Miracle-dz/UniFAR-FTUs huggingface dataset:
Surgical Repair of Acetabular Fracture Using Unidirectional Porous β-Tricalcium Phosphate

We report a case of an acetabular fracture treated using a unidirectional porous β-tricalcium phosphate artificial bone (Affinos®) to surgically repair bone defects. An 82-year-old man sustained an acetabular fracture on the left side and presented with shock on arrival along with impaired vital signs and systolic blood pressure. Upon stabilization, we performed an open reduction and internal fixation. However, there were significant bone defects, which were then fixed using Affinos® (both blocks and granules), an artificial β-tricalcium phosphate bone with a porosity of 57% (pore size: 25–300 μm), characterized by a novel unidirectional porous structure. By 18 months postoperatively, the patient was able to perform stair climbing and absorption and bone fusion around the artificial bone were observed. Affinos® has a frost-like structure, which endows it with good tissue-invasive properties because of the capillary effect. Moreover, it has excellent osteoconduction capability. In this case, both Affinos® blocks and granules showed good affinity, absorption, and bone substitution. Further prospective studies are required to confirm our findings.
The longevity gap between Black and White men in the United States at the beginning and end of the 20th century.

OBJECTIVES We sought to assess whether the disparity in mortality rates between Black and White men decreased from the beginning to the end of the 20th century. METHODS We used Cox proportional hazard models for mortality to estimate differences in longevity between Black and White Civil War veterans from 1900 to 1914 (using data from a pension program) and a later cohort of male participants (using data from the 1992 to 2006 Health and Retirement Study). In sensitivity analysis, we compared relative survival of veterans for alternative baseline years through 1914. RESULTS In our survival analysis, the Black-White male difference in mortality, both unadjusted and adjusted for other influences, did not decrease from the beginning to the end of the 20th century. A 17% difference in Black-White mortality remained for the later cohort even after we controlled for other influences. Although we could control for fewer other influences on longevity, the Black-White differences in mortality for the earlier cohort was 18%. CONCLUSIONS In spite of overall improvements in longevity, a major difference in Black-White male mortality persists.
Interaction in the psychoanalytic situation

In psychoanalytic psychotherapy we are primarily interested in psychic change and how to facilitate it for the better. Change is a universal property of matter, living or inanimate: everything in nature is influenced by everything else; interaction is ubiquitous. In the early years of psychoanalysis, the prevailing view was that therapeusis was essentially informational—insight and awareness would bring about changes in the ways one would experience events and respond to them. Over time, there has been a subtle shift from the informational perspective to the transformational, where insight is often retrospective rather than the active agent. The growing awareness of the need to be deeply recognized and responded to by another human being is reflective of this shift and has loomed ever larger in the interactive arena known as psychoanalysis. This paper focuses on a single facet of the need for recognition by another—the role of enacted response in effecting psychic change. It also addresses another level o.
Generation of Internal Lee Waves by Lateral Circulation in a Coastal Plain Estuary

Recent mooring observations at a cross-channel section in Chesapeake Bay showed that internal solitary waves regularly appeared during certain phases of a tidal cycle and propagated from the deep channel to the shallow shoal. It was hypothesized that these waves resulted from the nonlinear steepening of internal lee waves generated by lateral currents over channel-shoal topography. In this study numerical modeling is conducted to investigate the interaction between lateral circulation and cross-channel topography and discern the generation mechanism of the internal lee waves. During ebb tides, lateral bottom Ekman forcing drives a counterclockwise (looking into estuary) lateral circulation, with strong currents advecting stratified water over the western flank of the deep channel and producing large isopycnal displacements. When the lateral flow becomes supercritical with respect to mode-2 internal waves, a mode-2 internal lee wave is generated on the flank of the deep channel and subsequently propagates onto the western shoal. When the bottom lateral flow becomes near-critical or supercritical with respect to mode-1 internal waves, the lee wave evolves into an internal hydraulic jump. On the shallow shoal, the lee waves or jumps evolve into internal bores of elevation.
Climate-driven technical change: seasonality and the invention of agriculture

During the Neolithic Revolution, seven populations independently invented agriculture. In this paper, I argue that this innovation was a response to a large increase in climatic seasonality. Hunter-gatherers in the most affected regions became sedentary in order to store food and smooth their consumption. I present a model capturing the key incentives for adopting agriculture, and I test the resulting predictions against a global panel dataset of climate conditions and Neolithic adoption dates. I find that invention and adoption were both systematically more likely in places with higher seasonality. The findings of this paper imply that seasonality patterns 10,000 years ago were amongst the major determinants of the present day global distribution of crop productivities, ethnic groups, cultural traditions, and political institutions. ∗email: andrea.matranga@upf.edu. I am grateful to my advisor Joachim Voth, for his continuous guidance and support. I also thank for useful comments Ran Abramitzky, Francesco Amodio, Leonardo Bursztyn, Paula Bustos, Enriqueta Camps, Davide Cantoni, Bruno Caprettini, John Cherry, Paul David, Christian Dippel, James Fenske, Oded Galor, Nicola Gennaioli, Fabrizio Germano, Albrecht Glitz, Libertad Gonzalez, Avner Greif, Jonathan Hersh, Peter Koudijs, Thomas Leppard, Lorenzo Magnolfi, Alberto Martin, Stelios Michalopoulos, Mrdjan Mladjan, Omer Moav, Michele Monti, Luigi Pascali, Gonçalo Pina, Andrei Potlogea, Marta Reynal, Paul Seabright, Yannay Spitzer, Jaume Ventura, Nico Voigtlander, Noam Yuchtman, Romain Wacziarg, and David Weil. This project benefited greatly from audience comments received at the World Economic History Congress in Utrecht, the EHS Annual Conference in Oxford, The TSE Brow Bag seminar, the Yellow Pad Seminar at Santa Clara University, the SSH seminar at Stanford, the Macro Lunch at Brown, the History Tea at Harvard, the Cliometric Conference in Tucson, the World Cliometric Conference in Honolulu, the BGSE Jamboree, and the International Lunch and Applied Seminar at Pompeu.
An Adaptive Security Scheme for Secret Data using Cryptography and Steganography

In today’s information age, information sharing and transfer has increased exponentially. The information vulnerable to unauthorised access and interception, while in storage or transmission. The threat of an intruder and Steganalysis accessing secret information for sharing information over an unsecure or covert communication channels are vulnerable to intruder attacks. Although, these techniques are often combined together to achieve higher levels of security but still there is a need of a highly secure system to transfer information over any communication media that minimizes the threat of intrusion. Therefore, to maintain secrecy either we need to make more robust steganography techniques against steganalysis or discover new and better techniques. This paper presents a new generalized model by combining cryptographic and steganographic Technique. These two techniques encrypt the data as well as hide the encrypted data in another medium so the fact that a message being sent is concealed. In cryptography we are using Advanced Encryption Standard (AES) algorithm to encrypt secret message and then alteration component method is used to hide encrypted message. By using these two techniques the security of secret data increases to two tier and a high quality of stego image is obtained.
Solution Structure of the 2A Protease from a Common Cold Agent, Human Rhinovirus C2, Strain W12

Human rhinovirus strains differ greatly in their virulence, and this has been correlated with the differing substrate specificity of the respective 2A protease (2Apro). Rhinoviruses use their 2Apro to cleave a spectrum of cellular proteins important to virus replication and anti-host activities. These enzymes share a chymotrypsin-like fold stabilized by a tetra-coordinated zinc ion. The catalytic triad consists of conserved Cys (C105), His (H34), and Asp (D18) residues. We used a semi-automated NMR protocol developed at NMRFAM to determine the solution structure of 2Apro (C105A variant) from an isolate of the clinically important rhinovirus C species (RV-C). The backbone of C2 2Apro superimposed closely (1.41–1.81 Å rmsd) with those of orthologs from RV-A2, coxsackie B4 (CB4), and enterovirus 71 (EV71) having sequence identities between 40% and 60%. Comparison of the structures suggest that the differential functional properties of C2 2Apro stem from its unique surface charge, high proportion of surface aromatics, and sequence surrounding the di-tyrosine flap.
Mathematics teaching in the Seychelles: The challenges of reforming practices in a small developing state

This paper is drawn from my PhD research which aimed at investigating the outcomes of a primary mathematics teaching reform in Seychelles. As part of the research, 4 primary schools were chosen for the fieldwork. In each school the following activities were achieved: a) samples of mathematics lessons were observed followed by post lesson interviews with the teachers, b) a six-teacher focus group interview was held to gauge the teachers’ experiences enacting the reform, and c) relevant documents were reviewed to acquire background data about the school and the reform. This paper is based on findings from the focus group interviews. The findings revealed that the reform benefited the implementers’ practices but the schools were challenging sites for pedagogical reform. The results have implications for teacher in-service education and policy making on pedagogical reforms.
Modeling and Predicting DNS Server Load

The DNS relies on caching to ensure high scalability and good performance. In optimizing caching, TTL adjustment provides a means of balancing between query load and TTL-dependent performances such as data consistency, load balancing, migration time, etc. To gain the desired balance, TTL adjustment depends on predictions of query loads under alternative TTLs. This paper proposes a model of DNS server load, which employs the uniform aggregate caching model to simplify the complexity of modeling clients' requests and their caching. A method of predicting DNS server load is developed using that model. The prediction method is solely based on the unilateral measurements or observations at authoritative servers. Without reliance on lots of multi-point measurements nor distributed measuring facilities, the method is best suited for DNS authoritative operators. The proposed model and prediction method are validated through extensive simulations. Finally, global sensibility analysis is conducted to evaluate the impacts of measurement uncertainties or errors on the predictions.
The Effect of Repeated Ad Exposure and Physical Avoidance on Consumers' Mobile Ad Response

Opt-in mobile advertisement platform services are increasingly popular. In this paper, we explore how repeated ad exposure and physical avoidance influence consumers’ responses to mobile ads. Using a unique panel-level data of 1,193,131 million users from Cash Slide, a Korean mobile advertising platform, we find that 1) repeated ad exposure and physical avoidance has a negative impact on a consumer’s response to mobile ads; 2) information richness in a mobile ad has a negative impact on mobile users’ response to ads; 3) the negative effect of a user’s repeated exposures to a mobile ad and physical avoidance increases in magnitude when the advertisement involves a context of higher information richness. Our finding bridges the gap in the extant literatures by examining the impact of repeated ad exposure and avoidance on consumers’ mobile ad response. Moreover, we provide practical implications to advertisers on the mobile platform.
RefineNet: Iterative refinement for accurate object localization

We investigate a new strategy for improving localization accuracy of detected vehicles using a deep convolutional neural network. Specifically, we implement an iterative bounding box refinement on top of a state-of-the-art object detector. The bounding box refinement is achieved by iteratively pooling features from previous object location predictions. On KITTI vehicle detection benchmark, we achieve up to 6% improvement in average precision over the baseline results. Furthermore, the proposed refinement framework is computationally light, allowing for object detection at high run-time speeds. Our method runs at ~0.22 seconds per image on images of size 1242 × 375, making it one of the fastest detectors reported on the KITTI object detection benchmark.
Non-drug treatment for social phobia

When controlled studies on currently available non-drug treatments for social phobia were reviewed, short-term effectiveness was evident for social skills training, exposure therapy and cognitive therapy. The combination of exposure and cognitive therapy may have some superiority over the separate treatments. Group therapy and individual therapy did not differ clearly in outcome, while attempts to match treatment and patient characteristics have so far failed to produce consistently better results. With follow-up periods of either less than a year or more than a year, the therapies reviewed appeared to lead to stable results, although the available data allowed no firm conclusions because of attrition and additional treatments during the follow-up period. Except for the recent landmark study by Heimberg and Liebowitz, cognitive behavior therapy has not been adequately compared to medication. Studies are needed to assess the value of cognitive behavior therapy in combination with medication and to explore the possibilities of self-help manuals in such combination studies.
Extending Linear Discriminant Analysis by Using Unlabeled Data

Linear discriminant analysis(LDA) is a traditional dimension reduction method which finds projective directions to maximize separability between classes. However, when the number of labeled data points is small, the performance of LDA is degraded severely. In this paper, we propose two improved methods for LDA which utilizes abundant unlabeled data. Instead of using all the unlabeled data as in most of semi-supervised dimension reduction methods, we select confident unlabeled data and develop extended LDA algorithms. In the first method, a graph-based LDA method is developed to utilize confidence scores for chosen unlabeled data so that unlabeled data with a low confidence score contributes smaller than unlabeled data with a high confidence score. In the second method, selected unlabeled data points are used to modify the centroids of classes in an objective function of LDA. Extensive experimental results in text classification demonstrates the effectiveness of the proposed methods compared with other semi-supervised dimension reduction methods.
Mindfulness Training for Teachers Changes the Behavior of Their Preschool Students

We measured the effects of preschool teachers attending an 8-week mindfulness course on the behavior of the students in their classroom. Results showed that decreases in the students’ challenging behaviors and increases in their compliance with teacher requests began during mindfulness training for the teachers and continued to change following the training. While the students did not show a change in positive social interactions with peers, they did show a decrease in negative social interactions and an increase in isolate play. Our results indicate that mindfulness training for teachers was effective in changing teacher-student interactions in desirable ways.
Characterization of the Microbiome along the Gastrointestinal Tract of Growing Turkeys

The turkey microbiome is largely understudied, despite its relationship with bird health and growth, and the prevalence of human pathogens such as Campylobacter spp. In this study we investigated the microbiome within the small intestine (SI), caeca (C), large intestine (LI), and cloaca (CL) of turkeys at 6, 10, and 16 weeks of age. Eight turkeys were dissected within each age category and the contents of the SI, C, LI, and CL were harvested. 16S rDNA based QPCR was performed on all samples and samples for the four locations within three birds/age group were sequenced using ion torrent-based sequencing of the 16S rDNA. Sequencing data showed on a genus level, an abundance of Lactobacillus, Streptococcus, and Clostridium XI (38.2, 28.1, and 13.0% respectively) irrespective of location and age. The caeca exhibited the greatest microbiome diversity throughout the development of the turkey. PICRUSt data predicted an array of bacterial function, with most differences being apparent in the caeca of the turkeys as they matured. QPCR revealed that the caeca within 10 week old birds, contained the most Campylobacter spp. Understanding the microbial ecology of the turkey gastrointestinal tract is essential in terms of understanding production efficiency and in order to develop novel strategies for targeting Campylobacter spp.
Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables

Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep learning to substitute for well-established analysis techniques that rely on hand-crafted feature extraction and classification techniques. From these isolated applications of custom deep architectures it is, however, difficult to gain an overview of their suitability for problems ranging from the recognition of manipulative gestures to the segmentation and identification of physical activities like running or ascending stairs. In this paper we rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors. We describe how to train recurrent approaches in this setting, introduce a novel regularisation approach, and illustrate how they outperform the state-of-the-art on a large benchmark dataset. Across thousands of recognition experiments with randomly sampled model configurations we investigate the suitability of each model for different tasks in HAR, explore the impact of hyperparameters using the fANOVA framework, and provide guidelines for the practitioner who wants to apply deep learning in their problem setting.
Marginal Calibration in Multi-agent Probabilistic Systems

The multiply sectioned Bayesian network (MSBN) model successfully extends the traditional Bayesian network (BN) model for the support of probabilistic inference in distributed multi-agent systems. However, existing MSBN inference methods do not allow agents to reason about their own problem sub-domains right after the initialization process. Extensive amount of inter-agent message passings are needed to calibrate each agent's local subnet into a correct prior marginal distribution. In this paper, we introduce the concept of prior marginal factors to facilitate this process. Based on the analysis of the prior marginal factors, minimum message passing is required during calibration. Furthermore, we have removed the requirement of maintaining a consistent junction tree (JT) during message calculation. Therefore, our marginal calibration algorithm guarantees that a prior marginal in each MSBN subnet is formed with greatly reduced communication and computational cost. Our preliminary experiments have confirmed the improved time efficiency of the proposed algorithm.
Please Scroll down for Article Cybernetics and Systems toward Combining Automatic Resolution with Social Mediation for Resolving Multiuser Conflicts toward Combining Automatic Resolution with Social Mediation for Resolving Multiuser Conflicts Combining Automatic Resolution with Social Mediation

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. In spite of intensive effort to resolve conflicts between multiple users of context-aware applications in a smart space, there has been no practical solution for flexibly resolving them based on the situation of the users. In this paper, we propose a mixed resolution method to combine automatic resolution with social participation for resolving multiuser conflicts. For combining the two resolution approaches, various contexts such as preferences, priority, and types of applications are used to select an appropriate resolution method for the encountered conflict. Through an evaluation, we found that the performance of selection algorithms mainly depended on the number of users and the similarity between their preferences, and we derived an appropriate threshold for determining whether users were similar or not. With a user study of 3 applications in a smart-space test-bed, we observed that the combination of automatic resolution when preferences are similar and social mediation when preferences are different effectively resolved multiuser conflict even though social pressure played an important role, and the 3 different applications had different thresholds. INTRODUCTION With new computing paradigms such as ubiquitous computing and pervasive computing, smart-space applications have been evolving toward meeting the needs of residents by exploiting contextual information of users and their surroundings (Dey et al. 2001; Schmidt and Van Laerhoven 2001). In the case where a single user is 147 using smart-space applications, a user has complete control over all applications, and the aim of the applications is to provide the user with his/her desired personalized services and responses. In contrast, for the case where multiple users are using smart-space applications, all users are occupants of the space and expect the applications to respond to their individual desires. Family members, in smart homes, regularly …
Validation of Software Quality Models using Machine Learning: An Empirical Study

Software Quality is that significant nonfunctional requirement which is not fulfilled by many software products. In order to identify the faulty classes we can use prediction models using object oriented metrics. This paper empirically analyses the relationship between object oriented metrics and fault proneness of NASA Data sets using six machine Learning classifiers. It has been exhibited that Random Forest provides optimum values for accuracy, precision, sensitivity and specificity by performing Multivariate analysis of NASA Data sets. General Terms Software Maintenance, Software quality, confusion matrix, Accuracy, Precision, Recall, F-Measure, Sensitivity, Specificity, Bug