Miracle-dz/UniFAR-FTUs huggingface dataset:
Collective bargaining and the changing governance of hospitals : a comparison between United Kingdom, Italy and France

In the face of growing budgetary pressures and changes in demand for both the volume and quality of the health care, public authorities across Europe have moved to reform the organization and governance of publicly-provided care through hospitals. In all countries these reforms have been strongly influenced by the ‘New Public Management’ (NPM) approach, though not in a uniform manner. The paper identifies four key processes shaping the directions of the reforms: managerialization, corporatization, marketization and privatization. It then explores the implications for collective bargaining and other workforce governance arrangements in hospitals providing publicly funded health care in the United Kingdom, Italy and France. Drawing on the findings from coordinated studies of developments over the past two decades in the three countries, but with a focus on the 2000s, two sets of questions are addressed. First, to what extent have hospital reforms prompted changes in arrangements for collective bargaining and/or other workforce governance mechanisms? Second, what is the resulting direction of change in the mechanisms of workforce governance? How far has the embrace of ‘new public management’, which aims to propel the organization, management and provision of public services towards private sector practice, resulted in workforce governance arrangements which also more closely resemble those found in the private sector? And how do political contingency and actors’ interests and responses impact on this dynamic? The first section identifies the main processes characterising reforms to publicly provided hospital care, and the implications for collective bargaining and other mechanisms of workforce governance. In the second part we introduce the design of, and methods utilised in, the research. The third part establishes the trajectories of change in hospital governance in the three countries, whilst the fourth examines the implications of the respective reforms for the governance of health workforce through collective bargaining and other mechanisms. The final section discusses the findings and draws conclusions. Reform of hospital health care provision and implications for employment regulation: mapping the field Health care provision through hospitals is confronted by a number of medium-term pressures, which reach across the countries of the EU. These include the ageing population, and associated growth in both acute and chronic health problems requiring care; new demands for health care as medical technologies improve and the range of available treatments increases; growing attention to the quality of care; and budgetary / financial constraints, which are becoming even tighter as a
The difference of information diffusion for Seoul tourism destination according to user certification on Sina Weibo: through data crawling method

Abstract Consumer’s information search and sharing behaviour is critical to firm’s stratigic decision-making in SNS tourism marketing. Although there are many studies on SNS marketing, unlike previous research, our empirical study investigates the relationship between user certification and information elaboration degrees on Sina Weibo. Tourism information diffusion is used as an indication of rationality in order to provide a test to the explanation of consumer behaviour towards ‘Repost’ and ‘Like’. To understand this phenomenon, we collected data from Sina Weibo with a data harvesting programme. After filtering data, anlaysis was carried out on 5746 units. Espeically, we focused on ‘Seoul’, one of the biggest cities in Asia for wanna-visit tour destination. The focus of this study is the two types of user certification (official vs. personal) in concept of a rational information search and the moderating role of information elaboration level. To verify hypotheses, the Poisson Regression Method was applied. Empirical results indicated that ‘information shoppers’ for tourism purpose would react differently by information source certifications. Consumers are less likely to share ‘Like’ count generated by an official verified user when content elaboration level is relatively high. To the contrary, for a personal certification user, people are less likely to repost under high elaboration.
Content Recommendation in APOSDLE using the Associative Network

One of the success factors of Work Integrated Learning (WIL) is to provide the appropriate content to the users, both suitable for the topics they are currently working on, and their experience level in these topics. Our main contributions in this paper are (i) overcoming the problem of sparse content annotation by using a network based recommendation approach called Associative Network, which exploits the user context as input; (ii) using snippets for not only highlighting relevant parts of documents, but also serving as a basic concept enabling the WIL system to handle text-based and audiovisual content the same way; and (iii) using the Web Tool for Ontology Evaluation (WTE) toolkit for finding the best default semantic similarity measure of the Associative Network for new domains. The approach presented is employed in the software platform APOSDLE, which is designed to enable knowledge workers to learn at work.
Learning from within: a longitudinal case study of an education research group

Purpose Professionals in higher education are expected to be informed consumers of knowledge who seek out scholarship, critical evaluators of the applicability of extant knowledge, and contributors who build new knowledge for higher education practice. Despite the understood importance of developing research competencies, many have limited opportunities to develop these skills. This study aims to explore one way individuals develop research competencies: through participation in team-based research experiences. Design/methodology/approach A longitudinal case study approach was used to investigate what participants in an education research group learn, and how their participation in the group changes the ways in which they think about themselves as researchers and scholars. Four group members participated in two focus group interviews (at the end of the fall 2015 and spring 2016 academic semesters). Interviews were analyzed using thematic analysis. Findings Study participants report gaining knowledge about research, developing an identity as a researcher, and learning about faculty roles. Particular group practices and activities (e.g. full group meetings, subgroup meetings, professional development moments) helped mediate members’ learning and identity development. Originality/value Research groups should be considered valuable contexts where teaching and learning take place. By learning – and integrating what we learn – from research group participation, the higher education and student affairs fields may become better able to generate innovative practices and activities that provide students and professionals with opportunities to develop important research competencies.
Molecular Cloud Formation in Shock-compressed Layers

We investigate the propagation of a shock wave into a warm neutral medium and a cold neutral medium by one-dimensional hydrodynamic calculations with detailed treatment of thermal and chemical processes. Our main result shows that thermal instability inside the shock-compressed layer produces a geometrically thin, dense layer in which a large amount of hydrogen molecules are formed. Linear stability analysis suggests that this thermally collapsed layer will fragment into small molecular cloudlets. We expect that frequent compression due to supernova explosions, stellar winds, spiral density waves, etc., in the Galaxy causes the interstellar medium to be occupied by these small molecular cloudlets.
Using Model-Based Reflection to Guide Reinforcement Learning

In model-based reflection, an agent contains a model of its own reasoning processes organized via the tasks the agents must accomplish and the knowledge and methods required to accomplish these tasks. Utilizing this self-model, as well as traces of execution, the agent is able to localize failures in its reasoning process and modify its knowledge and reasoning accordingly. We apply this technique to a reinforcement learning problem and show how model-based reflection can be used to locate the portions of the state space over which learning should occur. We describe an experimental investigation of model-based reflection and self-adaptation for an agent performing a specific task (defending a city) in a computer war strategy game called FreeCiv. Our results indicate that in the task examined, model-based reflection coupled with reinforcement learning enables the agent to learn the task with effectiveness matching that of hand coded agents and with speed exceeding that of non-augmented reinforcement learning.
Artificial neural network as a tool to compensate for scatter and attenuation in radionuclide imaging.

UNLABELLED This study investigates the ability of artificial neural networks (ANN) to simultaneously correct for attenuation and Compton scattering in scintigraphic imaging. METHODS Three sets of experiments are conducted using images of radioactive sources with various shapes and distributions in a homogeneous medium. Numerical Monte Carlo simulations and physical phantom acquisitions of radioactive geometric sources provide the basic material for correction. Our method is based on the following assumptions: information needed to correct for scattering can be extracted from the energy spectrum at each pixel without any assumption concerning the source distribution, and two diametrically opposed energy spectrum acquisitions yield enough information on the source location in the diffusing medium for simultaneous correction for attenuation and scattering. RESULTS Qualitative and quantitative evaluations of scatter correction by ANN demonstrate its ability to perform scatter correction from the energy spectra observed in each pixel. By using the energy spectra of incident photons detected in two diametrically opposed images, multilayer neural networks are able to perform a proper restitution of projection images without any assumption on geometry or position of radioactive sources in simple geometric cases. ANN corrections compare favorably to those provided by five of the most popular methods. A satisfying correction of both scatter and attenuation is observed for a human pelvis scan obtained during routine clinical practice. CONCLUSION An ANN is an efficient tool for attenuation and Compton scattering in simple model cases. The results obtained for routine scintigrams in a much more complex situation are strong incentives for performing further studies.
An Efficient Proxy Re-Encryption Based on (Semi) Group Factorization Problems

Proxy re-encryption (PRE) enables a semi-trustedproxy to transfer Alice's secrets into Bob's secrets but withoutseeing the secrets. This functionality is very interesting formaking balance between the information confidentiality and the mutual accessibility in various scenarios such as public cloudstorage systems. During the past decades, many smart PREschemes were built based on intractability assumptions suchas integer factorization problems (IFP) and discrete logarithmproblems (DLP). However, Shor's efficient quantum algorithmsfor IFP and DLP manifests a threat towards the securitybaseline of these schemes. Enlightened by Gu et al.'s recent workon resisting known quantum attacks, we propose an efficientPRE scheme based on the intractability of the (semi) groupfactorization problems in this paper. The security of the proposedscheme is analyzed according to some conceivable attacks.
Native vs. non-native language fluency implications on multimodal interaction for interpersonal skills training

New technological developments in the field of multimodal interaction show great promise for the improvement and assessment of public speaking skills. However, it is unclear how the experience of non-native speakers interacting with such technologies differs from native speakers. In particular, non-native speakers could benefit less from training with multimodal systems compared to native speakers. Additionally, machine learning models trained for the automatic assessment of public speaking ability on data of native speakers might not be performing well for assessing the performance of non-native speakers. In this paper, we investigate two aspects related to the performance and evaluation of multimodal interaction technologies designed for the improvement and assessment of public speaking between a population of English native speakers and a population of non-native English speakers. Firstly, we compare the experiences and training outcomes of these two populations interacting with a virtual audience system designed for training public speaking ability, collecting a dataset of public speaking presentations in the process. Secondly, using this dataset, we build regression models for predicting public speaking performance on both populations and evaluate these models, both on the population they were trained on and on how they generalize to the second population.
A new approach to compress the configuration information of programmable devices

During the last decade programmable devices have gained an impressive diffusion, tackling some traditional ASIC marked domains. In particular, multi-million gates FPGAs have become a very appealing low-cost solution even for consumer applications. However, one of the big issues that can arise with modern FPGA devices is the need for large and expensive external non-volatile memory to keep the configuration data. In this work we developed an alternative technique to compress FPGA bitstreams based on the knowledge of the device internal structure. The proposed method performs a two-step coder: in the first step the bitstream is adoptively "filtered" to remove data redundancy, while in the second step an arithmetic coder is used to actually compress the information. The effectiveness of the proposed technique has been demonstrated on a set of case studies. As a result conventional approaches are outperformed reaching a compression ratio of 4.26 against 3.3 times
Critical thinking in nursing: Scoping review of the literature

This article seeks to analyse the current state of scientific knowledge concerning critical thinking in nursing. The methodology used consisted of a scoping review of the main scientific databases using an applied search strategy. A total of 1518 studies published from January 1999 to June 2013 were identified, of which 90 met the inclusion criteria. The main conclusion drawn is that critical thinking in nursing is experiencing a growing interest in the study of both its concepts and its dimensions, as well as in the development of training strategies to further its development among both students and professionals. Furthermore, the analysis reveals that critical thinking has been investigated principally in the university setting, independent of conceptual models, with a variety of instruments used for its measurement. We recommend (i) the investigation of critical thinking among working professionals, (ii) the designing of evaluative instruments linked to conceptual models and (iii) the identification of strategies to promote critical thinking in the context of providing nursing care.
3D curve-skeletons extraction and evaluation

A novel definition of the 3D curve-skeleton is presented. Many existing approaches to the problem can be formalized in the given definition. The main advantage of the presented mathematical model is that it allows strict quality assesment of the produced curveskeleton. The definition is based on the usage of fat curves. A fat curve is a 3D object which allows to approximate tubular fragments of the shape. A set of fat curves is used to approximate the entire shape; such a set can be considered as a generalization of the 2D medial axis. An example algorithm which obtains curve-skeletons due to the given definition is also presented. The algorithm is robust and efficient.
Audio fingerprinting based on normalized spectral subband centroids

For multimedia fingerprinting, it is crucial to extract relevant features that allow direct access to the distinguishing characteristics of a multimedia object. Features used for fingerprinting directly relate to the performance of the entire fingerprinting system. The paper proposes a novel audio fingerprinting method based on normalized spectral subband centroids. The spectral subband centroid is selected due to its resilience against equalization, compression, and noise addition. Both reliability and robustness issues in the fingerprinting system are addressed. Experimental results show that the proposed method is not only reliable, but also robust against various audio processing steps, including MP3 compression, equalization, random start, time-scale modification, and linear speed change.
Evolutionary Computational Method of Facial Expression Analysis for Content-based Video Retrieval using 2-Dimensional Cellular Automata

In this paper, Deterministic Cellular Automata (DCA) based video shot classification and retrieval is proposed. The deterministic 2D Cellular automata model captures the human facial expressions, both spontaneous and posed. The determinism stems from the fact that the facial muscle actions are standardized by the encodings of Facial Action Coding System (FACS) and Action Units (AUs). Based on these encodings, we generate the set of evolutionary update rules of the DCA for each facial expression. We consider a Person-Independent Facial Expression Space (PIFES) to analyze the facial expressions based on Partitioned 2D-Cellular Automata which capture the dynamics of facial expressions and classify the shots based on it. Target video shot is retrieved by comparing the similar expression is obtained for the query frame's face with respect to the key faces expressions in the database video. Consecutive key face expressions in the database that are highly similar to the query frame's face, then the key faces are used to generate the set of retrieved video shots from the database. A concrete example of its application which realizes an affective interaction between the computer and the user is proposed. In the affective interaction, the computer can recognize the facial expression of any given video shot. This interaction endows the computer with certain ability to adapt to the user's feedback.
Energy Efficient Zone Division Multihop Hierarchical Clustering Algorithm for Load Balancing in Wireless Sensor Network

Wireless sensor nodes are use most embedded computing application. Multihop cluster hierarchy has been presented for large wireless sensor networks (WSNs) that can provide scalable routing, data aggregation, and querying. The energy consumption rate for sensors in a WSN varies greatly based on the protocols the sensors use for communications. In this paper we present a cluster based routing algorithm. One of our main goals is to design the energy efficient routing protocol. Here we try to solve the usual problems of WSNs. We know the efficiency of WSNs depend upon the distance between node to base station and the amount of data to be transferred and the performance of clustering is greatly influenced by the selection of cluster-heads, which are in charge of creating clusters and controlling member nodes. This algorithm makes the best use of node with low number of cluster head know as super node. Here we divided the full region in four equal zones and the centre area of the region is used to select for super node. Each zone is considered separately and the zone may be or not divided further that’s depending upon the density of nodes in that zone and capability of the super node. This algorithm forms multilayer communication. The no of layer depends on the network current load and statistics. Our algorithm is easily extended to generate a hierarchy of cluster heads to obtain better network management and energy efficiency.
(Dis)embodied Job Search Communication Training: Comparative critical ethnographic analysis of materiality and discourse during the unequal search for work

Unemployment can be extremely challenging to manage. Depending on an individual’s social status, unemployment experiences can differ greatly. The longer people grapple with unemployment the more likely they are to seek help from unemployment support organizations. This study takes a comparative, critical ethnographic approach to the study of job search communication training at two separate unemployment support organizations considering intersections of social class and race. The analysis uses a communication lens in order to unpack communication expectations and assumptions embedded into the culture of unemployment support organizations that are tailored to different social class and racial groups. The findings reveal that the job search communication trainings are communicatively biased and divergent. Inequality molds and shapes the process of job search communication training and the progress of job searches. Working-class job seekers are required to communicatively assimilate during training while they manage material reality to survive. Upper-middle-class job seekers refine existing communication skill sets in order to search for work and rarely struggle to manage material needs. The findings point to important implications and areas for future research in workforce studies.
Antenna and Node Selection for Multi-Antenna Relay Networks in Correlated Channels

In this paper, we investigate the antenna and node selection issues for amplify-and-forward (AF) and decode-and-forward (DF) multi-antenna relay networks in correlated channels. Based on the channel statistics, optimal selection criteria for antenna and relay node are derived jointly, aiming to maximize the ergodic capacity. Instantaneous channel knowledge-based selection schemes, motivated by traditional antenna selection algorithms, are investigated as well. It is shown that the proposed node selection schemes derived from antenna selection on relay nodes are feasible and effective in relay systems. Statistical selection shows considerable capacity gain compared to full complexity scheme and random selection strategy in AF mode, while instantaneous selection performs better in DF relaying. Furthermore, the proposed schemes are shown to be robust to channel estimation errors due to their correlation-oriented nature.
Planning for Two: A Literature Review on Caregiver Stress near the End-of-Life

_Advances in end-of-life research have highlighted the risks associated with informal caregiving before and during bereavement. However, there are diverse conclusions on how to predict negative mental health outcomes and how to treat caregivers upon the development of psychological distress. In order to address this deficit in consensus, we searched PubMed and PsychInfo for relevant articles that identified predictors of negative health, assessed differences based on caregiver type, analyzed the development of complicated grief and depression, and discussed implications for interventions. Results emphasized the need for further research into specific interventions, on behalf of the caregiver, which can be implemented during end-of-life care planning. _