bluuebunny/crossref_metadata_2025 huggingface dataset:
INTERSTELLAR PICKUP ION ACCELERATION IN THE TURBULENT MAGNETIC FIELD AT THE SOLAR WIND TERMINATION SHOCK USING A FOCUSED TRANSPORT APPROACH
Authors: Ye Junye , Roux Jakobus A. le , Arthur Aaron D.
Published: July 2016

We study the physics of locally born interstellar pickup proton acceleration at the nearly perpendicular solar wind termination shock (SWTS) in the presence of a random magnetic field spiral angle using a focused transport model. Guided by Voyager 2 observations, the spiral angle is modeled with a q-Gaussian distribution. The spiral angle fluctuations, which are used to generate the perpendicular diffusion of pickup protons across the SWTS, play a key role in enabling efficient injection and rapid diffusive shock acceleration (DSA) when these particles follow field lines. Our simulations suggest that variation of both the shape (q-value) and the standard deviation (σ-value) of the q-Gaussian distribution significantly affect the injection speed, pitch-angle anisotropy, radial distribution, and the efficiency of the DSA of pickup protons at the SWTS. For example, increasing q and especially reducing σ enhances the DSA rate.
WHY IS A FLARE-RICH ACTIVE REGION CME-POOR?
Authors: Liu Lijuan , Wang Yuming , Wang Jingxiu , Shen Chenglong , Ye Pinzhong , Liu Rui , Chen Jun , Zhang Quanhao , Wang S.
Published: July 2016

Solar active regions (ARs) are the major sources of two of the most violent solar eruptions, namely flares and coronal mass ejections (CMEs). The largest AR in the past 24 years, NOAA AR 12192, which crossed the visible disk from 2014 October 17 to 30, unusually produced more than one hundred flares, including 32 M-class and 6 X-class ones, but only one small CME. Flares and CMEs are believed to be two phenomena in the same eruptive process. Why is such a flare-rich AR so CME-poor? We compared this AR with other four ARs; two were productive in both and two were inert. The investigation of the photospheric parameters based on the SDO/HMI vector magnetogram reveals that the flare-rich AR 12192, as with the other two productive ARs, has larger magnetic flux, current, and free magnetic energy than the two inert ARs but, in contrast to the two productive ARs, it has no strong, concentrated current helicity along both sides of the flaring neutral line, indicating the absence of a mature magnetic structure consisting of highly sheared or twisted field lines. Furthermore, the decay index above the AR 12192 is relatively low, showing strong constraint. These results suggest that productive ARs are always large and have enough current and free energy to power flares, but whether or not a flare is accompanied by a CME is seemingly related to (1) the presence of a mature sheared or twisted core field serving as the seed of the CME, or (2) a weak enough constraint of the overlying arcades.
Ketamine Sedation in Gastrointestinal Endoscopy in Children
Authors: Eskander Ayman E. , El Baroudy Nevine R. , El Refay Amira S.
Published: July 2016

BACKGROUND: Moderate sedation for gastrointestinal endoscopy has traditionally been provided by the endoscopist. Controversy has ensued over safe and efficient sedation practice as endoscopy has increased in numbers and complexity.AIM: To evaluate the safety of ketamine sedation given by non-anesthesiologist during gastrointestinal endoscopy in children.METHODS: A prospective study of 100 paediatric patients with gastrointestinal symptoms who were a candidate for upper or lower gastrointestinal endoscopy in paediatric endoscopy unit at Abo El-Reesh paediatric hospital, Cairo university. All children were > 2 years old and weighed > 6 kg. The analysis was performed in terms of sedation-related complications.RESULTS: A total 100 paediatric patients including 53 males and 47 females with mean age of 5.04 years were involved in the study. All children were medicated with ketamine with a mean dose of 3.77mg/kg. No complications occurred in 87% of cases. Desaturation occurred in 13% of the cases and was reversible by supplemental nasal oxygen. Desaturation was more frequent during Upper GI Endoscopy and with the intramuscular route (p value=0.049). No apnea, bradycardia, arrest or emergence reactions were recorded.CONCLUSION: Ketamine sedation found to be safe for paediatric gastrointestinal endoscopy in Egyptian children without co-morbidities. Transient Hypoxia (13%) may occur but easily reversed by nasal oxygen therapy.
Lateral Capsular Meniscal Tear in a Cross-country Runner
Authors: Pierce Todd P. , Wagner Casey , McInerney Vincent K. , Festa Anthony , Scillia Anthony J.
Published: July 2016

Tears of the meniscal capsular junction present unique diagnostic and treatment challenges for practitioners because they are rich in blood supply yet often not visualized on magnetic resonance imaging. The authors report the case of a young man with a meniscal capsular junction tear, difficulties related to his diagnosis, and his short-term outcomes following surgical treatment. An 18-year-old male cross-country runner presented to the authors for nonradiating lateral right knee pain after a failed course of physical therapy. This tear was only definitively diagnosed after inconclusive magnetic resonance imaging, an ultrasound-guided cortisone injection, and a direct arthroscopic visualization. He underwent an uncomplicated repair of his meniscal capsular junction tear with injection into the repair of bone marrow aspirate concentration taken from his iliac crest. At his 6-month follow-up visit, he had returned to cross-country running, achieved satisfactory Knee Outcome Scores, and reported minimal pain per the visual analog scale. This case is unique in that meniscal capsular junction tears often occur at the posteromedial portion. Given the rarity of these injuries, future studies should focus on longer-term outcomes following surgical repair of meniscal capsular junction tears and which techniques may portend to superior outcomes. [
Orthopedics.
2016; 39(6):e1201–e1204.]
Pediatric Diaphyseal Femur Fractures: Submuscular Plating Compared With Intramedullary Nailing
Authors: Sutphen Sean A. , Mendoza Juan D. , Mundy Andrew C. , Yang Jingzhen G. , Beebe Allan C. , Samora Walter P. , Klingele Kevin E.
Published: July 2016

This study compared the radiographic and clinical outcomes of pediatric diaphyseal femur fractures treated by submuscular plating, flexible retrograde intramedullary nailing, or rigid antegrade intramedullary nailing with a trochanteric entry point in skeletally immature patients who were 8 years and older. A retrospective review was conducted of skeletally immature patients 8 years and older who were treated for femur fracture with submuscular plating, flexible intramedullary nailing, or rigid intramedullary nailing from 2001 to 2014 with a minimum 12-week follow-up. Treatment outcomes were compared for statistical significance, including time to union, malunion, nonunion, heterotopic ossification, avascular necrosis, time to full weight bearing, limb length discrepancy, residual limp, painful hardware, and infection. The study identified 198 femur fractures in 196 patients (mean age, 11.9 years). Each femur fracture was treated with submuscular plating (35), flexible intramedullary nailing (61), or rigid intramedullary nailing (102). Mean follow-up across the cohort was 48 weeks, ranging from 12 to 225 weeks. Flexible nailing was associated with an increased incidence of malunion (
P
<.0001) and hardware irritation (
P
=.0204) and longer time to full weight bearing (
P
=.0018). Rigid nailing was associated with an increased incidence of limp at 12-week followup (
P
=.0412). Additionally, 23.5% of patients who were treated with rigid nailing had heterotopic ossification. Of all surgical methods, submuscular plating allowed for the most rapid return to full weight bearing (mean, 7 weeks) and offered the fastest healing rate (mean, 6 weeks). Submuscular plating resulted in faster times to union and full weight bearing, with minimal complication rates. Rigid intramedullary nailing with trochanteric entry resulted in a lower incidence of malunion and hardware-related complications; however, these patients had an increased incidence of heterotopic ossification and residual limp postoperatively. Flexible retrograde intramedullary nailing resulted in the highest rates of malunion and hardware irritation and the longest time to full weight bearing. [
Orthopedics.
2016; 39(6):353–358.]
Timing of Hip Fracture Surgery and 30-Day Outcomes
Authors: Neufeld Michael E. , O'Hara Nathan N. , Zhan Min , Zhai Yongliang , Broekhuyse Henry M. , Lefaivre Kelly A. , Abzug Joshua M. , Slobogean Gerard P.
Published: July 2016

The goal of this study was to determine the proportion of patients admitted with a hip fracture to participating American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) hospitals who were treated within the United Kingdom's National Institute for Health and Care Excellence (NICE) time-to-hip-fracture-surgery benchmark. The secondary goals were to identify factors associated with missing the benchmark and to determine whether the benchmark was associated with improved 30-day patient outcomes. Patients aged 60 years or older who underwent hip fracture surgery between 2005 and 2013 were identified from the ACS-NSQIP database. Of the 26,066 patients who met the enrollment criteria, 71.4% were treated within the NICE benchmark. Many variables, including sex, race, procedure type, and hip fracture diagnosis, were statistically significant predictors of missing the benchmark (
P
<.001). Meeting the NICE benchmark was not associated with reductions in major complications (odds ratio [OR], 0.93; 95% confidence interval [CI], 0.83–1.05;
P
=.234); however, it was associated with reductions in 30-day mortality (OR, 0.88; 95% CI, 0.78–0.99;
P
=.028), minor complications (OR, 0.92; 95% CI, 0.84–0.995;
P
=.038), and postoperative length of stay (beta=−0.77;
P
<.001). Current practice at participating ACS-NSQIP hospitals is compatible with the NICE time-to-surgery benchmark. However, the findings highlight the importance of further prospective investigation to monitor the effect of early-treatment benchmarks on 30-day patient outcomes. [
Orthopedics.
2016; 39(6):361–368.]
Benefits and Challenges of Teaching Nursing Online: Exploring Perspectives of Different Stakeholders
Authors: Wingo Nancy P. , Peters Gary B. , Ivankova Nataliya V. , Gurley D. Keith
Published: July 2016

Background:

Recruiting and retaining faculty to teach courses is crucial for online nursing programs to succeed. The purpose of this study was to gain a fuller understanding of the benefits and challenges of teaching nursing courses online by exploring the perspectives of faculty, administrators, and instructional designers at three schools of nursing in the southeastern United States.

Method:

This qualitative multiple case study explored perspectives of 21 participants from different stakeholder groups. Researchers used cross-case analysis and determined emerging themes in data collected from interviews, course demonstrations, and course documents.

Results:

Data analysis revealed themes regarding benefits and challenges for nursing instructors in (a) teaching strategies, (b) instructor availability, (c) training and support, and (d) institutional issues.

Conclusion:

This study found gaps in perspectives between participant groups that indicated a need for institutions to address communication issues, training program objectives, and institutional policies and procedures regarding online course design and delivery to promote faculty success and satisfaction. [
J Nurs Educ.
2016;55(8):433–440.]
Attitudes Toward Nursing Students With Disabilities: Promoting Social Inclusion
Authors: Shpigelman Carmit-Noa , Zlotnick Cheryl , Brand Rachel
Published: July 2016

Background:

Nursing education programs rarely refer to individuals with disabilities as potential nursing students; more often, the assumption is that they are patients. Thus, this study aimed to capture nursing students' perspectives of social inclusion through examination of their attitudes toward nursing student colleagues with disabilities.

Method:

Paper-and-pencil structured surveys containing two validated scales were collected from Israeli nursing students (N= 270). Analyses included measuring associations using Pearson's correlation coefficient and general linear regression models.

Results:

Nursing students held relatively negative attitudes toward colleagues with disabilities, and these negative attitudes were correlated to attitudes toward people with disabilities in general, even after adjusting for noted confounders.

Conclusion:

Nurse educators and nursing students should be aware of prejudicial attitudes with their respective communities toward nursing student colleagues with disabilities, and they should work toward a better understanding that cultural competence and awareness extends not only to patients but also to one's colleagues. [J Nurs Educ.2016;55(8):441–449.]
Transgender Health Care for Nurses: An Innovative Approach to Diversifying Nursing Curricula to Address Health Inequities
Authors: McDowell Alex , Bower Kelly M.
Published: July 2016

Background:

Transgender people experience high rates of discrimination in health care settings, which is linked to decreases in physical and mental wellness. By increasing the number of nurses who are trained to deliver high-quality care to transgender patients, health inequities associated with provider discrimination can be mitigated. At present, baccalaureate nursing curricula do not adequately prepare nurses to care for transgender people, which is a shortcoming that has been attributed to limited teaching time and lack of guidance regarding new topics.

Method:

We developed transgender health content for students in a baccalaureate nursing program and used a student–faculty partnership model to integrate new content into the curriculum.

Results:

We incorporated new transgender health content into five required courses over three semesters.

Conclusion:

We mitigated common barriers to developing and integrating new, diversity-related topics into a baccalaureate nursing curriculum. Added transgender health content was well received by students and faculty. [
J Nurs Educ.
2016;55(8):476–479.]
A Mobile WSN Sink Node Using Unmanned Aerial Vehicles: Design And Experiment
Authors: Cao Huiru , Yang Zhi , Li Ye-Qian
Published: July 2016

In this paper, we construct a new type of mobile wireless data sinking platform for data collection based on unmanned aerial vehicle (UAV) technology, which aims to address the increasing demand for wireless sensor network (WSN) distribution in different monitoring areas and enlarge the coverage for various application scenarios. A wireless environmental monitoring system is firstly studied, and then wireless communication capacity and data collection experiments are performed. The communication capacity test results show that when the RF modules operate with a transmission power above 1 dBm and a communication distance below 100 m, the UAV wireless sink node can maintain a high quality communication data link. Additionally, an outdoor data collection experiment is performed using this UAV platform within a mountainous area. In this outdoor experiment, the data analysis results show that the validity rate of the environmental data that is obtained from the WSN cluster head node on the ground is higher than 92%, and most of the missing data results from WSN communication failures. This experiment proves the feasibility of introducing UAV as a sink node in a clustering WSN. The overall contributions of this paper can provide guidance for building a UAV cooperative WSN system in future.
Some Properties on the Capability of Associative Memory for Higher Order Neural Networks
Authors: Miyajima Hiromi , Yatsuki Shuji , Shigei Noritaka , Miyajima Hirofumi
Published: July 2016

Higher order neural networks (HONNs) have been proposed as new systems. In this paper, we show some theoretical results of associative capability of HONNs. As one of them, memory capacity of HONNs is much larger than one of the conventional neural networks. Further, we show some theoretical results on homogeneous higher order neural networks (HHONNs), in which each neuron has identical weights. HHONNs can realize shift-invariant associative memory, that is, HHONNs can associate not only a memorized pattern but also its shifted ones.
Evolutionary Algorithms
Authors: Wong Ka-Chun
Published: July 2016

Inspired from nature, evolutionary algorithms have been proven effective and unique in different real world applications. Comparing to traditional algorithms, its parallel search capability and stochastic nature enable it to excel in search performance in a unique way. In this chapter, evolutionary algorithms are reviewed and discussed from concepts and designs to applications in bioinformatics. The history of evolutionary algorithms is first discussed at the beginning. An overview on the state-of-the-art evolutionary algorithm concepts is then provided. Following that, the related design and implementation details are discussed on different aspects: representation, parent selection, reproductive operators, survival selection, and fitness function. At the end of this chapter, real world evolutionary algorithm applications in bioinformatics are reviewed and discussed.
Parallel Multi-Criterion Genetic Algorithms
Authors: Mishra Bhabani Shankar Prasad , Mishra Subhashree , Singh Sudhansu Sekhar
Published: July 2016

The objective of this paper is to study the existing and current research on parallel multi-objective genetic algorithms (PMOGAs) through an intensive experiment. Many early efforts on parallelizing multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution of them with various examples. Further, the authors tried to identify some of the issues that have not yet been studied systematically under the umbrella of parallel multi-objective genetic algorithms. Finally, some of the potential application of parallel multi objective genetic algorithm is discussed.
Developmental Swarm Intelligence
Authors: Shi Yuhui
Published: July 2016

In this article, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.
Neural Network Models
Authors: Pessa Eliano
Published: July 2016

The Artificial Neural Network (ANN) models gained a wide popularity owing to a number of claimed advantages such as biological plausibility, tolerance with respect to errors or noise in the input data, learning ability allowing an adaptability to environmental constraints. Notwithstanding the fact that most of these advantages are not typical only of ANNs, engineers, psychologists and neuroscientists made an extended use of ANN models in a large number of scientific investigations. In most cases, however, these models have been introduced in order to provide optimization tools more useful than the ones commonly used by traditional Optimization Theory. Unfortunately, just the successful performance of ANN models in optimization tasks produced a widespread neglect of the true – and important – objectives pursued by the first promoters of these models. These objectives can be shortly summarized by the manifesto of connectionist psychology, stating that mental processes are nothing but macroscopic phenomena, emergent from the cooperative interaction of a large number of microscopic knowledge units. This statement – wholly in line with the goal of statistical mechanics – can be readily extended to other processes, beyond the mental ones, including social, economic, and, in general, organizational ones. Therefore this chapter has been designed in order to answer a number of related questions, such as: are the ANN models able to grant for the occurrence of this sort of emergence? How can the occurrence of this emergence be empirically detected? How can the emergence produced by ANN models be controlled? In which sense the ANN emergence could offer a new paradigm for the explanation of macroscopic phenomena? Answering these questions induces to focus the chapter on less popular ANNs, such as the recurrent ones, while neglecting more popular models, such as perceptrons, and on less used units, such as spiking neurons, rather than on McCulloch-Pitts neurons. Moreover, the chapter must mention a number of strategies of emergence detection, useful for researchers performing computer simulations of ANN behaviours. Among these strategies it is possible to quote the reduction of ANN models to continuous models, such as the neural field models or the neural mass models, the recourse to the methods of Network Theory and the employment of techniques borrowed by Statistical Physics, like the one based on the Renormalization Group. Of course, owing to space (and mathematical expertise) requirements, most mathematical details of the proposed arguments are neglected, and, to gain more information, the reader is deferred to the quoted literature.
A Method for Classification Using Data Mining Technique for Diabetes
Authors: Al-Khasawneh Ahmad
Published: July 2016

Many researchers in the health information system field have been attracted to develop computer applications that help in the diagnosis process. Imperatively, data mining algorithms address the vital role in all of these applications. Many contributions were made in this area. There has always been a debate on the algorithm that gives the best classifier, the parameters to be used, the dataset pre-processing steps, etc. In this paper, the author largely emphasizes that the best way to build a predictive model with relatively high classification accuracy is to build several predictive models and to choose the model that gives the best results through parameters optimization. Diagnosing diabetes mellitus has gained considerable attention in the last few decades due to the increased severity of the disease. In this research, the author reviews four predictive data mining approaches that are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset; k-nearest neighbour, support vector machine, multilayer perceptron neural network, and naive bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.
MEMS Microrobot with Pulse-Type Hardware Neural Networks Integrated Circuit
Authors: Saito Ken , Takato Minami , Sekine Yoshifumi , Uchikoba Fumio
Published: July 2016

Hexapod locomotive Micro-Electro Mechanical Systems (MEMS) microrobot with Pulse-type Hardware Neural Networks (P-HNN) locomotion controlling system is presented in this chapter. MEMS microrobot is less than 5 mm width, length, and height in size. MEMS microrobot is made from a silicon wafer fabricated by micro fabrication technology to realize the small size mechanical components. The mechanical components of MEMS microrobot consists of body frames, legs, link mechanisms, and small size actuators. In addition, MEMS microrobot has a biologically inspired locomotion controlling system, which is the small size electrical components realized by P-HNN. P-HNN generates the driving pulses for actuators of the MEMS microrobot using pulse waveform such as biological neural networks. The MEMS microrobot emulates the locomotion method and the neural networks of an insect with small size actuator, link mechanisms, and P-HNN. As a result, MEMS microrobot performs hexapod locomotion using the driving pulses generated by P-HNN.
Swarm Intelligence for Biometric Feature Optimization
Authors: Kumar Santosh , Datta Deepanwita , Singh Sanjay Kumar
Published: July 2016

Swarm Intelligence (SI) and bio-inspired computation has gathered great attention in research in the last few years. Numerous SI-based optimization algorithms have gained huge popularity to solve the complex combinatorial optimization problems, non-linear design system optimization, and biometric features selection and optimization. These algorithms are inspired by nature. In biometrics, face recognition is a non-intrusive method, and facial characteristics are probably the most common biometric features to identify individuals and provide a competent level of security. This chapter presents a novel biometric feature selection algorithm based on swarm intelligence (i.e. Particle Swarm Optimization [PSO] and Bacterial Foraging Optimization Algorithm [BFOA] metaheuristics approaches). This chapter provides the stepping stone for future researchers to unveil how swarm intelligence algorithms can solve the complex optimization problems to improve the biometric identification accuracy. In addition, it can be utilized for many different areas of application.
Application of Biologically Inspired Techniques for Industrial and Environmental Research via Air Quality Monitoring Network
Authors: Cai Tianxing
Published: July 2016

Industrial and environmental research will always involve the study of the cause-effect relationship between the emissions and the surrounding environment. Qualitative and mixed methods researchers have employed a variety of Information and Communication Technology (ICT) tools, simulated or virtual environments, information systems, information devices, and data analysis tools in this field. Machine-enhanced analytics has enabled the identification of aspects of interest such as correlations and anomalies from large datasets. Chemical facilities have high risks to originate air emission events. Based on an available air-quality monitoring network, the data integration technologies are applied to identify the scenarios of the possible emission source and the dynamic pollutant monitor result, so as to timely and effectively support diagnostic and prognostic decisions. In this chapter, the application of artificial neural networks for such applications have been developed according to the real application purpose. It includes two stages of modeling and optimization work: 1) the determination of background normal emission rates from multiple emission sources and 2) single-objective or multi-objective optimization for impact scenario identification and quantification. They have the capability to identify the potential emission profile and spatial-temporal characterization of pollutant dispersion for a specific region, including reverse estimation of the air quality issues. The methodology provides valuable information for accidental investigations and root cause analysis for an emission event; meanwhile, it helps evaluate the regional air quality impact caused by such an emission event as well. Case studies are employed to demonstrate the efficacy of the developed methodology.
Parameter Optimization of Photovoltaic Solar Cell and Panel Using Genetic Algorithms Strategy
Authors: Tarik Benmessaoud Mohammed , Zerhouni Fatima Zohra , Stambouli Amine Boudghene , Tioursi Mustapha , M'harer Aouad
Published: July 2016

In this chapter, we propose to perform a numerical technique based on genetic algorithms (GAs) to identify the electrical parameters (Is, Iph, Rs, Rsh, and n) of photovoltaic (PV) solar cells and modules. The one diode type approach is used to model the I–V characteristic of the solar cell. To extract electrical parameters, the approach is formulated as optimization problem. The GAs approach was used as a numerical technique in order to overcome problems involved in the local minima in the case optimization criteria. Compared to other methods, we find that the GAs is a very efficient technique to estimate the electrical parameters of photovoltaic solar cells and modules. Compared with other parameter extraction techniques, based on statistical study, results indicate the consistency and uniformity of method in terms of the quality of final solutions. In parallel, the simulated data with the extracted parameters of method base with GAs are in very good agreement with the experimental data in all cases.
Schematic Classification Model of Green Computing Approaches
Authors: Kesswani Nishtha , Jain Shelendra Kumar
Published: July 2016

Every electronic device has an impact on the environment, and computing devices are not an exception. Green computing is a highly motivated smart computing which tries to save energy and environment by minimizing harmful impacts of computing resource's production and their uses. As the 21st Century is known for Information Technology Revolution, major negative impact of computing resources on environment are day by days increasing contribution to carbon dioxide emission, hazardous substances, e-Waste and high consumption of energy. Nowadays Global Warming is a big issue which is responsible for climate changes. And according to many available facts computing resources are highly affecting it directly and indirectly. So the world's moral duties motivate it to save earth and environment from these harmful impacts, whose outcome is Green Computing. This paper focus on classification of various existing approaches which are used for Green Computing. This classification enhances researcher's concentration on particular classified category for their future contribution so that Green Computing's main aims can be fulfilled.
Toward a Sustainable Fishery Management Policy
Authors: Coy Steven P. , Shipley Margaret F. , Shipley-Lozano J. Brooke
Published: July 2016

This article proposes an Artificial Neural Network (ANN) model to predict neonatal and juvenile bull shark habitat usage in the Sabine Pass, located within the Gulf of Mexico between Louisiana and Texas. Given continuing discussion regarding overfishing of all shark species, including bull sharks, in the northwestern Atlantic and Gulf of Mexico, the research objective was to analyze environmental data proven to be conducive to bull shark early life stages and to use these data in an ANN to predict bull shark presence during late spring and throughout summer months at designated sampling sites. The results of this analysis can both aid decision making in a fisheries context and inform the discussion on bull shark habitat usage in general, thereby contributing to the discussion of whether or not the need exists for conservation efforts to ensure sustainability of the species as part of an effective bull shark management plan.
The Contribution of Teleworking towards a Green Computing Environment
Authors: Egbuta Iheanyi Chuku , Thomas Brychan , Al-Hasan Said
Published: July 2016

The aims of the chapter are to consider the strategic green issues of teleworking in terms of the environment, transport, location, office space, and resource use for modern organisations and business sectors and to formulate a conceptual model of the processes involved. In fact, teleworking technologies are variously implemented for green computing initiatives, and the many advantages include lower greenhouse gas emissions related to travel, greater worker satisfaction, and as a result of lower overhead office costs, increased profit margins. The chapter initially investigates the appropriateness of a working definition of teleworking with regard to green computing, and following this, explores the benefits and barriers of teleworking in a green computing environment. The theoretical frameworks and models of teleworking are then considered, and a conceptual model of the contribution of teleworking to green computing is formulated. It is the intention of the chapter to identify and articulate those teleworking concepts that will be useful to academicians, scientists, business entrepreneurs, practitioners, managers, and policy makers, and to indicate future research directions for research scholars and students with similar interests.
Improving Performance of Higher Order Neural Network using Artificial Chemical Reaction Optimization
Authors: Nayak Sarat Chandra , Misra Bijan Bihari , Behera Himansu Sekhar
Published: July 2016

Multilayer neural networks are commonly and frequently used technique for mapping complex nonlinear input-output relationship. However, they add more computational cost due to structural complexity in architecture. This chapter presents different functional link networks (FLN), a class of higher order neural network (HONN). FLNs are capable to handle linearly non-separable classes by increasing the dimensionality of the input space by using nonlinear combinations of input signals. Usually such network is trained with gradient descent based back propagation technique, but it suffers from many drawbacks. To overcome the drawback, here a natural chemical reaction inspired metaheuristic technique called as artificial chemical reaction optimization (ACRO) is used to train the network. As a case study, forecasting of the stock index prices of different stock markets such as BSE, NASDAQ, TAIEX, and FTSE are considered here to compare and analyze the performance gain over the traditional techniques.
FcγRIIB-Independent Mechanisms Controlling Membrane Localization of the Inhibitory Phosphatase SHIP in Human B Cells
Authors: Pauls Samantha D. , Ray Arnab , Hou Sen , Vaughan Andrew T. , Cragg Mark S. , Marshall Aaron J.
Published: July 2016

SHIP is an important regulator of immune cell signaling that functions to dephosphorylate the phosphoinositide phosphatidylinositol 3,4,5-trisphosphate at the plasma membrane and mediate protein–protein interactions. One established paradigm for SHIP activation involves its recruitment to the phospho-ITIM motif of the inhibitory receptor FcγRIIB. Although SHIP is essential for the inhibitory function of FcγRIIB, it also has critical modulating functions in signaling initiated from activating immunoreceptors such as B cell Ag receptor. In this study, we found that SHIP is indistinguishably recruited to the plasma membrane after BCR stimulation with or without FcγRIIB coligation in human cell lines and primary cells. Interestingly, fluorescence recovery after photobleaching analysis reveals differential mobility of SHIP–enhanced GFP depending on the mode of stimulation, suggesting that although BCR and FcγRIIB can both recruit SHIP, this occurs via distinct molecular complexes. Mutagenesis of a SHIP–enhanced GFP fusion protein reveals that the SHIP–Src homology 2 domain is essential in both cases whereas the C terminus is required for recruitment via BCR stimulation, but is less important with FcγRIIB coligation. Experiments with pharmacological inhibitors reveal that Syk activity is required for optimal stimulation-induced membrane localization of SHIP, whereas neither PI3K or Src kinase activity is essential. BCR-induced association of SHIP with binding partner Shc1 is dependent on Syk, as is tyrosine phosphorylation of both partners. Our results indicate that FcγRIIB is not uniquely able to promote membrane recruitment of SHIP, but rather modulates its function via formation of distinct signaling complexes. Membrane recruitment of SHIP via Syk-dependent mechanisms may be an important factor modulating immunoreceptor signaling.
Symptomatic vergence disorders in junior high school children in Ghana
Authors: Darko-Takyi Charles , Khan Naimah E. , Nirghini Urvashni
Published: July 2016

Background: Optometrists in Ghana are hampered in performing comprehensive binocular vision assessments, because of the lack of appropriate instruments leading to a paucity of data on vergence disorders and their association with asthenopia among Ghanaian school children.Aim: To establish the prevalence of symptomatic vergence disorders among junior high school (JHS) children in Cape Coast Metropolis, Ghana, in their habitual (vision) states and investigate if there were any associations between these disorders and specific asthenopic symptoms.Methods: A prospective cross-sectional school-based study using a multistage sample of 627 participants aged 12–17 years from JHSs in Cape Coast Metropolis. Participants completed a reliable asthenopic symptoms questionnaire and 220 participants who expressed two or more severe or very severe symptoms were selected for comprehensive binocular vision assessment in their habitual vision state.Results: The prevalence of symptomatic vergence disorders among JHS children in Cape Coast Metropolis was 14.8%. For specific symptomatic vergence disorders, the prevalence was: 1.4% basic esophoria, 1.4% basic exophoria, 8.6% convergence insufficiency, 1.8% convergence excess, 0.8% fusional vergence dysfunctions and 0.8% divergence excess. No participant had symptomatic divergence insufficiency. The study revealed significant associations between some specific symptomatic vergence disorders and specific asthenopic symptoms even though all of these asthenopic symptoms overlapped in other vergence disorders.Conclusion: Presenting complaints of specific asthenopic symptoms does not differentiate between specific types of vergence disorders. A comprehensive binocular vision assessment is vital in the diagnosis and management of these disorders to relieve asthenopia.
Writer’s cramp in spinocerebellar ataxia Type 1
Authors: Khwaja Geeta Anjum , Srivastava Abhilekh , Ghuge Vijay Vishwanath , Chaudhry Neera
Published: July 2016

Dystonia can be encountered in a small subset of patients with spinocerebellar ataxia (SCA), but task specific dystonia is extremely rare. We report a case of a 48-year-old male with confirmed SCA Type 1 (SCA1) with mild progressive cerebellar ataxia and a prominent and disabling Writer’s cramp. This case highlights the ever-expanding phenotypic heterogeneity of the SCA’s in general and SCA1 in particular.
Public opinion mining on Sochi-2014 Olympics
Authors: Stepchenkova Svetlana , Kirilenko Andrei
Published: July 2016

The requirements of evidence-based policymaking promote interest to realtime monitoring of public’s opinions on policy-relevant topics, and social media data mining allows diversification of information portfolio used by public administrators. This study discusses issues in public opinion mining with respect to extraction and analysis of information posted on Twitter about Sochi-2014 Olympic. It focuses on topics discussed on Twitter and sentiment analysis of tweets about the Games. Final database contained 613,333 tweets covering time span from November 1, 2013 until March 31, 2014. Using hash tags the data were classified into the following categories: Events (21%); News (14%); Sports (12%); Anticipation of the Games (12%); Cheering of the teams (6%) and Problems & Politics (2%). Research reveals considerable differences in the outcomes of machine sentiment classifiers: Deeply Moving, Pattern, and SentiStrength. SentiStrength produced the most suitable results in terms of minimization of incorrectly classified tweets. Methodological implications and directions for future research are discussed.
Effects of colored contrast of mobile websites on behavioral intentions
Authors: Pelet Jean-Eric , Taieb Basma
Published: July 2016

This study examines the effects of the mobile-phone website colored contrasts and the affective states of the consumer (emotions and moods) and trust respectively on intention to revisit, buy on and recommend the mobile website. For this purpose, a factorial plan 2x2 was developed and a mobile website, with two different alternatives, was designed especially for the experiment: positive contrast (yellow text on green background) and negative contrast (green text on yellow background). The research was conducted on French consumers. 312 valid responses were collected through online and personal survey questionnaires. Data was analysed using the method of structural equations. The results show the significant effects of mobile website’s color contrast on behavioral intentions. Perceived dominance and trust towards the website have positive effects on behavioral intentions, whereas mood has non-significant effects on behavioral intentions. Managerial implications are discussed.
Scale of Attitudes Towards ICT (SATICT): Factor Structure and Factorial Invariance in Distance University Students
Authors: Ordóñez Xavier Giovanni , Romero Sonia Janeth
Published: July 2016

Attitudes Towards Information and Communication Technology (ICT) are preconceived beliefs about influence of the ICT tools in the process of learning. Studies carried out in this area shows that attitudes may influence cognitive and learning processes and also motivation of students. Despite its importance, very few instruments have been proposed to measure student attitudes, and none analyzes the factor structure or the factorial invariance of the scores, for that reason, the aim of the present study is to analyze the factor structure of SATICT, a new instrument proposed to measure the attitudes towards ICT on distance university students. A second aim is to test the factorial invariance across gender and educational level in a sample of 1080 university students of Madrid Open University using multi-group CFA. The results provide high support of the proposed factor structure with significant loadings and adequate model fit, however, the results also showed that factor structure couldn't be considered invariant across groups.
The impact of companies’ websites on competitiveness and productivity performance
Authors: Vecchi Michela , Domenech Josep , Rizov Marian
Published: July 2016

We investigate the role of the Internet in inducing market competition and firm productivity performance using a sample of UK and Spanish firms over the 1995-2010 period. For each firm we collect unique information on its online status (website) and the number of years of Internet activity. Our results show that the Internet is associated with reduced market concentration in both countries. Using a semiparametric estimation algorithm we find that firms’ Internet presence is positively associated with the level of TFP but not with its rate of growth. This suggests that selection is likely to drive website adoption and that the Internet by itself cannot replace traditional sources of competitive and productivity advantage.
Online CASE CPI
Authors: Radzikowski Bartosz , Śmietanka Adam
Published: July 2016

Online CASE CPI is an example of using Big data in public statistics. In principle, it is a consumer price index based entirely on online prices: a combination of Central Statistical Office of Poland’s methodology and online data sets. An innovative method of data collection – data scrapping – allowed us to substantially reduce a time delay between data collection and a publication of results. A short, nine-month period of data collection has not given rise to make important conclusions, hence the aims of this paper are: to discuss a general framework of measuring consumer inflation online, to present preliminary results for Poland and to highlight the strengths and weaknesses of this approach. Finally, we believe that online consumer price indices have a complementary nature to conventional inflation measurement, but it might be a serious alternative, having in mind a huge growth potential of e-commerce in coming years.
Quantifying and comparing web news portals’ article salience using the VoxPopuli tool
Authors: Bonacci Duje , Jelinić Antonija , Jurišić Jelena , Vesnić-Alujević Lucija
Published: July 2016

VoxPopuli tool enables quantification of absolute and relative salience of news articles published on daily news web portals. Obtained numerical values for the two types of salience enable direct comparison of audience impact of different news articles in specified time period. Absolute salience of a news article in a specified time period is determined as the total number of distinct readers who commented on the story in that period. Hence, articlesthat appear on web portals with larger audiences will in general be (absolutely) more salient as there are more potential commentators to comment on them. On the other hand, relative salience of a particular article during a particular time period is calculated as the quotient of a number of distinct readers who comented on that particular story and the number of all readers who in the same period commented on any news story published on the same news portal. As such relative salience will always be a number between 0 and 1, irrespective of the popularity of particular news portal, the (relative) salience of news stories on different news portals can be compared.
Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
Authors: Munar Antoni , Chiner Esteban
Published: July 2016

Know your customer is a core element of any customer relationship management system for mass service organizations. The emergence of social networking services has provided a radically new dimension, creating a more personalized, deeper, ubiquitous and almost real time relation with customers. At the same time, some of the more widespread social network platforms seem to be evolving not only as social networks between individuals but also as mass information distribution media. When knowing your customer through social networking services, it may be of interest to disambiguate which part of the customer context in the network relates to his peers from other sources. In this paper we present an algorithmic approach to disambiguate one aspect of such relation, as expressed in the nature of the contacts established in the social network: with peers or with organizations, news media or influencers. We focus in the case of Twitter where a simple supervised linear regression can provide a ranking score, effectively discriminating and ordering by closeness peer and other types of contacts (mass media or influencers). Such discrimination can serve as a preliminary step for deeper analysis or privacy protection of customer interaction and is suitable for implementation in automated Big Data systems.
Identification of Influencers in eWord-of-Mouth communities using their Online Participation Features
Authors: Toral Sergio L. , Olmedilla Maria , Arenas-Márquez Francisco José , Martinez-Torres M. Rocio
Published: July 2016

The identification of influencers in any type of online social network is of paramount importance, as they can significantly affect consumers’ purchasing decisions. This paper proposes the utilization of a self-designed web scraper to extract meaningful information for the identification of influencers and the analysis of how this new set of variables can be used to predict them. The experimental results from the Ciao UK website will be used to illustrate the proposed approach and to provide new insights in the identification of influencers. Obtained results show the importance of the trust network, but considering the intensity and the quality of both trustors and trustees.
Limited Resettlement and Ongoing Uncertainty: responses to and experiences of people seeking asylum in Australia and Indonesia
Authors: Fleay Caroline , Hartley Lisa
Published: July 2016

In the wake of the Coalition Government’s narrow victory in the first Australian election since the adoption of policies known as Operation Sovereign Borders, this special edition of Cosmopolitan Civil Societies focuses its attention on the treatment of refugees and asylum seekers . It explores some of the experiences of people both in Australia and Indonesia who are seeking a life of safety, as well as the responses of civil society groups and governments, following the commencement of policies that have vastly reduced the opportunities for refugee resettlement in Australia.