Complex Area Of Research Research Articles

Mathematical Modeling of Brain Activity under Specific Auditory Stimulation.

Understanding the connection between different stimuli and the brain response represents a complex research area. However, the use of mathematical models for this purpose is relatively unexplored. The present study investigates the effects of three different auditory stimuli on cerebral biopotentials by means of mathematical functions. The effects of acoustic stimuli (S1, S2, and S3) on cerebral activity were evaluated by electroencephalographic (EEG) recording on 21 subjects for 20 minutes of stimulation, with a 5-minute period of silence before and after stimulation. For the construction of the mathematical models used for the study of the EEG rhythms, we used the Box-Jenkins methodology. Characteristic mathematical models were obtained for the main frequency bands and were expressed by 2 constant functions, 8 first-degree functions, a second-degree function, a fourth-degree function, 6 recursive functions, and 4 periodic functions. The values obtained for the variance estimator are low, demonstrating that the obtained models are correct. The resulting mathematical models allow us to objectively compare the EEG response to the three stimuli, both between the stimuli itself and between each stimulus and the period before stimulation.

Identification of Normal and Abnormal Mammographic Images Using Deep Neural Network

This article presents an image retrieval strategy that considers the similarity of a selected image (CBIR-Content-Based Image Retrieval). The analogy is defined by the wavelet technique, combined with Hu moments, for removing features. The classification of mammography’s is conducted through the Artificial Neural Networks Auto-organizing System (SME). A Data Base (QUALIM), University of the Federal Republic of Sao Paulo (UNIFESP), and Medical Image Classification Laboratory are used to test the method. The system suggested is tested. We have employed two widely used pattern recognition approaches—facial recognition digital mammograms for examination. The techniques are based on the new classification schemes Ada Boost and Support Vector Machines (SVM).Several experiments were carried out to evaluate the accuracy of these two algorithms in different circumstances. The results for the Ada Boost classification system are positive, especially for mass lesions classification. In all cases, the algorithm was 76% exact, and only 90% accurate for mass. The SVM-based algorithm was not available. To improve the precision of the process, we need to choose enhanced image functionality for digital mammograms than those commonly used. Detection of breast cancer is the most challenging aspect in the field of health monitoring. This document has been used to evaluate breast cancer detection through a dataset of the mammographic image analysis company (MIAS). The suggested method has four key steps: preprocessing, segmentation, retrieval, and classification of images. Initially in mammograms, laplacian filtration was used to describe the edges' area and was thus particularly susceptible to noise. The modified adjustable Fuzzy-C-MEANS (ARKFCM) was used in the following segmentation to find the object within the complicated module. The conventional ARKFCM masses of undefined mass were painful to divide into mammograms. The Euclidean distance in ARKFCM was replaced with the correlation function to solve this problem to increase its segmental efficiency. In the segmented cancer region, the removal of representative subsets was performed by extracting hybrid properties (histogram of guided grade (HOG), uniformity, and energy). Each feature value was defined for the Deep Neural classifier—network (DNN) for detection in normal and pathological areas of mammograms. The findings of the study show that the technique shows an improvement of up to 3-9% in precision compared with other methods currently in use in breast cancer classifications. Describes a new way to identify borders between different brightness areas. The goal is to create distinctions between regions that are unclearly distinct and defined by tonal shifts. The limits are set by the coordinates of the start and endpoints. The proposed method can be used as a stage in advanced techniques for hierarchical image analysis that increases the semantically awareness of picture content with each step. This study applies and evaluates mammograms. Interpretation of mammograms is a complex research area discussed by many authors. The treatment of breast cancer is achieved using these approaches.

Identification of Normal and Abnormal Mammographic Images Using Deep Neural Network

This article presents an image retrieval strategy that considers the similarity of a selected image (CBIR-Content-Based Image Retrieval). The analogy is defined by the wavelet technique, combined with Hu moments, for removing features. The classification of mammography’s is conducted through the Artificial Neural Networks Auto-organizing System (SME). A Data Base (QUALIM), University of the Federal Republic of São Paulo (UNIFESP), and Medical Image Classification Laboratory are used to test the method. The system suggested is tested. We have employed two widely used pattern recognition approaches—facial recognition digital mammograms for examination. The techniques are based on the new classification schemes Ada Boost and Support Vector Machines (SVM).Several experiments were carried out to evaluate the accuracy of these two algorithms in different circumstances. The results for the Ada Boost classification system are positive, especially for mass lesions classification. In all cases, the algorithm was 76% exact, and only 90% accurate for mass. The SVM-based algorithm was not available. To improve the precision of the process, we need to choose enhanced image functionality for digital mammograms than those commonly used. Detection of breast cancer is the most challenging aspect in the field of health monitoring. This document has been used to evaluate breast cancer detection through a dataset of the mammographic image analysis company (MIAS). The suggested method has four key steps: preprocessing, segmentation, retrieval, and classification of images. Initially in mammograms, laplacian filtration was used to describe the edges' area and was thus particularly susceptible to noise. The modified adjustable Fuzzy-C-MEANS (ARKFCM) was used in the following segmentation to find the object within the complicated module. The conventional ARKFCM masses of undefined mass were painful to divide into mammograms. The Euclidean distance in ARKFCM was replaced with the correlation function to solve this problem to increase its segmental efficiency. In the segmented cancer region, the removal of representative subsets was performed by extracting hybrid properties (histogram of guided grade (HOG), uniformity, and energy). Each feature value was defined for the Deep Neural classifier—network (DNN) for detection in normal and pathological areas of mammograms. The findings of the study show that the technique shows an improvement of up to 3-9% in precision compared with other methods currently in use in breast cancer classifications. Describes a new way to identify borders between different brightness areas. The goal is to create distinctions between regions that are unclearly distinct and defined by tonal shifts. The limits are set by the coordinates of the start and endpoints. The proposed method can be used as a stage in advanced techniques for hierarchical image analysis that increases the semantically awareness of picture content with each step. This study applies and evaluates mammograms. Interpretation of mammograms is a complex research area discussed by many authors. The treatment of breast cancer is achieved using these approaches.

Analyzing the Ecological Environment of Mining Area by Using Moving Window Remote Sensing Ecological Index

Objectives Mine environmental problems are intensifying, and the environmental monitoring and evaluation of the mining area is an indispensable part of mine management. Previous researches have obtained effective results by evaluating the mining ecological environment through the remote sensing ecological index (RSEI). However, the calculation does not take into account that the environmental influence is regional under natural conditions of RSEI, and the geo-graphical location of the mining area has different effects on its surrounding ecological environment. The main purpose of this research is to propose a new remote sensing ecological index that is more in line with the ecological response mechanism based on the characteristics of the ecological environment of the mining area. Methods Aiming at the complex research area of large area feature type, this paper presents an improved ecological index named moving window-based remote sensing ecological index (MW-RSEI) which based on RSEI and moving window evaluation unit. MW-RSEI divides the ecological environment into four influencing factors: Greenness, wetness, dryness and heat. Each pixel in the study area is taken as the research object, and the extended window of the pixel is taken as the calculation range of each factor. The experiment assigns weight to the center pixel of each window through principal component analysis. Results The MW-RSEI and RSEI models were used to calculate in the study area, the results show that the mean value of MW-RSEI and RSEI in the study area were 0.522 and 0.459, the greenness and wetness had positive effect on the environment, and the dryness and heat had negative effects. Based on the MW-RSEI model, mine geomorphology destroys the regional ecological environment grade is worst and poor accounted for 85.40% of the overall area. MW-RSEI in line with the first law of geography. Its results are consistent with RSEI, and it shows more gradient information of the ecological environment around the mining area, which confirms the theory of dissipative structure in the ecological environment. Conclusions MW-RSEI refines the local characteristics of the ecological environment, and it can provide an effective basis for ecological environment monitoring and evaluation in mining areas.

Engineering Technology-Based Social Innovations Accommodating Functional Decline of Older Adults

Abstract Social innovation is a complex area of research that has received a great deal of attention, especially recently. The development of digital technologies such as cyber-physical systems, big data, machine learning, blockchain, artificial intelligence and Internet-of-Things presents new opportunities for technology-based social innovations. Our paper places social innovation in the context of engineering -and proposes the integration of social innovation methodology in the development of products or services for older people. Governments need to help social enterprises in the field of engineering by laying the foundations for ‘what works’ policymaking and the development of evidence-based initiatives. The purpose of this paper is to review the development of social innovations supporting multiple intelligences of older people and to provide examples of good practice in order to develop a model and propose a future research agenda. Cybersecurity and the ethical dimension within technically based social innovation are highlighted.

Cyber Risk Perception in the Maritime Domain: A Systematic Literature Review

This paper aims to present an approach to investigate cyber risk perception with use of recognized psychological models, and to give an overview of state-of-the-art research within the field of cyber risk perception in general and in the context of the maritime domain. The focus will be on determinative dimensions within the psychometric paradigm and cognitive biases, and to give recommendations on further research within these fields. Okoli and Schabram’s eight-step guide to plan, select, extract, and execute a systematic literature review is used as guidance. The search process resulted in 25 relevant articles which describes 24 dimensions of cyber risk perception in different online environments. Research within the area of maritime cyber security is increasing, however, no studies relevant for our literature review were found within the maritime domain. The nine dimensions in the psychometric model, perceived benefit and the optimistic bias is presented and discussed in a maritime context. Cyber risk perception is a complex research-area where both determinative factors and other cognitive processes can be influenced by each other. This can indicate that the dimensions differ across populations and professions, creating grounds for why context-specific studies are important. Further research may benefit from more multidisciplinary, descriptive, and inductive approaches, and contextual studies within maritime cyber risk perception can contribute to develop targeted tools for risk mitigation to enhance safety at sea.

A novel comprehensive framework for analyzing and assessing water quality and failure consequences based on Bayesian networks.

The study of the effects of mixing potable water with wastewater is a complex and difficult research area. This difficulty is because water and sewage networks are subject to various physical, environmental, and operational factors. The main objective of the study was to propose a new comprehensive framework for analyzing and assessing water quality based on Bayesian networks. An intervention plan was proposed to reduce the consequences of water quality and networks failure. The proposed framework was applied to water distribution network of Mdaourouch city (Souk Ahras, Algeria) to demonstrate its effectiveness. The results indicated that the water contamination rate has reached 33.9 %, which caused severe consequences. The effectiveness of the proposed plan has been verified theoretically using simulations, and the results have proven to be very satisfactory. The proposed model is a decision support tool, which is expected to assist decision-makers and engineers in reviewing their plans and making the right decision. PRACTITIONER POINTS: This paper proposes a novel comprehensive framework for analyzing and assessing water quality and failure consequences based on Bayesian networks. This paper revisits the failure consequences. An intervention plan is proposed to reduce failure consequences. Results demonstrate that the proposed plan leads to fewer consequences probabilities. The proposed method can give the probability of failure of water and sewer network.

Intuitionistic Fuzzy Sets in Multi-Criteria Group Decision Making Problems Using the Characteristic Objects Method

Over the past few decades, several researchers and professionals have focused on the development and application of multi-criteria group decision making (MCGDM) methods under a fuzzy environment in different areas and disciplines. This complex research area has become one of the more popular topics, and it seems that this trend will be increasing. In this paper, we propose a new MCGDM approach combining intuitionistic fuzzy sets (IFSs) and the Characteristic Object Method (COMET) for solving the group decision making (GDM) problems. The COMET method is resistant to the rank reversal phenomenon, and at the same time it remains relatively simple and intuitive in practical problems. This method can be used for both symmetric and asymmetric information. The Triangular Intuitionistic Fuzzy Numbers (TIFNs) have been used to handle uncertain data. This concept can ensure the preference information about an alternative under specific criteria more comprehensively and allows for easy modelling of symmetrical or asymmetrical linguistic values. Each expert provides the membership and non-membership degree values of intuitionistic fuzzy numbers (IFNs). So this approach deals with a different kind of uncertainty than with hesitant fuzzy sets (HFSs). The proposed combination of COMET and IFSs required an adaptation of the matrix of expert judgment (MEJ) and allowed to capture the behaviour aspects of the decision makers (DMs). Therefore, we get more reliable solutions while solving MCGDM problems. Finally, the proposed method is presented in a simple academic example.

Realist evaluation of public engagement and involvement in data-intensive health research

BackgroundHigh quality public engagement and involvement (PEI) in data-intensive health research is seen as one way of ensuring that social legitimacy, i.e. a social license, is conferred through public acceptance of the need for research use of their data. This is a complex research area, and portfolios of involvement have been suggested, but not yet evaluated, to support the role of public contributors. The study aim was to evaluate if and how membership of a data-intensive research public forum can act as a mechanism for enhancing members’ personal development. Our objective was to understand the circumstances and mechanisms that help to explain how, why and for whom involvement with a public forum enhanced those members’ personal development.MethodQualitative data were collected from 15 current and previous members, via semi-structured interviews, notes from meetings, and consultations with and feedback from members. Data were critically compared, contrasted and reviewed until no new themes could be discerned and then condensed into context-mechanism-outcome (CMO) configurations. Realist evaluation was used to generate a theoretical and empirical appreciation of the contextual circumstances and mechanisms which help to explain the extent to which involvement with a public forum would enhance members’ personal development and, if so, how, why, and for whom.ResultsThree CMO configurations were identified. All of them showed that using the portfolio facilitated growth in forum members’ personal development, but only where the members valued using the portfolio. This was particularly so for female members. Members valued the portfolio in one or more of three ways: as a tool to record and evidence activities, to facilitate reflective practice or as a guiding framework.ConclusionsData analysis and consideration of the three CMO configurations suggests a refined middle range theory that ‘The use of a portfolio as a framework for learning in a public forum will facilitate members’ personal development if they value its use as a framework for learning’. Further work is needed to confirm these findings both elsewhere in data-intensive health research and in other complex research areas using public forums for PEI.Plain English summaryPublic engagement and involvement in health research is now well established and makes a valuable contribution to the research process. However, little is known about its impact on participants. This article investigates how involvement in a data-intensive health research public forum impacts on public forum members, rather than the research process. Personal involvement portfolios were used to support their involvement work and help evaluate if and how involvement in research activities enhanced members’ personal development. Taking a realist evaluation approach, ‘Context-Mechanism-Outcome’ configurations were used to explore how membership of a public forum might enhance public forum members’ personal development. The Context-Mechanism-Outcome configuration refers to an exploration of what influences the extent to which an intervention is successful or unsuccessful in producing positive outcomes and tries to identify the reasons why it is successful for some and unsuccessful for others. However, evidence from this realist evaluation recommends that engagement and involvement should always be underpinned by procedures which ensure that public contributors receive ongoing and tailored guidance and support throughout the process.

The New Visibility of Religion and Its Impact on Populist Politics

The marginalised research field of populism and religion has mainly focused on the positive aspects of how religion and populism can be combined with mutual benefits for both parties, whereas the critical potential and limitations that religion and theology pose to populist politics has often been overlooked. The following essay intends to contribute to the complex research area of religion and populism, by focusing on the negative side, that is, the incompatibilities of religion and theology with populism. It is suggested that the very nature of religious belief and theological convictions impose limits on their use in populist politics.

Industrial Control Systems (ICS): Cyber-attacks & Security Optimization

Cyber-security of digital industrial control system in reality is complex and challenging research area, due to various interconnections of electro-mechanical related components driving national critical infrastructures. These networked system components performs monitoring and controlling tasks in several industries and organization through the access of Internet connectivity across the world. More recently, there are myriad of security threats and attacks by malicious elements on ICS which now presents a priority to organizations and researchers for optimal security solutions. Development of the Internet and communication systems has also exacerbated such security concerns. Activities of cyber-attacks malicious elements on ICS may result in serious disaster in industrial environments, human casualties and loss. This paper critically looks at the SCADA/industrial control systems, architecture, cyber-attacks. Other aspect of the paper examines current ICS security technologies including a computational secured algorithm for PLC

Multidimensional analysis of embeddedness and cooperation in a cluster – a literature and empirical study

Cooperation of enterprises within inter-organizational networks is a complex research and cognitive area due to the multidimensional nature of cooperation, which is often a mixture of motives, intentions, goals and operating conditions. Literature studies have revealed the so far very meager and scattered work in the field of embedding cooperation between enterprises in inter-organizational networks, which definitely does not sufficiently describe the impact of the context of collaboration on enterprise cooperation. The main aim of the article was to understand and explain, on the basis of the concept of embeddedness, whether there are relations between the participation of enterprises in a cluster-type inter-organizational network (embeddedness and its dimensions) and their cooperation in the cluster. Implementation of research goals required the development of a research process covering three stages. The essence and dimensions of embeddedness of cooperating enterprises in inter-organizational networks (structural, relational, social, positional, territorial, geographical, spatial, institutional, ecological, political and temporal) were determined. Next, a survey of European cluster managers (study 1) on cooperation in a cluster was carried out in order to better understand the dimensions of embeddedness of enterprises cooperating in clusters (qualitative research, IDI). In the next stage, a survey of cluster enterprises was carried out (study 2) to determine the relations between their embeddedness in the cluster and cooperation with other cluster companies (quantitative research, CAWI). Then, triangulation of data sources, research methods and context was used. As a result, it was established that four dimensions of embeddedness are of key importance for cooperating enterprises embedded in the cluster, namely: structural, geographical, institutional and relational. Moreover, the results of the research show that there is a positive relation between embedding enterprises in the cluster and their cooperation.

What Mergers and Acquisitions Research is about – Theories, Topics, and the Scientific Community

Mergers and Acquisitions (M&A) have become a central instrument of organizational development. Consequently, research on M&A has developed into a complex research area of management science in recent years. However, the different perspectives are so heterogeneous that a holistic view is often difficult to achieve. The present research paper aims to take a holistic view of M&A research and, based on a bibliometric analysis, to allocate the various studies to research clusters, to map interrelationships between documents, authors, countries, and institutions and to trace the development of M&A research over time. For this purpose, a sample of 580 articles from 9 leading management science journals was bibliometrically examined and a total of 42,630 citations and 18,734 unique references were considered for the years 1963–2019.

ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER

In computer vision, one of the complex research areas is video surveillance. It is very important to monitor the abnormal events in public places. Due to the technical advances, the usage of cameras is increased for surveillance purpose. As human operators are employed for the observation, their visual attention is reduced after long periods. Hence, an automated Abnormal Event Detection (AED) technique is designed in this study. It uses Generalized Autoregressive Conditional Heteroscedasticity (GARCH) which is a statistical model to model the events occurs in the pedestrian pathway. Before modeling, a series of preprocessing steps are employed to detect the moving objects. Multilayer Perceptron (MLP) is used to classify the parameters of GARCH model as normal event or abnormal event. Results show that the events are modeled by GARCH in an efficient manner which provides promising results for AED.

A Content Analysis on the Small and Medium-Sized Enterprises in the United States of America

AbstractThe present research was constructed with the idea of offering a detailed view on one of the most important legislative papers of the United States of America (USA), namely, the Small Business Act of 1953. The public policy sector in the USA is a rather complex area of research, and the qualitative study performed through the content analysis has been based on external factors within the Small Business Administration (SBA) for the small and medium-sized enterprise (SME) sector in the USA, in terms of financial assistance, regulations and directives, as well as access to information, knowledge and know-how regarding the national and international markets. As a result, the article underlines important elements for both theory and practice. The content analysis performed on the SBA has suggested that, given the administration’s organisational chart and programmes of the American SBA, the latter pushes towards an impersonal connection between the administration and the SME sector through the existence of the business development centres created with the main purpose of assisting the SMEs in their activity.

A framework linking ecosystem services and human well‐being: Saltmarsh as a case study

Abstract The ecosystem services approach is based on the interdependencies between nature and human well‐being. However, while the ecosystem services aspect of this approach is well‐developed, the human well‐being aspect remains unstructured and vaguely defined. An integrated conceptual framework was developed by adapting and linking the UK National Ecosystem Assessment‐Follow On framework with human well‐being domains. As well as benefits, the notion of disbenefits was incorporated to recognise the potentially detrimental effects from interacting with nature. Benefits and disbenefits occur at the social–ecological interface and are classified by the seven domains of human well‐being they affect. The framework is applied to saltmarsh habitat as a case study, highlighting knowledge gaps and the potential applicability and usefulness of the framework. In saltmarsh, benefits mainly accrue at larger scales with a greater impact affecting local to global individuals, while disbenefits tend to occur at a smaller scale and impact in‐situ individuals. The framework provides in‐depth insight into links, trade‐offs and dichotomies between benefits and disbenefits and human well‐being, and improves accessibility to the complex research area of human well‐being. This research can be a useful tool to guide environmental and health policy and management, as well as stakeholder engagement. A free Plain Language Summary can be found within the Supporting Information of this article.

Untangling the nonlinear “knots” of UK’s housing prices

PurposeHousing prices in the UK offer an inspiring, yet a complex and under-explored research area. The purpose of this paper is to investigate the critical factors that affect UK’s housing prices.Design/methodology/approachThe authors utilize the recently developed nonlinear ARDL approach of Shin et al. (2014) over the period 1969–2016.FindingsThe authors find that both the long-run and short-run impact of the price-to-rent (PTR) ratio and credit-to-GDP ratio on house prices (HP) is asymmetric whilst ambiguous results are established for mortgage rates, industrial production and equities. Apart from the novel framework of analysis, this study also establishes a positive association between HP and the PTR ratio which suggests a speculative behaviour and could imply the formation of a housing bubble.Originality/valueIt is the first study for the UK housing market that explores the underlying fundamental relationships by looking at nonlinearities hence, allowing HP to be tied by asymmetric relationships in the long as well as in the short run. Modelling the inherent nonlinearities enhances significantly the understanding of UK housing market which can prove useful for policymaking and forecasting purposes.

A computational approach to lexical polysemy in Ancient Greek

AbstractLanguage is a complex and dynamic system. If we consider word meaning, which is the scope of lexical semantics, we observe that some words have several meanings, thus displaying lexical polysemy. In this article, we present the first phase of a project that aims at computationally modelling Ancient Greek semantics over time. Our system is based on Bayesian learning and on the Diorisis Ancient Greek corpus, which we have built for this purpose. We illustrate preliminary results in light of expert annotation, and take this opportunity to discuss the role of computational systems and human analysis in a complex research area like historical semantics. On the one hand, computational approaches allow us to model large corpora of texts. On the other hand, a long and rich scholarly tradition in Ancient Greek has provided us with valuable insights into the mechanisms of semantic change (cf. e.g. Leiwo, M. (2012). Introduction: variation with multiple faces. In Leiwo, M., Halla-aho, H., and Vierros, M. (eds), Variation and Change in Greek and Latin, Helsinki: Suomen Ateenan-instituutin säätiö, pp. 1–11.). In this article, we show that these qualitative analyses can be leveraged to support and complement the computational modelling.

Strengthening the Child- and Youth-Serving Workforce: Surveying the Landscape, Overcoming Challenges.

This special issue addresses a neglected but important topic in our field: strengthening the child- and youth-serving workforce. Investing in this workforce should be a national priority because considerable evidence has shown that investments in early childhood education and development, particularly in low-resource contexts, that are reinforced through skills-based programs in adolescence and adulthood, have beneficial impacts throughout life. Investing in quality child- and youth-serving programs also has the additional benefit of creating a productive and capable future workforce. The editors and authors of the special issue should be commended for producing the first special issue on this topic in our field or in related fields, one that is long overdue. Articles in this issue survey the landscape of an amorphous and complex area of practice and research and describe key challenges for the field. In this commentary, I offer organizing frameworks to characterize the child- and youth-serving workforce, note emerging issues when addressing specific challenges, and identify areas for future research.

Application of Neural Network Models for Mathematical Programming Problems: A State of Art Review

Artificial neural networks or neural networks (NN) are new computational models based on the working of biological neurons of human body. A NN model consists of an interactive system through which external or internal information flows. Nowadays, NN models are being used to deal with complex real problems. On the other hand, mathematical programming problems (MPPs) are a particular class of optimization problems with mathematical structure of objective function(s) and set of constraints. Use of NN models in solving MPPs is a complex area of research and researchers have tried to contribute to apply NN models on different mathematical programming problems. This paper describes classification of MPPs, different neural network models and the detailed literature review on application of NN models for solving different MPPs along with comprehensive analysis on references. Some new research issues and scopes are also discussed on the use of different NN models on MPPs. This paper aims to present the state of art literature review on the use of NNs for solving MPPs with constructive analysis to elaborate future research scope and new directions in this area for future researchers.