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The purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data.

Based on GM(1,1) and neural networks, a co-training model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions.

The experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results.

Fine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points.

The paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.

Training given to 250 UK sales representatives of the global pharmaceutical company Eli Lilly and Co. helped to make the launch of the firm’s male anti‐impotence drug Cialis the most successful in the firm’s 128‐year history. The training won a UK National Training Award. It also earned the UK training department a Lilly global training award for being the best of the 96 that the company has across the world.

A necessary preprocessing of document classification is to label some documents so that a classifier can be built based on which the remaining documents can be classified. Because each document differs in length and complexity, the cost of labeling each document is different. The purpose of this paper is to consider how to select a subset of documents for labeling with a limited budget so that the total cost of the spending does not exceed the budget limit, while at the same time building a classifier with the best classification results.

In this paper, a framework is proposed to select the instances for labeling that integrate two clustering algorithms and two centroid selection methods. From the selected and labeled instances, five different classifiers were constructed with good classification accuracy to prove the superiority of the selected instances.

Experimental results show that this method can establish a training data set containing the most suitable data under the premise of considering the cost constraints. The data set considers both “data representativeness” and “data selection cost,” so that the training data labeled by experts can effectively establish a classifier with high accuracy.

No previous research has considered how to establish a training set with a cost limit when each document has a distinct labeling cost. This paper is the first attempt to resolve this issue.

The purpose of this paper is to analyse factors influencing perceptions of preparedness in the response to terrorist attacks of operational personnel in Swedish emergency organizations.

Data were collected using a questionnaire distributed to operational personnel from the police, rescue and ambulance services in eight Swedish counties; 864 responses were received and analysed.

Three aspects of the perception of preparedness for terrorist attacks among Swedish emergency responders were studied: willingness to respond; level of confidence with tasks; and estimated management capability. Factors which positively influenced these perceptions were male sex, training in first aid and dealing with mass casualty incidents, terrorism-related management training (MT), table-top simulations, participation in functional exercises, and access to personal protective equipment (PPE); work experience was inversely related. Occupation in police or rescue services was positively associated with willingness to respond whereas occupation within the emergency medical services was positively associated with estimated management capability.

These findings show that terrorism-related MT and access to PPE increase the perceptions of preparedness for terrorism among the emergency services, aiding judgements about investments in preparedness by crisis management planners.

Limited research in disaster management and hazard preparedness has been conducted in a European context, especially regarding terrorism. Little is known about aspects of preparedness for terrorism in Sweden, particularly from the perspective of the emergency responders.

As a result of the fundamental reorganisation of local government functions, boundaries and structures implemented in 1974, the UK's Local Government Training Board launched a pilot Management Development Adviser (MDA) Programme in the following year. Initial programme design reflected the LGTB's belief that reorganisation, coupled with changes in the political, economic and social environment within which local authorities operated, placed greater emphasis on individuals' abilities to manage change at all levels within the organisation.

The purpose of the current study was two‐fold: to explore police officers' perceptions of the daily challenges involved in child abuse investigation and how those challenges affect their ability to undertake child abuse investigations, and to explore how these challenges are managed on a daily basis.

This study employed a qualitative research design. In‐depth interviews were conducted with a diverse sample of 25 police officers working in child abuse units across three Australian states.

Inductive thematic analysis revealed that heavy caseload and collaboration with other professional groups are two key sources of negative work stress frequently associated with child abuse investigation. Further, despite the provision of organisational strategies aimed at reducing work stress, the officers tended to rely predominantly on informal coping mechanisms.

This study has raised many questions for further research aimed at developing interventions to assist police organisations in managing work stress.

This paper provides an in‐depth analysis of the key challenges associated with child abuse investigation and the coping mechanisms employed for overcoming these challenges from the unique perspective of police officers authorised to investigate child abuse.

Utilizing data drawn from 18 in-depth case studies the authors explore in detail the factors shaping employment in a diverse range of Irish small- and medium-sized enterprises. Existing theory in HRM is deemed inadequate in capturing the complexity of HRM in SMEs especially as it treats organizations as hermetically sealed entities. In an effort to animate the criticism directed at normative models of HRM the authors use a conceptual framework with an emergent, open systems theoretical proposition to examine the parameters, dynamics and determining factors of HRM at each of the case study companies. The results show that the notion of a normative HRM model was not coherent in terms of actual practices but rather reactive, and emergent HRM-related processes were often imposed to meet legislative requirements or to reinforce owner–manager legitimacy and control. The authors conclude that an appreciation of the interaction between structural factors both inside and outside the immediate work milieu is crucial if the heterogeneity of HRM in SMEs is to be adequately accommodated and understood.

Understanding the outcomes of co-creation (CC) in healthcare is increasingly gaining multidisciplinary scientific interest. Although more and more service management scholars have pointed out the benefits of cross-fertilization between the various research fields, the literature on this topic is still scattered and poorly integrated. This study aims to summarize and integrate multiple strands of extant knowledge CC by identifying the outcomes of health CC and the determinants of these outcomes and their relationships.

A structured literature review was conducted per PRISMA guidelines. A total of 4,189 records were retrieved from the six databases; 1,983 articles were screened, with 161 included in the qualitative thematic analysis.

This study advances a comprehensive framework for healthcare CC based on a thorough analysis of the outcomes and their determinants, that is, antecedents, management activities and institutional context. Extant research rarely evaluates outcomes from a multidimensional and systemic perspective. Less attention has been paid to the relationship among the CC process elements.

This study offers an agenda to guide future studies on healthcare CC. Highlighting some areas of integration among different disciplines further advances service literature.

The framework offers an operational guide to better shape managerial endeavors to facilitate CC, provide direction and assess multiple outcomes.

This is the first extensive attempt to synthesize and integrate multidisciplinary knowledge on CC outcomes in healthcare settings by adopting a systematic perspective on the overall process.

As a foundational issue of social mining, sentiment classification suffered from a lack of unlabeled data. To enhance accuracy of classification with few labeled data, many semi-supervised algorithms had been proposed. These algorithms improved the classification performance when the labeled data are insufficient. However, precision and efficiency are difficult to be ensured at the same time in many semi-supervised methods. This paper aims to present a novel method for using unlabeled data in a more accurate and more efficient way.

First, the authors designed a boosting-based method for unlabeled data selection. The improved boosting-based method can choose unlabeled data which have the same distribution with the labeled data. The authors then proposed a novel strategy which can combine weak classifiers into strong classifiers that are more rational. Finally, a semi-supervised sentiment classification algorithm is given.

Experimental results demonstrate that the novel algorithm can achieve really high accuracy with low time consumption. It is helpful for achieving high-performance social network-related applications.

The novel method needs a small labeled data set for semi-supervised learning. Maybe someday the authors can improve it to an unsupervised method.

The mentioned method can be used in text mining, image classification, audio processing and so on, and also in an unstructured data mining-related field. Overcome the problem of insufficient labeled data and achieve high precision using fewer computational time.

Sentiment mining has wide applications in public opinion management, public security, market analysis, social network and related fields. Sentiment classification is the basis of sentiment mining.

According to what the authors have been informed, it is the first time transfer learning be introduced to AdaBoost for semi-supervised learning. Moreover, the improved AdaBoost uses a totally new mechanism for weighting.

The British Library Lending Division serials system is unusual both in its size and in the fact that it has been automated for more than 20 years. The present version involves an on‐line database in‐house, using a PDP‐11/34, together with external operations which produce an index on microfiche. The in‐house part of the system is concerned with purchase control. This paper describes the system and indicates the philosophy which led to its design.