Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Fibre-based Force Sensing Needle Driver for Minimally Invasive Surgery

Minimally Invasive Surgery (MIS) has been limited from its inception by insufficient haptic feedback to surgeons. The loss of haptic information threatens patient’s safety and results in longer operation times. To address this problem, various force sensing systems have been developed to provide information about tool– tissue interaction forces. However, the provided results for axial and grasping forces have been inaccurate in most of these studies due to considerable amount of error and uncertainty in their force acquisition method. Furthermore, sterilizability of the sensorized instruments plays a pivotal role in accurate measurement of forces inside a patient’s body. Nonetheless, none of the force sensing devices described in the literature can be fully cleaned or sterilized for use inside a patient’s body during a surgery. The objective of this thesis was to develop a sterilizable needle-driver type grasper using fibre Bragg gratings. In order to measure more accurate and reliable tool–tissue interaction forces, optical force sensors were integrated in the grasper jaw to measure axial and grasping forces directly at their exertion point on the tool tip. Two sets of sensor prototypes were developed to prove the feasibility of the proposed concept. Implementation of this concept into a needle-driver instrument resulted in the final proposed model of the sensorized laparoscopic instrument. Fibre Bragg gratings were used for measuring forces due to their many advantages for this application such as small size, sterilizability and high sensitivity. Visual force feedback was provided for users based on the acquired real-time force data. Improvement and consideration points related to the current work were identified and potential areas to continue this project in the future are discussed.
One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM

This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.
Analysis of entropy generation for nanofluid flow over a stretching sheet with an inclined magnetic field and uniform heat source/sink

This investigation presents an analytical study of entropy generation of the nanofluid over a stretching sheet with inclined magnetic field and uniform heat generation/absorption. Similarity variables are used to convert the governing PDEs into ODEs. Then it solved analytically by hyper geometric function. The effect of physical parameters such as nanosolid volume fraction, aligned angle, magnetic, slip and suction parameters are discussed for both temperature and velocity profiles. Also, the effects of the same parameters and uniform generation/absorption and dimensionless group parameters on entropy generation are discussed for Ag nanofluid.
Relationship of Social Determinants of Health with the Three-year Survival Rate of Breast Cancer

Background: Social determinants of health are among the key factors affecting the pathogenesis of diseases. Considering the increasingly high prevalence of breast cancer and the association of social determinants of health with its occurrence, related morbidity and mortality and survival rate, this study sought to assess the relationship of three-year survival rate of breast cancer with social determinants of health. Materials and Methods: This cohort study was conducted on males and females presenting to the Cancer Research Center of Shohada-E-Tajrish Hospital from 2006 to 2010 with definite diagnosis of breast cancer. Data were collected via phone interviews. Kaplan-Meier and Cox regression was fitted using SPSS (version 18) and PH assumption was tested by STATA (version 11) software. Results: The study was performed on 797 breast cancer patients, aged 25-93 years with mean age of 54.66 (SD=11.86) years. After 3 years from diagnosing cancer 700 (87.8%) patients were alive and 97 (12.2%) patients were dead. Using log rank test, there was relationship between 3-year survivals with age, education, childhood residence, sibling, treatment type, and district were significant (p<0.05). Using Cox PH regression, 3-year survival was related to age, level of education, municipal district of residence and childhood condition (p<0.05). Conclusion: Social determinants of health such as childhood condition, city region residency, level of education and age affect the three-year survival rate of breast cancer. Future studies must focus on the effect of childhood social class on the survival rates of cancers, which have been paid less attention to.
Crystal Structure of a Hidden Protein, YcaC, a Putative Cysteine Hydrolase from Pseudomonas aeruginosa, with and without an Acrylamide Adduct

As part of the ongoing effort to functionally and structurally characterize virulence factors in the opportunistic pathogen Pseudomonas aeruginosa, we determined the crystal structure of YcaC co-purified with the target protein at resolutions of 2.34 and 2.56 Å without a priori knowledge of the protein identity or experimental phases. The three-dimensional structure of YcaC adopts a well-known cysteine hydrolase fold with the putative active site residues conserved. The active site cysteine is covalently bound to propionamide in one crystal form, whereas the second form contains an S-mercaptocysteine. The precise biological function of YcaC is unknown; however, related prokaryotic proteins have functions in antibacterial resistance, siderophore production and NADH biosynthesis. Here, we show that YcaC is exceptionally well conserved across both bacterial and fungal species despite being non-ubiquitous. This suggests that whilst YcaC may not be part of an integral pathway, the function could confer a significant evolutionary advantage to microbial life.
Mining Revision Log of Language Learning SNS for Automated Japanese Error Correction of Second Language Learners

We present an attempt to extract a largescale Japanese learners’ corpus from the revision log of a language learning SNS. This corpus is easy to obtain in largescale, covers a wide variety of topics and styles, and can be a great source of knowledge for both language learners and instructors. We also demonstrate that the extracted learners’ corpus of Japanese as a second language can be used as training data for learners’ error correction using an SMT approach. We evaluate different granularities of tokenization to alleviate the problem of word segmentation errors caused by erroneous input from language learners. Experimental results show that the character-wise model outperforms the word-wise model.
Experimental investigations of ice slurry flow based on monoethylene glycol at high ice fractions

Ice slurry has so many advantages as phase-change slurries due to the high latent heat of its particles. Ice slurry is one of a competitive alternative to conventional secondary refrigeration systems that have been successfully employed in many applications. In this research, ice slurry flow with monoethylene glycol (MEG) at high ice fractions was investigated. The experiment was carried out by measuring shear stress and flow rate as variable to calculate power law index. The experiment was performed in 14 mm, 21 mm, and 24 mm circular tubes, respectively. The Initial concentration of 5 wt%, 7 wt%, and 10 wt% of MEG was investigated. Ice fraction was maintained above 30% in a storage tank. Experimental results of the Darcy friction factor were compared to Poiseuille and Blasius model. The ice slurry flows exhibited a shear-thickening flow (n>1) for all initial concentration in the range 1.07 - 1.58. The Greater initial concentration of MEG tends to give smaller friction compared with another ice slurry flow with smaller initial concentration.
Joint Variant and De Novo Mutation Identification on Pedigrees from High-Throughput Sequencing Data

The analysis of whole-genome or exome sequencing data from trios and pedigrees has being successfully applied to the identification of disease-causing mutations. However, most methods used to identify and genotype genetic variants from next-generation sequencing data ignore the relationships between samples, resulting in significant Mendelian errors, false positives and negatives. Here we present a Bayesian network framework that jointly analyses data from all members of a pedigree simultaneously using Mendelian segregation priors, yet providing the ability to detect de novo mutations in offspring, and is scalable to large pedigrees. We evaluated our method by simulations and analysis of WGS data from a 17 individual, 3-generation CEPH pedigree sequenced to 50X average depth. Compared to singleton calling, our family caller produced more high quality variants and eliminated spurious calls as judged by common quality metrics such as Ti/Tv, Het/Hom ratios, and dbSNP/SNP array data concordance. We developed a ground truth dataset to further evaluate our calls by identifying recombination cross-overs in the pedigree and testing variants for consistency with the inferred phasing, and we show that our method significantly outperforms singleton and population variant calling in pedigrees. We identify all previously validated de novo mutations in NA12878, concurrent with a 7X precision improvement. Our results show that our method is scalable to large genomics and human disease studies and allows cost optimization by rational sequencing capacity distribution.
A Comparative Study of Link Analysis Algorithms

With the advancement of technology, the internet has become a growing place with huge amounts of information. Retrieving information is not a hard task, but ranking the retrieved information according to the relevancy is a very complex task. Search engines provides users to query and assess results instantly. Behind these search engines there runs vast amounts of algorithms to give out the best results. One such algorithm type is the link analysis algorithms which is used to rank pages and content. This paper reviews and compares the most commonly used link analysis algorithms.
The paediatric airway: Basic principles and current developments

Perioperative airway problems frequently result in significant morbidity and mortality in children. Therefore, proficiency in airway management is one of the most important key elements in the safe conduct of paediatric anaesthesia. This review includes important anatomical and physiological aspects of the paediatric airway, challenges encountered, and their management with commonly available resources. The importance of early recognition and treatment of anatomical or functional airway obstruction using locally adapted algorithms is highlighted. Children deemed at risk of aspiration require a controlled rapid sequence induction with sufficiently deep anaesthesia, confirmed complete muscle paralysis and intermittent ventilation prior to tracheal intubation. The benefits of a supraglottic airway device and a cuffed tracheal tube in paediatric airway management are discussed. The primary goal of mastering the paediatric airway is to ensure oxygenation and ventilation. This requires intricate knowledge, regular practice and experience.
Triple Degree-of-freedom Piezoelectric Ultrasonic Micromotor via Flexural-axial Coupled Vibration

—Actuators remain a limiting factor in robotics, especially in microrobotics where the power density of actua-tors is a problem. A 3 × 3 × 8.7 mm 3-axis piezoelectric ultra-sonic micromotor system is described here in an effort to help solve this problem. Formed from 4 bulk lead zirconate titanate (PZT) thickness-polarized elements placed around the periphery of a rectangular rod, the stator is designed to combine axial and flexural vibrations in a way that permits rotation of a hardened steel ball as a rotor about an arbitrary axis. A simple prototype of the micromotor was found to produce at least a rotation speed of 10.4 rad/s with 4 μN-m torque about all 3 orthogonal directions at an excitation frequency of about 221 kHz, demonstrating the feasibility of a 3 degree-of-freedom millimeter-scale piezoelectric motor.
Dynamic matching market design

We introduce a simple benchmark model of dynamic matching in networked markets, where agents arrive and depart stochastically and the network of acceptable transactions between agents forms a random graph. We analyze our model from three perspectives: waiting time, optimization, and information. The main insight of our analysis is that waiting to thicken the market can be substantially more important than increasing the speed of transactions, and this is quite robust to the presence of waiting costs. From an optimization perspective, naive local algorithms, that choose the right time to match agents but do not exploit global network structure, can perform very close to optimal algorithms. From an information perspective, algorithms that employ even partial information on agents' departure times perform substantially better than those that lack such information. Information and waiting are complements; information about departure times is necessary for waiting to yield large gains. To elicit agents' departure times, we design an incentive-compatible continuous-time dynamic mechanism without transfers. LINK: www.ssrn. com/abstract=2394319
MOLE: A mobile agent system

Due to its salient properties, mobile agent technology has received rapidly growing attention over the last few years. Many developments ofmobile agent systems are under way in both academic and industrial environments. MOLE is one of the first mobile agent systems that has been developed in the Java language; the first version came out in 1995. Since then MOLE has been constantly improved, and provides a stable environment for development and usage of mobile agents in the area of distributed applications. Furthermore, it has been used extensively in our research at the University of Stuttgart.
The Purdue Mechanics Freeform Classroom: A New Approach to Engineering Mechanics Education

Jeffrey F. Rhoads is an Associate Professor in the School of Mechanical Engineering at Purdue University and is affiliated with both the Birck Nanotechnology Center and Ray W. Herrick Laboratories at the same institution. He received his B.S., M.S., and Ph.D. degrees, each in mechanical engineering, from Michigan State University in 2002, 2004, and 2007, respectively. Dr. Rhoads’ current research interests include the predictive design, analysis, and implementation of resonant micro/nanoelectromechanical systems (MEMS/NEMS); the behavior of electromechanical and thermomechanical systems operating in rich, multi-physics environments; and mechanics education. Dr. Rhoads is a member of the American Society for Engineering Education (ASEE) and the American Society of Mechanical Engineers (ASME), where he serves on the Student Design Committee and the Design Engineering Division’s Technical Committees on Micro/Nanosystems and Vibration and Sound. Dr. Rhoads is a recipient of the National Science Foundation’s Faculty Early Career Development (CAREER) Award, the Purdue University School of Mechanical Engineering’s Harry L. Solberg Best Teacher Award (twice), and the ASEE Mechanics Division’s Ferdinand P. Beer and E. Russell Johnston, Jr. Outstanding New Mechanics Educator Award.
Robust Color-Based Multi-Face Detection Using Skin Color Segmentation

Human face detection is the first step for face recognition. Variation of illumination, the existence of occlusion and orientation effect on the performance of the face-detection algorithms. Using the skin-color segmentation technique we can overcome the above referred problem. In this paper we propose a novel color-based multiple face detection approach which is based on the color-features and skin-color segmentation. At first the image is converted into YCbCr and HSV color model. Next the resultant image is converted into skin and non-skin regions using the designed HSCbCr color model. Next the segmented image is morphed and then skin-regions are constructed. Finally the face is detected by drawing the bounding box around the faces. The proposed approach is able to localize the occluded facial features.
Genetic Variation in the Proximal Promoter of ABC and SLC Superfamilies: Liver and Kidney Specific Expression and Promoter Activity Predict Variation

Membrane transporters play crucial roles in the cellular uptake and efflux of an array of small molecules including nutrients, environmental toxins, and many clinically used drugs. We hypothesized that common genetic variation in the proximal promoter regions of transporter genes contribute to observed variation in drug response. A total of 579 polymorphisms were identified in the proximal promoters (−250 to +50 bp) and flanking 5′ sequence of 107 transporters in the ATP Binding Cassette (ABC) and Solute Carrier (SLC) superfamilies in 272 DNA samples from ethnically diverse populations. Many transporter promoters contained multiple common polymorphisms. Using a sliding window analysis, we observed that, on average, nucleotide diversity (π) was lowest at approximately 300 bp upstream of the transcription start site, suggesting that this region may harbor important functional elements. The proximal promoters of transporters that were highly expressed in the liver had greater nucleotide diversity than those that were highly expressed in the kidney consistent with greater negative selective pressure on the promoters of kidney transporters. Twenty-one promoters were evaluated for activity using reporter assays. Greater nucleotide diversity was observed in promoters with strong activity compared to promoters with weak activity, suggesting that weak promoters are under more negative selective pressure than promoters with high activity. Collectively, these results suggest that the proximal promoter region of membrane transporters is rich in variation and that variants in these regions may play a role in interindividual variation in drug disposition and response.
Improving Researcher Homepage Classification with Unlabeled Data

A classifier that determines if a webpage is relevant to a specified set of topics comprises a key component for focused crawling. Can a classifier that is tuned to perform well on training datasets continue to filter out irrelevant pages in the face of changing content on the Web? We investigate this question in the context of identifying researcher homepages. We show experimentally that classifiers trained on existing datasets of academic homepages underperform on “non-homepages” present on current-day academic websites. As an alternative to obtaining labeled datasets to retrain classifiers for the new content, in this article we ask the following question: “How can we effectively use the unlabeled data readily available from academic websites to improve researcher homepage classification? ” We design novel URL-based features and use them in conjunction with content-based features for representing homepages. Within the co-training framework, these sets of features can be treated as complementary views enabling us to effectively use unlabeled data and obtain remarkable improvements in homepage identification on the current-day academic websites. We also propose a novel technique for “learning a conforming pair of classifiers” that mimics co-training. Our algorithm seeks to minimize a loss (objective) function quantifying the difference in predictions from the two views afforded by co-training. We argue that this loss formulation provides insights for understanding co-training and can be used even in the absence of a validation dataset. Our next set of findings pertains to the evaluation of other state-of-the-art techniques for classifying homepages. First, we apply feature selection (FS) and feature hashing (FH) techniques independently and in conjunction with co-training to academic homepages. FS is a well-known technique for removing redundant and unnecessary features from the data representation, whereas FH is a technique that uses hash functions for efficient encoding of features. We show that FS can be effectively combined with co-training to obtain further improvements in identifying homepages. However, using hashed feature representations, a performance degradation is observed possibly due to feature collisions. Finally, we evaluate other semisupervised algorithms for homepage classification. We show that although several algorithms are effective in using information from the unlabeled instances, co-training that explicitly harnesses the feature split in the underlying instances outperforms approaches that combine content and URL features into a single view.
Slotted Gaussian Multiple Access Channel: Stable Throughput Region and Role of Side Information Vaneet

We study the relation between the stable throughput regions and the capacity regions for a Gaussian multiple-access channel. Our main focus is to study how the extent of side information about source arrival statistics and/or instantaneous queue states at each transmitter influence the achievable stable throughput region. Two notions of MAC capacity are studied. The first notion is the conventional Shannon capacity which relies on large coding block lengths for finite SNR, while the second uses finite code blocks with high SNR. We find that the stable throughput region coincides with the Shannon capacity region for many scenarios of side information, where side information is defined as a mix of statistical description and instantaneous queue states. However, a lack of sufficient side information about arrival statistics can lead to a significant reduction in the stable throughput region. Finally, our results lend strong support to centralized architectures implementing some form of congestion/rate control to achieve Shannon capacity, primarily to counter lack of detailed information about source statistics at the mobile nodes.