Miracle-dz/UniFAR-FTUs huggingface dataset:
Femoral Nerve Block versus Adductor Canal Block for Analgesia after Total Knee Arthroplasty

Inadequate pain management after total knee arthroplasty (TKA) impedes recovery, increases the risk of postoperative complications, and results in patient dissatisfaction. Although the preemptive use of multimodal measures is currently considered the principle of pain management after TKA, no gold standard pain management protocol has been established. Peripheral nerve blocks have been used as part of a contemporary multimodal approach to pain control after TKA. Femoral nerve block (FNB) has excellent postoperative analgesia and is now a commonly used analgesic modality for TKA pain control. However, FNB leads to quadriceps muscle weakness, which impairs early mobilization and increases the risk of postoperative falls. In this context, emerging evidence suggests that adductor canal block (ACB) facilitates postoperative rehabilitation compared with FNB because it primarily provides a sensory nerve block with sparing of quadriceps strength. However, whether ACB is more appropriate for contemporary pain management after TKA remains controversial. The objective of this study was to review and summarize recent studies regarding practical issues for ACB and comparisons of analgesic efficacy and functional recovery between ACB and FNB in patients who have undergone TKA.
On the numerical solution of some semilinear elliptic problems.

We discuss a general framework for the numerical solution of a family of semilinear elliptic problems whose leading differential operator is the Laplacian. A problem is ﬁrst transformed to one on a standard domain via a conformal mapping. The boundary value problem on the standard domain is then reduced to an equivalent integral operator equation. We employ the Galerkin method to solve the integral operator equation, using the eigenfunctions of the Laplacian on the standard domain. An error analysis of the method is given.
Mapping of the normal human corneal sub-Basal nerve plexus by in vivo laser scanning confocal microscopy.

PURPOSE To produce a two-dimensional reconstruction map of the living human sub-basal corneal nerve plexus using in vivo confocal microscopy. METHODS Laser scanning in vivo confocal microscopy was performed on three normal eyes of three healthy human subjects. Subjects were asked to fixate on targets arranged in a grid to enable examination of the cornea in a wide range of positions. Using the section mode, a mean of 573 +/- 176 images of the sub-basal plexus were obtained for each subject. The data were arranged and images were mapped for each subject into confluent montages. RESULTS Mean dimensions of the corneal areas mapped were 4.95 +/- 0.53 mm horizontally and 5.14 +/- 0.53 mm vertically. In all subjects, the sub-basal nerve plexus appeared to radiate toward a whorl-like complex centered 1 to 2 mm inferior to the corneal apex. Outside this area, the nerve fiber bundles in the remainder of the cornea were arranged in a radiating pattern. Mean nerve density was significantly higher in the inferocentral whorl region (25,249 +/- 616 microm/mm2) compared with the central cornea (21,668 +/- 1411 microm/mm2) (Mann-Whitney U test; P = 0.05). CONCLUSIONS This is the first study to elucidate the overall distribution of sub-basal nerves in the healthy, live central to mid-peripheral human cornea by laser scanning in vivo confocal microscopy. The whorl pattern of the sub-basal nerves is similar to that seen in the epithelium in corneal verticillata and may lend support to the theory that epithelial cells and nerves migrate centripetally in tandem.
The analysis of attenuation of electromagnetic field by woven structures based on hybrid fancy yarns

In this paper we present an analysis of attenuation by woven structures produced from hybrid yarns. The woven structures have been designed for protection against electromagnetic fields (EMFs). The idea of creating hybrid yarns that may intensively protect against EMFs comes from the analogy discovered between the construction of a fancy bunch yarn and an electric system composed of electro-conductive solenoids on a ferromagnetic material connected in series and then in parallel. The application of hybrid fancy yarn as a weft made of copper as a special effect thread insulated with lacquer and wrapped around a core of steel thread in a textile fabric will enable EMF attenuation as a result of inducing an electromotive force in the special effect thread and magnetizing the core thread. In this way, the designed electric circuit (textile fabric) in the variable primary EMF will be the source of a secondary EMF directed opposite to the direction of the primary field. The fabricated woven structures present electrical and magnetic components of EMF attenuation in some narrow ranges.
An Approach for Finding Attribute Dependencies in an Anonymized Data

The modern digital world leveraged both public and private organization in collecting personal information of the individuals. Researchers collect non aggregate data for mining or survey purpose from these organizations. However, this data contains individual’s sensitive information like credit card number, salary, disease etc.,. An adversary with hypothetical nature may have access to various external resources like voters/medical list for mapping the individuals so that he/she can identify the individuals potentially. Privacy Preserving models like k-anonymity, l-diversity have been proposed to thwart the privacy attacks. These models use anonymization methods like generalization, suppression and make the data privacy-safe. However, due to the presence of associations between attributes in the anonymized dataset, individual information is being compromised. This paper proposes a novel approach for detecting the associations.
Federal Reserve Bank of Minneapolis Research Department Staff Report 237 Mixture of Normals Probit Models

This paper generalizes the normal probit model of dichotomous choice by introducing mixtures of normals distributions for the disturbance term. By mixing on both the mean and variance parameters and by increasing the number of distributions in the mixture these models effectively remove the normality assumption and are much closer to semiparametric models. When a Bayesian approach is taken, there is an exact finite-sample distribution theory for the choice probability conditional on the covariates. The paper uses artificial data to show how posterior odds ratios can discriminate between normal and nonnormal distributions in probit models. The method is also applied to female labor force participation decisions in a sample with 1,555 observations from the PSID. In this application, Bayes factors strongly favor mixture of normals probit models over the conventional probit model, and the most favored models have mixtures of four normal distributions for the disturbance term. *Geweke wishes to acknowledge financial support from National Science Foundation grant SBR-9514865, Keane from NSF grant SBR-9511186. We thank the editor, Robin Carter, Daniel Houser, and seminar participants at the University of Western Ontario for comments on an earlier draft, but blame only each other for remaining errors. We thank John Landon-Lane and Lance Schibilla for research assistance. The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System.
Analysis of Multi-Crack Growth in Asphalt Pavement Based on Extended Finite Element Method

An extended finite element method (XFEM) for multiple crack growth in asphalt pavement is described. A discontinuous function and the two-dimensional asymptotic crack-tip displacement fields are added to the finite element approximation to account for the crack using the notion of partition of unity. This enables the domain to be modeled by finite element with no explicit meshing of the crack surfaces. Computational geometry issues associated with the representation of the crack and the enrichment of the finite element approximation are discussed. Finally, the propagation path of the cracks in asphalt pavement under different load conditions is presented.
Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing in Wireless Sensor Networks

There are many challenges when designing and deploying wireless sensor networks (WSNs). One of the key challenges is how to make full use of the limited energy to prolong the lifetime of the network, because energy is a valuable resource in WSNs. The status of energy consumption should be continuously monitored after network deployment. In this paper, we propose coverage and connectivity aware neural network based energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with coverage and connectivity aware constraints. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage and connectivity aware routing with data transmission. The proposed scheme is compared with existing schemes with respect to the parameters such as number of alive nodes, packet delivery fraction, and node residual energy. The simulation results show that the proposed scheme can be used in wide area of applications in WSNs.
A preliminary study into the use of an evolutionary algorithm hyper-heuristic to solve the nurse rostering problem

This paper reports on an initial attempt to solve the nurse rostering problem using an evolutionary algorithm selection perturbative hyper-heuristic. The main aim of this study is to get a feel for the potential of such a hyper-heuristic in solving the nurse rostering problem. This will be used to direct future extensions of this work. This study identifies low-level perturbative heuristics for this domain as well as a representation, initial population generation method, evaluation and selection methods, and genetic operator for the evolutionary algorithm hyper-heuristic. The approach was tested on six problems from the first international nurse rostering competition. The performance of the hyper-heuristic was found to be comparable to that of other methods applied to the same problems. The study has shown the potential of this approach and also identified future extensions of this work.
Traveling with children.

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.
Latin American Labor Markets in the 1990s: Deciphering the Decade

This paper analyzes the recent performance of Latin American labor markets. We find that unemployment rates are on the rise in most countries and sub-regions of Latin America. The rise in unemployment over the 1990s is not driven by a higher proportion of women, adults or urban workers in the labor force, nor can it be attributed to an increasing demand for skilled workers. In a few countries, increasing unemployment is caused by falling employment rates. In the rest, unemployment rates can be explained by a large rise in participation that has not been fully absorbed by increases in employment. We also find that a large and in many countries growing proportion of workers are employed at jobs that pay very low wages. Finally, returns to higher education are increasing while returns to secondary education are declining in almost all countries.
Perinatal asphyxia.

INTRODUCTION In resource-rich countries, the incidence of severe perinatal asphyxia (causing death or severe neurological impairment) is about 1/1000 live births. In resource-poor countries, perinatal asphyxia is probably much more common. Data from hospital-based studies in such settings suggest an incidence of 5-10/1000 live births. METHODS AND OUTCOMES We conducted a systematic review and aimed to answer the following clinical question: What are the effects of interventions in term or near-term newborns with perinatal asphyxia? We searched: Medline, Embase, The Cochrane Library and other important databases up to June 2006 (Clinical Evidence reviews are updated periodically, please check our website for the most up-to-date version of this review). We included harms alerts from relevant organisations such as the US Food and Drug Administration (FDA) and the UK Medicines and Healthcare products Regulatory Agency (MHRA). RESULTS We found 25 systematic reviews, RCTs or observational studies that met our inclusion criteria. We performed a GRADE evaluation of the quality of evidence for interventions. CONCLUSIONS In this systematic review we present information relating to the effectiveness and safety of the following interventions: anticonvulsants (prophylactic), antioxidants, calcium channel blockers, corticosteroids, fluid restriction, head and/or whole body hypothermia, hyperbaric oxygen treatment, hyperventilation, Iinotrope support, magnesium sulphate, mannitol, opiate antagonists, and resuscitation (in air versus higher concentrations of oxygen).
Purification and N-terminal analysis of urease from Helicobacter pylori

Urease of Helicobacter pylori (formerly Campylobacter pylori) is believed to represent a critical virulence determinant for this species. Ammonia generated by hydrolysis of urea may protect the acid-sensitive bacterium as it colonizes human gastric mucosa. An H. pylori strain, cultured from a gastric biopsy of a patient with complaints of abdominal pain and a history of peptic ulcer disease, was isolated on selective medium and cultured in Mueller-Hinton broth supplemented with 4% fetal calf serum. Whole cells were ruptured by French pressure cell lysis, and soluble protein was chromatographed on DEAE-Sepharose, phenyl-Sepharose, Mono-Q, and Superose 6 resins. Purified urease represented 6% of the soluble protein of crude extract, was estimated to have a native molecular size of 550 kilodaltons (kDa), and was composed of two distinct subunits of apparent molecular sizes of 66 and 29.5 kDa. On the basis of subunit size, a 1:1 subunit ratio as measured by scanning densitometry of Coomassie blue-stained sodium dodecyl sulfate-polyacrylamide gels, and estimated native molecular size, the data are consistent with a stoichiometry of (29.5 kDa-66 kDa)6 for the structure of the native enzyme. Km for urea was estimated at 0.2 mM. By N-terminal analysis, the 29.5-kDa subunit of H. pylori urease was found to share significant amino acid sequence similarity with the smallest of three subunits of the Proteus mirabilis and Morganella morganii ureases, as well as to the amino terminus of the unique jack bean subunit. The 66-kDa subunit also shared up to 80% similarity with the largest of three subunits of P. mirabilis, M. morganii, and Klebsiella aerogenes ureases and to internal sequences (amino acids 271 to 285) of the jack bean urease subunit. Thus, the amino acid sequence is conserved among ureases with one, two, and three distinct subunits, suggesting a common ancestral urease gene. Also, urease subunits of M. morganii and jack bean were specifically recognized by antisera raised against the 66-kDa subunit of H. pylori urease, demonstrating that at least some antigenic determinants were conserved among ureases from different species.
Performance Analysis of Advanced Metering Infrastructure with Multihop Hybrid Communication System

Advanced Metering Infrastructure (AMI) has been attracting considerable attention for effective use of energy. In the system, smart meters installed in houses, factories, and other buildings automatically and periodically measure power consumption data and transmit them to electric power companies, which enables real-time monitoring and power demand control. If the transmission bandwidth allocated to AMI is more than the necessary capacity for periodical collection of power consumption data, other types of communication such as gas and water consumption data collection, demand response for electricity, and inquiries to electric power companies can be conducted by using surplus bandwidth. Multihop wireless networks will be adopted as an FAN (Field Area Network) for communication via smart meters, and it is important to discuss the optimal network topology in order to improve communication performance. In this paper, we propose a mathematical model of AMI taking multihop topology into consideration, and investigate the impact of the configuration of network topology on performance measures.
Efficient and Secure Implementation of Elliptic Curve Scalar Multiplication Against Power Analysis Attacks

The Elliptic Curve Cryptosystems(ECC) are proved to be the cryptosystem of future generation because of its smaller key size and uncompromised security. It is well suited for applications running in resource-restricted devices such as smart cards. At present, there is no efficient algorithm or known sub-exponential algorithm to break ECC theoretically. However, a hardware implementation of ECC leaks secret key information due to power analysis attacks particularly differential power analysis attack(DPA). These attacks break the system with far less effort when compared to all other attacks based on algebraic weaknesses of the algorithms. There are many solutions to overcome the power analysis attack, but all the available solutions have their own advantages and disadvantages by compromising either its security or performance. In this paper, we present a secure and efficient algorithm to solve the elliptic curve scalar multiplication(ECSM) using initial points randomization and by delaying the point addition operation. The implementation results and performance analysis shows that the proposed algorithm is efficient and secure against power analysis attacks.
Study of Emotion Detection in Tunes Using Machine Learning

The main objective of this paper is to study possible emotions generation in listener’s mind due to listening of tunes. Such emotions can be detected automatically using the audio features such as zero crossing, compactness, spectral centroid, spectral Flux, spectrum Roll off and Beat Histogram etc. We have explored machine Learning algorithms such as SVM (support vector machine) and ANN (Artificial Neural Network) for classification. The proposed technique of emotion detection is done in two parts as feature extraction and classification of tunes using machine learning techniques. We have studied different tools for extracting the features of tunes. These extracted features can be further given to the classifiers to categorize the emotions.
Genetic Risk Prediction and a 2-Stage Risk Screening Strategy for Coronary Heart Disease

Objective—Genome-wide association studies have identified several genetic variants associated with coronary heart disease (CHD). The aim of this study was to evaluate the genetic risk discrimination and reclassification and apply the results for a 2-stage population risk screening strategy for CHD. Approach and Results—We genotyped 28 genetic variants in 24 124 participants in 4 Finnish population-based, prospective cohorts (recruitment years 1992–2002). We constructed a multilocus genetic risk score and evaluated its association with incident cardiovascular disease events. During the median follow-up time of 12 years (interquartile range 8.75–15.25 years), we observed 1093 CHD, 1552 cardiovascular disease, and 731 acute coronary syndrome events. Adding genetic information to conventional risk factors and family history improved risk discrimination of CHD (C-index 0.856 versus 0.851; P=0.0002) and other end points (cardiovascular disease: C-index 0.840 versus 0.837, P=0.0004; acute coronary syndrome: C-index 0.859 versus 0.855, P=0.001). In a standard population of 100 000 individuals, additional genetic screening of subjects at intermediate risk for CHD would reclassify 2144 subjects (12%) into high-risk category. Statin allocation for these subjects is estimated to prevent 135 CHD cases over 14 years. Similar results were obtained by external validation, where the effects were estimated from a training data set and applied for a test data set. Conclusions—Genetic risk score improves risk prediction of CHD and helps to identify individuals at high risk for the first CHD event. Genetic screening for individuals at intermediate cardiovascular risk could help to prevent future cases through better targeting of statins.
Multiple Radar Time Series Similarity Matching Algorithm

The paper provide a new algorithm for multiple radar time series similarity matcing.The main difficulty for multiple radar time series similarity matching lies in huge data amount and too many factors will be considered.So,the previous algorithm on this area has existance of false dismissals and false alarms.The traditional methods Mostly based on Euclidean Distance and pay little attention to the influence of amplitude change, frequency persistence and internal structure of sequence.The matching method held out in the paper firstly devided the radar spatial data into certatin segments based on its' spatial position . Then,the mutidemession time series in each data segments can maped to a time interval with certain length. The step can transform multiple radar time series to three-demession time series and greatly reduce the complexity of algotithm. In order to improve the accuracy of the method, some weighted factor with consideration of amplitude change,frequency persistence and internal structure of radar sequence is carried out to define an accuracy distance measure for two sequences.With the predefined distance measure,a multiple radar time series similarity measure modal and a algrothm for similarity matching is discussed in detail. At the end of the paper,the method in the paper is applied in actually radar observed data and proved its better performance. Keywords-meteorological radar;multiple time series;distance measure;similarity modal;SBMDTSM algorithm
Ephemeral Context to Support Robust and Diverse Music Recommendations

While prior work on context-based music recommendation focused on fixed set of contexts (e.g. walking, driving, jogging), we propose to use multiple sensors and external data sources to describe momentary (ephemeral) context in a rich way with a very large number of possible states (e.g. jogging fast along in downtown of Sydney under a heavy rain at night being tired and angry). With our approach, we address the problems which current approaches face: 1) a limited ability to infer context from missing or faulty sensor data; 2) an inability to use contextual information to support novel content discovery.
Investigating scaling of an abstracted LCS utilising ternary and s-expression alphabets

Utilising the expressive power of S-Expressions in Learning Classifier Systems often prohibitively increases the search space due to increased flexibility over the ternary alphabet. Selection of appropriate S-Expressions functions through domain knowledge improves scaling, as expected. Considering the Cognitive Systems roots, abstraction was included in LCS - episodic learning generalises prior to abstraction for semantic learning. This novel method is shown to provide compact results (135-MUX) and exhibits potential for scaling well (1034-MUX).