Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost-utility analyses in orthopaedic surgery.

BACKGROUND The rising cost of health care has increased the need for the orthopaedic community to understand and apply economic evaluations. We critically reviewed the literature on orthopaedic cost-utility analysis to determine which subspecialty areas are represented, the cost-utility ratios that have been utilized, and the quality of the present literature. METHODS We searched the English-language medical literature published between 1976 and 2001 for orthopaedic-related cost-utility analyses in which outcomes were reported as cost per quality-adjusted life year. Two trained reviewers independently audited each article to abstract data on the methods and reporting practices used in the study as well as the cost-utility ratios derived by the analysis. RESULTS Our search yielded thirty-seven studies, in which 116 cost-utility ratios were presented. Eleven of the studies were investigations of treatment strategies in total joint arthroplasty. Study methods varied substantially, with only five studies (14%) including four key criteria recommended by the United States Panel on Cost-Effectiveness in Health and Medicine. According to a reader-assigned measure of study quality, cost-utility analyses in orthopaedics were of lower quality than those in other areas of medicine (p = 0.04). While the number of orthopaedic studies has increased in the last decade, the quality did not improve over time and did not differ according to subspecialty area or journal type. For the majority of the interventions that were studied, the cost-utility ratio was below the commonly used threshold of $50,000 per quality-adjusted life year for acceptable cost-effectiveness. CONCLUSIONS Because of limitations in methodology, the current body of literature on orthopaedic cost-utility analyses has a limited ability to guide policy, but it can be useful for setting priorities and guiding research. Future research with clear and transparent reporting is needed in all subspecialty areas of orthopaedic practice.
A Cognitive Linguistics Application for Second Language Pedagogy: The English Preposition Tutor

This study investigated the effects of teaching English prepositions using schematic diagrams inspired by cognitive linguistics in a computer-based tutorial system called the English Preposition Tutor. Training was designed based on the theoretical framework of the Competition Model and a cognitive linguistic analysis of prepositions. Sixty-four Cantonese-speaking intermediate learners of English were trained using a sentence– picture matching task. They received one of the three types of feedback: schematic diagram feedback, metalinguistic rule feedback, or correctness feedback. Only the schematic diagram feedback group was exposed to chaining between spatial senses and nonspatial senses. Results showed that instruction was effective in all three feedback groups, as measured by a cloze test and a translation test. In the translation test, the group receiving schematic diagram feedback outperformed the correctness feedback group. The effects of the three feedback conditions were not significantly different in the cloze test.
Application-Level Perceptual ARQ for H.264 Video Streaming over 802.11 Wireless LAN's

— A new ARQ algorithm for video streaming over 802.11 wireless networks is presented. The algorithm operates at the application level in order to exploit information about the perceptual and temporal importance of each packet. A priority value is associated to each packet to determine which one to retransmit at each retransmission opportunity. With respect to the standard 802.11 MAC-layer ARQ scheme, the proposed cross-layer technique delivers higher perceptual quality because it retransmits only the most perceptually important packets. Video streaming of H.264 test sequences has been simulated using ns in a realistic home network scenario, in which data ﬂows have been assigned to different 802.11e access categories according to their QoS requirements. Results show that the proposed method consistently outperforms the standard link-layer 802.11 retransmission scheme, delivering more than 1.5 dB of PSNR gain with a very limited impact on concurrent trafﬁc.
Efficient wireless power transfer using underground relay coils

Underground Wireless Sensor Network (UWSN) is a newly emerging technology that is capable of replacing existing traditional wired connections. Research has proved that magnetic induction based communication performs better than electromagnetic wave communication, especially in the dynamic underground environment. Currently, data recorders are being used to collect the data from underground sensors. Data recorders have a few disadvantages such as the inability to produce data on time and the difficulty to deploy them. Many problems related to data recorders can be solved using UWSN's magnetic induction technique. One of the main challenges associated with the UWSN is the lack of efficient power transfer to underground sensors as the distance between the transmitter and the receiver coils increases. This paper introduces a unique technique where relay resonators are used between the transmitter and the receiver coils to increase the efficiency for charging sensors which are present underground. Experimental results that prove the presence of relay coils for efficient power transfer are presented.
Identification of Fat Mass and Obesity Associated (FTO) Protein Expression in Cardiomyocytes: Regulation by Leptin and Its Contribution to Leptin-Induced Hypertrophy

The recently-identified fat mass and obesity-associated (FTO) protein is associated with various physiological functions including energy and body weight regulation. Ubiquitously expressed, FTO was identified in heart homogenates although its function is unknown. We studied whether FTO is specifically expressed within the cardiac myocyte and its potential role pertaining to the hypertrophic effect of the adipokine leptin. Most experiments were performed using cultured neonatal rat cardiomyocytes which showed nuclei-specific FTO expression. Leptin significantly increased FTO expression which was associated with myocyte hypertrophy although both events were abrogated by FTO knockdown with siRNA. Administration of a leptin receptor antibody to either normal or obese rats significant reduced myocardial FTO protein expression. Responses in cardiomyocytes were accompanied by JAK2/STAT3 activation whereas JAK2/STAT3 inhibition abolished these effects. Expression of the cut-like homeobox 1(CUX1) transcriptional factor was significantly increased by leptin although this was restricted to the cathepsin L-dependent, proteolytically-derived shorter p110CUX1 isoform whereas the longer p200CUX1 protein was not significantly affected. Cathepsin L expression and activity were both significantly increased by leptin whereas a cathepsin L peptide inhibitor or siRNA specific for CUX1 completely prevented the leptin-induced increase in FTO expression. The cathepsin L peptide inhibitor or siRNA-induced knockdown of either CUX1 or FTO abrogated the hypertrophic response to leptin. Two other pro-hypertrophic factors, endothelin-1 or angiotensin II had no effect on FTO expression and FTO knockdown did not alter the hypertrophic response to either agent. This study demonstrates leptin-induced FTO upregulation in cardiomyocytes via JAK2/STAT3- dependent CUX1 upregulation and suggests an FTO regulatory function of leptin. It also demonstrates for the first time a functional role of FTO in the cardiomyocyte.
Optimized Production of Vanillin from Green Vanilla Pods by Enzyme-Assisted Extraction Combined with Pre-Freezing and Thawing

Production of vanillin from natural green vanilla pods was carried out by enzyme-assisted extraction combined with pre-freezing and thawing. In the first step the green vanilla pods were pre-frozen and then thawed to destroy cellular compartmentation. In the second step pectinase from Aspergillus niger was used to hydrolyze the pectin between the glucovanillin substrate and β-glucosidase. Four main variables, including enzyme amount, reaction temperature, time and pH, which were of significance for the vanillin content were studied and a central composite design (CCD) based on the results of a single-factor tests was used. Response surface methodology based on CCD was employed to optimize the combination of enzyme amount, reaction temperature, time, and pH for maximum vanillin production. This resulted in the optimal condition in regards of the enzyme amount, reaction temperature, time, and pH at 84.2 mg, 49.5 °C, 7.1 h, and 4.2, respectively. Under the optimal condition, the experimental yield of vanillin was 4.63% ± 0.11% (dwb), which was in good agreement with the value predicted by the model. Compared to the traditional curing process (1.98%) and viscozyme extract (2.36%), the optimized method for the vanillin production significantly increased the yield by 133.85% and 96%, respectively.
A PA-noise cancellation technique for next generation highly integrated RF front-ends

The evolution of efficiency in RF power amplifiers has been very gradual. Even today, they dissipate a lot of energy as heat which reduces the efficiency and increases the noise level. This PA noise is generally attenuated down to the thermal noise floor of the receiver by using highly selective duplexers. High selectivity necessarily carries high insertion loss and hence the PA output power requirement further increases. In the next generation of radios, efforts are underway to substantially reduce duplexer size and selectivity specifications. This will lead to a significant increase in the PA noise falling into the receive band. This work looks at a new technique to mitigate this receive-band noise by means of a mixed signal architecture using analog and digital components. We demonstrate that the cancellation of this noise can be accomplished by the receiver, and SNR gains can be demonstrated using commercial RFICs.
Defect Bash - Literature Review

Defect bash is a co-located testing session performed by a group of people. We performed a systematic review of the academic and grey literature, i.e. informally published writings, of the defect bash. Altogether, we found 44 items (17 academic and 27 grey literature sources) that were identified useful for the review. Based on the review the definition of defect bash is presented, benefits and limitations of using defect bash are given. Finally, the process of doing defect bash is outlined. This review provides initial understanding on how defect bash could be useful in achieving the software quality and lays foundation for further academic studies of this topic.
A general-purpose sentence-level nonsense detector

I have constructed a sentence-level nonsense detector, with the goal of discriminating well-formed English sentences from the large volume of fragments, headlines, incoherent drivel, and meaningless snippets present in internet text. For many NLP tasks, the availability of large volumes of internet text is enormously helpful in combating the sparsity problem inherent in modeling language. However, the derived models can be easily polluted by ungrammatical text and spam, and automated means are necessary to filter this and provide high-quality training data. There is scarce precedent in the literature for a direct nonsense-detection system, but similar problems exist in the context of spam filtering and computational sentence completion. For spam filtering, recent research has focused on the problem of “Bayesian poisoning” (Hayes, 2007), whereby Naive Bayes-based filters are defeated by the inclusion of random words or text snippets that make the spam vocabulary appear similar to normal text. Solutions to this problem propose combining multiple sources of information, such as higher n-grams, in order to introduce greater context (Upasana and Chakravarty, 2010). Sentence completion is an example of a standard NLP task where the system must consider a variety of possible solutions and discriminate between “sensical” and “nonsensical” answers. While generally operating on a more restricted domain (e.g. SAT questions), the fundamental goal is similar enough that similar techniques can be applied. The Microsoft Research Sentence Completion Challenge (Zweig and Burges, 2011) highlights several approaches to this task, including neural networks and dependency parsing but also showing strong performance with lighterweight n-gram and distributional models (Zweig et al., 2012). I take a lightweight approach, using a mix of heuristics, lightweight token-based features, part-of-speech features, and language model scores as features to avoid computationally-intensive parsing or neural networks. I structure my system as a binary classification task on a heterogeneous feature space, consisting of to produce a final answer of “sentence” or “nonsense” for a given line of text. I implement this project in a mix of Java and Python, using Java to interface with Stanford CoreNLP for feature extraction, and Python (with the excellent pandas and scikit-learn libraries) for data management, classifier implementation, and analysis.
Automatic Analysis of Radiographic Images for Non Destructive Test Applications

Radiographic inspection is a reliable non-destructive test, widely used for integrity evaluation of structures and equipments. Nowadays, high quality images with very accurate resolutions have been supported by modern digital radiographic systems. However, the image analysis for internal defect detection and geometric characterization is still a not totally automated task. The main reason is that image analysis is usually a very complex task, which involves heuristic decisions based on experiences, as object detection and recognition. For that reason, a new automatic radiographic image analysis system was developed in order to identify important components or component parts, which must be inspected separately, as weld joints, pipe walls, pipe wall thicknesses, valves and mechanical parts. The developed methodology involves the use of a genetic algorithm search to find desirable patterns on the image. Image indexing procedures are used for a final verification process. As a result, the system offers quick and correct answers and also flexibility to be applied in others applications.
Building a semantic role labelling system for Vietnamese

Semantic role labelling (SRL) is a task in natural language processing which detects and classifies the semantic arguments associated with the predicates of a sentence. It is an important step towards understanding the meaning of a natural language. There exists SRL systems for well-studied languages like English, Chinese or Japanese but there is not any such system for the Vietnamese language. In this paper, we present the first SRL system for Vietnamese with encouraging accuracy. We first demonstrate that a simple application of SRL techniques developed for English could not give a good accuracy for Vietnamese. We then introduce a new algorithm for extracting candidate syntactic constituents, which is much more accurate than the common node-mapping algorithm usually used in the identification step. Finally, in the classification step, in addition to the common linguistic features, we propose novel and useful features for use in SRL. Our SRL system achieves an F1 score of 73.53% on the Vietnamese PropBank corpus. This system, including software and corpus, is available as an open source project and we believe that it is a good baseline for the development of future Vietnamese SRL systems.
Reconstruction of SAXS Profiles from Protein Structures

Small angle X-ray scattering (SAXS) is used for low resolution structural characterization of proteins often in combination with other experimental techniques. After briefly reviewing the theory of SAXS we discuss computational methods based on 1) the Debye equation and 2) Spherical Harmonics to compute intensity profiles from a particular macromolecular structure. Further, we review how these formulas are parameterized for solvent density and hydration shell adjustment. Finally we introduce our solution to compute SAXS profiles utilizing GPU acceleration.
Boltzmann Machine and Mean-Field Approximation for Structured Sparse Decompositions

Taking advantage of the structures inherent in many sparse decompositions constitutes a promising research axis. In this paper, we address this problem from a Bayesian point of view. We exploit a Boltzmann machine, allowing to take a large variety of structures into account, and focus on the resolution of a marginalized maximum a posteriori problem. To solve this problem, we resort to a mean-field approximation and the “variational Bayes expectation-maximization” algorithm. This approach results in a soft procedure making no hard decision on the support or the values of the sparse representation. We show that this characteristic leads to an improvement of the performance over state-of-the-art algorithms.
Design of micromachined resonators for fish identification

The ID tag presented here was designed to give a tag of small size that could be produced at a low prize, and that could be read remotely in live fish, even in seawater. The last condition precludes use of electromagnetic waves for interrogation of the tags, and acoustic interrogation is then a clear alternative. The solution presented is a passive tag with a set of acoustic resonances that may be detected acoustically. The tag operates in the 200 to 400 kHz range. The identity of the tag is given by a unique combination of resonances in this frequency range. For the tags presented here there are five resonances per tag. If five or more resonances are chosen from a predetermined set of say 17 resonance frequencies, a total number of at least 3000 to 4000 different tags are available. This is adequate for classification of fish at the batch level in fish farms, or of local wild fish tribes. The resonators on a tag consists of a thin, nominally 500 nm thick silicon nitride membrane suspended over separate evacuated cavities, made by bulk silicon micromachining. The resonators were designed to have Q-factors in the range 27 to 35 with viscous losses in the water neglected. The resonators have been measured in water and in dead or live anesthetized fish from distances up to 30 cm. Sharp resonances in fair accordance with the tag design were achieved. Some alterations of the tag response with change of the angular orientation of the tag relative to the ultrasound beam are seen. This is also theoretically expected.
Interpolation of Orientation : Axiomatic Approach and Applications

This paper develops an axiomatic approach of vector field orientation, seen as angles on the unit circle. Two operators will be singled out: the curvature operator, appearing in total variation minimisation for image restoration and inpainting/disocclusion, as a direct solution and the Absolutely Minimizing Lipschitz Extension (AMLE), already known as a robust and coherent scalar image interpolation technique, if we relax slightly the axioms. Numerical results are presented on real and artificial images using a multiresolution finite differences scheme. The computed field is shown to be able to extend geometrical information from images in accordance with the human perception of edges. First applications are shown, including a Fast Marching contour extraction algorithm and a LIC-based smoothing method.
Analysis and improvement of a toroidal wound permanent magnet flux-switching machine

A 6/8 flux-switching machine with large air-gap and toroidal winding design is presented. The fractional-slot winding of C-core flux-switching machine is wound around the stator yoke instead of the stator pole in this machine design. On the basis of the C-core flux-switching machine several modifications of the stator core shape and of PM arrangement are evaluated by means of 2D Finite Element Analysis. A significant enhancement of the torque capability of the toroidal winding design can be achieved by some minor changes of the stator core shape. Additionally, the application of Halbach shaped magnets leads to even more torque and a reduction of the leakage flux at the stator outer diameter.
Analytical electron microscopy study of submicroscopic metal particles in glassy constituents of lunar breccias 15015 and 60095

3 Printed in the 1 nited States of Ante ica Analytical electron microscopy study of submicroscopic metal particles in glassy constituents of lunar breccias 15015 and 60095 Abstract—Transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) X-ray analytic techniques were employed to characterize the structure and composition of submicroscopic metal inclusions in lunar glasses 15015 and 60095. Inclusions (l000 A) in 15015 glass Contain microtwins and grade from a cubic to a subspherical morphology with increasing size. These inclusions are a-Fe metal and contain from I to 3.8 wt% Ni. This metal probably formed from reduction of Fe2 during impact induced melting of soils previously saturated with solar wind-implanted components. The gradation in the metal grain size from 40 A to 0.4 jim suggests that some metal grains may have grown while the rock was in a hot debris cloud. In 60095 glass, coexistence of two different types of submicroscopic metal was observed. The Ni content of 200 A spherical inclusions ranged from 17 to 57 wt'. The 500 A cube crystals were
The fear of being laughed at (gelotophobia) and personality

Gelotophobia is defined as the fear of being laughed at. First empirical studies of the concept (Ruch & Proyer, in press, 2008) show that it is a valid new construct. Furthermore, it can be assessed reliably by means of a self-report instrument (GELPH ). Gelotophobes interpret the laughter and smiling of other persons not in a positive way but as a weapon used to put them down. They are persistently concerned about being laughed at by others and are convinced of being ridiculous. While gelotophobia was first observed in clinical case studies, it is now considered as an individual difference phenomenon within the range of normality. We investigated whether it can be located in a comprehensive model of personality, namely, the Five Factor Model (FFM). A study investigating N = 119 participants showed that gelotophobia is high among emotionally instable introverts. Furthermore, there were lower but significant negative correlations with friendliness, openness and social desirability. However, only emotional stability and extraversion entered a stepwise regression analysis. Overall, the results fit the predictions very well and led to a revised version of a putative model of origins and consequences of gelotophobia. The concept of gelotophobia is of relevance in Romania, too. Results from the first data collection (N = 200) with a Romanian version of the short form of the self-report measure (GELOPH ) showed that approx. 13% of the participants exceeded a cut-off score indicating at least a slight expression of the fear of being laughed at. The Romanian version showed good psychometric properties in terms of high reliability and—as in the German and English form—a unidimensional factor solution. Based on these first promising experiences with this new concept, further studies on the fear of being laughed at in Romania are encouraged.
Chinese Unknown Words Extraction Based on Word-Level Characteristics

The automatic recognition of unknown words is an important problem in Chinese information processing. Based on the characteristics of words, this paper proposes a method to recognize new words using high frequent strings. Firstly, the high frequent strings from each single document are extracted as candidate strings. Then the strings that cannot satisfy the characteristics of word’s distribution and word’s independently usage are removed. Finally, segment the entire corpus with these candidate strings, and count the word-frequency for further filtering. Experimental results show that, on the documents about basketball downloaded from Zaobao Newspaper, this method achieves an F-score of 79.39%.