Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing guidelines to routinely prevent chronic vascular disease in primary care: the Preventive Evidence into Practice cluster randomised controlled trial

Objective To evaluate an intervention to improve implementation of guidelines for the prevention of chronic vascular disease. Setting 32 urban general practices in 4 Australian states. Randomisation Stratified randomisation of practices. Participants 122 general practitioners (GPs) and practice nurses (PNs) were recruited at baseline and 97 continued to 12 months. 21 848 patient records were audited for those aged 40–69 years who attended the practice in the previous 12 months without heart disease, stroke, diabetes, chronic renal disease, cognitive impairment or severe mental illness. Intervention The practice level intervention over 6 months included small group training of practice staff, feedback on audited performance, practice facilitation visits and provision of patient education and referral information. Outcome measures Primary: 1. Change in proportion of patients aged 40–69 years with smoking status, alcohol intake, body mass index (BMI), waist circumference (WC), blood pressure (BP) recorded and for those aged 45–69 years with lipids, fasting blood glucose and cardiovascular risk in the medical record. 2. Change in the level of risk for each factor. Secondary change in self-reported frequency and confidence of GPs and PNs in assessment. Results Risk recording improved in the intervention but not the control group for WC (OR 2.52 (95% CI 1.30 to 4.91)), alcohol consumption (OR 2.19 (CI 1.04 to 4.64)), smoking status (OR 2.24 (1.17 to 4.29)) and cardiovascular risk (OR 1.50 (1.04 to 2.18)). There was no change in recording of BP, lipids, glucose or BMI and no significant change in the level of risk factors based on audit data. The confidence but not reported practices of GPs and PNs in the intervention group improved in the assessment of some risk factors. Conclusions This intervention was associated with improved recording of some risk factors but no change in the level of risk at the follow-up audit. Trial registration number Australian and New Zealand Clinical Trials Register (ANZCTR): ACTRN12612000578808, results.
Three methodologies for the calibration of industrial manipulators: Experimental results on a SCARA robot

This article presents and compares three algorithms for the geometric parameter identification of industrial robots to increase its accuracy (static calibration). The estimation is based on the measure of the gripper pose errors when the robot follows suitable trajectories. The algorithms are general and can be applied to any robot providing that its kinematics is known. After a theoretical introduction to the general methodologies, these are applied to a selective compliance assembly robot arm (SCARA) robot analyzing its performance (precision, efficiency). Experimental results obtained with three methodologies are presented and discussed. The measure of the gripper pose error is based on a laser triangulation technique whose working principles are also recalled. © 2000 John Wiley & Sons, Inc.
Multiple Instance Learning for Computer Aided Diagnosis

Many computer aided diagnosis (CAD) problems can be best modelled as a multiple-instance learning (MIL) problem with unbalanced data: i.e., the training data typically consists of a few positive bags, and a very large number of negative instances. Existing MIL algorithms are much too computationally expensive for these datasets. We describe CH, a framework for learning a Convex Hull representation of multiple instances that is significantly faster than existing MIL algorithms. Our CH framework applies to any standard hyperplane-based learning algorithm, and for some algorithms, is guaranteed to find the global optimal solution. Experimental studies on two different CAD applications further demonstrate that the proposed algorithm significantly improves diagnostic accuracy when compared to both MIL and traditional classifiers. Although not designed for standard MIL problems (which have both positive and negative bags and relatively balanced datasets), comparisons against other MIL methods on benchmark problems also indicate that the proposed method is competitive with the state-of-the-art.
Optimized health parameters using PSO: a cost effective rfid based wearable gadget with less false alarm rate

The motive of contriving the proposed research was originated after recognizing some false alarm in measuring health parameters and practical issues in hospitals for example approachability and presence of patient at designated place for the evaluation of the health parameters like Blood pressure, sugar, body temperature, pulse and some other parameters. The manual entry of the data into the systems have been become a critical problem. To vanquish this problem, a wearable gadget has been designed, so that a patient can carry it feasibly. Various approaches like Bayesian classifier has been applied to minimize the false alarm. Fuzzy logic, Kalman filtering, extended Kalman filtering, support vector machine, multi-layer perceptron, adaptive neuro fuzzy inference system and cuckoo search have been applied to eliminate the false alarm and give the best optimum solution. Results have proved that PSO worked betted as its convergence time is less than the others algorithms and produced best optimum value for the body vitals. Moreover, PSO has been applied to achieve the best optimal results and to increase the performance. Monitoring of heart pulse rate and body temperature readings that were recorded in database were then tested and validated by comparing digital thermometer and digital inflator. PSO will converge and give the global best position of data by updating the velocity. Results proved that PSO produced better results by optimizing with better accuracy and precision and can be acknowledged as cost-effective solution.
PaRQ: A Privacy-Preserving Range Query Scheme Over Encrypted Metering Data for Smart Grid

Smart grid, envisioned as an indispensable power infrastructure, is featured by real-time and two-way communications. How to securely retrieve and audit the communicated metering data for validation testing is, however, still challenging for smart grid. In this paper, we propose a novel privacy-preserving range query (PaRQ) scheme over encrypted metering data to address the privacy issues in financial auditing for smart grid. Our PaRQ allows a residential user to store metering data on a cloud server in an encrypted form. When financial auditing is needed, an authorized requester can send its range query tokens to the cloud server to retrieve the metering data. Specifically, the PaRQ constructs a hidden vector encryption based range query predicate to encrypt the searchable attributes and session keys of the encrypted data. Meanwhile, the requester's range query can be transferred into two query tokens, which are used to find the matched query results. Security analysis demonstrates that in the PaRQ, only the authorized requesters can obtain the query results, while the data confidentiality and query privacy are also preserved. The simulation results show that our PaRQ can significantly reduce communication and computation costs.
Preliminary study on visual guidance for autonomous vehicle in rain forest terrain

In this paper, a review of visual guidance systems of autonomous vehicle is presented. The challenges of autonomous navigation in rainforest or tropical terrain are highlighted. There are several methods used in navigation system of the autonomous vehicle and it is shown that vision system remains a key-enabling component for successful navigation system. Color feature is used to examine the general requirement for visual guidance applied in rain forest terrain. The potential usage of polarizer to detect water bodies is demonstrated. The aim of this study is to give overview of visual guidance system and examining the key challenges in autonomous rain forest navigation.
Neural Networks in Multi-Relational Data Mining

Neural networks are non-parametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Multi-relational data mining framework is based on the search for interesting patterns in the relational database. Multi-relational data mining algorithms search a large hypothesis space in order to find a suitable model for a given data set. Although neural networks may have complex structure, long training time, and uneasily understandable representation of results, neural networks have high acceptance ability for noisy data and high accuracy and are preferable in data mining. One of the goals of this paper is to point out the similarities and overlap between these two fields. This paper presents some challenges to multi-relational data mining and the application of soft computing methodologies (involving neural networks, genetic algorithms, fuzzy sets, and rough sets).
Video steganalysis of multiplicative spread spectrum steganography

In this paper we propose a video steganalysis method toward multiplicative spread spectrum embedding. We use the redundancies of the video frames to estimate the cover frame and after extracting some features from the video frames and the estimated ones, the received video is classified as suspicious or not suspicious. In the case that the video declared suspicious, we estimate the hidden message and the gain factor used in the embedder. We also propose a new method for estimating the gain factor in multiplicative spread spectrum embedding. Using the estimated hidden message and gain factor, we are able to reconstruct the original video. Simulation results verify the success of our steganalysis method.
Computational Detection of Alternative Exon Usage

Background: With the advent of the GeneChip Exon Arrays, it is now possible to extract “exon-level” expression estimates, allowing for detection of alternative splicing events, one of the primary mechanisms of transcript diversity. In the context of (1) a complex trait use case and (2) a human cerebellum vs. heart comparison on previously validated data, we present a transcript-based statistical model and validation framework to allow detection of alternative exon usage (AEU) between different groups. To illustrate the approach, we detect and confirm differences in exon usage in the two of the most widely studied mouse genetic models (the C57BL/6J and DBA/2J inbred strains) and in a human dataset. Results: We developed a computational framework that consists of probe level annotation mapping and statistical modeling to detect putative AEU events, as well as visualization and alignment with known splice events. We show a dramatic improvement (∼25 fold) in the ability to detect these events using the appropriate annotation and statistical model which is actually specified at the transcript level, as compared with the transcript cluster/gene-level annotation used on the array. An additional component of this workflow is a probe index that allows ranking AEU candidates for validation and can aid in identification of false positives due to single nucleotide polymorphisms. Discussion: Our work highlights the importance of concordance between the functional unit interrogated (e.g., gene, transcripts) and the entity (e.g., exon, probeset) within the statistical model. The framework we present is broadly applicable to other platforms (including RNAseq).
Preventive effect of protein-energy supplementation on the functional decline of frail older adults with low socioeconomic status: a community-based randomized controlled study.

BACKGROUND Chronic undernutrition is a common condition in older people with low socioeconomic status and is clearly an important component of frailty. However, it is uncertain whether protein-energy supplementation can prevent functional decline in this group. METHODS Eighty-seven frail older adults (usual gait speed, <0.6 m/second; Mini Nutritional Assessment, <24) were enrolled in this randomized controlled trial. Participants were randomly assigned to either an intervention group, which was provided two 200-mL cans of commercial liquid formula (additional 400 kcal of energy, 25g of protein, 9.4g of essential amino acids, 400mL of water) per day for 12 weeks, or the controls group, which did not receive this supplement. The primary outcomes were the change of the Physical Functioning and Short Physical Performance Battery. Usual gait speed, timed up-and-go test, hand grip strength, and one-legged stance were also measured as secondary outcome variables. RESULTS Physical Functioning increased by 5.9% (1 point) in the intervention group, although no change was observed in the control group (p =.052). Short Physical Performance Battery remained stable in the intervention group, although it decreased by 12.5% (1 point) in controls (p = .039). Usual gait speed decreased by 1.0% in the intervention group versus 11.3% (0.04 m/second) in controls (p = .039). Timed up-and-go improved by 7.2% (1.1 seconds) in the intervention group and worsened by 3.4% (0.9 seconds) in controls (p = .038). There were no differences between groups in hand grip strength or one-legged stance performance. CONCLUSIONS The results indicate that protein-energy supplementation administered to frail older adults with low socioeconomic status shows evidence of reducing the progression of functional decline.
Near-lossless image authentication transparent to near-lossless coding

In this paper attention is paid to the problem of remote-sensing image authentication by relying on a digital watermarking approach. Both transmission and storage is usually cumbersome for remote-sensing imagery and compression is unavoidable to make it feasible. In the case of multi-spectral images, used for classification purposes, too large valuemetric changes can cause miss-classification errors, so near-lossless compression is used to grant a given peak compression error. Similarly it is likely that also watermarking based authentication should grant such a maximum peak error and the requirements to be satisfied are: to allow valuemetric authentication, to control the peak watermark embedding error, to tolerate near-lossless compression. To achieve these requirements, a methodology has been designed by integrating into the standard JPEG-LS compression algorithm, by means of a stripe approach, a known authentication technique derived from Fridrich. This procedure points out two advantages: firstly, the produced bit-stream is perfectly compliant with the JPEG-LS standard, secondly, when the image has been decoded, it is always authenticated because information has been embedded in the reconstructed values. Near-lossless coding does not harm authentication procedure and robustness against different attacks is preserved. JPEG-LS coding/decoding has not got worse from the point of view of computational time.
High-accuracy positioning for indoor applications: RFID, UWB, 5G, and beyond

Highly accurate and reliable indoor positioning - at accuracy levels in the 10 cm range - will enable a large a number of innovative location-based applications because such accuracy levels essentially allow for a useful real-time interaction of humans and cyber-physical systems. Activity recognition, navigation at "shelf" level, geofencing, process monitoring and process control are among the envisioned services that will yield numerous applications in various domains. This paper reviews the difficulties faced by indoor positioning systems, motivating the requirement for a large signal bandwidth and how a lack of bandwidth can be compensated by multi-antenna systems. The potential capabilities of upcoming generations of wireless systems will increasingly make high-accuracy positioning available in near future.
Structure and Determinants of Illness Representations in Chronic Disease

Although the clinical relevance of illness representations has been demonstrated in several studies, research on the structure and determinants of illness representations is rare. This article examines the illness representations of chronically ill patients, using a structured interview technique and taking chronic fatigue syndrome (CFS) and Addison's disease (AD) as examples. Considerable differences were found between the group of CFS patients (n = 98) and the group of AD patients (n = 63) with regard to their ideas about the identity, time line, control/cure , and consequences of their illness. Despite these differences, the pattern of correlations among these four dimensions of illness representation was found to be similar for the two groups. Moreover, the strength of the correlations points to the coherent nature of illness representations. The relations between the illness representations, personal variables, and disease- related variables were also explored. Regression analyses showed the dimensions of illness representation to be explained rather well by personal and disease-related variables. Disease-related variables were the most important predictors for the dimensions of identity and consequences; personal variables showed strong associations with time line and control/cure.
A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series

The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of ± 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set.
Robust derivation of epicardium and its differentiated smooth muscle cell progeny from human pluripotent stem cells

The epicardium has emerged as a multipotent cardiovascular progenitor source with therapeutic potential for coronary smooth muscle cell, cardiac fibroblast (CF) and cardiomyocyte regeneration, owing to its fundamental role in heart development and its potential ability to initiate myocardial repair in injured adult tissues. Here, we describe a chemically defined method for generating epicardium and epicardium-derived smooth muscle cells (EPI-SMCs) and CFs from human pluripotent stem cells (HPSCs) through an intermediate lateral platemesoderm (LM) stage.HPSCswere initially differentiated toLM in the presence of FGF2 and high levels of BMP4. The LM was robustly differentiated to an epicardial lineage by activation of WNT, BMP and retinoic acid signalling pathways. HPSC-derived epicardium displayed enhanced expression of epithelialand epicardium-specific markers, exhibited morphological features comparable with human foetal epicardial explants and engrafted in the subepicardial space in vivo. The in vitro-derived epicardial cells underwent an epithelial-tomesenchymal transition when treated with PDGF-BB and TGFβ1, resulting in vascular SMCs that displayed contractile ability in response to vasoconstrictors. Furthermore, the EPI-SMCs displayed low density lipoprotein uptake and effective lowering of lipoprotein levels upon treatment with statins, similar to primary human coronary artery SMCs. Cumulatively, these findings suggest that HPSC-derived epicardium and EPI-SMCs could serve as important tools for studying human cardiogenesis, and as a platform for vascular disease modelling and
Lean transformation efforts of the wood industry in Virginia

This manuscript reports preliminary results from a study examining the status of Lean implementation and the need for Lean transformation support among participants in two segments of Virginia's manufacturing industry. A census survey with 180 responses (population size 1033) was conducted and the results confirm previous research that enterprises in these industries are not uniformly aware of Lean and its potential benefits. Enterprises also have implemented Lean to varying degrees or not at all. Interestingly, small enterprises indicated a lower need for Lean implementation support from third parties than did medium or large enterprises.
Mechanical noise suppression based on non-negative matrix factorization and multi-band spectral subtraction for digital cameras

This paper proposes a new non-stationary noise suppression method to reduce the mechanical noise generated when audio signals are recorded with a digital camera. The proposed method first utilizes a non-negative matrix factorization (NMF) technique to estimate the noise spectrum of the mechanical noise from the noisy audio spectrum. After that, the mechanical noise contaminated in the audio signal is suppressed by multi-band spectral subtraction. In particular, the NMF technique estimates the noise spectrum in a frame-wise manner in order for the proposed method to operate in real-time. The performance of the proposed mechanical noise suppression method is evaluated in terms of log-spectral distortion, cepstral distortion, subjective quality, and computational complexity. In addition, it is compared with the performance of conventional methods. It is shown from the evaluation that the proposed method provides lower log-spectral and cepstral distortions with better subjective preference than conventional methods. Moreover, the complexity of the proposed method is low enough to be implemented on a commercially available digital camera.
How do Takeovers Create Synergies? Evidence from France

Abstract Based on bidder-target asymmetry, our study investigates the source of synergy gains derived from corporate takeovers and their specific contribution to bidder value creation. Prior researches have focused on the relevance of only one source of potential synergy. We find that French takeovers tend to create long-term operating and financial synergies. These two synergy components are positive and significant with a large contribution of the former. Furthermore, cutbacks in investment expenditures represent the most significant source of operating synergies, while post-acquisition market power is non-significant. Moreover, both total and operating synergies are higher in focused takeovers initiated by “value” as opposed to “glamour” bidders. Lastly, financial synergies are likely to arise from bidder leverage level and target relative size.
Localization Service Using Sparse Visual Information Based on Recent Augmented Reality Platforms

The ability to localize a device or user precisely within a known space, would allow many use cases on the context of location-based augmented reality. We propose a localization service based on sparse visual information using ARCore [4], a state-of-the-art augmented reality platform for mobile devices. Our service is constituted by two components: front-end and back-end. On the front-end, using the point cloud generated by ARCore as feature points, a corresponding binary keypoint descriptor algorithm like ORB [6] or FREAK [1] is computed to describe the place. On the back-end, this binary descriptor is searched in a map using the bags of binary words technique [3], responding with the position of the recognized place.