bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach
Authors: Xu Yang , Shaw Shih-Lung , Fang Zhixiang , Yin Ling
Published: July 2016

This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation method to partition cellphone trajectories into trip chain segments. By selecting trip chain segments that can potentially be served by bicycles, two indicators (inflow and outflow) are generated at the cellphone tower level to estimate the potential demand of incoming and outgoing bicycle trips at different places in the city and different times of a day. A maximum coverage location-allocation model is used to suggest locations of bike sharing stations based on the total demand generated at each cellphone tower. Two measures are introduced to further understand characteristics of the suggested bike station locations: (1) accessibility; and (2) dynamic relationships between incoming and outgoing trips. The accessibility measure quantifies how well the stations could serve bicycle users to reach other potential activity destinations. The dynamic relationships reflect the asymmetry of human travel patterns at different times of a day. The study indicates the value of mobile phone data to intelligent spatial decision support in public transportation planning.
Stability of Naturally Relevant Ternary Phases in the Cu–Sn–S System in Contact with an Aqueous Solution
Authors: Giaccherini Andrea , Montegrossi Giordano , Di Benedetto Francesco
Published: July 2016

A relevant research effort is devoted to the synthesis and characterization of phases belonging to the ternary system Cu–Sn–S, mainly for their possible applications in semiconductor technology. Among all ternary phases, kuramite, Cu3SnS4, mohite, Cu2SnS3, and Cu4Sn7S16 have attracted the highest interest. Numerous studies were carried out claiming for the description of new phases in the ternary compositional field. In this study, we revise the existing literature on this ternary system, with a special focus on the phases stable in a temperature range at 25 °C. The only two ternary phases observed in nature are mohite and kuramite. Their occurrence is described as very rare. A numerical modelling of the stable solid phases in contact with a water solution was underwent to define stability relationships of the relevant phases of the system. The numerical modelling of the Eh-pH diagrams was carried out through the phreeqc software with the lnll.dat thermodynamic database. Owing to the complexity of this task, the subsystems Cu–O–H, Sn–O–H, Cu–S–O–H and Sn–S–O–H were firstly considered. The first Pourbaix diagram for the two naturally relevant ternary phases is then proposed.
Green Tea Leaves Extract: Microencapsulation, Physicochemical and Storage Stability Study
Authors: Zokti James , Sham Baharin Badlishah , Mohammed Abdulkarim , Abas Faridah
Published: July 2016

Green tea polyphenols have been reported to possess many biological properties. Despite the many potential benefits of green tea extracts, their sensitivity to high temperature, pH and oxygen is a major disadvantage hindering their effective utilization in the food industry. Green tea leaves from the Cameron Highlands Malaysia were extracted using supercritical fluid extraction (SFE). To improve the stability, green tea extracts were encapsulated by spray-drying using different carrier materials including maltodextrin (MD), gum arabic (GA) and chitosan (CTS) and their combinations at different ratios. Encapsulation efficiency, total phenolic content and antioxidant capacity were determined and were found to be in the range of 71.41%–88.04%, 19.32–24.90 (g GAE/100 g), and 29.52%–38.05% respectively. Further analysis of moisture content, water activity, hygroscopicity, bulk density and mean particles size distribution of the microparticles were carried out and the results ranged from; 2.31%–5.11%, 0.28–0.36, 3.22%–4.71%, 0.22–0.28 g/cm3 and 40.43–225.64 µm respectively. The ability of the microparticles to swell in simulated gastric fluid (SGF) and simulated intestinal fluid (SIF) was determined as 142.00%–188.63% and 207.55%–231.77%, respectively. Release of catechin polyphenol from microparticles in SIF was higher comparable to that of SGF. Storage stability of encapsulated catechin extracts under different temperature conditions was remarkably improved compared to non-encapsulated extract powder. This study showed that total catechin, total phenolic content (TPC) and antioxidant activity did not decrease significantly (p ≥ 0.05) under 4 °C storage conditions. The half-life study results were in the range of 35–60, 34–65 and 231–288 weeks at storage temperatures of 40 °C, 25 °C and 4 °C respectively, therefore, for improved shelf-life stability we recommend that microparticles should be stored at temperatures below 25 °C.
Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain
Authors: Doña Carolina , Chang Ni-Bin , Caselles Vicente , Sánchez Juan , Pérez-Planells Lluís , Bisquert Maria , García-Santos Vicente , Imen Sanaz , Camacho Antonio
Published: July 2016

The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013–2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.
Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
Authors: Gu Changzhan
Published: July 2016

Short-range noncontact sensors are capable of remotely detecting the precise movements of the subjects or wirelessly estimating the distance from the sensor to the subject. They find wide applications in our day lives such as noncontact vital sign detection of heart beat and respiration, sleep monitoring, occupancy sensing, and gesture sensing. In recent years, short-range noncontact sensors are attracting more and more efforts from both academia and industry due to their vast applications. Compared to other radar architectures such as pulse radar and frequency-modulated continuous-wave (FMCW) radar, Doppler radar is gaining more popularity in terms of system integration and low-power operation. This paper reviews the recent technical advances in Doppler radars for healthcare applications, including system hardware improvement, digital signal processing, and chip integration. This paper also discusses the hybrid FMCW-interferometry radars and the emerging applications and the future trends.
Green Energy for a Green City—A Multi-Perspective Model Approach
Authors: Wątróbski Jarosław , Ziemba Paweł , Jankowski Jarosław , Zioło Magdalena
Published: July 2016

The basis for implementing demands for a green city is the use of, among other things, innovative “clean” technologies. However, it is mostly and directly connected to the increased use of electric energy. Green transport is an appropriate example of this. By contrast, conventional sources of energy (e.g., based on coal) have a very negative impact on people and the environment. Therefore, this article mentions an attempt to solve a complex problem of employing renewable energy sources (RES) as an element of the “green city” system. The research was carried out on the basis of a feasibility study (decision game) for the location of a wind farm in the vicinity of the city of Szczecin, Poland. When constructing the decision models, multiple-criteria decision analysis (MCDA) methods were applied, especially analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE).
Assessing Risk in Chinese Shale Gas Investments Abroad: Modelling and Policy Recommendations
Authors: Li Hui , Sun Renjin , Lee Wei-Jen , Dong Kangyin , Guo Rui
Published: July 2016

As the shale gas revolution expands globally, the future potential and economic profits of overseas shale gas reserves have attracted the interest of Chinese investors. Overseas shale gas development is becoming an investment hotspot for Chinese oil companies. However, this multibillion-dollar venture is surrounded by a complex and uncertain environment. Therefore, this paper carries out an integrated and publicly available model for assessing risk in overseas shale gas investments. The purpose of this model is to address the index weight calculation and risk ranking and provide investor with risk information. In view of this, the comprehensive weights are obtained based on an analytic hierarchy process (AHP) and entropy weight methods; and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is performed to rank target countries. First, the paper identities five categories of risks with full consideration of the economic risk, political risk, geological risk, technological risk, and internal managements risk. Based on the risk identification, the assessment index system is established and valued. Secondly, China is taken as an example nation to use this model to prove the effectiveness of the proposed model and help the investor make wise decisions. According to the results, low-risk countries, such as Canada, Argentina, United States, and Algeria can be considered to be future key targets of shale gas investment abroad, while investors should be more cautious of high-risk countries such as South Africa and Brazil. Finally, policy recommendations are proposed to optimize the overseas shale gas investments from both the government and investor perspectives.
Distributed Leadership in a Low-Carbon City Agenda
Authors: Mohamed Azalia , Ibrahim Zelina , Silong Abu , Abdullah Ramdzani
Published: July 2016

This paper uses Spillane’s (2001) theory and Gronn’s (2000) concerted efforts approach to examine distributed leadership in a low-carbon city agenda. The main purpose of the paper is to find empirical evidence of a relationship between distributed leadership and the achievement of the agenda. Eight constructs emerged that informed our understanding of distributed leadership dimensions within the low-carbon city framework: vision, organizational framework, organizational culture, consensus, instructional programs, expertise, team leader leadership, and team member leadership. The evidence shows that there is a positive relationship between distributed leadership and the outcome of the low-carbon city agenda, and that a dispersed pattern in distributing leadership is required to enhance community engagement. The findings also suggest that an organizational culture that facilitates multiple sources of leadership may largely contribute to the effectiveness of distributed leadership practices in realizing the low-carbon city agenda.
Curricular Innovations in Tobacco Cessation Education for Prelicensure Baccalaureate Nursing Students
Authors: Schwindt Rhonda G. , McNelis Angela M. , Agley Jon
Published: July 2016

Background:

Tobacco use is the primary preventable cause of morbidity and mortality in the United States, resulting in enormous health care expenditures. The burden of smoking is higher among disadvantaged populations, such as individuals with mental illness. As the largest group of health care providers, nurses must assume a leading role in tobacco control efforts to decrease the deleterious impact on health outcomes.

Method:

Investigators used a randomized control group design to assess the effectiveness of a theory-based tobacco education program on the perceived competence and intrinsic motivation of prelicensure BSN students (
N
= 134) to engage in cessation interventions with patients with mental illness.

Results:

Students completing the program reported a significant increase in perceived competence, compared with their peers who received standard instruction only. Intrinsic motivation did not increase significantly for either group.

Conclusion:

Findings suggest that the program improves students' perceived competence, but further research is needed to determine its effect on motivation and its usefulness in other health care contexts. [
J Nurs Educ.
2016;55(8):425–431.]
A Summer Prematriculation Program: Bridging the Gap for Disadvantaged Underrepresented Minority Students Interested in a Nursing Career
Authors: Norris Tommie L. , Wicks Mona N. , Cowan Patricia A. , Davison Erwin Story
Published: July 2016

Background:

The nursing and health care workforce needs diverse clinicians who can provide culturally competent and high-quality care to an increasingly diverse U.S. population. Achieving this goal requires creating learning environments that foster the success of disadvantaged underrepresented minority (URM) students seeking nursing careers.

Method:

This 4-week summer prematriculation program introduced 33 URM individuals from disadvantaged backgrounds to nursing as a career through financial support, academic enrichment, and social support to enhance nursing program admission success. Federal guidelines were used to establish URM and economically disadvantaged status.

Results:

To date, one third of program participants have been admitted to nursing programs.

Conclusion:

Fundamental reforms in pre-college education systems, such as the evidence-based strategies implemented in our summer prematriculation program, may be needed to achieve a diverse, culturally competent workforce that can help eliminate persistent health and health care disparities. [
J Nurs Educ
. 2016;55(8):471–475.]
A Support Vector Machine Classification of Computational Capabilities of 3D Map on Mobile Device for Navigation Aid
Authors: Abubakar Adamu , Mantoro Teddy , Moedjiono Sardjoeni , Ayu Media Anugerah , Chiroma Haruna , Waqas Ahmad , Muhammad Abdulhamid Shafi’i , Fatihu Hamza Mukhtar , Gital Abdulsalam Ya'u
Published: July 2016

3D map for mobile devices provide more realistic view of an environment and serves as better navigation aid. Previous research studies shows differences in 3D maps effect on acquiring of spatial knowledge. This is attributed to the differences in mobile device computational capabilities. Crucial to this, is the time it takes for 3D map dataset to be rendered for a required complete navigation task. Different findings suggest different approach on solving the problem of time require for both in-core (inside mobile) and out-core (remote) rendering of 3D dataset. Unfortunately, studies on analytical techniques required to shows the impact of computational resources required for the use of 3D map on mobile device were neglected by the research communities. This paper uses Support Vector Machine (SVM) to analytically classify mobile device computational capabilities required for 3D map that will be suitable for use as navigation aid. Fifty different Smart phones were categorized on the bases of their Graphical Processing Unit (GPU), display resolution, memory and size. The result of the proposed classification shows high accuracy
Fill Me App: An Interactive Mobile Game Application for Children with Autism
Authors: Eder Marylene Saldon , L. Diaz John Maruel , S. Madela Joanne Ruth , U. Mag-usara Marife , M. Sabellano Dhally Dith
Published: July 2016

FillMeApp is an interactive mobile game application which is a supplementary learning material intended for children with Autism that helps them motivate in their learning process. This game application is focus mainly on Science basically on identifying the human’s body parts. Accumulating the best time for focus monitoring, eye-catching graphics, simple level of exercises, video tutorial and background music that coincide with the current educational teachings are among the primary features that this application has to offer. The researchers analyzed the results of the test survey and proved that the application is user friendly, interactive, the logic of the game is understandable and would learn to use this application very useful in their learning. Based also from the researchers testing and result on the students motivational rating from the teacher, before, the students with Autisms’ motivation level were LOW but after the game application was deployed and tested their motivation status begin to grow and become HIGH.
A Proposed System for Push Messaging on Android
Authors: El Stohy Rasha Abdel sattar , El Ghetany Nashaat El Khamessy , El Gharib Haitham Abdul Menem
Published: July 2016

Abstract— There is a remarkable growing interest in utilizing mobile communications to enhance the interactions between different partners in educational institutions, particularly in schools. However, how communicating with parents via applications while aren't actively running is quite a challenging demand. On the other hand, the addressed success of the Android supporting features provided a good candidate to support applications with real time information. Moreover the Android platforms supports efficiently customizable notifications, which allow applications based on Google Cloud Messaging (GCM) to specify the pushed events. In this paper, we look at different push messaging alternatives available for Android. Push messaging provides an important aspect of server to device communication, and we specifically focus on the integration of cloud computing with mobile devices through the use of push-based technologies. The paper presents a proposed notification system; ToParent, that actively provide alerts and messaging between parents and schools. Also, we investigate the performance of two relevant push technologies for the Android devices .The proposed system was implemented using Eclipse. The system has been tested and validated. Evaluation have proven that the proposed ToParent enabled better efficiency, and good overall satisfaction of both parties; parents and school administrations.
A Theoretical Framework for Parallel Implementation of Deep Higher Order Neural Networks
Authors: Xu Shuxiang , Liu Yunling
Published: July 2016

This chapter proposes a theoretical framework for parallel implementation of Deep Higher Order Neural Networks (HONNs). First, we develop a new partitioning approach for mapping HONNs to individual computers within a master-slave distributed system (a local area network). This will allow us to use a network of computers (rather than a single computer) to train a HONN to drastically increase its learning speed: all of the computers will be running the HONN simultaneously (parallel implementation). Next, we develop a new learning algorithm so that it can be used for HONN learning in a distributed system environment. Finally, we propose to improve the generalisation ability of the new learning algorithm as used in a distributed system environment. Theoretical analysis of the proposal is thoroughly conducted to verify the soundness of the new approach. Experiments will be performed to test the new algorithm in the future.
A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms
Authors: Maitre Julien , Gaboury Sébastien , Bouchard Bruno , Bouzouane Abdenour
Published: July 2016

Knowledge on asynchronous machine parameters (resistances, inductances…) has become necessary for the manufacturing industry in the interest of optimizing performances in a production system (roll-to-roll processing, wind generator…). Indeed, accurate values of this machine allow improving control of the torque, speed and position, managing power consumption in the best way possible, and predicting induction machine failures with great effectiveness. In these regards, the authors of this paper propose a black-box modeling for a powerful identification of asynchronous machine parameters relying on stochastic research algorithms. The algorithms used for the estimation process are a single objective genetic algorithm, the well-known NSGA II and the new ?-NSGA III (multi-objective genetic algorithms). Results provided by those show that the best estimation of asynchronous machines parameters is given by ?-NSGA III. In addition, this outcome is confirmed by performing the identification process on three different induction machines.
Cosine and Sigmoid Higher Order Neural Networks for Data Simulations
Authors: Zhang Ming
Published: July 2016

New open box and nonlinear model of Cosine and Sigmoid Higher Order Neural Network (CS-HONN) is presented in this paper. A new learning algorithm for CS-HONN is also developed from this study. A time series data simulation and analysis system, CS-HONN Simulator, is built based on the CS-HONN models too. Test results show that average error of CS-HONN models are from 2.3436% to 4.6857%, and the average error of Polynomial Higher Order Neural Network (PHONN), Trigonometric Higher Order Neural Network (THONN), and Sigmoid polynomial Higher Order Neural Network (SPHONN) models are from 2.8128% to 4.9077%. It means that CS-HONN models are 0.1174% to 0.4917% better than PHONN, THONN, and SPHONN models.
Ultra High Frequency SINC and Trigonometric Higher Order Neural Networks for Data Classification
Authors: Zhang Ming
Published: July 2016

This chapter develops a new nonlinear model, Ultra high frequency SINC and Trigonometric Higher Order Neural Networks (UNT-HONN), for Data Classification. UNT-HONN includes Ultra high frequency siNc and Sine Higher Order Neural Networks (UNS-HONN) and Ultra high frequency siNc and Cosine Higher Order Neural Networks (UNC-HONN). Data classification using UNS-HONN and UNC-HONN models are tested. Results show that UNS-HONN and UNC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UNS-HONN and UNC-HONN models can classify the data with error approaching 0.0000%.
A Survey on Swarm Robotics
Authors: Tan Ying
Published: July 2016

In this chapter, the current work on swarm robotics is briefly reviewed. Swarm robotics, inspired from nature swarm, is a combination of swarm intelligence and robotic, and shows great potential in several aspects. Firstly of all, the cooperation in nature swarm and swarm intelligence is briefly introduced, and the special features of the swarm robotics compared with single robot and other multi-individual systems is also presented. Then we describe the modeling method for swarm robotics and list several widely used swarm robotics entity projects and simulation platforms for interested researchers. Finally, as the main point of this chapter, we summarize the current researches on swarm robotic algorithms, i.e., cooperative control mechanisms for swarm robotics for flocking, navigating and searching applications.
Using a Hybrid System Composed of Neural Networks and Genetic Algorithms for Financial Forecasting
Authors: Dumitrescu Mihaela
Published: July 2016

The possibility of applying artificial neural networks in different areas determined the discovery of more complex structures. This chapter describes the characteristic aspects of using a back-propagation neural network algorithm in making financial forecasting improved by a different technology: genetic algorithms. These can help build an automatic artificial neural network by two adaptive processes: first, genetic search through the data entry window, the forecast horizon, network architecture space, and control parameters to select the best performers; second, back propagation learning in individual networks to evaluate the selected architectures. Thus, network performance population increases from generation to generation. This chapter also presents how genetic algorithms can be used both to find the best network architecture and to find the right combination of inputs, the best prediction horizon and the most effective weight. Finally, this study shows how the results obtained using these technologies can be applied to obtain decision support systems that can lead to increased performance in economic activity and financial projections.
Studies of Computational Intelligence Based on the Behaviour of Cockroaches
Authors: Neogi Amartya
Published: July 2016

In this chapter, the author expands the notion of computational intelligence using the behavior of cockroaches. An introduction to cockroach as swarm intelligence emerging research area and literature review of its growing concept is explained in the beginning. The chapter also covers the ideas of hybrid cockroach optimization system. Next, the author studies the applicability of cockroach swarm optimization. Thereafter, the author presents the details of theoretical algorithm and an experimental result of integration of robot to some cockroaches to make collective decisions. Then, the author proposes his algorithm for traversing the shortest distance of city warehouses. Then, a few comparative statistical results of the progress of the present work on cockroach intelligence are shown. Finally, conclusive remarks are given. At last, the author hopes that even researchers with little experience in swarm intelligence will be enabled to apply the proposed algorithm in their own application areas.
Evaluation of Genetic Algorithm as Learning System in Rigid Space Interpretation
Authors: Singh Bhupesh Kumar
Published: July 2016

Genetic Algorithm (GA) (a structured framework of metaheauristics) has been used in various tasks such as search optimization and machine learning. Theoretically, there should be sound framework for genetic algorithms which can interpret/explain the various facts associated with it. There are various theories of the working of GA though all are subject to criticism. Hence an approach is being adopted that the legitimate theory of GA must be able to explain the learning process (a special case of the successive approximation) of GA. The analytical method of approximating some known function is expanding a complicated function an infinite series of terms containing some simpler (or otherwise useful) function. These infinite approximations facilitate the error to be made arbitrarily small by taking a progressive greater number of terms into consideration. The process of learning in an unknown environment, the form of function to be learned is known only by its form over the observation space. The problem of learning the possible form of the function is termed as experience problem. Various learning paradigms have ensured their legitimacy through the rigid space interpretation of the concentration of measure and Dvoretzky theorem. Hence it is being proposed that the same criterion should be applied to explain the learning capability of GA, various formalisms of explaining the working of GA should be evaluated by applying the criteria, and that learning capability can be used to demonstrate the probable capability of GA to perform beyond the limit cast by the No Free Lunch Theorem.
Using Self Organizing Maps for Banking Oversight
Authors: Lopez-Iturriaga Felix , Pastor-Sanz Iván
Published: July 2016

This chapter combines two methods based on neural networks (trait recognition and self-organizing maps) to develop a model of bankruptcy prediction. The authors apply the method to the Spanish savings banks, most of them rescued by the Government between 2008 and 2013 in a costly massive process. First, the authors detect the combinations of variables (performance, asset structure, and capitalization) that best describe the profile of the rescued savings banks. Then, the authors use these combinations on a yearly basis to generate bi-dimensional maps in which banks are placed according to their risk and similarities. This method provides a visual tool that can improve the oversight of policy makers on the whole financial system and enable time pertinent answers to some threatens to the country financial stability. The maps are useful means to detect and understand how the financial threats emerge over time too.
Customer Profiling in Complex Analytical Environments Using Swarm Intelligence Algorithms
Authors: Klepac Goran
Published: July 2016

Customer profiling is always an interesting task from perspective of business. It became even bigger challenge in situation of complex analytical environment. Complex analytical environment can be caused by multiple modality of output variable as well as from big data environment, which cause data complexity in way of data quantity. As an illustration of presented concept particle swarm optimization algorithm will be used as a tool, which will find profiles from developed predictive model of neural network. Presented methodology has practical value for decision support in business, where information about customer profiles which prefers to buy some product or group products are valuable information for campaign planning and customer portfolio management.
Hybrid BFO and PSO Swarm Intelligence Approach for Biometric Feature Optimization
Authors: Kumar Santosh , Singh Sanjay Kumar
Published: July 2016

Nature-inspired novel swarm intelligence algorithms have gained more proliferation due to a variety of applications and uses in optimization of complex problems and selection of discriminatory sets of features to classify huge datasets during the past few decades. Feature selection is an efficient and useful pre-processing technique for solving classification problems in computer vision, data mining and pattern recognition. The major challenges of solving the feature selection problems lay in swarm intelligence algorithms which are capable of handling the vast number of feature sets from involved databases. In biometric based recognition systems, face recognition is a non-intrusive approach to identify individuals based on their discriminatory sets of facial feature vectors. In this paper, the authors tend to propose a unique novel hybrid based on Bacterial Foraging Optimization (BFO) and Particle swarm optimization (PSO) approach for the selection of best facial feature vectors that enhance the identification accuracy of the individual recognition because concerned facial info will contain useless and redundant face expression. The proposed hybrid approach mitigates irrelevant facial features in the feature space and selects the relevant set of features from the facial feature space. The proposed feature selection approach presents promising experimental results with respect to the number of facial feature subsets. The identification accuracies are superior to other approaches from the literature.
From Optimization to Clustering
Authors: Vora Megha , Mirnalinee T. T.
Published: July 2016

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.
Mucosal Inducible NO Synthase–Producing IgA+ Plasma Cells in Helicobacter pylori–Infected Patients
Authors: Neumann Laura , Mueller Mattea , Moos Verena , Heller Frank , Meyer Thomas F. , Loddenkemper Christoph , Bojarski Christian , Fehlings Michael , Doerner Thomas , Allers Kristina , Aebischer Toni , Ignatius Ralf , Schneider Thomas
Published: July 2016

The mucosal immune system is relevant for homeostasis, immunity, and also pathological conditions in the gastrointestinal tract. Inducible NO synthase (iNOS)–dependent production of NO is one of the factors linked to both antimicrobial immunity and pathological conditions. Upregulation of iNOS has been observed in human Helicobacter pylori infection, but the cellular sources of iNOS are ill defined. Key differences in regulation of iNOS expression impair the translation from mouse models to human medicine. To characterize mucosal iNOS-producing leukocytes, biopsy specimens from H. pylori–infected patients, controls, and participants of a vaccination trial were analyzed by immunohistochemistry, along with flow cytometric analyses of lymphocytes for iNOS expression and activity. We newly identified mucosal IgA-producing plasma cells (PCs) as one major iNOS+ cell population in H. pylori–infected patients and confirmed intracellular NO production. Because we did not detect iNOS+ PCs in three distinct infectious diseases, this is not a general feature of mucosal PCs under conditions of infection. Furthermore, numbers of mucosal iNOS+ PCs were elevated in individuals who had cleared experimental H. pylori infection compared with those who had not. Thus, IgA+ PCs expressing iNOS are described for the first time, to our knowledge, in humans. iNOS+ PCs are induced in the course of human H. pylori infection, and their abundance seems to correlate with the clinical course of the infection.
Identification of Stage-Specific Surface Markers in Early B Cell Development Provides Novel Tools for Identification of Progenitor Populations
Authors: Jensen Christina T. , Lang Stefan , Somasundaram Rajesh , Soneji Shamit , Sigvardsson Mikael
Published: July 2016

Whereas the characterization of B lymphoid progenitors has been facilitated by the identification of lineage- and stage-specific surface markers, the continued identification of differentially expressed proteins increases our capacity to explore normal and malignant B cell development. To identify novel surface markers with stage-specific expression patterns, we explored the reactivity of CD19+ B cell progenitor cells to Abs targeted to 176 surface proteins. Markers with stage-specific expression were identified using a transgenic reporter gene system subdividing the B cell progenitors into four surface IgM− stages. This approach affirmed the utility of known stage-specific markers, as well as identifying additional proteins that selectively marked defined stages of B cell development. Among the stage-specific markers were the cell adhesion proteins CD49E, CD11A, and CD54 that are highly expressed selectively on the most immature progenitors. This work identifies a set of novel stage-specific surface markers that can be used as a complement to the classical staining protocols to explore B lymphocyte development.
Nummular headache: Clinico-epidemiological features in South Indian population
Authors: Rammohan K. , Mundayadan Shyma M. , Mathew Robert
Published: July 2016

Context: Nummular headache (NH) is a primary disorder characterized by head pain exclusively felt in a small-rounded area typically 2–6 cm in diameter. Aims: The aim of this review is to study the clinical and epidemiological features of NH in a patient population of South India and to compare this with that of described in the international literature. Settings and Design: A prospective, observational study conducted in a tertiary care center. Materials and Methods: Patients attending the medicine and neurology outpatient departments of a tertiary referral hospital in South India diagnosed to have NH as per the International Classification of Headache Disorders 3 beta (2013) criteria were studied over 30 months. All of the patients had a normal neurological examination. Neuroimaging findings were normal, except in one patient. Results: A total of 19 females and 10 males were studied. The mean age of onset was 47.62 years (range 36–60). The duration of headache varied from a minimum of 3 months to a maximum of 5 years, with a mean of 24.17 months. The site of headache was predominantly in the parietal area 15 (51.72%), followed by the occipital area 7 (24.13%). The mean diameter of the affected area was 3.23 cm. The intensity of the headache proved to be mild to moderate with a mean visual analog scale score of 5.03. The quality of pain was mainly felt as burning dysesthesia 12 (41.38%). In the majority of patients, i.e. 21 (72.41%), pain was chronic and continuous. None of the patients had any significant trophic change even though paresthesias, dysesthesias, and allodynia were reported by a significant minority of patients, i.e. 9 (31.03%). Only one (3.45%) patient gave a history of head injury. Ten (34.48%) out of 29 patients had other types of concurrent headaches; the majority of which proved to be migrainous, i.e. 7 (24.14%), 2 patients (6.89%) with tension headache, and 1 patient (3.45%) with trigeminal neuralgia. Conclusion: Our study proves the existence of the newly described primary headache syndrome called NH in South Indian population. In comparing our results with the international literature, the number of similarities is much greater than the differences. The etiology of pain in our series appeared to be primarily peripheral with a role for central pain sensitization in some cases due to a variety of concurrent central causes of head pain.
Recursive Partitioning Method on Competing Risk Outcomes
Authors: Xu Wei , Che Jiahua , Kong Qin
Published: July 2016

In some cancer clinical studies, researchers have interests to explore the risk factors associated with competing risk outcomes such as recurrence-free survival. We develop a novel recursive partitioning framework on competing risk data for both prognostic and predictive model constructions. We define specific splitting rules, pruning algorithm, and final tree selection algorithm for the competing risk tree models. This methodology is quite flexible that it can corporate both semiparametric method using Cox proportional hazards model and parametric competing risk model. Both prognostic and predictive tree models are developed to adjust for potential confounding factors. Extensive simulations show that our methods have well-controlled type I error and robust power performance. Finally, we apply both Cox proportional hazards model and flexible parametric model for prognostic tree development on a retrospective clinical study on oropharyngeal cancer patients.
Notes on the Statistical Power of the Binary State Speciation and Extinction (BiSSE) Model
Authors: Gamisch Alexander
Published: July 2016

The Binary State Speciation and Extinction (BiSSE) method is one of the most popular tools for investigating the rates of diversification and character evolution. Yet, based on previous simulation studies, it is commonly held that the BiSSE method requires phylogenetic trees of fairly large sample sizes (>300 taxa) in order to distinguish between the different models of speciation, extinction, or transition rate asymmetry. Here, the power of the BiSSE method is reevaluated by simulating trees of both small and large sample sizes (30, 60, 90, and 300 taxa) under various asymmetry models and root state assumptions. Results show that the power of the BiSSE method can be much higher, also in trees of small sample size, for detecting differences in speciation rate asymmetry than anticipated earlier. This, however, is not a consequence of any conceptual or mathematical flaw in the method per se but rather of assumptions about the character state at the root of the simulated trees and thus the underlying macroevolutionary model, which led to biased results and conclusions in earlier power assessments. As such, these earlier simulation studies used to determine the power of BiSSE were not incorrect but biased, leading to an overestimation of type-II statistical error for detecting differences in speciation rate but not for extinction and transition rates.
Comparación de modelos de interceptación de agua de lluvia en individuos aislados de Pinus pinea y Cistus ladanifer
Authors: Pérez-Arellano R. , Moreno-Pérez M. F. , Roldán-Cañas J.
Published: July 2016

En este trabajo se realiza una comparación de varios modelos de simulación del proceso de interceptación, comúnmente utilizados en numerosos estudios, como son las versiones clásicas de Rutter y de Gash, además de la versión adaptada por Valente a bosques dispersos. El objetivo es analizar la aplicabilidad de los diferentes modelos en ejemplares aislados de dos especies de clima mediterráneo, Pinus pinea y Cistus ladanifer. La toma de datos se ha realizado en la cuenca de “El Cabril” (Córdoba), desde octubre de 2010 a junio de 2015. Las diferencias obtenidas entre las medidas de campo y los resultados de los diferentes modelos son inferiores al 6%, siendo el modelo de Rutter en su versión original el que mejor se ajusta en pino y el modelo original de Gash el que mejor se ajusta en el caso de la jara.
Growing Pains: The Asia Pacific Refugee Rights Network at Seven Years
Authors: Taylor Savitri
Published: July 2016

The mission of the Asia Pacific Refugee Rights Network (APRRN), as stated in its Constitution, is ‘to advance the rights of refugees and other people in need of protection in the Asia Pacific region’. This article describes and analyses APRRN’s internal governance and resourcing and the manner in which it is going about achieving its mission. It argues that APRRN’s organisational strength is inadequate to support all that it is trying to do. The article concludes by considering what APRRN could do to improve the likelihood of achieving success in the pursuit of its mission and reflecting on the lessons of the APRRN case study for wider civil society.
Mental health and legal representation for asylum seekers in the ‘legacy caseload’
Authors: Kenny Mary Anne , Procter Nicholas , Grech Carol
Published: July 2016

This article examines the legal challenges asylum seekers arriving by boat to Australia experience when seeking assistance with their claims and its impact on their mental health. The authors outline the experiences of asylum seekers in the “legacy caseload” group who have been waiting up to four years to have their protection claims assessed. The complex interplay between legal assistance to support refugee claims and the way those making claims inevitably struggle to understand, engage and participate in the process is analysed. It is argued that provision of legal assistance for this group will be essential to ensuring that the refugee status determination process is fair and allows asylum seekers to understand and participate more fully in the process. Recent changes to the assessment of claims combined with a reduction in funding for legal assistance create significant hurdles and combine to compound existing stress and emotional trauma leading to detrimental outcomes on the mental health of asylum seekers.
Model-based aviation advice on distal volcanic ash clouds by
assimilating aircraft in situ measurements
Authors: Fu Guangliang , Heemink Arnold , Lu Sha , Segers Arjo , Weber Konradin , Lin Hai-Xiang
Published: July 2016

Abstract. The forecast accuracy of distal volcanic ash clouds is important for providing valid aviation advice during volcanic ash eruption. However, because the distal part of volcanic ash plume is far from the volcano, the influence of eruption information on this part becomes rather indirect and uncertain, resulting in inaccurate volcanic ash forecasts in these distal areas. In our approach, we use real-life aircraft in situ observations, measured in the northwestern part of Germany during the 2010 Eyjafjallajökull eruption, in an ensemble-based data assimilation system combined with a volcanic ash transport model to investigate the potential improvement on the forecast accuracy with regard to the distal volcanic ash plume. We show that the error of the analyzed volcanic ash state can be significantly reduced through assimilating real-life in situ measurements. After a continuous assimilation, it is shown that the aviation advice for Germany, the Netherlands and Luxembourg can be significantly improved. We suggest that with suitable aircrafts measuring once per day across the distal volcanic ash plume, the description and prediction of volcanic ash clouds in these areas can be greatly improved.
The importance of plume rise on the concentrations and atmospheric impacts of biomass burning aerosol
Authors: Walter Carolin , Freitas Saulo R. , Kottmeier Christoph , Kraut Isabel , Rieger Daniel , Vogel Heike , Vogel Bernhard
Published: July 2016

Abstract. We quantified the effects of the plume rise of biomass burning aerosol and gases for the forest fires that occurred in Saskatchewan, Canada, in July 2010. For this purpose, simulations with different assumptions regarding the plume rise and the vertical distribution of the emissions were conducted. Based on comparisons with observations, applying a one-dimensional plume rise model to predict the injection layer in combination with a parametrization of the vertical distribution of the emissions outperforms approaches in which the plume heights are initially predefined. Approximately 30 % of the fires exceed the height of 2 km with a maximum height of 8.6 km. Using this plume rise model, comparisons with satellite images in the visible spectral range show a very good agreement between the simulated and observed spatial distributions of the biomass burning plume. The simulated aerosol optical depth (AOD) with data of an AERONET station is in good agreement with respect to the absolute values and the timing of the maximum. Comparison of the vertical distribution of the biomass burning aerosol with CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) retrievals also showed the best agreement when the plume rise model was applied. We found that downwelling surface short-wave radiation below the forest fire plume is reduced by up to 50 % and that the 2 m temperature is decreased by up to 6 K. In addition, we simulated a strong change in atmospheric stability within the biomass burning plume.