bluuebunny/crossref_metadata_2025 huggingface dataset:
The Use of Social Media by Local Governments
Authors: Leocadia Diaz Romero
Published: February 2018

This chapter describes how social media, if correctly used, can enhance cultural, political, economic and social engagement. They also represent key communication tools for administrators to highlight the principles of openness, transparency, and to promote civic engagement. Nowadays, local governments have launched social media strategies. After reviewing necessary categories such as E-Government and E-Democracy, this chapter explores in what ways the use of Information and Communication Technologies (ICTs) can benefit governance, and foster transparency, participation. The chapter describes contemporary setbacks and challenges officials at the local level which have been encountered in the implementation and development of social media. Finally, it offers an empirical approach of the utilization of ICTs by the Office of the Mayor of New York City and, therefore, describes the portal NYC.gov.
Mediation Is the Message
Authors: Briziarelli Marco , Flores Joseph
Published: February 2018

In this chapter, the authors explore the deep level of ambiguities that characterizes the relationship between social media and capitalism, and the social, political economic context in which in several regions of world they operate. This reflection is provided in a particular moment of history in which media may have become at the same time the main “language” in order to decipher contemporary economics as well as the material terrain in which those economic activities develop. Yet, while social media seems to have been more and more integrated within the logic of capitalism, and capitalism has increasingly assumed the morphology of an informational dispersal, their relationship is not straightforward but defined by a series of deep rooted tensions.
The Transmission of the Great Recession (2008-2009) and the Sovereign Debt Crisis (2010-2012) to Latin America
Authors: Santillán-Salgado Roberto J. , Ayala-Gaytán Edgardo A.
Published: February 2018

In this work we discuss econometric evidence on four major issues that relate to the six largest Latin American economies (Argentina, Brazil, Chile, Colombia, Mexico, and Peru) during the two consecutive international financial crises between 2008 and 2012. Our first concern has to do with the mechanism of transmission of the international financial crisis and its secondary effects on the real and financial sectors of our sample countries. The second aspect that we explore refers to the actual magnitude of both, real and financial effects of the crisis. Our third objective has to do with an evaluation of the role played by individual countries' external macroeconomic vulnerability. And, finally, we propose a contra-factual analysis of the growth performance of our sample of Latin American economies, with the growth performance they would have experienced in a hypothetical scenario of no external turmoil.
City Group Optimization
Authors: Yang Yijun
Published: February 2018

City group refers to a collection of cities. Through the development and growth, these cities form a chain of metropolitan areas. In a city group, cities are divided into central cities and subordinate cities. Generally, central cities have greater chances to develop. However, subordinate cities may not have great chances to develop unless they are adjacent to central cities. Thus, a city is more likely to develop well if it is near a central city. In the process, the spatial distribution of cities changes all the time. Urbanologists call the above phenomena the evolution of city groups. In this chapter, the city group optimization algorithm is presented, which is based on urbanology and mimics the evolution of city groups. The robustness and evolutionary process of the proposed city group optimization algorithm are validated by testing it on 15 benchmark functions. The comparative results show that the proposed algorithm is effective for solving complexly continuous problems due to a stronger ability to escape from local optima.
Magnetotactic Bacteria Optimization Algorithm (MBOA) for Function Optimization
Authors: Liu Lili , Mo Hongwei
Published: February 2018

Magnetotactic bacteria is a kind of prokaryotes with the characteristics of magnetotaxis. Magnetotactic bacteria optimization algorithm (MBOA) is an optimization algorithm based on the characteristics of magnetotaxis. It mimics the development process of magnetosomes (MTSs) in magnetotactic bacteria. In this chapter, four pairwise MTSs regulation schemes based on the best individual and randomly chosen one are proposed to study which scheme is more suitable for solving optimization problems. They are tested on 14 functions and compared with many popular optimization algorithms, including PSO, DE, ABC, and their variants. Experimental results show that all the schemes of MBOA are effective for solving most of test functions but have different performance on a few test functions. The fourth MBOA scheme has superior performance to the compared methods on many test functions. In this scheme, the algorithm searches around the current best individual to enhance the convergence of MBOA and the individual can migrate to the current best individual to enhance the diversity of the MBOA.
Assessment of Gamma-Ray-Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures
Authors: Alamaniotis Miltiadis , Tsoukalas Lefteri H.
Published: February 2018

The analysis of measured data plays a significant role in enhancing nuclear nonproliferation mainly by inferring the presence of patterns associated with special nuclear materials. Among various types of measurements, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this chapter, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular, FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, where non-zero coefficients express the detected signatures. FWA is tested on a set of experimentally obtained measurements optimizing various objective functions—MSE, RMSE, Theil-2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Furthermore, FWA is benchmarked against genetic algorithms and multiple linear regression, showing its superiority over those algorithms regarding precision with respect to MAE, MAPE, and MAP measures.
Multi-Thresholding of Histopathological Images Using Fuzzy Entropy and Parameterless Cuckoo Search
Authors: Dhal Krishna Gopal Gopal , Sen Mandira , Das Sanjoy
Published: February 2018

This chapter presents a multi-level histopathological image thresholding approach based on fuzzy entropy theory. This entropy measure is maximized to obtain the optimal thresholds of the image. In order to solve this problem, one self-adaptive and parameter-less cuckoo search (CS) algorithm has been employed, which leads to an accurate convergence towards the optima within less computational time. The performance of the proposed CS is also compared with traditional CS (TCS) algorithm and particle swarm optimization (PSO). The outcomes of the proposed fuzzy entropy-based model are compared with Shannon entropy-based model both visually and statistically in order to establish the perceptible difference in image.
Particle Swarm Optimization for Model Predictive Control in Reinforcement Learning Environments
Authors: Hein Daniel , Hentschel Alexander , Runkler Thomas A. , Udluft Steffen
Published: February 2018

This chapter introduces a model-based reinforcement learning (RL) approach for continuous state and action spaces. While most RL methods try to find closed-form policies, the approach taken here employs numerical online optimization of control action sequences following the strategy of nonlinear model predictive control. First, a general method for reformulating RL problems as optimization tasks is provided. Subsequently, particle swarm optimization (PSO) is applied to search for optimal solutions. This PSO policy (PSO-P) is effective for high dimensional state spaces and does not require a priori assumptions about adequate policy representations. Furthermore, by translating RL problems into optimization tasks, the rich collection of real-world-inspired RL benchmarks is made available for benchmarking numerical optimization techniques. The effectiveness of PSO-P is demonstrated on two standard benchmarks mountain car and cart-pole swing-up and a new industry-inspired benchmark, the so-called industrial benchmark.
State-of-the-Art Information Retrieval Tools for Biological Resources
Authors: Lal Shashi Bhushan , Sharma Anu , Chaturvedi Krishna Kumar , Farooqi Mohammad Samir , Kumar Sanjeev , Mishra Dwijesh Chandra , Jha Mohit
Published: February 2018

With the advancements in sequencing technologies, there is an exponential growth in the availability of the biological databases. Biological databases consist of information and knowledge collected from scientific experiments, published literature and statistical analysis of text, numerical, image and video data. These databases are widely spread across the globe and are being maintained by many organizations. A number of tools have been developed to retrieve the information from these databases. Most of these tools are available on web but are scattered. So, finding a relevant information is a very difficult, and tedious task for the researchers. Moreover, many of these databases use disparate storage formats but are linked to each other. So, an important issue concerning present biological resources is their availability and integration at single platform. This chapter provides an insight into existing biological resources with an aim to provide consolidated information at one place for ease of use and access by researchers, academicians and students.
Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search
Authors: Chawla Suruchi
Published: February 2018

This chapter explains the multi-agent system for effective information retrieval using information scent in query log mining. The precision of search results is low due to difficult to infer the information need of the small size search query and therefore information need of the user is not satisfied effectively. Information Scent is used for modeling the information need of user web search session and clustering is performed to identify the similar information need sessions. Hyper Link-Induced Topic Search (HITS) is executed on clusters to generate the Hubs and authorities for web page recommendations to users who search with similar intents. This multi-agent system based on clustered query sessions uses query operations like expansion and recommendation to infer the information need of user search queries and recommends Hubs and authorities for effective web search.
The KnowledgeStore
Authors: Corcoglioniti Francesco , Rospocher Marco , Cattoni Roldano , Magnini Bernardo , Serafini Luciano
Published: February 2018

Although the quantity of structured information on the Web and within organizations is increasing, the majority of information remains available only in unstructured form. While different in form, both unstructured and structured information sources provide information about entities in the world and their properties and relations; still, frameworks for their seamless integration have not been deeply investigated. In this paper the authors describe the KnowledgeStore, a scalable, fault-tolerant, and Semantic Web grounded open-source storage system for interlinking structured and unstructured data. They present the concept, design, function and implementation of the system, and report on its concrete usage in three application scenarios within the NewsReader EU project, where it stores and supports the querying of millions of news articles interlinked with millions of RDF triples extracted from text and imported from Linked Open Data sources. The authors report on data population and data retrieval performances of the system measured through a number of experiments, and they also discuss the practical issues and lessons learned from these experiences.
Nesting Strategies for Enabling Nimble MapReduce Dataflows for Large RDF Data
Authors: Ravindra Padmashree , Anyanwu Kemafor
Published: February 2018

Graph and semi-structured data are usually modeled in relational processing frameworks as “thin” relations (node, edge, node) and processing such data involves a lot of join operations. Intermediate results of joins with multi-valued attributes or relationships, contain redundant subtuples due to repetition of single-valued attributes. The amount of redundant content is high for real-world multi-valued relationships in social network (millions of Twitter followers of popular celebrities) or biological (multiple references to related proteins) datasets. In MapReduce-based platforms such as Apache Hive and Pig, redundancy in intermediate results contributes avoidable costs to the overall I/O, sorting, and network transfer overhead of join-intensive workloads due to longer workflows. Consequently, providing techniques for dealing with such redundancy will enable more nimble execution of such workflows. This paper argues for the use of a nested data model for representing intermediate data concisely using nesting-aware dataflow operators that allow for lazy and partial unnesting strategies. This approach reduces the overall I/O and network footprint of a workflow by concisely representing intermediate results during most of a workflow's execution, until complete unnesting is absolutely necessary. The proposed strategies are integrated into Apache Pig and experimental evaluation over real-world and synthetic benchmark datasets confirms their superiority over relational-style MapReduce systems such as Apache Pig and Hive.
Three-Dimensional Information Retrieval (3DIR)
Authors: Demian Peter , Ruikar Kirti , Sahu Tarun , Morris Anne
Published: February 2018

An increasing amount of information is packed into BIMs, with the 3D geometry serving as a central index leading to other information. The 3DIR project investigates information retrieval from such environments. Here, the 3D visualization can be exploited when formulating queries, computing the relevance of information items, or visualizing search results. The need for such a system was specified using workshops with end users. A prototype was built on a commercial BIM platform. Following an evaluation, the system was enhanced to exploit model topology. Relationships between 3D objects are used to widen the search, whereby relevant information items linked to a related 3D object (rather than linked directly to objects selected by the user) are still retrieved but ranked lower. An evaluation of the enhanced prototype demonstrates its effectiveness but highlights its added complexity. Care needs to be taken when exploiting topological relationships, but that a tight coupling between text-based retrieval and the 3D model is generally effective in information retrieval from BIMs.
A Similarity Measure Across Ontologies for Web Services Discovery
Authors: Fellah Aissa , Malki Mimoun , Elci Atilla
Published: February 2018

Given the critical and difficult nature of discovering Web services in the development process of service oriented architectures, several studies have been proposed to solve this problem. There is a real need to work for matching semantic Web services which use different ontologies. In responding to this need, measuring semantic similarity between SWS may be reduced to the calculation of similarity between ontological concepts. This work is a contribution to achieve semantic interoperability for Web services in a multi-ontology environment, for which the authors present a generic framework for Web services discovery. Here their focus is on the semantic similarity measure-based core of their framework and the authors present a novel algorithm for concepts matching between different ontologies. Results of the experiments confirm the viability of the semantic similarity measure.
TemporalClassifier
Authors: Pradhan Rahul , Sharma Dilip Kumar
Published: February 2018

Users issuing query on search engine, expect results to more relevant to query topic rather than just the textual match with text in query. Studies conducted by few researchers shows that user want the search engine to understand the implicit intent of query rather than looking the textual match in hypertext structure of document or web page. In this paper the authors will be addressing queries that have any temporal intent and help the web search engines to classify them in certain categories. These classes or categories will help search engine to understand and cater the need of query. The authors will consider temporal expression (e.g. 1943) in document and categories them on the basis of temporal boundary of that query. Their experiment classifies the query and tries to suggest further course of action for search engines. Results shows that classifying the query to these classes will help user to reach his/her seeking information faster.
Filtering Method for the Annotated and Non-Annotated Web Pages
Authors: Dammak Sahar Maâlej , Jedidi Anis , Bouaziz Rafik
Published: February 2018

With the great mass of the pages managed through the world, and especially with the advent of the Web, it has become more difficult to find the relevant pages after an interrogation. Furthermore, the manual filtering of the indexed Web pages is a laborious task. A new filtering method of the annotated Web pages (by our semantic annotation process) and the non-annotated Web pages (retrieved from search engine “Google”) is then necessary to group the relevant Web pages for the user. In this paper, the authors will first synthesize their previous work of the semantic annotation of Web pages. Then, they will define a new filtering method based on three activities. The authors will also present their querying and filtering component of Web pages; their purpose is to demonstrate the feasibility of the filtering method. Finally, the authors will present an evaluation of this component, which has proved its performance for multiple domains.
Gravizor
Authors: Bouramoul Abdelkrim
Published: February 2018

Users of Web search engines are generally confronted to numerous responses that are rarely structured, making it difficult to analyze the available results. Indeed, the linear results displayed through lists ordered according to a relevance criterion, although still widely used, seem often limitless. A solution to this problem is to improve the interfaces for better visualization of large number of results. In this paper, we propose modeling and implementation of a tool for graphical visualization and manipulation of results returned by search engines. The goal is to facilitate the analysis, the interpretation and the supervision of users' information needs. The architecture of the ‘Gravisor' tool is based on Multi-Agent paradigm. It is composed of four agents working in full cooperation and coordination. We hope that besides the web information retrieval field, the three graphical visualization modes offered by the ‘Gravisor' tool will be a promising alternative for better information visualization in other areas.
Semantic Technologies and Big Data Analytics for Cyber Defence
Authors: Leenen Louise , Meyer Thomas
Published: February 2018

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.
A New Method for Building Probabilistic Ontology (Prob-Ont)
Authors: Hlel Emna , Jamoussi Salma , Ben Hamadou Abdelmajid
Published: February 2018

During the past years, ontologies are widely used for representing knowledge of complex domains. Despite that the ontologies (classical ontologies) have become standard for representing knowledge; however, they are not able to represent and reason with uncertainty which is one of the characteristics of the world that must be handled. Probabilistic Ontologies have come to remedy this defect. This paper is part of this framework in which the authors have proposed a new method of probabilistic ontology construction, named Prob-Ont, by integrating uncertainty to elements of OWL ontology (especially to instances and/or relations). As a case study, the authors have constructed a probabilistic ontology for the domain of scientific documentation system (dblp).
SPedia
Authors: Aslam Muhammad Ahtisham , Aljohani Naif Radi
Published: February 2018

Producing the Linked Open Data (LOD) is getting potential to publish high-quality interlinked data. Publishing such data facilitates intelligent searching from the Web of data. In the context of scientific publications, data about millions of scientific documents published by hundreds and thousands of publishers is in silence as it is not published as open data and ultimately is not linked to other datasets. In this paper the authors present SPedia: a semantically enriched knowledge base of data about scientific documents. SPedia knowledge base provides information on more than nine million scientific documents, consisting of more than three hundred million RDF triples. These extracted datasets, allow users to put sophisticated queries by employing semantic Web techniques instead of relying on keyword-based searches. This paper also shows the quality of extracted data by performing sample queries through SPedia SPARQL Endpoint and analyzing results. Finally, the authors describe that how SPedia can serve as central hub for the cloud of LOD of scientific publications.
Developing Engineering Students' Communication and Information Retrieval Skills Utilizing Capstone Projects
Authors: Blicblau Aaron S. , Naser Jamal
Published: February 2018

The pedagogy of engineering requires a better understanding of the requirements of students' abilities to learning the skills necessary for working in the engineering community. In many engineering courses around the world, one of the key aspects required of the students is that they complete an independent project in their final year of studies incorporating information retrieval and subsequent communication skills. The current work provides details teaching and learning approaches to enhance student abilities and expertise involving research skills, communication skills, and information retrieval integrated within capstone projects. Findings from this the work indicated that both domestic and international students benefited from the intensive tutorial activities involving computer based information retrieval skills. The implementation of active tutorial sessions resulted in increased grades for the majority of students, highlighting the importance of intensive active learning events for final year capstone engineering students.
Proximity-Based Good Turing Discounting and Kernel Functions for Pseudo-Relevance Feedback
Authors: Khennak Ilyes , Ezzouar Bab
Published: February 2018

During the last few years, it has become abundantly clear that the technological advances in information technology have led to the dramatic proliferation of information on the web and this, in turn, has led to the appearance of new words in the Internet. Due to the difficulty of reaching the meanings of these new terms, which play an essential role in retrieving the desired information, it becomes necessary to give more importance to the sites and topics where these new words appear, or rather, to give value to the words that occur frequently with them. For this purpose, in this paper, the authors propose a new robust correlation measure that assesses the relatedness of words for pseudo-relevance feedback. It is based on the co-occurrence and closeness of terms, and aims to select the appropriate words that best capture the user information need. Extensive experiments have been conducted on the OHSUMED test collection and the results show that the proposed approach achieves a considerable performance improvement over the baseline.
Overview of Approaches and Future Challenges for Development of Music Recommendation Socio-Technical Systems
Authors: Lugovic Sergej
Published: February 2018

This paper analyses the position of music recommendations in the wider context of music information behavior research and proposes five music information behavior dimensions: socio-cognitive information experience, information seeking, information retrieval, recommendations, and content consumption and analysis. It examines different approaches in the development of music recommendation systems (RS) which are applicable to all types of web information resources. These approaches are classified as content-based, collaborative, demographic, knowledge-based, meta-data-based, emotion-based and context-based, while the hybrid approach to RS development combines two or more approaches into one. Also, recent developments in the domain of music recommendations are discussed in detail. Finally, challenges and opportunities for collaboration between the scientific and the commercial communities on the development of new RS models are being explored.
Learning Analytics
Authors: Singh Rakesh Pratap
Published: February 2018

Learning analytics offers many exceptional opportunities, but how can a programme or institution use it successfully? It provides better insight into the teaching process and the feedback from college students ultimately improves schooling: this is the concept behind learning analytics. The use of learning analysis to guide the design and deployment of learning is becoming more popular. Educational institutions at all levels are realising that with the help of technology they are better able to gather data that would be very valuable when analysed. The aim of learning analytics is to apply facts from extraordinary structures and databases that help students to learn and teachers to improve their teaching strategies. To ensure that educational institutions obtain their greatest desires, as done through the usage of learning analytics, there is a vital need to increase the viable requirements analysis method. This is in addition to carrying out the performance evaluation consistent with it. This chapter discusses learning analytics and gives a critical analysis of the potential, protection, and privacy that learning analytics has in educational institutions.
Assessment and Evaluation of Students' Performance Without Learning Analytics
Authors: Zesu Sushant
Published: February 2018

Humans have constantly tried to assess and measure individual differences. But in the beginning of the prevailing century there has been a fast advancement on this discipline. The old methods of assessment have been defective and unreliable. In their area, reliable and standardized equipment of assessment have now been constructed. Every teacher desires that s/he must by some means assess the academic achievement of the student's performance that s/he has taught. This is why examinations are every day and use every tool of training. But today, the traditional system of exam is taken into consideration. Ruled defective and in its region, a new tool is deliberated and taken into consideration as goal related to real global duties. In this chapter, the author explains various types of examinations.
Learning Analytics in the Perspective of Enhancing Students' Performance
Authors: Malik Neha
Published: February 2018

Prior to the growing efforts for knowledge, predicting, and enhancing college students' learning in instructional settings, learning analytics emerges as a candidate studies region to address such problems. Hence, several studies have been conducted; in addition, numerous conceptual and theoretical perspectives have been arisen. Moreover, quite exciting and beneficial results have been produced all through the learning analytics brief life time. However, a clean concept of numerous questions is still pending (e.g., What does getting to know analytics suggest? What are its backgrounds, related domain names, and underlying factors? Which can be the devices of its applications? and What about the developments and demanding situations to be taken into consideration?). That is why the chapter explains the learning analytics background, nature, including new and applicable methods, and a relation of risks and possibilities.
Synergy With the World Soul
Authors: Schafer Stephen Brock
Published: February 2018

It may be stipulated that people live in an emergent media reality. Based on robust scientific research in diverse fields, this article shows how research on Jungian dream analysis and neurobiological “coherence” comprise a synchronistic formula for social activism on a scale necessary to achieve synergy with the World Soul that exists as high frequency “bandwidths”—sentience that may be accessed by maintaining high energy coherent frequencies of a humanitarian collective. This is also by using the media-sphere as biofeedback according to the Jungian process of “Compensation” to heal contextual human collectives. In order to establish a practical framework for dealing collectively with the global crisis of values and to avoid capitulation to the forces of darkness, our assumption is that—insofar as people now know—sentience consists of infinite cosmic dimensions or “bandwidths”—an admixture of frequency, color, and sound that exists in a superconductive field approximating the holographic theories of David Bohm and Karl Pribram.
Finding Inner Strength in the Face of Adversity Kgopolano GROW Group Empowerment Journey
Authors: Nthomang Keitseope
Published: February 2018

This article describes how numerous studies in Botswana have found high levels of poverty among women, in particular, those in female-headed households. Extreme poverty among women continues despite government programme interventions designed to end poverty. This article reports the findings of an initially donor-funded women's empowerment project in an urban village, Molepolole, Botswana. At the center of this project are 14 women who are experimenting with the GROW model to transform their lives. Through in-depth interviews and focus group discussions the women's views on the past, present, and future of the project were solicited. Content and thematic analysis were utilized on the data. The results confirm the potential of the GROW model in transforming the lives of women facing insurmountable hardships. The Grow model inspired women to gather inner strength and break the cycle of poverty by continuing the project beyond donor funding.
Named Entity Based Ranking with Term Proximity for XML Retrieval
Authors: Roko Abubakar , Doraisamy Shyamala , Azman Azreen , Jantan Azrul Hazri
Published: February 2018

In this article, an indexing scheme that includes the named entity category for each indexed term is proposed. Based on this, two methods are proposed, one to infer the semantics of an XML element based on its data content, called the confidence value of the element, and the second method computes the proximity scores of the query terms. The confidence value of an element is obtained based on the probability of a named entity category in the data content of the underlying XML element. The proximity score of the query terms measures the proximity and ordering of the query term within an XML element. The article then shows how a ranking function uses the confidence value of an XML element and proximity score to mitigate the impact of higher frequency terms and compute the relevance between a keyword query and an XML fragment. Finally, a keyword search system is introduced and experiments show that the proposed system outperforms existing approaches in terms of search quality and achieve a higher efficiency.
Identifying the Opinion Orientation of Online Product Reviews at Feature Level
Authors: Chauhan Nilanshi , Singh Pardeep
Published: February 2018

This article describes how e-commerce has become so vast that almost every product and service can be purchased online, to be delivered at our doorsteps. This has led to a striking increase in the number of online customers. In an attempt to make the online shopping more appealing and transparent to the online customers, the e-retailers allow their customers to express their opinion about the purchased products and services. Recently, analysis of such online reviews has become an active topic of research. This is because it is of immense concern to various stakeholders vs. online merchants, potential customers and the manufacturers of the particular product or service providers. The present article addresses the problem of summarization of such opinions expressed online and aims to create an organized feature-based summary as a solution. The proposed system depends on the frequency of occurrences of the potential features. A number of pruning methods are applied in order to obtain the final feature set and sentiment analysis has been done for each such feature.
A Survey to Nature Inspired Soft Computing
Authors: Kumar Deepak , Kumar Sushil , Bansal Rohit , Singla Parveen
Published: February 2018

This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.
An Efficient Batch Scheduling Model for Hospital Sterilization Services Using Genetic Algorithm
Authors: Xu Shubin , Wang John
Published: February 2018

A major challenge faced by hospitals is to provide efficient medical services. The problem studied in this article is motivated by the hospital sterilization services where the washing step generally constitutes a bottleneck in the sterilization services. Therefore, an efficient scheduling of the washing operations to reduce flow time and work-in-process inventories is of great concern to management. In the washing step, different sets of reusable medical devices may be washed together as long as the washer capacity is not exceeded. Thus, the washing step is modeled as a batch scheduling problem where washers have nonidentical capacities and reusable medical device sets have different sizes and different ready times. The objective is to minimize the sum of completion times for washing operations. The problem is first formulated as a nonlinear integer programming model. Given that this problem is NP-hard, a genetic algorithm is then proposed to heuristically solve the problem. Computational experiments show that the proposed algorithm is capable of consistently obtaining high-quality solutions in short computation times.
Sexbots
Authors: Mackenzie Robin
Published: February 2018

This article describes how sexbots: sentient, self-aware, feeling artificial moral agents created soon as customised potential sexual/intimate partners provoke crucial questions for technoethics. Coeckelbergh's model of human/robotic relations as co-evolving to their mutual benefit through mutual vulnerability is applied to sexbots. As sexbots have a sustainable claim to moral standing, benefits and vulnerabilities inherent in human/sexbots relations must be identified and addressed for both parties. Humans' and sexbots' vulnerabilities are explored, drawing on the philosophy and social science of dehumanisation and inclusion/exclusion. This article argues humans as creators owe a duty of care to sentient beings they create. Responsible innovation practices involving stakeholders debating ethicolegal conundrums pertaining to human duties to sexbots, and sexbots' putative interests, rights and responsibilities are essential. These validate the legal recognition of sexbots, the protection of their interests through regulatory oversight and ethical limitations on customisation which must be put in place.
Transcatheter Pulmonary Valve Replacement in Patients With Congenital Heart Disease
Authors: Bartra Shantelle
Published: February 2018

Transcatheter pulmonary valve replacement is now a feasible alternative to surgical pulmonary valve replacement in children and adults with dysfunctional right ventricular outflow conduits. Currently, 2 types of valves can be used for this application. This article provides an overview of the procedure and how it is performed, indications and contraindications for transcatheter pulmonary valve replacement, and short- and long-term outcomes. Nursing considerations mainly focus on educating patients, preventing bleeding and infection, monitoring renal function, and preventing injury to the catheter insertion site. This article enhances the knowledge of nurses working in cardiac catheterization laboratories and post-procedure recovery and cardiac units so that the nurses can anticipate interventions and understand the management of patients who have transcatheter pulmonary valve replacement.
Changing the Time of Blood Collection to Determine Vancomycin Concentrations in Intensive Care Unit Patients
Authors: Hammond Drayton A. , James Taylor B. , Atkinson Lexis N. , Painter Jacob T. , Lusardi Katherine
Published: February 2018

BACKGROUND

Clinical practice guidelines for initiation and therapeutic drug monitoring, but not timing, of vancomycin dosing exist at many institutions. Scheduling vancomycin trough measurements and doses around the morning blood sample collection could yield more interpretable troughs and increase patient safety.

OBJECTIVE

To evaluate the appropriateness of blood sample collection times for vancomycin trough measurements before and after an initiative to change the timing of blood sampling to determine vancomycin doses and trough levels in a medical intensive care unit.

METHODS

A retrospective cohort study was conducted of patients in a medical intensive care unit who received intravenous vancomycin at a scheduled interval. Differences in continuous and categorical data were compared between pre- and postintervention groups. The primary outcome was proportion of blood samples collected for vancomycin trough measurements within 30 minutes of the next scheduled vancomycin dose.

RESULTS

Baseline characteristics were similar between the preintervention (n = 68) and postintervention (n = 176) groups except for the percentage of blood samples drawn for trough measurements and morning laboratory tests (6% vs 81%; P < .001). Frequency of loading doses was similar between patients in the pre- and postintervention groups, as was weight-based maintenance dosing. There was no significant difference in the percentage of blood samples collected to measure vancomycin trough levels appropriately at 30, 60, or 75 minutes from the next scheduled dose.

CONCLUSION

Measuring vancomycin trough levels in morning blood samples did not affect the percentage of inappropriately collected blood samples used to measure vancomycin trough levels.
MI: Not a Heart Attack but a Gut Attack
Authors: Lee Rosemary K. , Cabrera Ana M.
Published: February 2018

Mesenteric ischemia and infarction are infrequent but often deadly conditions in acute and critically ill patients. Mesenteric ischemia may be a primary admission diagnosis or may develop secondary to another diagnosis. Having a high index of suspicion for patients at risk of mesenteric ischemia and mesenteric infarction can alter a poor outcome. This article reviews the pathophysiology, risk factors, assessment, medical and nursing diagnoses, as well as collaborative management for mesenteric ischemia. Early identification of patients at risk and the appropriate diagnostic testing are stressed. Nurses armed with the knowledge of this condition are better able to provide safe care to their patients.
The Fontan Procedure for Single-Ventricle Physiology
Authors: Jones Melissa Beaudet
Published: February 2018

The Fontan procedure is the final procedure in staged palliation for patients with functional single-ventricle physiology. The goal of the procedure is to separate systemic and pulmonary blood flow by directing systemic venous return through the Fontan connection to the pulmonary arteries and the lungs without ventricular contribution. Following the procedure, pulmonary blood flow is completely passive and dependent on pressure gradients, resulting in complex postoperative cardiopulmonary interactions. Understanding the physiology is essential to effectively manage these patients. Critical care nurses caring for patients after a Fontan procedure must understand preoperative data, risk factors, and unique postoperative physiology so they can anticipate specific postoperative problems, recognize trends in clinical status, and develop an appropriate plan of care. This paper reviews the first 2 stages of single-ventricle palliation, relevant modifications to the Fontan procedure, important preoperative cardiac catheterization data, common postoperative problems, and outcomes after the Fontan procedure.
A Teaching Simulation is Effective in Improving Athletic Training Students' Football Helmet Facemask Removal Clinical Skills and Confidence
Authors: Popp Jennifer K. , Walker Stacy E.
Published: February 2018

Context:

Patient encounters related to acute care skills rarely occur in clinical education, leaving a potential gap in students' skills and confidence.

Objective:

Investigate the effects of an acute care simulation requiring football helmet facemask removal on clinical skill application and confidence in athletic training students.

Design:

Cross-sectional.

Patients or Other Participants:

Forty-four students (16 male, 28 female; age = 20.9 ± 1.12 years) enrolled in 2 accredited professional athletic training programs.

Intervention(s):

Participants completed a confidence assessment survey and were pretested on football helmet facemask removal skills. Pairs of participants engaged in a simulation where they evaluated and managed a football player who required facemask removal, followed by a debriefing session. Participants repeated the confidence assessment survey and were posttested to evaluate skills.

Main Outcome Measure(s):

Dependent variables were clinical skills scores and confidence, as measured by a confidence assessment survey. Simulation served as the independent variable. Paired samples t test determined changes in clinical skills scores. Wilcoxson Signed-Rank Test determined changes in confidence.

Results:

Paired samples t test revealed a significant increase in performance on the posttest, including: primary survey (t43 = 4.13, P < .001), facemask removal (t43 = 4.00, P < .001), vital signs assessment (t43 = 5.57, P < .001), and secondary survey (t43 = 8.85, P < .001). Wilcoxon Signed-Rank Test revealed increased confidence in participants' recognition of (Z = 4.96, n = 44, P < .001), knowledge of (Z = 5.03, n = 44, P < .001), and skills (Z = 4.78, n = 43, P < .001) needed for football helmet facemask removal.

Conclusions:

With the inability to ensure students have authentic, real-time evaluation of acute care skills during clinical experiences, a simulation can assist in acquisition of skills, while also improving confidence in managing acute care conditions.