Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Health Care Providers’ Perceptions of the Needs of Stroke Carers: Informing Development of an Optimal Health Program

Abstract Background: Health care provider experiences of the carer have been researched, but little is written about how these can inform development of support programs. Objectives: This study aimed to (1) explore health care provider perceptions of stroke carer roles and support needs and (2) examine carer needs across the stroke care trajectory to assist with development of an Optimal Health Program (OHP) to support carers. This study is part of a staged program of research that will evaluate and refine the OHP. Methods: Four dual-moderated semi-structured focus groups of stroke health care providers across acute, subacute, and community rehabilitation services were conducted. Facilitators used a semi-structured focus group schedule to guide discussion. Sessions were recorded, transcribed, and analyzed using thematic and content analysis. Results: Three key themes emerged: transition, information, and impact of stroke. A number of subthemes highlighted the distinct roles of health care providers and carers. Specific elements of the OHP were identified as having the potential to advance support for carers across the stroke care trajectory. Discussion: Findings support the integration of an OHP for carers within existing stroke care services in Australian public hospital and community settings. Conclusion: This study suggests how health care provider experiences could inform a self-management OHP to assist carers in navigating stroke services and to address their health-related concerns.
An exploratory evaluation of the feasibility, acceptability, and efficacy of the mental fitness disordered eating program in schools

ABSTRACT The present study examines the feasibility, acceptability, and efficacy of a tiered professional development disordered eating prevention program. A pretest/posttest quasi-experimental design assessed elementary and junior high school faculty and staff’s disordered eating knowledge and attitudes. Findings indicate participants experienced increased awareness of students’ susceptibility to and the severity of disordered eating, increased self-efficacy about referring students to appropriate resources, and overall perceived acceptability of the training. Improved knowledge and attitudes suggest faculty and staff are better equipped to identify at-risk individuals at an early stage, increasing the likelihood of recovery from disordered eating behaviors or disordered eating.
Insider threat detection using principal component analysis and self-organising map

An insider threat can take on many aspects. Some employees abuse their positions of trust by disrupting normal operations, while others export valuable or confidential data which can damage the employer's marketing position and reputation. In addition, some just lose their credentials which are then abused in their name. In this paper, we use Principal Component Analysis (PCA) in conjunction with Self-Organising Map (SOM) for insider threat detection within an organisation. The results show that using PCA before SOM increases the clustering accuracy.
Piccolo: Building Fast, Distributed Programs with Partitioned Tables

Piccolo is a new data-centric programming model for writing parallel in-memory applications in data centers. Unlike existing data-flow models, Piccolo allows computation running on different machines to share distributed, mutable state via a key-value table interface. Piccolo enables efficient application implementations. In particular, applications can specify locality policies to exploit the locality of shared state access and Piccolo's run-time automatically resolves write-write conflicts using user-defined accumulation functions. Using Piccolo, we have implemented applications for several problem domains, including the PageRank algorithm, k-means clustering and a distributed crawler. Experiments using 100 Amazon EC2 instances and a 12 machine cluster show Piccolo to be faster than existing data flow models for many problems, while providing similar fault-tolerance guarantees and a convenient programming interface.
Blind high dynamic range image quality assessment using deep learning

In this paper we propose a No-Reference Image Quality Assessment (NR-IQA) method on High Dynamic Range (HDR) images by combining deep Convolutional Neural Networks (CNNs) with saliency maps. The proposed method utilises the power of deep CNN architectures to extract quality features which can be applied cross HDR and Standard Dynamic Range (SDR) domains. To introduce human visual system to CNNs, a saliency map algorithm is used to select a subset of salient image patches to evaluate on. Our CNN-based method delivers a state-of-the-art performance in HDR NR-IQA experiment, competitive with full reference IQA methods.
Gene expression and ultrastructure of meso‐ and thermophilic methanotrophic consortia

Summary The sulfate‐dependent, anaerobic oxidation of methane (AOM) is an important sink for methane in marine environments. It is carried out between anaerobic methanotrophic archaea (ANME) and sulfate‐reducing bacteria (SRB) living in syntrophic partnership. In this study, we compared the genomes, gene expression patterns and ultrastructures of three phylogenetically different microbial consortia found in hydrocarbon‐rich environments under different temperature regimes: ANME‐1a/HotSeep‐1 (60°C), ANME‐1a/Seep‐SRB2 (37°C) and ANME‐2c/Seep‐SRB2 (20°C). All three ANME encode a reverse methanogenesis pathway: ANME‐2c encodes all enzymes, while ANME‐1a lacks the gene for N5,N10‐methylene tetrahydromethanopterin reductase (mer) and encodes a methylenetetrahydrofolate reductase (Met). The bacterial partners contain the genes encoding the canonical dissimilatory sulfate reduction pathway. During AOM, all three consortia types highly expressed genes encoding for the formation of flagella or type IV pili and/or c‐type cytochromes, some predicted to be extracellular. ANME‐2c expressed potentially extracellular cytochromes with up to 32 hemes, whereas ANME‐1a and SRB expressed less complex cytochromes (≤ 8 and ≤ 12 heme respectively). The intercellular space of all consortia showed nanowire‐like structures and heme‐rich areas. These features are proposed to enable interspecies electron exchange, hence suggesting that direct electron transfer is a common mechanism to sulfate‐dependent AOM, and that both partners synthesize molecules to enable it.
Histological Quantification in Temporal Lobe Epilepsy

Approximately 30 percent of epilepsy patients suffer from refractory temporal lobe epilepsy which is commonly treated with resection of the epileptogenic tissue. However, surgical treatment presents many challenges in locating the epileptogenic focus and thus not all patients become seizure-free following surgery. Advances in techniques can lead to improved localization of the epileptogenic zone and may be validated by correlating MRI with neuropathology of the excised cortical tissue. Focal cortical dysplasias are a neuropathological group of cortical malformations that are often found in cases of refractory epilepsy, however, they are subtle and difficult to quantify. The purpose of this research is to employ histology image analysis techniques to better characterize these abnormalities at the neuronal and laminar level, allowing for correlative MRI-histology studies and improved lesion detection in medically intractable TLE.
A Novel Immune Optimization with Shuffled Frog Leaping Algorithm - A Parallel Approach for Unsupervised Data Clustering

Data clustering is one of the data mining task, it is used to group the data objects according to their similarity. It is an optimization problem to find optimal results apply the proposed parallel approach called P-AISFLA. This hybrid algorithm is developed by utilizing the benefits of both social and immune mechanisms. The social algorithm Shuffled Frog Leaping Algorithm is a new parameter free population based algorithm combined with Clonal selection algorithm CSA. This hybrid algorithm performs the parallel computation of social behavior based SFLA and Immune behavior based CSA to improve the ability to reach the global optimal solution with a faster and a rapid convergence rate. The proposed algorithm PAISFL is applied for the data clustering applications and proved that it produces optimal results than SFLA and PSO.
New Matching Method for Accelerometers in Gravity Gradiometer

The gravity gradiometer is widely used in mineral prospecting, including in the exploration of mineral, oil and gas deposits. The mismatch of accelerometers adversely affects the measuring precision of rotating accelerometer-based gravity gradiometers. Several strategies have been investigated to address the imbalance of accelerometers in gradiometers. These strategies, however, complicate gradiometer structures because feedback loops and re-designed accelerometers are needed in these strategies. In this paper, we present a novel matching method, which is based on a new configuration of accelerometers in a gravity gradiometer. In the new configuration, an angle was introduced between the measurement direction of the accelerometer and the spin direction. With the introduced angle, accelerometers could measure the centrifugal acceleration generated by the rotating disc. Matching was realized by updating the scale factors of the accelerometers with the help of centrifugal acceleration. Further simulation computations showed that after adopting the new matching method, signal-to-noise ratio improved from −41 dB to 22 dB. Compared with other matching methods, our method is more flexible and costs less. The matching accuracy of this new method is similar to that of other methods. Our method provides a new idea for matching methods in gravity gradiometer measurement.
Serial measurements of higher-order aberrations after blinking in patients with dry eye.

PURPOSE To study the sequential postblink changes in ocular higher-order aberrations (HOAs) in patients with dry eye. METHODS A wavefront sensor was used to measure HOAs sequentially for 30 seconds in 20 eyes of 20 patients with dry eye. The 20 eyes were classified into two groups, with or without superficial punctate keratopathy (SPK) in the central cornea. During the measurement, subjects were required to blink every 10 seconds. The aberration data were analyzed in the central 4-mm diameter for coma-like, spherical-like, and total HOAs up to sixth-order Zernike polynomials. Total HOAs, as well as fluctuation index (FI) and stability index (SI) of the total HOAs over time were compared between the two groups. The sequential changes in coma-like aberration, spherical-like aberration, and total HOAs were also investigated. RESULTS The total ocular HOAs were significantly (P = 0.001) greater in dry eyes with central SPK than in dry eyes without central SPK. The sequential pattern of the total ocular HOAs had higher initial and consistently higher values in dry eyes with central SPK, whereas that of dry eyes without central SPK showed consistently lower total HOAs that were similar to the pattern of normal eyes. CONCLUSIONS Increased HOAs in dry eye at least partially result from SPK above the optical zone. The low tear volume in dry eye may not cause sequential increases in HOAs after blinking. Sequential measurement of HOAs may be useful for evaluating the sequential changes in optical quality in patients with dry eye.
Device for long term measurement of heart rate

In this contribution, a device for long term measurement of heart rate is described. The device is created based on development kit STM32-Primer2. Heart rate frequency is calculated from selected electrocardiograph lead from the external module. The device allows simultaneous recoding of acceleration which makes is appropriate for physical activity detection of the test subject. The recorded data is saved on a memory card as signals in raw form, which can be used for subsequent processing in various research areas. Modular solution is suitable for connection of other modules. This device is designed for research and educational purposes in the field of medical devices and signal processing.
Toward Open Data Blockchain Analytics: A Bitcoin Perspective

Bitcoin is the first implementation of what has become known as a 'public permissionless' blockchain. Guaranteeing security and protocol conformity through its elegant combination of cryptographic assurances and game theoretic economic incentives, it permits censorship resistant public read-write access to its append-only blockchain database without the need for any mediating central authority. Not until its advent has such a trusted, transparent, comprehensive and granular data set of digital economic behaviours been available for public network analysis. In this article, by translating the cumbersome binary data structure of the Bitcoin blockchain into a high fidelity graph model, we demonstrate through various analyses the often overlooked social and econometric benefits of employing such a novel open data architecture. Specifically we show (a) how repeated patterns of transaction behaviours can be revealed to link user activity across the blockchain; (b) how newly mined bitcoin can be associated to demonstrate individual accumulations of wealth; (c) through application of the naive quantity theory of money that Bitcoin's disinflationary properties can be revealed and measured; and (d) how the user community can develop coordinated defences against repeated denial of service attacks on the network. All of the aforementioned being exemplary benefits that would be lost with the closed data models of the 'private permissioned' distributed ledger architectures that are dominating enterprise level development due to existing blockchain issues of governance, scalability and confidentiality.
K-Divided Bloom Filter Algorithm and Its Analysis

By using a bit vector and a set of hash functions to represent data set, bloom filter can query a given data effectively. Bloom filter can be used to determine an element belongs to data set or not. Split bloom filter is amelioration to the bloom filter, which use a S times N bit matrix to represent data set. In distributed systems, if the number of the elements increases continually, the increasing error rate of bloom filter will make the representation nonsensically. Split bloom filter can only weaken this problem. In this paper, a new kind of bloom filter, named as K-divided bloom filter, is presented. Compared with split bloom filter, it can reduce space and time spending and has a resembling or better performance. K-divided bloom filter gets better tradeoff among error rate, space and time.
Micro-patterning of a conductive polymer and an insulation polymer using the Parylene lift-off method for electrochromic displays

We have developed a process to pattern the conductive polymer of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) film and the insulation polymer of the perfluoro polymer (Cytop) with a Parylene lift-off method for the purpose of fabricating a PEDOT:PSS-based electrochromic display device. Because conventional micro-patterning processes decrease the conductivity of organic electronic polymers due to the destructive solutions of photolithography, PEDOT:PSS and Cytop polymers were patterned by using the dry lift-off film of Parylene. Its patterning resolution of PEDOT:PSS was found to be as low as 20 µm. The insulation layer of Cytop was also patterned on the PEDOT:PSS pattern with the same resolution. This process was able to pattern the 300 µm wide wiring and 1 mm square pixels of PEDOT:PSS and the Cytop to cover the entire area except for the pixels constructed to form an electrochromic display. Finally, the fabricated 4 × 4 pixel device displayed a simple shape, a transverse line.
Efficient sampling of early signal arrival for estimation of perfusion and transit time in whole‐brain arterial spin labeling

Arterial spin labeling can be used to measure both cerebral perfusion and arterial transit time. However, accurate estimation of these parameters requires adequate temporal sampling of the arterial spin labeling difference signal. In whole‐brain multislice acquisitions, two factors reduce the accuracy of the parameter estimates: saturation of labeled blood in transit and inadequate sampling of early difference signal in superior slices. Label saturation arises when slices are acquired inferior‐to‐superior such that slice selection in proximal slices spoils the label for a distal slice. Inadequate sampling arises when the time spent acquiring inferior slices is too long to allow early sampling of the difference signal in superior slices. A novel approach to multislice imaging is proposed to address these two issues. In round‐robin arterial spin labeling, slices are acquired in a different order after every pair of control‐label acquisitions. Round‐robin arterial spin labeling enables the acquisitions of all slices across the same range of postlabel delays in a descending superior‐to‐inferior order. This eliminates the temporal sampling problem and greatly reduces label saturation. Arterial transit time estimates obtained for the whole brain with round‐robin arterial spin labeling show better agreement with a single‐slice acquisition than do conventional multislice acquisitions. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.
Efficient computation of spatial joins

Spatial joins are join operations that involve spatial data types and operators. Due to basic properties of spatial data, many conventional join strategies suffer serious performance penalties or are not applicable at all. The join strategies known from conventional databases that can be applied to spatial joins and the ways in which some of these techniques can be modified to be more efficient in the context of spatial data are discussed. A class of tree structures, called generalization trees, that can be applied efficiently to compute spatial joins in a hierarchical manner are described. The performances of the most promising strategies are analytically modeled and compared. <<ETX>>
V-Cloud: vehicular cyber-physical systems and cloud computing

The widespread use of smart phone devices and their ubiquity has brought up new application domains. One such interesting domain is vehicular networks. Furthermore, cloud computing is a next-generation information and communication technology that is gaining popularity due to its pay-as-you-go service model. In this paper, we propose our novel V-Cloud architecture which includes vehicular cyber-physical system (VCPS), vehicle-to-vehicle network (V2V) and vehicle-to-infrastructure network (V2I) layers. Each of these layers is explained in further details. We discuss research challenges in V-Cloud domain and introduce new services to show wide potential of such intelligent transportation system to meet safety and comfort requirements for driver.
Study on Hexagonal Mesoporous Silica Film for Antireflective Coating

In this paper, ordered mesoporous silica film as a novel antireflective coating was directly synthesized via evaporation induced self-assembly (EISA) process with cetyltrimethylammonium bromide (CTAB) as template surfactant. The mesoporous silica film exhibits ordered 2D hexagonal microstructure as demonstrated by transmission electron microscopy (TEM) and small angle X-ray diffraction (SAXRD). The average transmittance of the coated glass is about 98%, which indicates an increasing transmitted light of 7.5% in the wavelength between 400nm and 800nm. The mechanical performance and hydrophobicity tests prove that the films have excellent scratch resistance and long-time stability. The results of the characterizations and tests show that the film prepared by this convenient method would be very useful and promising for lots of commercial applications.
Influence of alkali fiber treatment and fiber processing on the mechanical properties of hemp/epoxy composites

Industrial hemp fibers were treated with a 5 wt % NaOH, 2 wt % Na2SO3 solution at 120°C for 60 min to remove noncellulosic fiber components. Analysis of fibers by lignin analysis, scanning electron microscopy (SEM), zeta potential, Fourier transform infrared (FTIR) spectroscopy, wide angle X-ray diffraction (WAXRD) and differential thermal/thermogravimetric analysis (DTA/TGA), supported that alkali treatment had (i) removed lignin, (ii) separated fibers from their fiber bundles, (iii) exposed cellulose hydroxyl groups, (iv) made the fiber surface cleaner, and (v) enhanced thermal stability of the fibers by increasing cellulose crystallinity through better packing of cellulose chains. Untreated and alkali treated short (random and aligned) and long (aligned) hemp fiber/epoxy composites were produced with fiber contents between 40 and 65 wt %. Although alkali treatment generally improved composite strength, better strength at high fiber contents for long fiber composites was achieved with untreated fiber, which appeared to be due to less fiber/fiber contact between alkali treated fibers. Composites with 65 wt % untreated, long aligned fiber were the strongest with a tensile strength (TS) of 165 MPa, Young's modulus (YM) of 17 GPa, flexural strength of 180 MPa, flexural modulus of 9 GPa, impact energy (IE) of 14.5 kJ/m2, and fracture toughness (KIc) of 5 MPa m1/2. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
Dancing like a superstar: Action guidance based on pose estimation and conditional pose alignment

Action Guidance (AG) aims at scoring how accurate the action is and giving guidance to the learners on how to correct their actions according to the standard instructive videos. AG has plenty of real-world applications such as sports training, rehabilitation treatment, and dance teaching. However, the problem of assessing the accuracy of action has almost no effective solution. In this paper, we describe a two-stage framework for action guidance. Firstly, we estimate the poses in the test video and standard video using person detection method and convolutional pose machines (CPMs). As for action guidance, we propose a network to compute the essential differences between two poses under different sizes, views, and locations. Extensive experiments with real-world and synthetic datasets demonstrate the effectiveness of our framework.
Facilitating change management with configurative reference modelling

Change management describes a structured approach to transitions from the present state to a desired state. Reference models can assist this process since they provide a solution schema for specific problems by adapting best or common-practice approaches. The configuration of reference models has been a field of research in the past. However, the integrative configuration of different reference models or respective reference models serving multiple purposes lacks applicable methods. In practice this is a common problem, as the simultaneous implementation and/or maintenance of business processes in an Enterprise Resource Planning (ERP) system and business intelligence system shows. We propose a methodology that allows the integrated configuration of conceptual models for transaction processing and decision support by integrating meta models for respective modelling languages. Additionally, its application is exemplified by extending an existing reference model in a business case.