Miracle-dz/UniFAR-FTUs huggingface dataset:
Left paraduodenal hernia: case report and review of the literature

Paraduodenal hernias are congenital internal hernias that usually present with non-specific symptoms, and are therefore rarely diagnosed preoperatively. Left-sided paraduodenal hernias are three times more likely to occur than right-sided ones. Both hernias present similarly, but have a differing embryological basis. Here, the case of a 76-year-old woman with a left paraduodenal hernia presenting with small bowel obstruction is presented, and a brief discussion of the literature on its diagnosis and management given.
The Cold Ocean-Warm Land Pattern: Model Simulation and Relevance to Climate Change Detection

Abstract Surface air temperatures from a 1000-yr integration of a coupled atmosphere–ocean model with constant forcing are analyzed by using a method that decomposes temperature variations into a component associated with a characteristic spatial structure and a residual. The structure function obtained from the coupled model output is almost identical to the so-called cold ocean–warm land (COWL) pattern based on observations, in which above-average spatial mean temperature is associated with anomalously cold oceans and anomalously warm land. This pattern features maxima over the high-latitude interiors of Eurasia and North America. The temperature fluctuations at the two continental centers exhibit almost no temporal correlation with each other. The temperature variations at the individual centers are related to teleconnection patterns in sea level pressure and 500-mb height that are similar to those identified in previous observational and modeling studies. As in observations, variations in the polarity.
Exploring Service Users’ Perceptions of Cognitive Behavioural Therapy for Psychosis: A User Led Study

Background and aims: This study explored individuals’ subjective experiences of Cognitive Behavioural Therapy for psychosis (CBTp) with the aim of identifying coherent themes consistent across individual accounts and any potential barriers to CBTp effectiveness. Method: Semi-structured interviews were conducted with nine individuals with experience of CBTp. A qualitative Interpretive Phenomenological Analysis was used to analyze the data collected to identify common themes. Results: Five super-ordinate themes emerged from our analyses: CBT as a process of person-centred engagement; CBT as an active process of structured learning; CBT helping to improve personal understanding; CBT is hard work; Recovery and outcomes of CBT for psychosis. Conclusions: The theoretical and clinical implications are discussed.
Packaging software process improvement issues: a method and a case study

Software process improvement is a challenge in general and in particular for small‐ and medium‐sized companies. Assessment is one important step in improvement. However, given that a list of improvement issues has been derived, it is often very important to be able to prioritize the improvement proposals and also look at the potential dependencies between them. This paper comes from an industrial need to enable prioritization of improvement proposals and to identify their dependencies. The need was identified in a small‐ and medium‐sized software development company. Based on the need, a method for prioritization and identification of dependencies of improvement proposals was developed. The prioritization part of the method is based on a multi‐decision criteria method and the dependencies are identified using a dependency graph. The developed method has been successfully applied in the company, where people with different roles applied the method. The paper presents both the method as such and the successful application of it. It is concluded that the method worked as a means for prioritization and identification of dependencies. Moreover, the method also allowed the employees to discuss and reason about the improvement actions to be taken in a structured and systematic way. Copyright © 2004 John Wiley & Sons, Ltd.
A Hierarchical Extractor-Based Visual Rail Surface Inspection System

Rail inspection based on visual inspection system (VIS) has drawn much attention recently, since VIS is automatic, fast, nondestructive, and objective. However, visual rail inspection is still a challenge to accurately identify possible defects involving a large variety of visual appearances. This paper presents an automatic visual inspection system to address these difficulties. Rail surface images are first obtained by the system, and then fed into the proposed hierarchical inspection framework containing coarse extractor and fine extractor. Specifically, the coarse extractor handles this defect inspection problem by exploring characteristics of rail background instead of defect itself, and focuses on finding the background modes. The fine extractor, which integrates the longitudinal context information and transversal prior information, is presented to largely reduce the impacts of other irregulars. The proposed method is evaluated on labeled Type-I and Type-II rail surface discrete defects data sets and a practical rail line. The experimental results demonstrate that our method outperforms the state-of-the-art works.
An apple-to-apple comparison of Learning-to-rank algorithms in terms of Normalized Discounted Cumulative Gain

The Normalized Discounted Cumulative Gain (NDCG) is a widely used evaluation metric for learning-to-rank (LTR) systems. NDCG is designed for ranking tasks with more than one relevance levels. There are many freely available, open source tools for computing the NDCG score for a ranked result list. Even though the definition of NDCG is unambiguous, the various tools can produce different scores for ranked lists with certain properties, deteriorating the empirical tests in many published papers and thereby making the comparison of empirical results published in different studies difficult to compare. In this study, first, we identify the major differences between the various publicly available NDCG evaluation tools. Second, based on a set of comparative experiments using a common benchmark dataset in LTR research and 6 different LTR algorithms, we demonstrate how these differences affect the overall performance of different algorithms and the final scores that are used to compare different systems.
Chronic Obstructive Pulmonary Disease Patient Well-Being and Its Relationship with Clinical and Patient-Reported Outcomes: A Real-Life Observational Study

Background: Quality of life is an umbrella concept that refers to all aspects of a person’s life, including health status and well-being. While health status measure focuses on the impact of the disease on physical functioning, well-being represents the self-representation of the emotional states related to the disease itself. Objectives: The objective of this study was to evaluate the psychological well-being and its determining factors in a real-life chronic obstructive pulmonary disease (COPD) population and to evaluate if patients with a different well-being differ in illness perception, health status and alexithymia. Methods: Psychological well-being (Psychological General Well-Being Index), health status (SF-36), illness perception (Illness Perception Questionnaire), alexithymia (Toronto Alexithymia Scale), as well as clinical parameters were assessed in COPD out-patients. Results: One hundred and sixty-four patients, with a mean forced expiratory volume in 1 s of 58.5%, were recruited. Forty percent of them had a moderately/severely impaired well-being, not correlated with forced expiratory volume in 1 s and the Charlson index value but significantly with the Medical Research Council score (p = 0.0001) that appeared to be the dominant factor. Patients with impaired well-being showed a different illness perception in terms of correct identification of symptoms, disease consequences, emotional representation and confidence in treatment compared with those having a positive well-being. The latter presented a lower alexithymia prevalence and a better health status. Conclusions: In order to minimize the disease-negative effects on patients’ lives, assessment of well-being and its determining factors, as well as planning specific behavioural, educational and therapeutic interventions seem to be relevant and useful.
Treatment of Pulmonary Embolism With Rivaroxaban: Outcomes by Simplified Pulmonary Embolism Severity Index Score from a Post Hoc Analysis of the EINSTEIN PE Study

Objectives The objective was to assess adverse outcomes in relation to the simplified Pulmonary Embolism Severity Index (PESI) score in patients treated with rivaroxaban or standard therapy in the phase III EINSTEIN PE study and to evaluate the utility of the simplified PESI score to identify low-risk pulmonary embolism (PE) patients. Methods A post hoc analysis of EINSTEIN PE data was performed to assess the efficacy and safety of rivaroxaban in patients with a range of simplified PESI scores. Recurrent venous thromboembolism, fatal PE, all-cause mortality, and major bleeding were stratified by simplified PESI scores of 0, 1, or ≥2 and according to treatment period at 7, 14, 30, and 90 days and at the end of the full intended treatment period. Results Simplified PESI scores could be calculated in 4,831 of the 4,832 randomized patients; of those, 53.6, 36.7, and 9.7% had PESI scores of 0, 1, and ≥2, respectively. Among patients with simplified PESI scores of 0 or 1, fatal PE, all-cause mortality, and other adverse outcomes were uncommon within the first 7, 14, and 30 days. Patients with simplified PESI scores of ≥2 had more frequent adverse outcomes. Major bleeding was lower in the rivaroxaban group, particularly in those with simplified PESI scores of 1 or ≥2. Conclusions The findings support using risk stratification with the simplified PESI score to identify low-risk patients with PE.
Treatment Patterns, Adherence, and Persistence in ADHD: A Canadian Perspective

Abstract Objective: To understand attention–deficit/hyperactivity disorder (ADHD) treatment patterns and estimate adherence and persistence in Quebec, Canada. Design: This cross–sectional, retrospective prescription claims analysis used a random sample of 15 838 patients with ADHD from a Quebec database (Régie de l'assurance maladie du Québec [RAMQ]) to assess treatment patterns, adherence (1–year medication possession ratio in new users), and persistence (proportion persistent at 3, 6, and 12 months after index prescription). Results: The mean patient age was 14 years; 72.6% were male. During the 5–year study period (2004–2009), 416 646 ADHD prescriptions were flled. Short–acting (SA) medications declined from 72.8% to 26.4% of all claims, while stimulant and nonstimulant long–acting (LA) medications increased from 27.2% to 73.6%. Approximately half of the patients used both SA and LA medications (either concomitantly or subsequently), and the others used only SA (30%) or LA (19%) drugs. Among patients using both, switching from SA to LA was the most frequent (27.9%) treatment pattern. More patients on LA methylphenidates (6.4%) compared with LA amphetamines (1.9%; P < 0.01) required augmentation with an SA drug. Fewer patients on SA stimulants (39.4%) were ≥ 80% adherent compared with LA stimulants (63%; P < 0.001) and LA nonstimulants (60.2%; P < 0.001). More patients on LA stimulants (81.1%) were persistent at 12 months compared with LA nonstimulants (61.7%; P < 0.001) and SA stimulants (59.6%; P < 0.001). Similar trends were observed at all time points measured. Conclusions: Switching from SA to LA medications and treatment augmentation are common in ADHD management, with implications for patient care and health care resource use. This analysis found poor adherence in ADHD treatment, although adherence and persistence were improved with LA stimulant formulations.
Using SSDs to scale up Google Fusion Tables, a database-in-the-cloud

Flash memory solid state drives (SSDs) have increasingly been advocated and adopted as a means of speeding up and scaling up data-driven applications. SSDs are becoming more widely available as an option in the cloud. However, when an application considers SSDs in the cloud, the best option for the application may not be immediate, among a number of choices for placing SSDs in the layers of the cloud. Although there have been many studies on SSDs, they often concern a specific setting, and how different SSD options in the cloud compare with each other is less well understood. In this paper, we describe how Google Fusion Tables (GFT) used SSDs and what optimizations were implemented to scale up its in-memory processing, clearly showing opportunities and limitations of SSDs in the cloud with quantitative analyses. We first discuss various SSD placement strategies and compare them with low-level measurements, and propose SSD-placement guidelines for a variety of cloud data services. We then present internals of our column engine and optimizations to better use the performance characteristics of SSDs. We empirically demonstrate that the optimizations enable us to scale our application to much larger datasets while retaining the low-latency and simple query processing architecture.
A Metric Model for Trustworthiness of Softwares

Trustworthiness of softwares describes the behavior of a software when it finishes its required task. The goal of this paper is to establish a metric model which measures the trustworthiness of a software based on the basic attributes of softwares. These attributes are classified into two classes: critical and non-critical attributes and four trust criteria of monotonicity, acceleration, sensitivity and substitutivity are proposed. A metric model of trustworthiness which satisfies these four criteria is introduced.
Semantic innovation management across the extended enterprise

Innovation provides the means for enterprises to survive and grow. Due to the distributed and heterogeneous characteristics, it's difficult to manage innovation across the extended enterprise. A novel vision for Semantic Innovation Management (SIM) is introduced. The system architecture combines ontology, inference and mediation technologies used to create a semantic web of innovation knowledge. A functional framework of the SIM System (SIMS) based on metadata harvesting and RDF access technologies is presented. An applied case study is explained in detail. This, in turn, facilitates all participating organisations and users in forming closer alliances based on the sharing of innovation information.
A combined LMS-SOM algorithm for time-varying non-linear channel equalization

The paper proposes a self-organizing map (SOM) approach to equalize time-varying non-linear channels. The approach is applied to a satellite mobile communication channel which is composed of time invariant linear filters, a non-linear memoryless amplifier and a time-varying multi-path propagation channel. The SOM is combined to a Transversal Linear Equalizer (TLE) which is trained by the LMS algorithm. The paper studies the performance and convergence behavior of the SOM. The paper also illustrates the capability of the SOM to track the channel changes in particular for complicated modulation schemes.
Comparison of the Robustness of RNN, MPC and ANN Controller for Residential Heating System

In this paper, a novel random neural network (RNN) controller is proposed to maintain a comfortable indoor environment in a single storey residential building having four rooms fitted with radiators for heating. This controller considers the effect of outside temperature and solar radiations on the building and is capable of maintaining a comfortable indoor environment on the basis of a PMV-based set point. The RNN controller is trained with a 30 day dataset from the living room of the building and the performance of the controller is evaluated by testing the controller in all four rooms of the building for 100 days. It is found that the RNN controller is not only capable of maintaining comfortable indoor environment as suggested by PMV-based set point but can also adjust the room temperature to a lower set point (not included in the training set) required by the user for unoccupied rooms. The RNN controller is further compared with similar artificial neural network (ANN) controller and model predictive control (MPC) controller. The results show that for maintaining comfortable indoor environment, the performance of the RNN controller is approximately equivalent to the MPC controller for the set points not covered in the training set, while ANN controller failed to maintain accurate comfortable environment for the operating points not covered in the training phase.
The Francisella tularensis migR, trmE, and cphA Genes Contribute to F. tularensis Pathogenicity Island Gene Regulation and Intracellular Growth by Modulation of the Stress Alarmone ppGpp

ABSTRACT The Francisella tularensis pathogenicity island (FPI) encodes many proteins that are required for virulence. Expression of these genes depends upon the FevR (PigR) regulator and its interactions with the MglA/SspA and RNA polymerase transcriptional complex. Experiments to identify how transcription of the FPI genes is activated have led to identification of mutations within the migR, trmE, and cphA genes that decrease FPI expression. Recent data demonstrated that the small alarmone ppGpp, produced by RelA and SpoT, is important for stabilizing MglA/SspA and FevR (PigR) interactions in Francisella. Production of ppGpp is commonly known to be activated by cellular and nutritional stress in bacteria, which indicates that cellular and nutritional stresses act as important signals for FPI activation. In this work, we demonstrate that mutations in migR, trmE, or cphA significantly reduce ppGpp accumulation. The reduction in ppGpp levels was similar for each of the mutants and correlated with a corresponding reduction in iglA reporter expression. In addition, we observed that there were differences in the ability of each of these mutants to replicate within various mammalian cells, indicating that the migR, trmE, and cphA genes are likely parts of different cellular stress response pathways in Francisella. These results also indicate that different nutritional and cellular stresses exist in different mammalian cells. This work provides new information to help understand how Francisella regulates its virulence genes in response to host cell environments, and it contributes to our growing knowledge of this highly successful bacterial pathogen.
Enhancing Coding Aware Routing and Handling Link Failure in WSN

The objective of this project is to handle link failure during distributed coding aware routing in wireless networks. Network coding is the technique in which the nodes of the network instead of simply relaying the packets they receive, the nodes will take several packets and combine them together for transmission. Network coding is becoming an emerging communication paradigm that can provide the performance improvement in throughput and energy efficiency. Network coding was originally proposed for wired networks. Recently, there is a growing interest to apply network coding onto wireless networks since the broadcast nature of wireless channel makes network coding particularly advantageous in terms of bandwidth efficiency and enables opportunistic encoding and decoding.This will greatly improve the performance of wireless networks. DCAR, the Distributed Coding-Aware Routing mechanism enhances the coding opportunity in the network and route the packets to the destination. However, in the case of link failure, the performance of the network would drop and there will be considerable loss of packets. To overcome this limitation, the routing protocol AODV with Direct Forward Routing Protocol is proposed. It enables routing the packets to another path effectively and reaches the destination. There is significant improvement in throughput. A result shows that the throughput has been improved by 32% and the packet drop reduced by 4.28% that of the existing system.
Customer Value Creation in CRM Initiatives: The Impacts of Organizational Learning and IT Capabilities

CRM initiatives are at the forefront of many organizations seeking to nurture long-term customer relationships through customer value creation. While the gains from CRM initiatives are apparent, the synergistic pathways that lead to customer value creation are understudied in literature. We take an organizational learning perspective, and posit that customer value creation in CRM initiatives can be obtained through dedicated organizational learning. Further, we draw upon the IT assets literature to posit that business analytics capability and IT infrastructure for CRM positively impact organizational learning. Human capital is postulated to play a moderating role on these impacts. We propose to conduct a matched-pair survey of senior executives and subsequent analysis to empirically validate our study. Findings from this study will help clarify the role of IT capabilities and organizational learning in CRM initiatives.
Finite Element Approximation of Elliptic Partial Differential Equations on Implicit Surfaces

The aim of this paper is to investigate finite element methods for the solution of elliptic partial differential equations on implicitely defined surfaces. The problem of solving such equations without triangulating surfaces is of increasing importance in various applications, and their discretization has recently been investigated in the framework of finite difference methods. For the two most frequently used implicit representations of surfaces, namely level set methods and phase-field methods, we discuss the construction of finite element schemes, the solution of the arising discretized problems, and provide error estimates. The convergence properties of the finite element methods are illustrated by computations for several test problems.
Recognizing Dynamic Scenes: Influence of Processing Orientation

From face recognition studies, it is known that instructions are able to change processing orientation of stimuli, leading to an impairment of recognition performance. The present study examined instructional influences on the visual recognition of dynamic scenes. A global processing orientation without any instruction was assumed to lead to highest recognition performance, whereas instructions focusing participants' attention on certain characteristics of the event should lead to a local processing orientation with an impairment of visual recognition performance as a direct consequence. Since the pattern of results provided evidence for this hypothesis, theoretical contributions were discussed.
Automated assists to the behavioral modeling process

The coding of behavioral models is a time consuming and error prone process. In this paper the authors describe automated assists to the behavioral modeling process which reduce the coding time and result in models which have a well defined structure making it easier to insure their accuracy. The approach uses a particular graphical representation for the model. An interactive tool then assists in converting the graphical representation to the behavioral HDL code. The authors discuss a pictorial representation for VHDL behavioral models. In VHDL an architectural body is used to define the behavior of a device. These architectural bodies are a set of concurrently running process. These processes are either process blocks or various forms of the signal assignment statements. One can give a pictorial representation to a behavioral architectural body by means of a process model graph (PMG). <<ETX>>