paper, we consider the case of a high-speed mobile receiver operating in an orthogonal frequency-division multiplexing (OFDM) communication system. We present an iterative algorithm for estimating multipath complex gains with intersubcarrier interference (ICI) mitigation (using comb-type pilots). Each complex gain variation is approximated by a polynomial representation within several OFDM symbols. Assuming knowledge of delay-related information, polynomial coefficients are obtained from time-averaged gain values, which are estimated using the least-square (LS) criterion. The channel matrix is easily computed, and the ICI is reduced by using successive interference suppression (SIS) during data symbol detection. The algorithm's performance is further enhanced by an iterative procedure, performing channel estimation and ICI mitigation at each iteration. Theoretical analysis and simulation results for a Rayleigh fading channel show that the proposed algorithm has low computational complexity and good performance in the presence of high normalized Doppler spread.
Asymmetric responses of land hydroclimatology to two types of El Niño in the Mississippi River Basin

El Niño events play important roles in influencing the hydroclimatology over the Mississippi River Basin (MRB). This study shows that the two types of El Niño events, the central Pacific (CP) El Niño and eastern Pacific (EP) El Niño, have opposite effects on spring soil water hydrology in the MRB. Above‐normal spring (March) precipitation during EP El Niño years leads to higher soil water levels during the subsequent 2 to 3 months in the central and western MRB. On the other hand, CP El Niño events induce below‐normal spring precipitation that causes lower soil water levels over the Ohio‐Mississippi Valley during the following 1 or 2 months. As a result, a springtime asymmetric response occurs in the MRB streamflow and soil water storage to the two types of El Niño. Subsurface hydrological storage processes are found to be essential to extend El Niño's influence in the MRB to late spring.
Explaining Area Variation in the Use of Medicare Home Health Services

This study examines the determinants of area-level variation in Medicare home health use in 1985 for the entire United States, using data from Medicare Home Health Bills, the Medicare/Medicaid Automated Certification System, the Medicare Provider Analysis and Review Files, and other sources. Weighted two-stage least squares regression was used to analyze variation in the number of home health users per 1,000 enrollees and the average number of visits received per user. The data were aggregated to the Metropolitan Statistical Area and the rural part of the state, resulting in 343 units of analysis. According to the study's results, higher proportions of Medicare enrollees use home health services in areas with fewer nursing home beds per enrollee, higher hospital discharge rates, and shorter mean lengths of stay, higher Medicare reimbursement ceilings for skilled nursing home health visits, and more home health agencies per enrollee. Other things being equal, beneficiaries in New England are 40% more likely to use home health services than their counterparts in other regions with similar climates. The average number of visits received by home health users appears to be higher in areas where there are more agencies per enrollee and a higher share of agencies that are proprietary. There also appear to be large regional differences in the number of visits received per user. Our results imply that constrained access to nursing home beds is leading to higher levels of Medicare home health use and that there may be further savings from the substitution of home health services for hospital days. The study shows that Medicare reimbursement ceilings may constrain use and that access may be a problem for beneficiaries in areas with fewer agencies per enrollee. This study also points to significant regional variation in the proportion of beneficiaries who use home health services, even with controls for many different explanatory variables. Overall, our results suggest the possibility of serious limitations in access to Medicare home health services.