bluuebunny/crossref_metadata_2025 huggingface dataset:
Biofilm Formation and Methicillin Resistance of Staphylococcus aureus Isolated from Clinical Samples
Authors: Elmanama Abdelraouf A. , Al-Aydi Islam Majdi , Al-Reefi Mariam R.
Published: March 2020

Background: Staphylococcus aureus including methicillin resistant S. aureus (MRSA) is one of the most effective biofilm-forming organisms, biofilm contribute in protecting the microorganism from host defenses and prevent the effective penetration of antimicrobial agents. Biofilm formation is considered as an important contributing factor for the initiation and establishment of chronic infection by S. aureus and known as a major obstacle in the treatment of S. aureus infections is their ability to develop resistance to antimicrobials.

Aims : To screen clinical Staphylococcus aureus including MRSA isolates for their biofilm forming abilities and their association with antimicrobial resistance.

Methods: A total of 196 clinical isolates of S. aureus were obtained from different sample sources using standard microbiological techniques from three major hospitals in Gaza strip. Biofilm formation of these isolates was determined by tissue culture plate (TCP) method and tube adherence method (TM). Antimicrobial susceptibility test was performed using the modified Kirby–Bauer disk diffusion method as per Clinical and Laboratory Standards Institute guidelines. MRSA was detected using the cefoxitin disk test.

Results: Biofilm formation was observed in 174 (88.8 %) and in 145 (74.0%) isolates of S. aureus via TCP method and TM, respectively. The highest resistance percent was for penicillin (92.9%), followed by cefoxitin (80.6%) and oxacillin (67.9%), while the lowest resistance percent was for linezolid and ceftaroline (1%). Among the 196 isolates 71.4% (N=140) were classified as MDR with a MAR index (≥ 0.2).

A total of 158 isolates (80.6%) were identified as MRSA distributed as 90.4%, 79.4% and 70.9% from Al-Shifa, Al-Nasser pediatrics and Al-Aqsa hospitals respectively. Large proportions (82.1%) of biofilm producers were identified as MRSA. Biofilm-producing MRSA exhibited a higher percent (90.5%) when compared with the biofilm non-producer MRSA (9.5%). Importantly, 89.2% of biofilm-producing S. aureus were multidrug resistant.

Conclusions: S. aureus isolates possessed high biofilm-forming ability. Biofilm-producing strains have very high tendency to exhibit antimicrobial resistance, multidrug resistance and methicillin resistance. Regular surveillance of biofilm formation by S. aureus and their antimicrobial resistance profile may lead more success in treating S. aureus infections.

 

Keywords: Biofilm, MDR, MRSA, TCP, TM, Gaza strip, Palestine.
Using a Higher-order Numerical Scheme to Study the Hall Magnetic Reconnection
Authors: Yang Yun , Manchester IV Ward B.
Published: March 2020

We use our recently developed higher-order conservation element and solution element scheme to investigate the evolutionary process of Hall magnetic reconnection. The purpose of this paper is twofold: (1) to take advantage of higher-order numerical schemes to capture some fine structures very well with fewer grid points and reduced computational cost; (2) to develop a better understanding of the magnetic reconnection described by Hall MHD; as Birn et al. pointed out, the Hall effect is a critical ingredient in determining collisionless reconnection rates in the magnetosphere. The contributions of this paper mainly include the following: (1) we capture a two-step magnetic reconnection process and describe the formation mechanism; (2) the simulations show complex formation and interaction of magnetic islands and we provide the ways by which the magnetic islands form and disappear; (3) we find an oscillatory nature of the reconnection and the transfer of energy from magnetic field to kinetic energy and thermal energy; (4) we identify the merging process of the central magnetic island and the outflow region magnetic island.
The Launching of Cold Clouds by Galaxy Outflows. III. The Influence of Magnetic Fields
Authors: Cottle J’Neil , Scannapieco Evan , Brüggen Marcus , Banda-Barragán Wladimir , Federrath Christoph
Published: March 2020

Motivated by observations of outflowing galaxies, we investigate the combined impact of magnetic fields and radiative cooling on the evolution of cold clouds embedded in a hot wind. We perform a collection of three-dimensional adaptive mesh refinement, magnetohydrodynamical simulations that span two resolutions, and include fields that are aligned and transverse to the oncoming, super-Alfvénic material. Aligned fields have little impact on the overall lifetime of the clouds over the non-magnetized case, although they do increase the mixing between the wind and cloud material by a factor of ≈3. Transverse fields lead to magnetic draping, which isolates the clouds, but they also squeeze material in the direction perpendicular to the field lines, which leads to rapid mass loss. A resolution study suggests that the magnetized simulations have somewhat better convergence properties than non-magnetized simulations, and that a resolution of 64 zones per cloud radius is sufficient to accurately describe these interactions. We conclude that the combined effects of radiative cooling and magnetic fields are dependent on field orientation, but are unlikely to enhance cloud lifetimes beyond the effect of radiative cooling alone.
Black Hole Mass Function of Coalescing Neutron Star Black Hole Binary Systems: The Prospect of Reconstruction with the Gravitational Wave Observations
Authors: Tang Shao-Peng , Wang Hao , Wang Yuan-Zhu , Han Ming-Zhe , Fan Yi-Zhong , Wei Da-Ming
Published: March 2020

The discovery of gravitational waves from compact objects coalescence opens a brand-new window to observe the universe. With more events being detected in the future, statistical examinations would be essential to better understand the underlying astrophysical processes. In this work we investigate the prospect of measuring the mass function of black holes that are merging with the neutron stars. Applying Bayesian parameter estimation for hundreds of simulated neutron star–black hole (NSBH) mergers, we find that the parameters for most of the injected events can be well recovered. We also take a Bayesian hierarchical model to reconstruct the population properties of the masses of black holes, in the presence of a low mass gap, both the mass gap and power-law index (α) of black hole mass function can be well measured, thus we can reveal where the α is different for binary black hole (BBH) and NSBH systems. In the absence of a low mass gap, the gravitational wave data as well as the electromagnetic data can be used to pin down the nature of the merger event and then measure the mass of these very light black holes. However, as a result of the misclassification of BBH into NSBH, the measurement of α is more challenging and further dedicated efforts are needed.
PSR J0030+0451, GW170817, and the Nuclear Data: Joint Constraints on Equation of State and Bulk Properties of Neutron Stars
Authors: Jiang Jin-Liang , Tang Shao-Peng , Wang Yuan-Zhu , Fan Yi-Zhong , Wei Da-Ming
Published: March 2020

Very recently the NICER collaboration published the first-ever accurate measurement of mass and radius together for PSR J0030+0451, a nearby isolated quickly rotating neutron star (NS). In this work we set the joint constraints on the equation of state (EoS) and some bulk properties of NSs with the data of PSR J0030+0451, GW170817, and some nuclear experiments. The piecewise polytropic expansion method and the spectral decomposition method have been adopted to parameterize the EoS. The resulting constraints are consistent with each other. Assuming the maximal gravitational mass of nonrotating NS M
TOV lies between 2.04M
⊙ and 2.4M
⊙, with the piecewise method the pressure at twice nuclear saturation density is measured to be

at the 90% level. For an NS with canonical mass of 1.4M
⊙, we have the moment of inertia

, tidal deformability

, radius

, and binding energy

at the 90% level, which are improved in comparison to the constraints with the sole data of GW170817. These conclusions are drawn for the mass/radius measurements of PSR J0030+0451 by Riley et al. For the measurements of Miller et al., the results are rather similar.
Forecasting [C ii] Line-intensity Mapping Measurements between the End of Reionization and the Epoch of Galaxy Assembly
Authors: Chung Dongwoo T. , Viero Marco P. , Church Sarah E. , Wechsler Risa H.
Published: March 2020

We combine recent simulation work on the SFR–[C ii] correlation at high redshift with empirical modeling of the galaxy–halo connection (via UniverseMachine) to forecast [C ii] auto power spectra from z ∼ 4 to z ∼ 8. We compare these to sensitivities realistically expected from various instruments expected to come online in the next decade. If the predictions of our model are correct, [C ii] should be detectable up to z ∼ 6 in this generation of surveys, but detecting [C ii] past the end of reionization will require a generational leap in line-intensity survey capabilities.
Kelvin–Helmholtz versus Tearing Instability: What Drives Turbulence in Stochastic Reconnection?
Authors: Kowal Grzegorz , Falceta-Gonçalves Diego A. , Lazarian Alex , Vishniac Ethan T.
Published: March 2020

Over the last few years it became clear that turbulent magnetic reconnection and magnetized turbulence are inseparable. It was not only shown that reconnection is responsible for violating the frozen-in condition in turbulence, but also that stochastic reconnection in 3D generates turbulence by itself. The actual mechanism responsible for this driving is still unknown. Processes such as the tearing mode or Kelvin–Helmholtz, among other plasma instabilities, could generate turbulence from irregular current sheets. We address the nature of the driving mechanism for this process and consider the relative role of tearing and Kelvin–Helmholtz instabilities for the process of turbulence generation. In particular, we analyze the conditions for development of these two instabilities within 3D reconnection regions. We show that both instabilities can excite turbulence fluctuations in reconnection regions. However, the tearing mode has a relatively slow growth rate, and at later times it becomes partially suppressed by a component of the magnetic field that runs transversely to the current sheet, which is generated during the growth of turbulent fluctuations. In contrast, the Kelvin–Helmholtz instability quickly establishes itself in the outflow region, and at later times, it dominates the turbulence generation compared to the contribution from the tearing mode. Our results demonstrate that the tearing instability is subdominant to the the Kelvin–Helmholtz instability in terms of generation of turbulence in the 3D reconnection layers, and therefore the self-driven reconnection is turbulent reconnection, and the tearing instability is only important at the initial stage of the reconnection.
The Association between Estrogen and Progesterone Receptors Expression with the Stages of Endometrioid-type Ovarian Carcinoma at Sanglah General Hospital, Bali, Indonesia: A Preliminary Study
Authors: Mahendra Dewi I Gusti Ayu Sri , Ekawati Ni Putu
Published: March 2020

BACKGROUND: Ovarian cancer is the most common cause of death among gynecologic malignancies. Poor prognosis is mainly due to the high incidence of advanced stage at the time of diagnosis as well as low estrogen receptor (ER) and progesterone receptor (PR) expression. Based on it, further understanding is needed to predict the course of disease.
AIM: This study aims to evaluate the expression of ER and PR with the stages of endometrioid-type ovarian carcinoma as the more frequent type carcinoma at Sanglah General Hospital, Bali.
METHODS: An analytical cross-sectional study was conducted among 36 samples of endometrioid-type ovarian carcinoma examined at Anatomical Pathology Laboratory, Faculty of Medicine, Udayana University/Sanglah General Hospital, Denpasar. The histopathological diagnosis, grade, and tumor size, as well as determination on hematoxylin-eosin staining were assessed. The expression of ER and PR was examined using immunohistochemical stain. Data were analyzed using the SPSS version 15 software for risk analysis and p < 0.05 was assumed statistically significant.
RESULTS: Most of the samples were from 41 to 50 years of age group (41.7%) and the average age was 51.1 ± 9.80 years old. Based on the degree of differentiation, Grade 2 was the most common cases (38.9%). However, the tumor size assessment revealed that T3 was predominant (41.7%). Positive ER and PR expressions were obtained in 18 samples (50.0%) and 14 samples (38.9%), respectively. Fisher’s exact test showed a significant association between ER expression with grade (odds ratio [OR]: 6.25; 95% confidence interval [CI] 1.327–29.432; p = 0.018) and tumor size (OR: 4.375; 95% CI 1.027–18.629; p = 0.043). A similar findings also found in PR expression with grade (OR: 15.60; 95% CI 1.728–140.829; p = 0.004) and tumor size (OR: 6.12; 95% CI 1.394–26.876; p = 0.016).
CONCLUSION: ER and PR expressions are significantly associated with grade and tumor size in the endometrioid-type ovarian carcinoma.
The Available Clinical Approaches to the Management of Patients with Acute and Chronic Hypernatremia
Authors: Moradi Asaad , Sabzghabaei Foroogh , Kalantar Mohammadshahab
Published: March 2020

Hypernatremia has been known as a conventional electrolyte problem and is defined as a serum sodium concentration growth up to a value above 145 mmol/l. This disease is also known as a hyperosmolar condition, which is resulted from a reduction in total body water compared to the electrolyte content. Since two types of hypernatremia, including acute and chronic, exist, in the present study, we will review the available clinical approaches for the management of patients with acute and chronic hypernatremia. To this end, 68 studies having the criteria of the present review and published from 2000 were chosen and their data were extracted. The inclusion criterion was to be mainly focused on hypernatremia clinical treatment and those having side topics were excluded from the study. Acute symptomatic hypernatremia is a disease that occurs in <24 h and needs to be treated immediately. On the other hand, there is a chronic hypernatremia that occurs in a duration of more than 48 h and is required to be treated at a lower speed due to cerebral edema risks during treatment. Finally, for providing beneficial outcomes to patients, both chronic and acute hypernatremia need to be compared thoroughly and also more investigation needs to be done.
Consideration of heterogeneous effects in preparing neutron multigroup constants in the CONSYST/BNAB-RF system
Authors: Andrianova Olga N. , Zherdev Gennady M. , Lomakov Gleb B. , Teplukhina Yevgeniya S.
Published: March 2020

The need for building mutually self-agreed computational models for high-precision and engineering neutronic codes is defined by requirements to certification and verification of software products and nuclear data in accordance with the Regulations for Verification and Expert Examination of Neutronic Calculation Software Tools (RB-061-11). The key requirement in RB-061-11 is that there shall be a methodically transparent and reproducible procedure to estimate the methodological and nuclear data component of the neutronic parameter uncertainties to be implemented only if there are mutually self-agreed computational neutronic models. Using an example of a series of measurements carried out on three critical BFS-61 configurations, factors are discussed which need to be taken into account when building such types of neutronic models and the peculiarities of their application for calculating the neutronic parameters of BFS-61 assemblies. Improved functional capabilities of updated software tools and nuclear data for the computational and experimental analysis of integral BFS experiments (ROSFOND/BNAB-RF, CONSYST and MMK-RF) have been demonstrated, allowing a much shorter time and the smallest risks of errors in preparing mutually self-agreed computational models for various neutronic codes, as well as correct estimation of the methodological and nuclear data components of the uncertainties in neutronic parameters in accordance with RB-061-11. The results of estimating the uncertainty in neutronic parameters with respect to the group approximation approach, are presented. It has been shown based on an analysis of the obtained results that the discrepancies in the calculations of the BFS-61 configurations in the transition from the ROSFOND evaluated neutron data library to its group version, BNAB-RF, does not exceed 0.3% in criticality (heterogeneous effects uncertainty of 0.2 to 0.8 %). The estimated spectral index data biases lie in the limits of the Monte Carlo statistical error. Based on the results of a computational and experimental analysis for the entire set of measurements performed on a series of BFS-61 assemblies, the ROSFOND library is the optimal nuclear data library.
Accueillir l’autre, ne pas se perdre
Authors: Dijon Xavier
Published: March 2020

L’ouvrage reprend la plupart des contributions données en 2017 lors de la 14 e édition des colloques Gesché à l’Université catholique de Louvain (UCLouvain) autour du thème des migrations. (…)
Considering the Impact of Aging When Caring For and Treating Adults With HIV
Authors: Frain Judy
Published: March 2020

The current study investigates how age impacts factors associated with successfully managing HIV. One hundred thirty adults with HIV were recruited for the study. Participants were divided into two groups, those age ≥50 and those age <50. Cognitive impairment and depressive symptoms were seen at higher rates in older adults, and the severity of depressive symptoms was also higher in older adults. Depressive symptoms impacted cognitive function to a higher degree in older adults compared to younger adults (
r
= −0.293,
p
= 0.018 vs.
r
= −0.109,
p
= 0.387). Polypharmacy was a greater concern in older adults, with 88% having polypharmacy compared with 60% of younger adults. Similarly, the prevalence of comorbidities was more than double in older adults compared to younger adults. Factors associated with aging complicate management of HIV. Gaining insight into the challenges of caring for this population will furnish nurses with information necessary to provide the best possible care for this growing population. [
Journal of Gerontological Nursing, 46
(4), 31–40.]
Tidying Up: Good for the Aging Brain
Authors: Sorrell Jeanne M.
Published: March 2020

As older adults experience physical and mental changes, they may have difficulty keeping up with tasks that were once routine and may be faced with a growing accumulation of once important possessions that are no longer needed. Problems with clutter have been associated with stress and life dissatisfaction. Helping older adults realize the benefits of decluttering their living environment and providing them with advice on how to do this can help them feel accomplished and in control. [
Journal of Psychosocial Nursing and Mental Health Services, 58
(4), 16–18.]
Synergetic Learning Model: The Sum is Greater
Authors: Thompson Kevin S.
Published: March 2020

Instructional design models provide process insight that guides learning solution designers in their work to meet learning objectives and improve performance. Human resource development scholars often focus on individual learning components that support meeting learning and performance objectives. Learning practitioners who design learning solutions must review significant amounts of research studies to extract information that may assist them in their roles. This paper introduces the evidence-based Synergetic Learning Model, focused exclusively on design, intended to leverage the work of scholars holistically for the benefit of learning practitioners.
Technological Pedagogical and Content Knowledge of ESP Teachers in Blended Learning Format
Authors: Mulyadi Dodi , Wijayatingsih Testiana Deni , Budiastuti Riana Eka , Ifadah Muhimatul , Aimah Siti
Published: March 2020

Abstract— The onslaught of technology in language learning necessitates ESP teachers to enhance their teaching quality by integrating technology, pedagogy, and subject matters. To this end, the present research was an effort to discern Technological Pedagogical and Content Knowledge (TPACK) of ESP teachers in a blended learning format. A modified online survey comprising 28 closed-ended questions were administered to 70 ESP instructors of nursing purposes from 35 Universities in Indonesia. The data were analyzed statistically to be depicted in descriptive statistics (percentages of frequencies, means, and standard deviations). As a result, three out of four TPACK subdomains, including technological content knowledge (TCK), and Technological Pedagogical Knowledge (TPK), Technological Pedagogical and Content Knowledge (TPACK) have been mastered by most ESP teachers. However, they have to improve their pedagogical content knowledge (PCK). The current research also contributes some empirical insights into how ESP teachers can construct the overarching ESP instruction in English for nursing purposes integrating TPACK.
Educating the Central Asian Librarian
Authors: Emmelhainz Celia
Published: March 2020

Why do Central Asian librarians enter the profession, and how do they decide which educational strategies to pursue in developing their careers? Using 13 conversations and 10 qualitative interviews with Kazakh and Kyrgyz librarians, this chapter finds that librarians enter the profession due to personal interest, by happenstance, or for university funding and continue when they perceive an opportunity for career growth as well as salary mobility. Central Asian librarians evaluate their educational options, including local bachelor's degrees; distance education from Russia; MLIS programs in Asia, Europe, or America; and short-term online training, while balancing family responsibilities and career prospects in and outside of librarianship. Prospects for creating a local MLIS or other improved professional training programs are discussed.
10,000 Newly Certified Librarians, 100 Secure Jobs
Authors: Okada Daisuke
Published: March 2020

This chapter discusses the status, challenges, and issues encountered in librarianship in Japan, especially school librarianship. Specifically, it focuses on the certifications for school library staff, the curriculum model, and the employability of certified librarians. Topics related to Library and Information Studies schools, training programs for certified librarians, summer and distance education, qualifications and accreditation of teacher librarians are discussed as well. Currently, librarianship and school librarianship are not close to implementing internationalization; however, this discussion cannot be avoided. Hence, this chapter argues that it is necessary to incorporate the specific trends in Japan along with global trends.
Role of Communicational Collaboration With Governmental Organizations as Stakeholder Groups
Authors: Genel Zeynep
Published: March 2020

By the increasing awareness of managers about the vital role of brands, the intangible elements of brands such as image and value become primary issues of their strategic plans. The widened communication platforms forced the significant role of reputation to sustain corporation life cycle, and this challenge triggers the recent discussion of reputation management. From this aspect, it can be seen that, for any organization, building the stakeholder communications and interactions accurately and maintaining the strong cooperation and communication with them play positive roles in organizational reputation. As well as the components of a strong reputation, the essential point is to understand the eco-system of communication. For this purpose, the chapter explains the communication eco-system of a corporation in terms of stakeholders, and the role of stakeholders in ultimate reputation is grounded on the congruity theory. As an example of the stakeholder role on communication eco-system, a qualitative research is applied in the research part.
A Case Study on Data Vulnerabilities in Software Development Lifecycle Model
Authors: Basha Syed Muzamil , Poluru Ravi Kumar , Janet J. , Balakrishnan S. , Santhosh D. Dharunya , Kousalya A.
Published: March 2020

Software security has become a very critical part of our lives. A software developer who has a fundamental understanding of software security can have an advantage in the workplace. In the massive Equifax breach that occurred in 2017 that exposed data of roughly 140 million people, attackers exploited a vulnerability in Apache Struts, CVE-2017-5638, which allows remote attackers to execute arbitrary commands when specially crafting user-controlled data in HTTP headers. Sensitive data exposure issues are essential to know to protect customer data. It is fascinating to understand how attackers can exploit application vulnerabilities to perform malicious activities. The authors also want the reader to be aware of the fact that we should always be thinking about how our applications handle user-controlled data so that we can put guards in place to minimize security issues in the development of new applications.
Information Security Policies and Procedures Guidance for Agencies
Authors: Kalyani Dasari
Published: March 2020

In today's digital e-commerce and m-commerce world, the information itself acts as an asset and exists in the form of hardware, software, procedure, or a person. So the security of these information systems and management is a big challenging issue for small and large-scale agencies. So this chapter discusses the major role and responsibility of the organization's management in identifying the need for information security policy in today's world of changing security principles and controls. It focuses on various policy types suitable for all kinds of security models and procedures with the background details such as security policy making, functionality, and its impact on an agency culture. Information security policies are helpful to identify and assess risk levels with the available set of technological security tools. The chapter describes the management strategies to write a good policy and selection of the right policy public announcement. The agencies must also ensure that the designed policies are properly implemented and ensure compliance through frequent intermediate revisions.
Cryptography Security Services
Authors: Kaplesh Pooja
Published: March 2020

This chapter gives an overview about the need for computer security and different types of network securities. Different types of attacks like active and passive are discussed that can harm system resources. This chapter also provides the basics of the cryptography system and basic terms used in cryptography. Security services provided by cryptography are also discussed such as data integrity, privacy/confidentiality, user authentication, message authentication, authorization, digital signatures, validation, access control, and non-repudiation along with their mechanisms.
Blockchain Technology and Its Applications
Authors: Lingareddy Lavanya , Krishnamoorthy Parthiban
Published: March 2020

Like other new generation information technologies such as IoT, big data, AI, and cloud, cryptocurrency and blockchain became buzz words in both industry and academia due to their advantageous features. With the features like decentralization, transparency, immutability, blockchain technology became more famous and is emerging in almost all fields like banking, education, healthcare, government, and real estate. Blockchain technology was introduced in the year 1991. It came into existence after this technology was created for bitcoin, a digital cryptocurrency by Santoshi Nakamoto in the year 2008. Since then, the blockchain is evolving rapidly. Even though blockchain usage is in high demand in all the sectors and it has received attention from many international organizations, most of the people lag in knowledge of blockchain technology and Cryptocurrency and how exactly they work. This chapter explores more in detail what blockchain technology is, how it works, and its applications.
Network Security Evaluation and Threat Assessments in Enterprise Systems
Authors: Goundar Sam , Chongkit Abraham Colin , Kumar Shalvendra , Bhardwaj Akashdeep
Published: March 2020

Companies have realized the need to have the competitive edge over their rivals. Enterprise systems provide this competitive edge. But with implementing enterprise system software (ESS), companies have to invest in the networking of their various business offices to interconnect to ESS. This chapter investigates the various ways two companies in Fiji do this and the various disadvantages they face from lack of knowledge of ESS to infrastructure limitations and issues faced in a developing country like Fiji. This chapter focuses on the network security issues of enterprise systems deployed in two companies in Fiji. It compares the information gathered to published papers on network infrastructure, network security issues, and threat assessments. It also proposes solutions to mitigate the security issues faced by enterprise systems networks.
A Review of Infrastructures to Process Big Multimedia Data
Authors: Salvador Jaime , Ruiz Zoila , Garcia-Rodriguez Jose
Published: March 2020

In the last years, the volume of information is growing faster than ever before, moving from small to huge, structured to unstructured datasets like text, image, audio and video. The purpose of processing the data is aimed to extract relevant information on trends, challenges and opportunities; all these studies with large volumes of data. The increase in the power of parallel computing enabled the use of Machine Learning (ML) techniques to take advantage of the processing capabilities offered by new architectures on large volumes of data. For this reason, it is necessary to find mechanisms that allow classify and organize them to facilitate to the users the extraction of the required information. The processing of these data requires the use of classification techniques that will be reviewed. This work analyzes different studies carried out on the use of ML for processing large volumes of data (Big Multimedia Data) and proposes a classification, using as criteria, the hardware infrastructures used in works of machine learning parallel approaches applied to large volumes of data.
It is All in the Design
Authors: Baran Mette L.
Published: March 2020

This chapter introduces the various design choices researchers need to decide on prior to conducting the study. In the first section of this chapter a detailed description of research design is followed by an explanation that the type of information that is collected is based on whether the research question is descriptive, explanatory, or exploratory. The major strategic implementation methods for quantitative, qualitative, and mixed methods are then discussed. The three strategies for mixed methods research Parallel Convergent, Sequential, and Embedded Design are presented in detail along with the rationale for their use. Finally, in the last section, the strands or sequencing of the data collection phase of the study is explained.
Designs of Mixed Method Research
Authors: Manzoor Amir
Published: March 2020

Mixed methods research is becoming an increasingly popular approach in the discipline fields of sociology, psychology, education and health sciences. Calls for the integration of quantitative and qualitative research methods have been advanced in these fields. A key feature of mixed methods research is its methodological pluralism, which frequently results in research which provides broader perspectives than those offered by mono-method designs. The central premise of mixed methods is that the use of quantitative and qualitative approaches in combination provides a better understanding of research problems and complex phenomena than either approach alone. The purpose of this chapter is to review designs of mixed methods research. The study surveys the common designs of mixed methods research and examine the main characteristics of each in terms of purposes, strengths, and issues, and posits suggestions on the application of these designs.
Classification of Sentiment of Reviews using Supervised Machine Learning Techniques
Authors: Tripathy Abinash , Rath Santanu Kumar
Published: March 2020

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.
Two-Level Data Collection for an Energy-Efficient Solution in Wireless Sensor Networks
Authors: Benaziz Besma , Kazar Okba , Kahloul Laid , Kitouni Ilham , Bourekkache Samir
Published: March 2020

Density in sensor networks often causes data redundancy, which is often the origin of high energy consumption. Data collection techniques are proposed to avoid retransmission of the same data by several sensors. In this paper, the authors propose a new data collection strategy based on static agents and clustering nodes in wireless sensor network (WSN) for an efficient energy consumption called: Two-Level Data Collection Strategy (TLDC). It consists in two-level hierarchy of nodes grouping. The technique is based on an experience building to perform a reorganization of the groups. Cooperation between agents can be used to reduce the communication cost significantly, by managing the data collection smartly. In order to validate the proposed scheme, the authors use the timed automata (TA) model and UPPAAL engine to validate the proposed strategy; the results after and before reorganization are compared. They establish that the proposed approach reduces the cost of communication in the group and thus minimizes the consumed energy.
Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set
Authors: Ke Han
Published: March 2020

In this paper, we present a new extreme learning machine network structure on the basis of tolerance rough set. The purpose of this paper is to realize the high-efficiency and multi-dimensional ELM network structure. Various published algorithms have been applied to breast cancer datasets, but rough set is a fairly new intelligent technique that applies to predict breast cancer recurrence. We analyze Ljubljana Breast Cancer Dataset, firstly, obtain lower and upper approximations and calculate the accuracy and quality of the classification. The high values of the quality of classification and accuracy prove that the attributes selected can well approximate the classification. Rough sets approach is established to solve the prolem of tolerance.
Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition
Authors: Trabelsi Imen , Bouhlel Med Salim
Published: March 2020

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.
Predicting Human Actions Using a Hybrid of ReliefF Feature Selection and Kernel-Based Extreme Learning Machine
Authors: Peker Musa , Ballı Serkan , Sağbaş Ensar Arif
Published: March 2020

Human activity recognition (HAR) is a growing field that provides valuable information about a person. Sensor-equipped smartwatches stand out in these studies in terms of their portability and cost. HAR systems usually preprocess raw signals, decompose signals, and then extract attributes to be used in the classifier. Attribute selection is an important step to reduce data size and provide appropriate parameters. In this chapter, classification of eight different actions (brushing teeth, walking, running, vacuuming, writing on the board, writing on paper, using the keyboard, and stationary) has been performed with smartwatch motion sensor data. Forty-two different features have been extracted from the motion sensor signals and the feature selection has been performed with the ReliefF algorithm. After that, performance evaluation has been performed with four different machine learning methods. With this study in which the best results have been obtained with the kernel-based extreme learning machine (KELM) algorithm, estimation of human action has been performed with high accuracy.
Creating and Implementing a Mixed Method Research Study
Authors: Baran Mette L. , Jones Janice E.
Published: March 2020

This chapter serves as a guideline for outlining the core characteristics of mixed methods research (MMR) and the various steps researchers undertake in order to conduct a research study. The purpose is to create a worksheet assisting the researcher step by step from beginning to end following the seven steps to conducting research. While the focus is on MMR the steps are similar for any type of research methodology. It is important to note that MMR is not a limiting form of research. Researchers need a MMR question and a mixed methods purpose statement for the research project. This chapter will also help explain why mixed method research is one of the best approaches in answering a research question. Finally, the chapter includes a suggestion to the importance of adding a visual diagram of the MMR into the research project and into the final report.
Quantitative Semantic Analysis and Comprehension by Cognitive Machine Learning
Authors: Wang Yingxu , Valipour Mehrdad , Zatarain Omar A.
Published: March 2020

Knowledge learning is the sixth and the most fundamental category of machine learning mimicking the brain. It is recognized that the semantic space of machine knowledge is a hierarchical concept network (HCN), which can be rigorously represented by formal concepts in concept algebra and semantic algebra. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic analysis based on machine learning. Semantic equivalence between formal concepts is rigorously measured by an Algorithm of Concept Equivalence Analysis (ACEA). The semantic hierarchy among formal concepts is quantitatively determined by an Algorithm of Relational Semantic Classification (ARSC). Experiments applying Algorithms ACEA and ARSC on a set of formal concepts have been successfully conducted, which demonstrate a deep machine understanding of formal concepts and quantitative relations in the hierarchical semantic space by machine learning beyond human empirical perspectives.
Intelligent Models to Predict the Prognosis of Premature Neonates According to Their EEG Signals
Authors: Al Hajjar Yasser , Al Hajjar Abd El Salam Ahmad , Daya Bassam , Chauvet Pierre
Published: March 2020

The aim of this paper is to find the best intelligent model that allows predicting the future of premature newborns according to their electroencephalogram (EEG). EEG is a signal that measures the electrical activity of the brain. In this paper, the authors used a dataset of 397 EEG records detected at birth of premature newborns and their classification by doctors two years later: normal, sick or risky. They executed machine learning on this dataset using several intelligent models such as multiple linear regression, linear discriminant analysis, artificial neural network and decision tree. They used 14 parameters concerning characteristics extracted from EEG records that affect the prognosis of the newborn. Then, they presented a complete comparative study between these models in order to find who gives best results. Finally, they found that decision tree gave best result with performance of 100% for sick records, 76.9% for risky and 69.1% for normal ones.
A Dynamic and Scalable Decision Tree Based Mining of Educational Data
Authors: Vaghela Dineshkumar B. , Sharma Priyanka , Passi Kalpdrum
Published: March 2020

The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.
Auto Associative Extreme Learning Machine Based Hybrids for Data Imputation
Authors: Gautam Chandan , Ravi Vadlamani
Published: March 2020

This chapter presents three novel hybrid techniques for data imputation viz., (1) Auto-associative Extreme Learning Machine (AAELM) with Principal Component Analysis (PCA) (PCA-AAELM), (2) Gray system theory (GST) + AAELM with PCA (Gray+PCA-AAELM), (3) AAELM with Evolving Clustering Method (ECM) (ECM-AAELM). Our prime concern is to remove the randomness in AAELM caused by the random weights with the help of ECM and PCA. This chapter also proposes local learning by invoking ECM as a preprocessor for AAELM. The proposed methods are tested on several regression, classification and bank datasets using 10 fold cross validation. The results, in terms of Mean Absolute Percentage Error (MAPE,) are compared with that of K-Means+Multilayer perceptron (MLP) imputation (Ankaiah & Ravi, 2011), K-Medoids+MLP, K-Means+GRNN, K-Medoids+GRNN (Nishanth & Ravi, 2013) PSO_Covariance imputation (Krishna & Ravi, 2013) and ECM-Imputation (Gautam & Ravi, 2014). It is concluded that the proposed methods achieved better imputation in most of the datasets as evidenced by the Wilcoxon signed rank test.
Mammogram Classification Using Support Vector Machine
Authors: Ben Youssef Youssef , Abdelmounim Elhassane , Belaguid Abdelaziz
Published: March 2020

Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.
Sentiment Analysis with Text Mining in Contexts of Big Data
Authors: Andrade Carina Sofia , Santos Maribel Yasmina
Published: March 2020

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.
Link Prediction in Complex Networks
Authors: Pujari Manisha , Kanawati Rushed
Published: March 2020

This chapter presents the problem of link prediction in complex networks. It provides general description, formal definition of the problem and applications. It gives a state-of-art of various existing link prediction approaches concentrating more on topological approaches. It presents the main challenges of link prediction task in real networks. There is description of our new link prediction approach based on supervised rank aggregation and our attempts to deal with two of the challenges to improve the prediction results. One approach is to extend the set of attributes describing an example (pair of nodes) calculated in a multiplex network that includes the target network. Multiplex networks have a layered structure, each layer having different kinds of links between same sets of nodes. The second way is to use community information for sampling of examples to deal with the problem of class imbalance. All experiments have been conducted on real networks extracted from well-known DBLP bibliographic database.