Miracle-dz/UniFAR-FTUs huggingface dataset:
SIZING FLUIDIC MUSCLE USING STATIC FORCE MODEL

Pneumatic actuators convert pneumatic energy into mechanical motion. This motion can be linear or rotary. Linear motion is feasible with pneumatic cylinders (e. g. single-acting cylinder, double-acting cylinder, rodless cylinder) and pneumatic artificial muscles (PAMs). Pneumatic artificial muscle is the newest and most promising type of pneumatic actuators. PAM is a membrane that expands radially and contracts axially when inflated, while generating high pulling forces along the longitudinal axis. The force and motion produced by PAM are linear and unidirectional. Different designs of PAM have already been developed. Recently Fluidic Muscle manufactured by Festo Company and Shadow Air Muscle manufactured by Shadow Robot Company are the most popular and commercially available. This paper describes a lifting device using PAM that is selected by our static force model.
Clinical practice guidelines for support of the family in the patient-centered intensive care unit: American College of Critical Care Medicine Task Force 2004–2005

Objective:To develop clinical practice guidelines for the support of the patient and family in the adult, pediatric, or neonatal patient-centered ICU. Participants:A multidisciplinary task force of experts in critical care practice was convened from the membership of the American College of Critical Care Medicine (ACCM) and the Society of Critical Care Medicine (SCCM) to include representation from adult, pediatric, and neonatal intensive care units. Evidence:The task force members reviewed the published literature. The Cochrane library, Cinahl, and MedLine were queried for articles published between 1980 and 2003. Studies were scored according to Cochrane methodology. Where evidence did not exist or was of a low level, consensus was derived from expert opinion. Consensus Process:The topic was divided into subheadings: decision making, family coping, staff stress related to family interactions, cultural support, spiritual/religious support, family visitation, family presence on rounds, family presence at resuscitation, family environment of care, and palliative care. Each section was led by one task force member. Each section draft was reviewed by the group and debated until consensus was achieved. The draft document was reviewed by a committee of the Board of Regents of the ACCM. After steering committee approval, the draft was approved by the SCCM Council and was again subjected to peer review by this journal. Conclusions:More than 300 related studies were reviewed. However, the level of evidence in most cases is at Cochrane level 4 or 5, indicating the need for further research. Forty-three recommendations are presented that include, but are not limited to, endorsement of a shared decision-making model, early and repeated care conferencing to reduce family stress and improve consistency in communication, honoring culturally appropriate requests for truth-telling and informed refusal, spiritual support, staff education and debriefing to minimize the impact of family interactions on staff health, family presence at both rounds and resuscitation, open flexible visitation, way-finding and family-friendly signage, and family support before, during, and after a death.
A Stability Control Method of Fractional Complex Lorenz System with Time-Delay

Recently, fractional complex Lorenz system has been interested by more and more scholars. In this article, based on the stability control theory, combined with the research achievements in mathematics, physics, automatic control and other scientific fields, the stability control of nonlinear dynamical system is discussed. First, we study the dynamic properties of a novel dynamic system, the fractional-order complex Lorenz system with time-delay. Then the feedback controllers are designed and the stability control of the fractional-order complex Lorenz system with time-delay is realized. At last, the numerical simulation is done by using Mat lab, and the simulation results are consistent well with the theoretical estimate, which points to the effectiveness of the feedback controllers.
EU ENTRANTS, CANDIDATES AND ASPIRANTS: HOW LARGE ARE THE DIFFERENCES AND HOW QUICKLY COULD THEY BE REDUCED?

This paper attempts to answer three questions on the status of real economic convergence in central and eastern Europe. First, how large are the differences, measured by key macroeconomic indicators, between the new member states that joined the EU in 2004, the four EU candidates (Bulgaria, Croatia, Romania and Turkey) and the EU “aspirants” from southeast Europe (Albania, Bosnia and Herzegovina, Macedonia and Serbia and Montenegro). Second, how long could it take the EU candidates and aspirants to overcome these differences and catch up with the new member states? And third, how long could it take the 16 countries in central and eastern Europe to catch up with living standards in Greece, Italy, Portugal and Spain? Due to the complexity of cross-country comparisons, answers to these questions are only illustrative. It is shown that differences in terms of macroeconomic indicators between the EU candidates and the new member states are not as large as is often thought. However, the starting position of countries in southeast Europe is considerably less favourable. Second, at the average speed of reforms exhibited by the new member states over the past 15 years, it would take between 11⁄2 and 71⁄2 years for the EU candidates and aspirants to reach the level of transition observed in the new member states at the time Dubravko Mihaljek*
Net-base Theory of Social Capital (特集 社会構造と社会関係資本)

1. Social Capital: Analogy and Beyond The concept of social capital is widely accepted in many academic disciplines and practical fields, particularly in the synthesized achievements of Ostrom and Ahn (2003) and Castiglione et al. (2008). Regardless of the competitive accumulation of studies, its definition remains ambiguous and its contribution to theoretical sociology is unclear. This confusion is mainly caused by its analogical property. On the other hand, theoretical sociology positively accepts this concept’s ambiguity for a variety of reasons. The merit of this analogy exists in that it highlights the mechanism of social structure that resembles the process of capital accumulation. Two implications are noteworthy in this case. Firstly, social capital works mainly at the meso-level of social structure. The process of capital accumulation, including social capital, can be captured from individual and societal viewpoints. Lin (2001) proposes an extended framework of status attainment to analyze how actors utilize social resources owned by others and what benefits they obtain; in contrast, Putnam (2000) discusses the decline of capital accumulation in the United Sates by analyzing macro indices of social capital. These studies do not necessarily deny the importance of meso-level mechanisms. In addition to Coleman’s (1988) meso-level definition that focuses on the function of social structure, we need a clearer stance regarding the meso-level viewpoint that suggests that the main mechanism of social capital, which corresponds to the market for economic capital, exists in social structure. In other words, the aim of social capital research is theorizing intermediary
A Study of Consciousness in Traditional Learning Explore With Classification Strategy

Data Mining is a tool that helps in understanding the patterns extracted with the set of rules. Mining the knowledge from the database and provides a better solution for a specific problem yields good outcome to the developers. The discovery of knowledge is essential in all sorts of application that uses data mining techniques. Data mining technique is used in various domains in order to give meaning for the available data. People are often prone to make mistakes during analyses or, possibly, when trying to establish relationships between multiple features which make it difficult for them to find solutions to certain problems. Machine learning can often be successfully applied to these problems, improving the efficiency of systems and the designs of machines. In the context of machine learning, classification is supervised learning and is capable of processing a wider variety of data and is growing in popularity. Classification techniques are being used in different industry to easily identify the type and group to which a particular tuple belongs. In this paper, different classification techniques are summarized in order to improve the learner's objective.
Increasing the coverage of a domain independent dialogue lexicon with VERBNET

This paper investigates how to extend coverage of a domain independent lexicon tailored for natural language understanding. We introduce two algorithms for adding lexical entries from VerbNet to the lexicon of the Trips spoken dialogue system. We report results on the efficiency of the method, discussing in particular precision versus coverage issues and implications for mapping to other lexical databases.
The Role of Transoral Robotic Surgery in the Management of Oropharyngeal Cancer: A Review of the Literature

Background. Transoral robotic surgery (TORS) is an emerging treatment option for the treatment of head and neck malignancies, particularly for oropharyngeal squamous cell carcinoma (OPSCC). Preliminary studies have demonstrated excellent oncologic and functional outcomes that have led to a resurgence of interest in the primary surgical management of OPSCC. The aim of the present study was to review the evidence base supporting the use of TORS in OPSCC. Methods. Studies evaluating the application of TORS in the treatment of head and neck squamous cell carcinoma (HNSCC), and more specifically OPSCC, were identified for review. Further searches were made of reference lists for complete evaluation of minimally invasive surgery (MIS) in treating OPSCC. Results. Seventeen results relating to the application of TORS in treatment of OPSCC were identified. Further results relating to the role of transoral laser microsurgery (TLM) in OPSCC were included for review. Feasibility, oncologic, and functional data is summarized and discussed. Discussion. Management strategies for patients with OPSCC continue to evolve. Minimally invasive surgical techniques including TORS and TLM offer impressive functional and oncologic outcomes particularly for patients with early T-classification and low-volume regional metastatic disease. Potential exists for treatment deintensification, particularly in patients who are HPV positive.
Dynamic ergosterol- and ceramide-rich domains in the peroxisomal membrane serve as an organizing platform for peroxisome fusion

We describe unusual ergosterol- and ceramide-rich (ECR) domains in the membrane of yeast peroxisomes. Several key features of these detergent-resistant domains, including the nature of their sphingolipid constituent and its unusual distribution across the membrane bilayer, clearly distinguish them from well characterized detergent-insoluble lipid rafts in the plasma membrane. A distinct set of peroxisomal proteins, including two ATPases, Pex1p and Pex6p, as well as phosphoinositide- and GTP-binding proteins, transiently associates with the cytosolic face of ECR domains. All of these proteins are essential for the fusion of the immature peroxisomal vesicles P1 and P2, the earliest intermediates in a multistep pathway leading to the formation of mature, metabolically active peroxisomes. Peroxisome fusion depends on the lateral movement of Pex1p, Pex6p, and phosphatidylinositol-4,5-bisphosphate–binding proteins from ECR domains to a detergent-soluble portion of the membrane, followed by their release to the cytosol. Our data suggest a model for the multistep reorganization of the multicomponent peroxisome fusion machinery that transiently associates with ECR domains.
REAL-TIME STATE ESTIMATION OF LINEAR PROCESSES OVER A SHARED AWGN CHANNEL WITHOUT FEEDBACK

We formulate the problem of estimating the state in real-time of multiple continuous-time linear dynamical systems over a shared Additive White Gaussian Noise (AWGN) channel without channel feedback and characterize the optimal encoders and decoder for minimum mean-squared error (MMSE) estimation. The first analyses are for a a single linear system being observed over an AWGN channel with a power constraint. One optimal encoder is a scaled innovation encoder and the decoder a scaling of the channel output. We then study the same problem for multiple linear systems communicating over a shared AWGN channel and characterize the optimal encoders and decoder. Finally the encoders and decoder are characterized in closed form for two identical linear sources with some correlation. We bound the optimal costs without the correlation assumption.
Increasing Adolescent HIV Prevalence in Eastern Zimbabwe – Evidence of Long-Term Survivors of Mother-to-Child Transmission?

Recent data from the Manicaland HIV/STD Prevention Project, a general-population open HIV cohort study, suggested that between 2004 and 2007 HIV prevalence amongst males aged 15–17 years in eastern Zimbabwe increased from 1.20% to 2.23%, and in females remained unchanged at 2.23% to 2.39%, while prevalence continued to decline in the rest of the adult population. We assess whether the more likely source of the increase in adolescent HIV prevalence is recent sexual HIV acquisition, or the aging of long-term survivors of perinatal HIV acquisition that occurred during the early growth of the epidemic. Using data collected between August 2006 and November 2008, we investigated associations between adolescent HIV and (1) maternal orphanhood and maternal HIV status, (2) reported sexual behaviour, and (3) reporting recurring sickness or chronic illness, suggesting infected adolescents might be in a late stage of HIV infection. HIV-infected adolescent males were more likely to be maternal orphans (RR = 2.97, p<0.001) and both HIV-infected adolescent males and females were more likely to be maternal orphans or have an HIV-infected mother (male RR = 1.83, p<0.001; female RR = 16.6, p<0.001). None of 22 HIV-infected adolescent males and only three of 23 HIV-infected females reported ever having had sex. HIV-infected adolescents were 60% more likely to report illness than HIV-infected young adults. Taken together, all three hypotheses suggest that recent increases in adolescent HIV prevalence in eastern Zimbabwe are more likely attributable to long-term survival of mother-to-child transmission rather than increases in risky sexual behaviour. HIV prevalence in adolescents and young adults cannot be used as a surrogate for recent HIV incidence, and health systems should prepare for increasing numbers of long-term infected adolescents.
The Application of KL Transform to Remove Direct Wave in Ground Penetrating Radar Records

In ground penetrating radar (GPR) records, because of different kinds of noises, especially direct wave of ground surface, the real target is not seen clearly in the radar echo section, which makes the detection of real targets difficult. Considering that the direct waves among traces are not change greatly and have strong correlation, so it can be restrained by certain methods. KL transform often be used to pick up coherent noises and random noises in multi channel seismic records[U 4]. In this paper, KL transform is used to GPR data processing, which fully uses the different second-order statistic characteristics of their correlations on the direct wave, target signals in multi channel GPR data. The target signals are successfully separated form noises according to the corresponding relation of the eigenvalue's magnitude of the correlation matrix and correlation strength of each component in the multi channel GPR data.
Welfare and macroeconomic policy in small open economies.

This thesis focuses on the analysis of welfare and macroeconomic policy in small open economies. The international dimension of monetary and fiscal policy is examined in a micro-founded New-Keynesian framework. The small open economy is characterized as a limiting case of a two-country dynamic general equilibrium model featuring imperfect competition and nominal rigidities. Under this specification, Chapter 1 formulates a utility-based loss function for a small open economy completely integrated with the rest of the world. The study investigates the role of the exchange rate in monetary policy and derives the optimal monetary policy rule. In this Chapter, the dynamics of the trade balance are shown to be crucial in determining the appropriate exchange rate regime. Chapter 2 analyses optimal monetary policy under alternative asset market structures; more specifically, it compares and contrasts the cases of incomplete asset markets, financial autarky and complete asset markets. Furthermore, the performance of standard monetary policy rules is evaluated under these different scenarios. The results show that the degree of substitutability between domestic and foreign goods and the level of risk sharing are important factors in determining the performance of policy rules. Finally, Chapter 3 incorporates fiscal policy in the general framework. This Chapter introduces distortionary taxation into the model and characterizes the optimal fiscal policy. In addition, a general monetary and fiscal policy problem is formulated in the presence of nominal rigidities. The Chapter demonstrates that the stabilization problem in an open economy is more complex than in a closed economy, even under flexible prices. Apart from the incentive to avoid the distortions implied by taxation, in a small open economy there is also an incentive to strategically affect the real exchange rate. That is, proportional taxation creates a distortion in the economy, but also introduces a policy instrument that can influence the terms of trade and the overall level of production and consumption in a welfare-improving manner.
Adding Physical Scalability to BSIM4 by Meta-Modeling of Fitting Parameters

A new approach for improving physical scalability of existing compact models is proposed. The behavior of internal BSIM4 model parameters in response to a change in fabrication process condition is modeled by physics-based meta-model equations. By recovering missing links between the fitting parameters and device design parameters, the model parameter sets for variant transistors with different channel implant dose can be predicted starting from a parameter set of existing reference transistors.
Soft Computing Techniques in Intelligent Control: A Review

The reason behind this interest of Soft Computing Techniques for Intelligent Control from its advantages which are: it is suitable for linear and non- linear model, able to handle imprecise knowledge and ill- defined system, has the ability to convert the observations and the operator experience into mathematical model, combines both linguistic and crisp information in the same frame work and it is more robust. In the present paper we review the soft computing techniques for three intelligent control techniques Fuzzy logic, Neural Network and genetic algorithm. We highlight the main difficulties and summarize the more recent developments in their polices.
CBCT findings of periapical cemento-osseous dysplasia: A case report

Periapical cemento-osseous dysplasia (PCOD) is a subtype of cemento-osseous dysplasia that usually occurs in middle-aged black women. This report described a case of a 45-year-old Iranian woman who was diagnosed with PCOD on the basis of cone beam computed tomographic (CBCT) findings. CBCT enabled detailed visualization of the bone changes. This report described the special radiographic characteristics of PCOD, including discontinuity of the lingual cortex on the CBCT sectional and three-dimensional images.
Database Constraints Applied to Metabolic Pathway Reconstruction Tools

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes.
Max-margin stacking with group sparse regularization for classifier combination

Multiple classifier systems are shown to be effective in terms of accuracy for multiclass classification problems with the expense of increased complexity. Classifier combination studies deal with the methods of combining the outputs of base classifiers of an ensemble. Stacked generalization, or stacking, is shown to be a strong combination scheme among combination algorithms; and in this thesis, we improve stacking's performance further in terms of both accuracy and complexity. We investigate four main issues for this purpose. First, we show that margin maximizing combiners outperform the conventional least-squares estimation of the weights. Second we incorporate the idea of group sparsity into regularization to facilitate classifier selection. Third, we develop non-linear versions of class-conscious linear combination types by transforming datasets into binary classification datasets; then applying the kernel trick. And finally, we derive a new optimization algorithm based on the majorization-minimization framework for a particular linear combination type, which we show is the most preferable one.
Prediction of Yahoo! Music Sequences on User’s Musical Taste

Sequential pattern mining is an important data mining problem with broad applications. In this paper, we have implemented Apriori a candidate generation algorithm and SPAM (Sequential Pattern Mining) algorithm on Yahoo! Music KDD Cup 2011, which is the annual Data Mining and Knowledge Discovery competition organized by ACM Special Interest Group on Knowledge Discovery and Data Mining, the leading professional organization of data miners. Yahoo! Music has amassed billions of user ratings for musical pieces. When properly analyzed, the raw ratings encode information on how the popularity of songs, albums and artists vary over time and above all, which songs users would like to listen to. Such an analysis introduces new scientific challenges. From these discovered patterns, we can know what patterns or music sequences which are frequently heard and in what order they are recommended. Experimental results have shown that SPAM performs well for large datasets like Yahoo! Music dataset is due to the bitmap representation of the data for efficient counting. Keywords— Data mining, sequence data, time interval, Yahoo! Music, User music tastes.