Our motivation for conducting this research is driven by the lack of studies focusing on the acknowledgments sections of published papers. Another motivation is the lack of a study examining the countries and organizations mentioned in the acknowledgments section and their influence—something that cannot be analyzed using a citation or co-authorship relationship. Concentrating on the qualitative aspects of acknowledgments has been limited because of the atypical pattern of the acknowledgment section. Our research aims to identify useful information hidden within the acknowledgment sections of the articles stored in the PubMed Central database and to analyze a map of influence via a country-acknowledgment network. To solve the problems, we use the topic modeling to analyze topics of acknowledgments and conduct a basic network analysis to find the difference in the co-the country network and acknowledgment network. A word-embedding model is used to compare the semantic similarity that exists between the authors and countries extracted from our original dataset. The result of topic modeling suggests that funding has become a critical topic in acknowledgments. The results of network analysis indicate that some large countries work as hubs in terms of both implicitly and explicitly while revealing that some countries such as China do not frequently work with other countries. The word-embedding model built by acknowledgments suggests that the authors frequently referenced in acknowledgments are also likely to be referred to in a similar context. It also implies that the publishing country of a paper has little effect on whether it receives an acknowledgment from any other specific country. Through these results, we conclude that the content in acknowledgments extracted from the papers can be divided into two categories—funding and appreciation. We also find that there is no clear relationship between the publication country and the countries mentioned in the acknowledgment section.
With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would be of great use to the doctors if an effective clinical decision support system is available to generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the usefulness of patients’ diagnosis information in supporting effective retrieval of relevant literature, but such diagnosis information is often missing in most cases. Furthermore, existing diagnosis prediction systems mainly focus on predicting a small range of diseases with well-formatted features, and it is still a great challenge to perform large-scale automatic diagnosis predictions based on noisy medical records of the patient. In this paper, we propose automatic diagnosis prediction methods for enhancing the retrieval in a clinical decision support system, where the prediction is based on evidences automatically collected from publicly accessible online knowledge bases such as Wikipedia and Semantic MEDLINE Database (SemMedDB). The assumption is that relevant diseases and their corresponding symptoms co-occur more frequently in these knowledge bases. Our methods use Markov Random Field (MRF) model to identify diagnosis candidates in the knowledge bases, and their performance was evaluated using test collections from the Clinical Decision Support (CDS) track in TREC 2014, 2015, and 2016. The results show that our methods can automatically predict diagnosis with about 75% accuracy, and such predictions can significantly improve the related biomedical literatures retrieval. Our methods can generate comparable retrieval results to the state-of-the-art methods, which utilize much more complicated methods and some manually crafted medical knowledge. One possible future work is to apply these methods in collaboration with real doctors.
Notes: a portion of this work was published in iConference 2017 as a poster, which won the best poster award. This paper greatly expands the research scope over that poster.
Book search is far from a solved problem. Complex information needs often go beyond bibliographic facts and cover a combination of different aspects, such as specific genres or plot elements, engagement or novelty. Conventional book metadata may not be sufficient to address these kinds of information needs. In this paper, we present a large-scale empirical comparison of the effectiveness of book metadata elements for searching complex information needs. Using a test collection of over 2 million book records and over 330 real-world book search requests, we perform a highly controlled and in-depth analysis of topical metadata, comparing controlled vocabularies with social tags. Tags perform better overall in this setting, but controlled vocabulary terms provide complementary information, which will improve a search. We analyze potential underlying factors that contribute to search performance, such as the relevance aspect(s) mentioned in a request or the type of book. In addition, we investigate the possible causes of search failure. We conclude that neither tags nor controlled vocabularies are wholly suited to handling the complex information needs in book search, which means that different approaches to describe topical information in books are needed.
Although user information disclosure behavior in the context of social network service(SNS) has been well studied in previous literature, there is a lack of understanding about user information withholding behavior. To fill this research gap, the present study assumes that there might be a three-way interaction among information sensitivity, prevention focus, and interdependent self-construal regarding information withholding. The proposed model is empirically tested through an online survey of 479 users in the context of WeChat, one of the most popular SNSs in China. The results of hierarchical regression analysis verify the three-way interaction that prevention focus positively moderates the relationship between information sensitivity and information withholding, and interdependent self-construal strengthens the moderating effect of prevention focus. Findings in light of theoretical and practical implications as well as limitations of the study are discussed.
Alan Bury, Dimitrios Paraskevadakis, Jun Ren and Farhan Saeed
The task of producing a generic model of the modal choice decision making process is a challenging one. Modal choice is strongly influenced by the infrastructure limitations and geographical constraints of the area in which the decision is being made. With this in mind, addressing modal choice on an individual basis for each region may be the optimal solution. This is the approach adopted in this paper. The creation of a modal choice model is a multistage process of which this paper addresses the first stage, the production a framework of the decision making process. Firstly, a number of criteria that are commonly used in modal choice models are identified. Then a number of gaps in the criteria utilized in previous papers are established. Subsequently, the method used to produce a framework of the decision making process within North West England’s Atlantic Gateway is outlined. Through consultation with transport industry experts in North West England, an initial list of sixty eight papers was reduced to thirty six that were considered to be of specific relevance to modern day freight transportation within their region. The criteria used in each of these papers were then, along with further industry input, used to create the foundation on which a modal choice framework specific to the Atlantic Gateway could be built. A greater understanding of what influences modal choice within this region will allow informed decisions to be made by policy makers on how to more efficiently utilize the available modes of freight transport. Having established this, future work can then go on to build upon these findings. This paper recommends that future work is performed to establish the weights of each criteria and sub-criteria within the framework. This should then be followed by establishing industry’s perceptions of the best and worst alternatives for moving freight within the Atlantic Gateway.
Oluwaseyi Joseph Afolabi and Kolawole Taofeek Gbadamosi
The significant of public transport of cities in many developing countries lies in the fundamental fact that mobility and accessibility are essential for economic growth and of necessity to provide efficient and effective movement for goods and services. The collapse of public intra-city transport system paved way for the rise of motorcycles as means of public transportation in Nigeria. This paper discusses the impact of commercial motorcycle operation as a means of urban transportation. Of the 200 questionnaires administered, 191 questionnaires were received for analysis using the Statistical Package for Social Sciences (SPSS). Secondary data was also sourced to serve as complement to the primary data, thus allowing for a robust research. Descriptive statistical tools such as percentages were adopted to present the socio - economic characteristics in the area. Findings showed that the majority of the users are adult between the aged 31 and 40 years. Most of them (53.8%) are married and are fairly educated. Furthermore, it was recommended that the Nigerian government should provide employment opportunities for our teeming youth as this will go a long way in the reduction of number of youth who as a result of unemployment took to motorcycle riding business.
The article explores the use of FMEA method in the logistics processes in manufacturing plants in Bulgaria. The surveyed enterprises have a system ISO 9001 and apply different methods of analysis and assessment of logistics processes. The purpose of this study is to present a model for improving the reliability of logistics processes through the FMEA (Failure Mode and Effect Analysis) method.
An inquiry among 14 organizations in the implementation of FMEA was conducted. The results show that FMEA is not used for assessment in logistics processes and provides useful insights for decision-making to improve the reliability of supply. A framework based on the survey is presented for determining the reliability of logistics processes in manufacturing plants. The study demonstrates the applicability of the method in logistics processes and the role FMEA can play in assessing logistics processes.
The pharmaceutical industry is one of the most competitive businesses in the world. Supply chain in this industry has been directed towards the production of large batches to avoid lack of supplies, and the achievement of regulatory requirements, at the cost of high level of inventory, higher costs and inventory write-off due to expiration or other reasons. In recent years this industry is facing major changes and challenges such as intense globalization processes, increased competition and innovations in technologies, which has broadened and deepened risks in supply chain.
The paper reports the results of the study of the risk in distribution processes of Slovenian pharmaceutical companies, which was conducted among five companies and aims to draw attention to risks that arise in supply chain, to emphasize the importance of their management and to present a model for an effective assessment of risk in companies, developed at the Faculty of Logistics.
The paper addresses an analysis of potential synergies in collaboration between an observed Port in the Mediterranean Sea and Central-European logistic railway-services based company. Both companies have established a strategic partnership. The main motive was cooperation in rail transport, with a particular emphasis on potential synergies that would a rail traffic have brought to a port’s business. For the purpose of synergies valuation under uncertain conditions, a Monte Carlo simulation-based framework with integrated discounted cash flow (DCF) model is applied. The possible values of future synergies are calculated via the DCF model by simultaneously changing values of different uncertain financial parameters at each repetition of a Monte Carlo scenario-playing mechanism. In this process, predicted forecasts of future synergetic throughputs are also used for various types of observed cargo. As it turned out, the generated synergies’ values follow the approximate normal distribution. Based on statistical inference and analysis of probability intervals it was discovered that there might indeed exist certain important synergies in the collaboration between both companies. This fact has convinced us into a belief in the correctness of companies′ decision to enter into such kind of strategic cooperation.