bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamical Analysis of Drug Efficacy and Mechanism of Action Using GFP Reporters
Authors: Hua Jianping , Sima Chao , Cypert Milana , Dougherty Edward R. , Trent Jeffery M. , Bittner Michael L.
Published: August 2016

To the development of effective cancer drug, it is necessary to, first, identify drugs and their possible combinations that could exert desired control over the type of cancer being considered; second, have a drug testing method that allows one to assess the variety of responses that can be provoked by drugs. To facilitate such an experiment-modeling-experiment cycle for drug development, a method based on the dynamical systems of pathways is presented. It involves a three-state experimental design: (1) formulate an oncologic pathway model of relevant cancer; (2) perturb the pathways with the drugs of known effects on components of the pathways of interest; and (3) measure process activity indicators at various points on cell populations. To evaluate the drug response in a high-throughput manner, a green fluorescent protein reporter-based technology has been developed. The authors apply the dynamical approach to several issues in the context of colon cancer cell lines.
A Hybrid Scheme for Breast Cancer Detection Using Intuitionistic Fuzzy Rough Set Technique
Authors: Chowdhary Chiranji Lal , Acharjya D. P.
Published: August 2016

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.
The E-Government Surveillance in the United States
Authors: McNeal Ramona Sue , Schmeida Mary , Holmes Justin
Published: August 2016

Since the 2001 U.S. Patriot Act passed in response to the 2001 terrorist attacks on the U.S., government wiretapping powers have evolved in scope and practice. Although overall public opinion favors government protection from terrorism, public support for government surveillance has ebbed and flowed. Recently, public opinion polls suggest that there has been a shift from supporting government wiretapping activities toward protecting individual civil liberty. A number of competing explanations have developed from sources ranging from the literature on Right Wing Authoritarianism (RWA) to evolving beliefs about personal information privacy. The purpose of this chapter is to analyze factors predicting changing public support for government surveillance. Multivariate regression analysis and individual level data from the 2012 American National Election Time Series Study are used to test rival explanations.
Dataveillance and Information Privacy Concerns
Authors: Connolly Regina , Kenny Grace
Published: August 2016

Information privacy research historically focuses on exploring individuals' concerns in the transaction environment. However, the recent growth of technology-enabled workplace surveillance is raising many concerns over employees' privacy. Employee surveillance practices are becoming increasingly prevalent, ranging from monitoring internet and email activities to capturing employees' interactions with customers and employees' personal health and fitness data using wearable health devices. Individuals may understand that employers can monitor their activities, but may not the potential uses or the repercussions of such monitoring. Moreover, employees may not feel they have the ability to opt-out of this monitoring. This chapter explores the privacy and ethical issues surrounding emerging means of workplace surveillance. The chapter considers both employee and employer perspectives and poses many questions to consider when deciding when does legitimate monitoring become an invasion of employee privacy?
Smart Surveillance Systems
Authors: Rothkrantz L. J. M.
Published: August 2016

To enable effective and efficient command and control in military operations it is necessary to have full awareness of all the actions in the field. In traditional C2 systems, human operators play key roles varying from observation in the field up to semantic interpretation of observed data in the Command and Control Centre. Networks are mainly used to transmit data between different components of the network. Observation by human operators will be replaced by sensor networks. The huge amount of incoming data is far beyond the capacity of operators, so the heterogeneous, multimodal data from the different sensor systems has to be fused, aggregated, and filtered. Automated surveillance sensor networks are discussed in this chapter. Sensors are modelled as a distributed system of smart agents. Methods and technology from Artificial Intelligence such as expert systems, semantic networks, and probabilistic reasoning is used to give a semantic interpretation of the sensed data from the environment.
Intuitionistic Fuzzy Filters for Noise Removal in Images
Authors: Radhika C. , Parvathi R. , Visalakshi N. Karthikeyani
Published: August 2016

Image processing is any form of information processing in which both input and output are images. Most of the image processing involves in treating the image as two dimensional representations and applying standard techniques to it. Images contain lot of uncertainties and are fuzzy/vague in nature. Various fuzzy filtering techniques are defined for noise removal in image processing and these existing filters helps to enhance the image using only the membership values. Further, by incorporating intuitionistic fuzzy filters, vagueness and ambiguity are managed by taking the non-membership values also into consideration. In this paper, light is thrown on some important types of noise and a comparative analysis is done. This paper also presents the results of applying different noise types to an image and investigates the results of various intuitionistic fuzzy filtering techniques. A comparison is made on the results of all the techniques.
Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform
Authors: Rathor Girraj Prasad , Gupta Sanjeev Kumar
Published: August 2016

Image fusion based on different wavelet transform is the most commonly used image fusion method, which fuses the source pictures data in wavelet space as per some fusion rules. But, because of the uncertainties of the source images contributions to the fused image, to design a good fusion rule to incorporate however much data as could reasonably be expected into the fused picture turns into the most vital issue. On the other hand, adaptive fuzzy logic is the ideal approach to determine uncertain issues, yet it has not been utilized as a part of the outline of fusion rule. A new fusion technique based on wavelet transform and adaptive fuzzy logic is introduced in this chapter. After doing wavelet transform to source images, it computes the weight of each source images coefficients through adaptive fuzzy logic and then fuses the coefficients through weighted averaging with the processed weights to acquire a combined picture: Mutual Information, Peak Signal to Noise Ratio, and Mean Square Error as criterion.
Improving the Efficiency of Color Image Segmentation Using an Enhanced Clustering Methodology
Authors: Nayak Nihar Ranjan , Mishra Bikram Keshari , Rath Amiya Kumar , Swain Sagarika
Published: August 2016

The findings of image segmentation reflects its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms normally used in image segmentation – the typical K-Means, its modified K-Means++ and their proposed Enhanced Clustering method. The idea is to present a brief explanation of the fundamental working principles implicated in these methods. They have analyzed the performance criterion which affects the outcome of segmentation by considering two vital quality measures namely – Structural Content (SC) and Root Mean Square Error (RMSE) as suggested by Jaskirat et al., (2012). Experimental result shows that, the proposed method gives impressive result for the computed values of SC and RMSE as compared to K-Means and K-Means++. In addition to this, the output of segmentation using the Enhanced technique reduces the overall execution time as compared to the other two approaches irrespective of any image size.
Mobile Health Care
Authors: Mangu Veda Prakash
Published: August 2016

This chapter gives a high-level view of the technology involved in the solution of Mobile Health Care with Cloud Computing as back-bone. It emphasizes on Hardware elements, Computation requirements when the solution covers huge scope of medical problems at the mega scales across wide areas. This chapter discusses sub-systems of the solution, that include Smart Phones, Computation Engines, High End Transportation Systems, Multi-Specialty Hospitals, Smart Phones/Digital Personal Assistants used by Medical Practitioners. Discusses on the accuracies, bandwidth requirements and latencies present in the systems, also emphasizes on the required accuracies as the problem area is Human Life. To address the challenges that arises when the solution gets high degree of maturity, this chapter proposes review of the current day protocols in the systems. Also proposes to integrate intelligent applications and different eco-systems like Big Data, Data Analytics and Internet of Things, and best adaptability of these areas with Nano-technologies to result in increased average life time of humans.
Cloud Based Secure Data Sharing Algorithm for Health Care
Authors: N. Saravana Kumar , Venkataramana Rajya Lakshmi Gubburi , B. Balamurugan
Published: August 2016

Cloud computing is one of most fast developing technology and many organizations are now offering a wide range of cloud services. Although the services provided are the same there is no common programming language, technology and protocol to access the entirety of the cloud services. Client who use a service provided by a certain organization are often limited and confined to that specific organization its structure and technologies. A Cloud federation is one solution to that interoperability through which computing resources of one Cloud Service Provider is rented or sold to another service provider or the services provided by one Cloud Service Provider is replicated into another Cloud Service Provider without having to lose any functionality and performance. This process is a tedious task and is prone to multiple limitations. In this paper we proposed the architectural framework and algorithm for the possible interoperability between the cloud service providers based on SLA in prospective of health sector as the application of cloud in health sector is highly needed in future.
Diabetes Patients Monitoring by Cloud Computing
Authors: Poorejbari Sepideh , Vahdat-Nejad Hamed , Mansoor Wathiq
Published: August 2016

The healthcare system is important due to the focus on human care and the interference with human lives. In recent years, we have witnessed a rapid rise in e-healthcare technologies such as Electronic Health Records (EHR) and the importance of emergency detection and response. Cloud computing is one of the new approaches in distributed systems that can handle some of the challenges of smart healthcare in terms of security, sharing, integration and management. In this study, an architecture design of a cloud-based pervasive healthcare system for diabetes treatment has been proposed. For this, three different components are defined as follows: (1) The home context manager which gathers necessary information from patients while simultaneously providing feedback, (2) a patient health record manager that is accessible by nurses or physicians at the hospital, and (3) a diabetes management system which is located with the cloud infra-structure for managing and accessing patient's information. The performance of proposed architecture is demonstrated through a user scenario.
IBM's Watson Analytics for Health Care
Authors: Aggarwal Mayank , Madhukar Mani
Published: August 2016

With the advent of Internet and Computers, Information Technology (IT) has become a major tool to aid medical issues. IBM Watson is one such initiative by IBM, which provides integration with any application to build Internet of Things (IoT), based health applications and also assists by its existing services. The strength of Watson is its data analytics and Artificial Intelligence. The four variants of Watsons are Watson Discovery Advisor, Oncology, Clinical Trial Matching and Curam. It is based on Open Source Apache UIMA, Apache Lucene. Its integration with IBM Bluemix Cloud, Platform as a Service (PaaS) makes it easily available to users.
Trust, Privacy, Issues in Cloud-Based Healthcare Services
Authors: Kaushik Shweta , Gandhi Charu
Published: August 2016

In recent era individuals and organizations are migrating towards the cloud computing services to store and retrieve the data or services. However, they have less confidence on cloud as all the task are handled by the service provider without any involvement of the data owner. Cloud system provides features to the owner, to store their data on some remote locations and allow only authorized users to access the data according to the role, access capability or attribute they possess. Storing the personal health records on cloud server (third party) is a promising model for healthcare services to exchange information with the help of cloud provider. In this chapter, we highlight the various security issues and concerns such as trust, privacy and access control in cloud based healthcare system that needs to be known while storing the patient's information over a cloud system.
Personalized Neuro-Fuzzy Expert System for Determination of Nutrient Requirements
Authors: Sajja Priti Srinivas , Trivedi Jeegar Ashokkumar
Published: August 2016

In the modern fast and stressful life, an individual does not have time to take an extra care for one's self. Support from general information about health and nutrient requirement through modern computing infrastructure is very limited and common. Generic information on the Web and other media sometime raises genuine queries about the good health. Further, typical solutions available may not interact with users in friendly way and deal with vague inputs provided by users. To resolve this issue, a system is required which knows its users, acts smartly and friendly, learns from past data & history and provides customised advisory. This chapter introduces a neuro-fuzzy architecture, based on which an expert system for determination of nutrient requirements is presented. The chapter includes in depth literature survey, concepts, implementation details with sample code, neural network structure, fuzzy membership functions used, sample input–output screens of the system and future work.
Approach for using Learner Satisfaction to Evaluate the Learning Adaptation Policy
Authors: Jeghal Adil , Oughdir Lahcen , Tairi Hamid , Radouane Abdelhay
Published: August 2016

The learning adaptation is a very important phase in a learning situation in human learning environments. This paper presents the authors' approach used to evaluate the effectiveness of learning adaptive systems. This approach is based on the analysis of learner satisfaction notices collected by a questionnaire on a learning situation; to analyze the data the authors will use the space Density-based clustering algorithm of clustering applications with noise. This proposed approach aims to help managers of human learning environments to improve policy used in the learning adaptation phase for learners from the analysis results of collected data.
Dynamic Group Formation based on a Natural Phenomenon
Authors: Zedadra Amina , Lafifi Yacine , Zedadra Ouarda
Published: August 2016

This paper presents a new approach of learners grouping in collaborative learning systems. This grouping process is based on traces left by learners. The goal is the circular dynamic grouping to achieve collaborative projects. The proposed approach consists of two main algorithms: (1) the circular grouping algorithm and (2) the dynamic grouping algorithm (used to update groups). The circular grouping is a novel algorithm to group learners based on their learning and collaborative traces. So, the aim is to form heterogeneous groups based on their profiles. The dynamic grouping algorithm is based on the behavior of penguins when they are moving in the winter season to secure their lives. The new proposed approach used the same behavior of penguins' colony. The proposed approach was applied on a collaborative learning system called LETline 2.0 (http://www.labstic.com/letline/). The developed system was experimented at an Algerian university. After the experiment, the authors observed that their system groups automatically the learners into homogeneous groups and improves their cognitive profiles.
Conceptual Design Model of Instructional Interfaces
Authors: Tosho Abdulrauf , Mutalib Ariffin Abdul , Abdul-Salam Sobihatun Nur
Published: August 2016

This paper describes an ongoing study related to a conceptual design model, which is specific to instructional interface design to enhance courseware usage. It was found that most of the existing courseware applications focus on the needs of certain target with most of the courseware offer too little to inclusive learners. In addition, the use of structure, layout, and navigation to improve the courseware instructional interfaces as part of usability strategies was also problematic for developers. Thus, this study aims at creating an alternative instructional interface as part of usability strategies for inclusive education systems called Instructional Interface Design for Courseware (IID4C). It is proposed as guidance for the developer to refer to. The study used comparative analysis technique to determine the elements of the model. In the end, this study finds that the IID4C model is useful for information accessibility and contributes to the designing of instructional courseware. Future works are to evaluate the proposed model among disabled and non-disabled learners.
Identifying Crucial Know-How and Knowing-That for Medical Decision Support
Authors: Ghrab Sahar , Saad Inès
Published: August 2016

In this paper, the authors propose a multi-criteria methodology for identifying “Crucial Know-How/Knowing-That”. Know-How and Knowing-That are two kinds of knowledge. Know-How is a disposition to perform a type of action whereas Knowing-That is a belief state and concerns a description which can be factual or propositional. The category of “crucial Know-How/Knowing-That” represents the subset of Know-How/Knowing-That sufficient for Know-How/Knowing-That capitalization. The authors are interested in special Know-How/Knowing-that called “Likely Crucial Know-How/Knowing-That” which represent the set of Know-How/Knowing-That under validation and experimentation and can be crucial in the short or medium term. For thus, a new decision class is attributed to this category of Know-How/Knowing-That. The methodology is composed of three phases: (i) the construction of the preference model of decision maker, (ii) the evaluation of “crucial Know-How/Knowing-That” and (iii) the sorting of this set of Know-How/Knowing-That. The methodology is experimented in the ASHMS organization and validated in the medical field.
Extracting-Transforming-Loading Modeling Approach for Big Data Analytics
Authors: Bala Mahfoud , Boussaid Omar , Alimazighi Zaia
Published: August 2016

Due to their widespread use, Internet, Web 2.0 and digital sensors create data in non-traditional volumes (at terabytes and petabytes scale). The big data characterized by the four V's has brought with it new challenges given the limited capabilities of traditional computing systems. This paper aims to provide solutions which can cope with very large data in Decision-Support Systems (DSSs). In the data integration phase, specifically, the authors propose a conceptual modeling approach for parallel and distributed Extracting-Transforming-Loading (ETL) processes. Among the complexity dimensions of big data, this study focuses on the volume of data to ensure a good performance for ETL processes. The authors' approach allows anticipating on the parallelization/distribution issues at the early stage of Data Warehouse (DW) projects. They have implemented an ETL platform called Parallel-ETL (P-ETL for short) and conducted some experiments. Their performance analysis reveals that the proposed approach enables to speed up ETL processes by up to 33% with the improvement rate being linear.
Advantages of Nonlinear over Segmentation Analyses in Path Models
Authors: Kock Ned
Published: August 2016

The recent availability of software tools for nonlinear path analyses, such as WarpPLS, enables e-collaboration researchers to take nonlinearity into consideration when estimating coefficients of association among linked variables. Nonlinear path analyses can be applied to models with or without latent variables, and provide advantages over data segmentation analyses, including those employing finite mixture segmentation techniques (a.k.a. FIMIX). The latter assume that data can be successfully segmented into subsamples, which are then analyzed with linear algorithms. Nonlinear analyses employing WarpPLS also allow for the identification of linear segments mirroring underlying nonlinear relationships, but without the need to generate subsamples. The author demonstrates the advantages of nonlinear over data segmentation analyses.
The Role of Perceived E-Collaborative Performance in an Extended Theory of Planned Behavior Model
Authors: Cheng Eddie W.L. , Chu Samuel K.W.
Published: August 2016

Online web technologies are adopted to improve students' collaborative work. However, the factors that affect students' engagement in online collaboration (e-collaboration) have not been understood in previous studies. This research aimed to examine the extent to which students' intentions to e-collaborate can be explained by the theory of planned behavior. A sample of secondary and tertiary students was used (N = 175). The results from the partial least square approach to structural equation modeling (PLS-SEM) indicated that the path coefficients of the relationships partially reinforced the a priori construction of the hypothesized model. In brief, attitudes toward e-collaboration and subjective norms were positively and significantly related to e-collaborative intentions, while perceived behavioral control indirectly predicted e-collaborative intentions via perceived e-collaborative performance. Research and practical implications have been presented in the paper.
What Lessons Can We Learn for “Good e-Government” From a User-Centred Evaluation of the Websites of European Capitals?
Authors: Scholl Margit Christa
Published: August 2016

From an E-Government perspective, modern public administrations require a high degree of accessibility, simplified modes of communication, transparent processes, and services that are available electronically. This article addresses the issue of whether, in a Europe-wide comparison, the websites in question offer products and services in a way that is not only intelligible and transparent for users but also efficient and functional. The websites were analysed using the evaluation tool TEDS*MOODLE as part of a student project, whose ratings are presented, to establish research-based and genetic learning as part of the study programme of administration and law. A general finding of the websites of seventeen European capitals applying an E-Government perspective is that the main challenges are in the evaluation categories Adjustment, Further Performance Features – as there are time and cost savings as well as security and safety aspects – and Affection, as indicated by the general satisfaction factor in the user evaluations. The article discusses the transferability of the student results to citizens. Moreover, the article discusses the fitness for purpose of the evaluation tool, and its utility for learning processes, in particular for research-based and genetic learning among students.
Smart City, Integrated Planning, and Multilevel Governance
Authors: Damurski Lukasz
Published: August 2016

The interdependent concepts of smart city, integrated planning and multilevel governance greatly determine the current discussion on planning theory and practice in Europe. Each of them presents new challenges for e-planning, pushing it into a priority position in performing planning tasks on various levels of administration. Thus e-planning is not just a way of describing current tools for governing space anymore, but becomes a new philosophy of public decision-making. The paper gives a discourse analysis of the key EU policy and research directions, defines the core attributes and values of contemporary planning concepts (such as governance, sustainability, communication, participation, responsiveness, innovation and coordination) and draws a “conceptual patchwork” situating e-planning on the intersection of smart city, integrated planning and multilevel governance.
Customer Choice of Super Markets using Fuzzy Rough Set on Two Universal Sets and Radial Basis Function Neural Network
Authors: Anitha A. , Acharjya Debi Prasanna
Published: August 2016

Information and communication technology made shopping more convenient for common man. Additionally, customers compare both online and offline price of a commodity. For this reason, offline shopping markets think of customer satisfaction and try to attract customers by various means. But, prediction of customer's choice in an information system is a major issue today. Much research is carried out in this direction for single universe. But, in many real life applications it is observed that relation is established between two universes. To this end, in this paper the authors propose a model to identify customer choice of super markets using fuzzy rough set on two universal sets and radial basis function neural network. The authors use fuzzy rough set on two universal sets on sample data to arrive at customer choice of super markets. The information system with customer choice is further trained with radial basis function neural network for identification of customer choice of supermarkets when customer size increases. A real life problem is presented to show the sustainability of the proposed model.
A Neuro-Fuzzy Rule-Based Classifier Using Important Features and Top Linguistic Features
Authors: Biswas Saroj Kr. , Bordoloi Monali , Singh Heisnam Rohen , Purkayastha Biswajit
Published: August 2016

The efficient feature selection for predictive and accurate classification is highly desirable in many application domains. Most of the attempts to neuro-fuzzy classifier lose information to build interpretable neuro-fuzzy classification model. This paper proposes an interpretable neuro-fuzzy classification model with significant features without loss of knowledge, which is an extension of an existing interpretable neuro-fuzzy classification model. The proposed model is designed based on the consideration of feature importance that is determined by frequency of linguistic features. The rules are then made based on important features. Therefore, the knowledge acquired in network can be comprehended to logical rules using only important features. The proposed model finally performs classification task by rule-based approach. The average accuracy calculated by 10-fold cross validation finds that the proposed model can increase performance of the already proven neuro-fuzzy system for classification tasks.
Warranty as an Effective Strategy for Remanufactured Product
Authors: Liao Bifeng , Li Bangyi
Published: August 2016

Consumers cannot fully evaluate the product quality before purchase. To reduce consumer risk, some manufacturers offer consumer warranty. Up to now, there are many studies investigating this role, but they seldom consider from the perspective of consumer utility. Consumers view remanufactured products as being of lower quality and are less willing to pay for them. It is important to incorporate the manufacturer's service and consumer heterogeneity in the model. Those employed in the remanufacturing industry are advised to increase the level of warranty associated with their remanufacturing products in order to command higher prices for their products in the marketplace. This paper firstly considers consumer utility, then develops three marketing settings to examine the impact of warranty services, and finally compares the optimal prices and profits among them. The results show that the manufacturer's warranty policy can enhance profit, and the profit is highest when both new and remanufactured products provided differentiated warranty.
Using Enhanced Lexicon-Based Approaches for the Determination of Aspect Categories and Their Polarities in Arabic Reviews
Authors: Smadi Mohammad Al , Obaidat Islam , Al-Ayyoub Mahmoud , Mohawesh Rami , Jararweh Yaser
Published: August 2016

Sentiment Analysis (SA) is the process of determining the sentiment of a text written in a natural language to be positive, negative or neutral. It is one of the most interesting subfields of natural language processing (NLP) and Web mining due to its diverse applications and the challenges associated with applying it on the massive amounts of textual data available online (especially, on social networks). Most of the current work on SA focus on the English language and work on the sentence-level or the document-level. This work focuses on the less studied version of SA, which is aspect-based SA (ABSA) for the Arabic language. Specifically, this work considers two ABSA tasks: aspect category determination and aspect category polarity determination, and makes use of the publicly available human annotated Arabic dataset (HAAD) along with its baseline experiments conducted by HAAD providers. In this work, several lexicon-based approaches are presented for the two tasks at hand and show that some of the presented approaches significantly outperforms the best-known result on the given dataset. An enhancement of 9% and 46% were achieved in the tasks aspect category determination and aspect category polarity determination respectively.
Polarity Classification of Arabic Sentiments
Authors: Al-Kabi Mohammed N. , Wahsheh Heider A. , Alsmadi Izzat M.
Published: August 2016

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.
Using MLP Neural Networks to Detect Late Blight in Brazilian Tomato Crops
Authors: Cruz Sergio Manuel Serra , Vianna Gizelle Kupac
Published: August 2016

The food quality is a major issue in agriculture, economics, and public health. The tomato is one the most consumed vegetables in the world, having a significant production chain in Brazil. Its culture permeates many economic and social sectors. This paper presents a technological approach focused on enhancing the quality of tomatoes crops. The authors developed intelligent computational strategies to support early detection of diseases in Brazilian tomato crops. Their approach consorts real field experiments with inexpensive computer-aided experiments based on pattern recognition using neural networks techniques. The recognition tasks aimed at the identification foliage diseases named late blight, which is characterized by the incidence of brown spots on tomato leaves. The identification method achieved a hit rate of 94.12%, by using digital images in the visible spectrum of the leaves.
Negotiation Strategies under Sigmoid Preferences
Authors: Neves Joao S. , Nakhai Behnam
Published: August 2016

The diminishing returns concept undergirds many economic theories and has led to the common assumption of concave preferences in the negotiation literature. Realizing that in practice negotiating parties are often confronted with very steep disagreements, negotiation researchers have investigated the impact of convex preferences on compromise and logrolling bargaining strategies. This article extends the previous work to the case of sigmoid preferences and examines the resulting possible shapes of the efficient frontier curve in two-party multi-issue negotiations. The implications for compromise and logrolling negotiation strategies are discussed.
Enabling RDF Stream Processing for Sensor Data Management in the Environmental Domain
Authors: Llaves Alejandro , Corcho Oscar , Taylor Peter , Taylor Kerry
Published: August 2016

This paper presents a generic approach to integrate environmental sensor data efficiently, allowing the detection of relevant situations and events in near real-time through continuous querying. Data variety is addressed with the use of the Semantic Sensor Network ontology for observation data modelling, and semantic annotations for environmental phenomena. Data velocity is handled by distributing sensor data messaging and serving observations as RDF graphs on query demand. The stream processing engine presented in the paper, morph-streams++, provides adapters for different data formats and distributed processing of streams in a cluster. An evaluation of different configurations for parallelization and semantic annotation parameters proves that the described approach reduces the average latency of message processing in some cases.
Segmentation analysis of financial savings markets through the lens of psycho-demographics
Authors: Matenge Tendy , Makgosa Rina , Mburu Paul T.
Published: August 2016

Purpose: This study seeks to contribute to the discourse of financial savings market segmentation. The study explores different segments of savers on the basis of demographic and psychographic characteristics that are unique to each segment relying on the perspectives of a sample of consumers of financial saving programmes. Design/methodology/approach: Principles of perceptual mapping were used to analyse 33 semi-structured interviews that gathered data on the participants’ psychographic make-up such as personal values, motives for saving, attitudes towards savings and perceived conditions of savings. Findings: Eight distinct segments emerged on each psychographic characteristic based on the participants’ demographics of income, gender and age. However, only five were sizeable enough to be interpreted, being three segments from the males’ category and two from the females’ category. The three segments that emerged within the male category are young low-income earners (YoLI), young high-income earners (YoHI) and old high-income earners (OHI) while the two female segments include YoLI and OHI. The most sizeable segment of savers in both gender-based categories is one of old adults who have a high income. These segments vary in terms of values, motives and perceptions. Originality/value: The study suggests that a multi-dimensional approach of segmenting financial savings markets is more effective, as neither the demographic nor the psychographic segmentation can fully describe the saving behaviour of consumers. Research implications: The findings of the present study provide strategic communication implications for financial institutions for the respective segments.
Update on ocular toxicity of ethambutol
Authors: Makunyane Priscilla , Mathebula Solani
Published: August 2016

The purpose of this review is to update clinicians on available literature on the ocular toxicity of ethambutol and the type of eye care to be provided to patients treated with these medications. Ethambutol is a commonly used first-line anti-tuberculosis drug. Since its first use in the 1960s, ocular toxicity is described as related to dose and duration, and it is reversible on therapy discontinuation. However, the reversibility of the toxic optic neuropathy remains controversial. The mechanism of ocular toxicity owing to ethambutol is still under investigation. Other than discontinuing the drug, no specific treatment is available for the optic neuropathy caused by ethambutol. Doctors prescribing ethambutol should be aware of the ocular toxicity, and the drug should be used with proper patient education and ophthalmic monitoring.
Attrition of undergraduate nursing students at selected South African universities
Authors: Roos Erna , Fichardt Anna E. , MacKenzie Margaret J. , Raubenheimer Jacques
Published: August 2016

Background: The nursing profession forms the backbone of many healthcare systems. It therefore needs a consistent supply of registered nurses to deliver continuous and safe quality healthcare, and to replace the nurses leaving or retiring from the profession. Attrition actively occurs among nursing students in South Africa and threatens the future supply of registered nurses. Aim: The aim of the study was to describe the attrition rate at selected South African universities and the factors influencing undergraduate nursing students to discontinue their nursing studies at these universities. Method: A quantitative descriptive design was followed. Heads of the nursing departments at the selected universities captured data with a specifically designed questionnaire. Thereafter their former nursing students provided information via a structured telephonic interview on the reasons why they discontinued the nursing programme. Results: The study revealed that attrition of undergraduate nursing students for three intake years (2007, 2008 and 2009) at the participating universities was between 39.3% and 58.7%. Academic and financial reasons as well as poor wellness and health were the main causes for attrition. Another factor was failure to cope with the demands of the clinical environment. Conclusion: Attrition might not occur immediately when a nursing student is challenged, as the student might exploit the various types of support offered. Although some nursing students do benefit from the offered support, a large number of nursing students still discontinue the undergraduate nursing programme.
’n Pastoraal hermeneutiese lees van Paulus se briewe: 1 Tessalonisense as toetsgeval
Authors: Putter Jaco
Published: August 2016

Paul’s letters, filled with pastoral content, are illustrating how he founded the church communities, shaping them and caring for them with various techniques. Paul shaped these communities within their own contexts after they had received and accepted the gospel. The outcome was that such a church community and its individuals created their own worldview from which they viewed and experienced the world. It is argued here that their worldview influenced the individuals’ and groups’ identity, values, norms and actions. All these aspects influenced each other in an interactive dynamic way with interdependence as a result. If one aspect changes, it affects all the other areas. These aspects are rooted in the individual’s emotive and cognitive areas. Paul addresses this in his letters by referring to these two areas on a constant basis by reflecting on how the converts had to change the way they thought and lived in order to imitate Christ. This motivated them to endure difficult situations they found themselves in. When reading Paul’s letters with this in mind a new pastoral understanding of it emerges, as illustrated with reference to 1 Thessalonians.
The electronic medical record in 2016: Advantages and disadvantages
Authors: Alpert Joseph S.
Published: August 2016

ABSTRACT

The electronic medical record (EMR) is now nearly ubiquitous in the USA. This article will review the EMR system with respect to goals, utilization, advantages compared with hand written records, as well as problems and/or disadvantages of the EMR system.
Motor cortex activation during motor imagery of the upper limbs in stroke patients
Authors: Wang Li , Zhang Jingna , Zhang Ye , Sang Linqiong , Yan Rubing , Liu Chen , Qiu Mingguo
Published: August 2016

ABSTRACT

Objective:

The objective of this study was to analyze the functional brain activation in acute stroke patients during motor execution (ME) and motor imagery (MI) and to discuss the association between damaged brain structure and impaired brain function in stroke patients.

Methods:

The functional magnetic resonance imaging technique was used to observe activation of the brain during ME/MI of the upper limbs in 12 acute stroke patients (with the left brain damage) and 12 healthy controls.

Results:

During ME, the stroke patients appeared to be activated more strongly than the healthy controls in the ipsilateral primary motor areas. The MI of the affected hand in the stroke patients was not significantly different from that of the healthy hand. The nonmotor areas, the angular gyrus, and the fusiform gyrus were also activated during ME/MI.

Conclusion:

Structural damage in the brain is associated with the activation of brain function in acute stroke patients. Ipsilateral inhibition is reduced in stroke patients during ME and the damaged brain needs to recruit more brain areas to complete the desired action due to motion difficulties resulting from brain damage. The participation of nonmotor brain areas in ME/MI indicates that cortical reorganization may contribute to the restoration of motor function following stroke. MI can be used to improve injured brain areas, helping with the rehabilitation of stroke patients.
Cognitive Performance and Iron Status are Negatively Associated with Hookworm Infection in Cambodian Schoolchildren
Authors: Kuong Khov , Fiorentino Marion , Perignon Marlene , Chamnan Chhoun , Berger Jacques , Sinuon Muth , Molyden Vann , Burja Kurt , Parker Megan , Ly Sou Chheng , Friis Henrik , Roos Nanna , Wieringa Frank T.
Published: August 2016

Soil-transmitted helminth (STH) infection has been associated with lower cognitive performance of schoolchildren. To identify pathways through which STH infection might affect school performance, baseline data from a large rice-fortification trial in Cambodian schoolchildren were used to investigate associations between STH infection, micronutrient status, anemia, and cognitive performance. Complete data on anthropometry, cognitive performance, and micronutrient status were available for 1,760 schoolchildren, 6–16 years of age. STH infection was identified using Kato–Katz, whereas cognitive performance was assessed using Raven's Colored Progressive Matrices (RCPM), block design, and picture completion. STH infection was found in 18% of the children; almost exclusively hookwork infection. After adjusting for age and gender, raw cognitive test scores were significantly lower in hookworm-infected children (−0.65; −0.78; −2.03 points for picture completion, RCPM, and block design, respectively; P < 0.05 for all). Hookworm infection was associated with iron status (total body iron), but not with vitamin A and zinc status, nor with inflammation or anthropometry. Body iron was negatively associated with increased intensity of hookworm infection (R = 0.22, P < 0.001). Hookworm infection in Cambodian schoolchildren was associated with lower cognitive performance, an effect most likely mediated through lower body iron. Interventions that are more effective against hookworm infection are needed to contribute to better health and improvement of cognitive performance.
Evaluation of a Rapid Lateral Flow Point-of-Care Test for Detection of Cryptosporidium
Authors: Fleece Molly E. , Heptinstall Jack , Khan Shaila S. , Kabir Mamum , Herbein Joel , Haque Rashidul , Petri William A.
Published: August 2016

A new rapid lateral flow fecal antigen detection test for Cryptosporidium was evaluated using diarrheal stool samples from a cohort of children in Bangladesh. The test had a sensitivity of 100% and a specificity of 94% when compared with enzyme-linked immunosorbent assay antigen detection.
Dengue Virus Seroconversion in Travelers to Dengue-Endemic Areas
Authors: Olivero Rosemary M. , Hamer Davidson H. , MacLeod William B. , Benoit Christine M. , Sanchez-Vegas Carolina , Jentes Emily S. , Chen Lin H. , Wilson Mary E. , Marano Nina , Yanni Emad A. , Ooi Winnie W. , Karchmer Adolf W. , Kogelman Laura , Barnett Elizabeth D.
Published: August 2016

We conducted a prospective study to measure dengue virus (DENV) antibody seroconversion in travelers to dengue-endemic areas. Travelers seen in the Boston Area Travel Medicine Network planning to visit dengue-endemic countries for ≥ 2 weeks were enrolled from 2009 to 2010. Pre- and post-travel blood samples and questionnaires were collected. Post-travel sera were tested for anti-DENV IgG by indirect IgG enzyme-linked immunosorbent assay (ELISA) and anti-DENV IgM by capture IgM ELISA. Participants with positive post-travel anti-DENV IgG or IgM were tested for pre-travel anti-DENV IgG and IgM; they were excluded from the seroconversion calculation if either pre-travel anti-DENV IgG or IgM were positive. Paired sera and questionnaires were collected for 62% (589/955) of enrolled travelers. Most participants were 19–64 years of age, female, and white. The most common purposes of travel were tourism and visiting friends and relatives; most trips were to Asia or Africa. Median length of travel was 21 days. DENV antibody seroconversion by either anti-DENV IgM or IgG ELISA was 2.9–6.8%; lower range percent excluded potential false-positive anti-DENV IgG due to receipt of yellow fever or Japanese encephalitis vaccines at enrollment; upper range percent excluded proven false-positive anti-DENV IgM. Eighteen percent of those with seroconversion reported dengue-like symptoms. Seroconversion was documented for travel to Africa as well as countries and regions known to be highly dengue endemic (India, Brazil, southeast Asia). Given widespread risk of dengue, travel medicine counseling should include information on risk of dengue in endemic areas and advice on preventing insect bites and seeking prompt medical attention for febrile illness.