bluuebunny/crossref_metadata_2025 huggingface dataset:
Blockchain Technology for the Internet of Things Applications in Apparel Supply Chain Management
Authors: Pal Kamalendu
Published: September 2020

Adoption of the internet of things (IoT) and blockchain technology opens new opportunities of business process automation in apparel supply chain management. The IoT technology helps to capture real-time information from different aspects of garment manufacturing activities by using radio frequency identification (RFID) tags and sensors. Blockchain technology is an emerging concept of computing that enable the decentralized and immutable storage of business transactions. In combination with IoT, blockchain technology can enable a broad range of application scenarios to enhance business value and trust. This chapter presents some of the blockchain-based IoT technology applications in apparel business processes. Moreover, the chapter provides a classification of threat models, which are considered by blockchain protocols in IoT networks. Finally, the chapter provides a taxonomy and a side-by-side comparison of the state-of-the-art methods towards secure and privacy-preserving blockchain technologies concerning the blockchain model, specific security goals, performance, and limitations.
Neuropathogenesis of Multiple Sclerosis and Huntington's Disease
Authors: El Hiba Omar , Zouhairi Nadia , Chatoui Hicham , Balzano Tiziano , Benammi Hind , Isbaine Faical , Bitar Abdelali , Idrissi Hamid Rguibi , El Moudden Ismail , Marhoume Fatima Zahra , Gamrani Halima
Published: September 2020

Substantial epidemiological studies have established a link between environmental factors and prevalence of neurodegenerative diseases. Increasingly, some studies report a direct cause-and-effect relation between environmental agents such as heavy metals and pesticides, and some neurodegenerative disorders including multiple sclerosis (MS) and Huntington's disease (HD). Interestingly, high blood level of heavy metals and pesticides has been shown in patients with MS and HD. Those agents could be involved directly or indirectly in the pathogenesis of MS and HD. The underlying mechanisms may imply an immune breaking of self-tolerance or neurodegeneration onset of several neurotransmission systems. The chapter will discuss the role of different metals and pesticides in the onset and progress of MS and HD with an overview of the possible underlying pathomechanisms.
Environmental Aspects of Alzheimer's and Parkinson's Diseases Neuropathologies
Authors: Zouhairi Nadia , El Hiba Omar , Lahouaoui Hasna , Benammi Hind , Chatoui Hicham , El Got Abdeljalil , Aimrane Abdelmohcine , Bitar Abdelali , Kahime Kholoud , Draoui Ahmed , El Kherchi Ouassil , Hmyed Wafa Ait
Published: September 2020

This chapter presents a literature review on the effect of environmental changes factors exposure in the etiology of Alzheimer's and Parkinson's diseases. The use of pesticides is more intense and somehow erratic as it aims to face climate change consequences like drought and water scarcity. The rural population is getting to be more vulnerable to have these neurodegenerative diseases. However, intense food production and economic models mean also the use of heavy metals in many stages as well during the production and the consumption processes and practices. Evidence from experimental studies shows that such heavy metals may also be a factor for the occurrence of Parkinson's and Alzheimer's diseases. At least, the environmental lifestyle and, likely, genetic factors, individually and collectively, play a significant role in the etiology of the diseases.
Deep Learning in Early Detection of Alzheimer's
Authors: Pillai Anitha S. , Menon Bindu
Published: September 2020

Advancement in technology has paved the way for the growth of big data. We are able to exploit this data to a great extent as the costs of collecting, storing, and analyzing a large volume of data have plummeted considerably. There is an exponential increase in the amount of health-related data being generated by smart devices. Requisite for proper mining of the data for knowledge discovery and therapeutic product development is very essential. The expanding field of big data analytics is playing a vital role in healthcare practices and research. A large number of people are being affected by Alzheimer's Disease (AD), and as a result, it becomes very challenging for the family members to handle these individuals. The objective of this chapter is to highlight how deep learning can be used for the early diagnosis of AD and present the outcomes of research studies of both neurologists and computer scientists. The chapter gives introduction to big data, deep learning, AD, biomarkers, and brain images and concludes by suggesting blood biomarker as an ideal solution for early detection of AD.
Analysis of Biomedical Image for Alzheimer's Disease Detection
Authors: Kumari Rashmi , Pushkar Shashank
Published: September 2020

Image analysis is giving a huge breakthrough in every field of science and technology. The image is just a collection of pixels and light intensity. The image capturing was done in two ways: (1) by using infrared sensors and (2) by using radiography. The normal images are captured by using the infrared sensors. Radiography uses the various forms of a light family, such as x-ray, gamma rays, etc., to capture the image. The study of neuroimaging is one of the challenging research topics in the field of biomedical image processing. So, from this note, the motivation for this work is to analyze 3D images to detect Alzheimer's disease and compare the statistical results of the whole brain image data with standard doctor's results. The authors also provide a very short implementation for brain slicing and feature extraction using Freesurfer and OpenNeuro dataset.
Soft Computing-Based Early Detection of Parkinson's Disease Using Non-Invasive Method Based on Speech Analysis
Authors: Ravi Chandrasekar
Published: September 2020

This chapter aims to use the speech signals that are a behavioral bio-marker for Parkinson's disease. The victim's vocabulary is mostly lost, or big gaps are observed when they are talking or the conversation is abruptly stopped. Therefore, speech analysis could help to identify the complications in conversation from the inception of the symptoms of Parkinson's disease in initial phases itself. Speech can be regularly logged in an unobstructed approach and machine learning techniques can be applied and analyzed. Fuzzy logic-based classifier is proposed for learning from the training speech signals and classifying the test speech signals. Brainstorm optimization algorithm is proposed for extracting the fuzzy rules from the speech data, which is used by fuzzy classifier for learning and classification. The performance of the proposed classifier is evaluated using metrics like accuracy, specificity, and sensitivity, and compared with benchmark classifiers like SVM, naïve Bayes, k-means, and decision tree. It is observed that the proposed classifier outperforms the benchmark classifiers.
Tremor Identification Using Machine Learning in Parkinson's Disease
Authors: Saikia Angana , Majhi Vinayak , Hussain Masaraf , Paul Sudip , Datta Amitava
Published: September 2020

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.
QSAR and QAAR Studies on Mixtures of 3-(Benzylidene)Indolin-2-One Isomers as Leads to Develop PET Radiotracers for Detection of Parkinson's Disease
Authors: Bhayye Sagar S. , Saha Achintya
Published: September 2020

Deposition of α–synuclein, tau and β–amyloid protein plaques in brain leads to neurodegeneration. A series of indolin derivatives, which can bind to α–synuclein and detect Parkinson's disease (PD), were used for development of QSAR and QAAR models. It is the first attempt of QSAR for any radiotracer agents used for detection of PD. The binding affinity against α–synuclein was used as dependent variable while independent variables, such as structural, topological, E-state keys, electronic, molecular shape analysis and spatial molecular descriptors were used for QSAR modeling. For QAAR modeling, the binding affinities of molecules for tau and β–amyloid along with different molecular descriptors were used as independent variables. All models were successfully developed using multiple linear regression method, and validated internally and externally, based on different standard criteria. This article describes how the derived models postulate that conformation of molecules and presence of unsaturated hydrocarbon chains, nitro, methoxy and amine functionalities play an important role in determining binding affinity.
Virtual Reality Therapeutic Environments in Autism Spectrum Disorder (ASD) and Alzheimer's
Authors: Sime David W.
Published: September 2020

This chapter is devoted to observing and analyzing the role of virtual reality environments in the therapeutic treatment, analysis, and ongoing treatment planning of autistic spectrum disorders (ASD), Alzheimer's, and dementia. Using live real-world examples of the above activities in action and a literature review, the chapter will examine the level of empirical data and pre-existing qualitative and quantitative research to support these ongoing approaches. Critical analysis will also be made of the current level of empirical research available highlighting areas that should be focused on for future research.
Dementia
Authors: Mukherjee Sudeshna
Published: September 2020

The high global prevalence, economic impact of dementia on families, caregivers, and communities, and the associated stigma and social exclusion present dementia as a significant public health challenge. Global as well as Indian data confirms a greater vulnerability of the women patients than their male counterparts. The global health community has recognized the need for action and to place dementia and its care and support services on the public health agenda. Dementia imposes significant physical, mental, and financial stress on caregivers who, again, often are women. In this context, the chapter aims to use capability approach and the concept of “disability paradox” to project dementia as a capability deprivation situation and through comparative analysis of various care models proposes a comprehensive economic care model and plan of action for a developing country like India to ensure dignified quality living for dementia patients and their caregivers.
Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI
Authors: Bhaskaran Bhuvaneshwari , Anandan Kavitha
Published: September 2020

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.
Assessment of Gait Disorder in Parkinson's Disease
Authors: Govindaraju Divya , Nagarajan Gururaj , Alagumariappan Paramasivam
Published: September 2020

Neurological disorders are some of the leading chronic disorders that impose a massive burden on low-income and developing countries. The disability resulting from the neurological disorder increases the severity and costs during the primary healthcare and for entire lifetime. Parkinson's disease (PD) is the second most common chronic neurodegenerative disorder which is slowly progressive with decrease in the motor and non-motor function of the nervous system due to cognitive impairment leading to gait abnormality. PD is most common in the age group of 40-65years leading to increase in gait disorders associated with slowing down of the movement, balance instability, rigidness in the muscles, and difficulty in performing everyday tasks. The assessment of gait plays a significant role in maintaining the balance disorders in Parkinson's disease. In patients with PD, the neurons present in substantia nigra region of the brain get injured, and they progressively decline during their lifetime. Therefore, the patients lose their ability to perform movement and also lose their stability. The symptoms of PD can be monitored and controlled by assessing gait parameters based on gait disorder.
Role of Wearable Technology and Fitness Apps in Obesity and Diabetes
Authors: Sherwani Shariq I. , Bates Benjamin R.
Published: September 2020

Rapid economic growth, industrialization, mechanization, sedentary lifestyle, high calorie diets, and processed foods have led to increased incidence of obesity in the United States of America. Prominently affected by the obesity epidemic are the most vulnerable such as the rural poor and those who have less access to nutritious and healthy foods due to barriers such as socioeconomic, infrastructural, and organizational. Wearable technology (WT) and health fitness applications (apps) have the potential to address some of the health disparities associated with obesity. Monitoring health parameters through WT and Apps using remote sensing technology generates personal health data which can be captured, analyzed, and shared with healthcare providers and others in social support network. Because captured data include protected health information, and breaches can occur, the concerns about health data privacy, personal ownership, and portability are addressed in this chapter.
Case Study
Authors: Torres Kylie , Stevenson Andrew , Hicks Justin
Published: September 2020

Under Armour purchased a fitness app, MyFitnessPal, that suffered a data breach shortly after Under Armour acquired the app. This breach made customers usernames, emails, and passwords available and for sale on the dark web. Under Armour informed its users of the breach and handled the crisis in the way they saw fit. During the investigation, it was found that Under Armour used a weaker encryption algorithm than it should have to encrypt customers' sensitive information. The company is currently in a lawsuit over the breach with one MyFitnessPal user.
Governmentality, Playbor, and Peak Performance
Authors: Bowman Nicholas David , Condis Megan
Published: September 2020

Gamification—the use of video game elements in non-gaming environments—is an effective and lucrative method of compelling individuals to engage with behaviors normally found aversive or uninteresting. Gamified applications are found in myriad areas, from education and social justice to health and wellness. A preponderance of evidence suggests that gamified health applications can have a positive effect on mental and physical health, but these benefits are often not balanced against the unanticipated or unknown consequences to individuals that come with coercing or “governing” players towards activities that might not be for the players' benefit. The chapter describes and explains gamification, discusses various health and wellness gamification programs, and then highlights existing and speculates on potential exploitative interactions stemming from uncritical engagement with health and wellness gamification. This critique is offered through Foucault's lens of “governmentality.”
Enhancing Socioscientific Reasoning Through Nature of Technology
Authors: Lee Hyunok , Lee Hyunju
Published: September 2020

Fostering informed Socioscientific Reasoning (SSR) is an essential component of developing scientific literacy. In this chapter, the authors suggest that enhancing Nature of Technology (NOT) understanding can be one way to leverage students' informed socioscientific reasoning. The authors describe a proposed NOT conceptual framework with four dimensions and detailed components, and present an analysis of students' reasoning of various socioscientific issues using this framework. Finally, the authors present the finding that NOT components were present in student discussions with varying levels of understanding. The SSR analysis reveals that students with NOT informed understanding can appreciate the integrated characteristics of technology, so as to make sophisticated decisions about science and technology that will change society in fundamental ways, for both better and worse.
Unifying Themes in Socioscientific Issues-Based Instruction for Scientific Literacy Development
Authors: Powell Wardell A.
Published: September 2020

This chapter presents the unifying themes in socioscientific issues-based instruction for scientific literacy development. Section 1 presented an overview of how to effectively implement socioscientific issues in the elementary grades to provide students with opportunities to apply science to their everyday lives. Section 2 built upon where the authors left off in section one. In this section, the authors used real-world scientific context to provide opportunities to use character and values and moral reasoning as they think about finding solutions to real-world scientific problems. Section 3 showed the continued use of socioscientific issues with an upward trajectory to enhance scientific literacy at the college level. Section 4 demonstrated socioscientific issues being successfully implemented at the core of the P-12 educational system. In Section 5, the authors revealed the integrative nature among STEM, model-based learning, and socioscientific issues in achieving scientific literacy.
New Marketing Strategy
Authors: Florido-Benitez Lazaro
Published: September 2020

Mobile applications (apps) on smartphones are being increasingly used to access news, entertainment, weather forecast, etc. As apps gain a stronger footing in the 21st century, large airports are taking advantage of this new marketing outlet by developing apps for branding and (business) communication purposes. In recent years, due to its multi-functionality, which provides added value to the service content offered, this marketing model has established itself as a communication tool. This research aims are twofold: First, to evaluate how an airport app is having a direct and positive influence on passengers' sense of security-control; second, to analyse how this tool can improve passengers' satisfaction and their perceived image of an airport. To analyse the influence an app has on passengers-users at airports, 103 passengers-users were surveyed. The results are particularly relevant for passengers as they are more efficient during their leisure time in terminals, and their stress levels decrease.
The Impact of AI on Disintermediation Processes in the Tourism Industry
Authors: Damoska Sekuloska Jovanka , Erceg Aleksandar
Published: September 2020

As an innovative technology, artificial intelligence (AI) leads to disruption and automation of almost every process and business model in almost any industry today. AI contributes to the process of disintermediation of value chains resulting in shrinking the producer-customer links. The chapter examines the influence of AI on disintermediation in the tourism industry. It investigates the changes and transformation of the value creation process and marketing in the tourism industry affected by AI technology. AI could transform and revolutionize every segment of the tourism industry. Thus, it can make tourism more efficient, with new value-added customer services. AI is considered an enabling tool for the creation of the so-called “smart tourism” as a new age of tourism development. As a digital tool, AI enables the tourism industry to get insight into the customer, understand tourist profiles and provide consumers with personalized and niche travel experiences.
Risk Management in the Oil and Gas Industry Related to the AI Tools
Authors: Popescu Catalin , Avram Lazăr , Mocanu Ion
Published: September 2020

The oil and gas industry is in a stage of intense focus on safety, preparing for better management of environmental risks and mitigating them. Given the policy of corporate social responsibility, technical and economic challenges, as well pressures in domain-specific regulations, it has become increasingly obvious that the management of these risks is essential for long-term sustainability of oil and gas companies. Research shows that safety and environmental issues, compliance with regulatory rules, price volatility, and rising challenges associated with access to oil and gas reserves and markets are the top risks identified by oil and gas industry executive directors. In this sense, the tools offered by artificial intelligence can contribute to the proper management of these risks and to the adequate monitoring of all the categories of processes that take place at the level of the optimized production generated by the gas wells, regarding the transport of petroleum products through the pipelines and especially with regard to offshore activities.
Successful Computer Forensics Analysis on the Cyber Attack Botnet
Authors: Leelasankar Kavisankar , C. Chellappan , P. Sivasankar
Published: September 2020

The success of computer forensics lies in the complete analysis of the evidence that is available. This is done by not only analyzing the evidence which is available but also searching for new concrete evidence. The evidence is obtained through the logs of the data during the cyberattack. When performing analysis of the cyberattack especially the botnet attacks, there are many challenges. First and the foremost is that it hides the identity of the mastermind, the botmaster. It issues the command to be executed using its subordinate, the command and control (C&C). The traceback of C&C itself is a complex task. Secondly, it victimizes the innocent compromised device zombies. This chapter discusses the analysis done in both proactive and reactive ways to resolve these challenges. The chapter ends by discussing the analysis to find the real mastermind to protect the innocent compromised system and to protect the victim system/organization affected by the botnet cyberattack.
Evaluation of the Attack Effect Based on Improved Grey Clustering Model
Authors: Yue Chen , Tianliang Lu , Manchun Cai , Jingying Li
Published: September 2020

There are a lot of uncertainties and incomplete information problems on network attack. It is of great value to access the effect of the attack in the current network attack and defense. This paper examines the characteristics of network attacks, there are problems with traditional clustering that index attribution is not clear and the cross of clustering interval. A two-stage grey synthetic clustering evaluation model based on center-point triangular whitenization weight function was proposed for the attack effect. The authors studied the feasibility of applying this model to the evaluation of network attack effect. Finally, an example is given, which showed the model could evaluate the effect of the denial-of-service attack precisely. It is also shown that the model is viable to evaluate the attack effect.
Cyber-Physical System and Internet of Things Security
Authors: Ulz Thomas , Haas Sarah , Steger Christian
Published: September 2020

An increase of distributed denial-of-service (DDoS) attacks launched by botnets such as Mirai has raised public awareness regarding potential security weaknesses in the Internet of Things (IoT). Devices are an attractive target for attackers because of their large number and due to most devices being online 24/7. In addition, many traditional security mechanisms are not applicable for resource constraint IoT devices. The importance of security for cyber-physical systems (CPS) is even higher, as most systems process confidential data or control a physical process that could be harmed by attackers. While industrial IoT is a hot topic in research, not much focus is put on ensuring information security. Therefore, this paper intends to give an overview of current research regarding the security of data in industrial CPS. In contrast to other surveys, this work will provide an overview of the big CPS security picture and not focus on special aspects.
UWDBCSN Analysis During Node Replication Attack in WSN
Authors: Kaur Harpreet , Saxena Sharad
Published: September 2020

Wireless sensor network is an emerging area in which multiple sensor nodes are present to perform many real-time applications like military application, industrialized automation, health monitoring, weather forecast, etc. Sensor nodes can be organized into a group which is led by a cluster head; this concept is known as clustering. Clustering of wireless sensor network is used when sensor nodes want to communicate simultaneously in a single network. The author organizes the sensor nodes by applying UWDBCSN (underwater density-based clustering sensor network) clustering approach in which routing of the packets is controlled by cluster head. The author also considers the security of sensor nodes which are harmful to different types of mischievous attacks like wormhole attack, denial of service attack, replication or cloning attack, blackhole attack, etc. Node replication is one of the types in which an attacker tries to capture the node and generate the replica or clone of that node in the same network. So, this chapter describes how to deal with these types of attacks. The author used the intrusion detection process to deal with this type of attack. All the detection procedure is combined with sleep/wake scheduling algorithm to increase the performance of sensor nodes in the network.
A Survey of KYC/AML for Cryptocurrencies Transactions
Authors: Moreno Suzana M. B. M. , Seigneur Jean-Marc , Gotzev Gueorgui
Published: September 2020

KYC (know your customer) and AML (anti-money laundering) practices have been designed and implemented in traditional financial transactions for some years now. However, it has been complicated to find a balance between business efficiency, innovations, financial inclusion, and compliance, both in the specification of what should be done and in the implementation of a risk-based approach that satisfies the required specification during real business operation. This chapter presents a survey of traditional practices to KYC/AML, highlighting a subset of existing challenges in these practices, taking into consideration the innovation of cryptocurrencies transactions and related innovations, such as digital identity, and the financial inclusion of unbanked people without identity papers. The authors finish this chapter by discussing existing solutions to these challenges both by adopting new KYC/AML practices and by using innovative technological solutions.
An Overview on Protecting User Private-Attribute Information on Social Networks
Authors: Alnasser Walaa , Beigi Ghazaleh , Liu Huan
Published: September 2020

Online social networks enable users to participate in different activities, such as connecting with each other and sharing different contents online. These activities lead to the generation of vast amounts of user data online. Publishing user-generated data causes the problem of user privacy as this data includes information about users' private and sensitive attributes. This privacy issue mandates social media data publishers to protect users' privacy by anonymizing user-generated social media data. Existing private-attribute inference attacks can be classified into two classes: friend-based private-attribute attacks and behavior-based private-attribute attacks. Consequently, various privacy protection models are proposed to protect users against private-attribute inference attacks such as k-anonymity and differential privacy. This chapter will overview and compare recent state-of-the-art researches in terms of private-attribute inference attacks and corresponding anonymization techniques. In addition, open problems and future research directions will be discussed.
Evolving Cyber Threats, Combating Techniques, and Open Issues in Online Social Networks
Authors: Rao Sanjeev , Verma Anil Kumar , Bhatia Tarunpreet
Published: September 2020

Online social networks (OSNs) are renowned powerful web tools that allow worldwide users to connect with their friends, families, professional groups, and social circle through social interaction for sharing common interests. With the proliferation, ease, and efficacy of OSNs, these are becoming an essential tool for communication. But many OSN users innocently uncover their personal information such as their home address, phone numbers, email id, etc. that can be used by the malicious user to perform various kind for cyber-crimes like cyber-bullying, spamming, click-jacking, identity theft, phishing, distrust, fake profiles, spreading malicious content, etc. This chapter presents a review of various privacy and security threats/attacks associated with OSN users and recommended combating techniques based on data-mining and machine learning algorithms. Also, the future directions for upcoming researchers in this field are suggested.
Hybrid Intelligence Framework for Improvement of Information Security of Critical Infrastructures
Authors: Ryjov Alexander P. , Mikhalevich Igor F.
Published: September 2020

Information infrastructures for corporations and governments (information and automated systems, telecommunication networks, and other elements) have dramatically changed in the last decades due to the broad usage of IoT, AI, mobile internet, and other advanced technologies. Protection against cyberattacks requires new solutions that correspond to an increased level of complexity for these infrastructures. Important tasks for these new tools are forecasting cyberattacks, developing and applying preventive protective measures to reduce the risk of information security incidents. For the prediction of cyberattacks and the development of preventive protective measures, the authors propose to use one of the hybrid (or augmented) intelligence (HI) frameworks – evaluation and monitoring of complex processes. The mission of the chapter is to discuss the main issues, challenges, and opportunities related to HI applications for protection against cyberattacks to information infrastructures.
Modeling a Cyber Defense Business Ecosystem of Ecosystems
Authors: Ishikawa Edison , Vianna Eduardo Wallier , da Silva João Mello , Fernandes Jorge Henrique Cabral , Gondim Paulo Roberto de Lira , Zelenovsky Ricardo
Published: September 2020

Providing cyber defense in a country is complex. It involves ensuring the security of various products and services that are part of a global supply chain. In this complex scenario, the challenge is the development of a cyber defense business ecosystem that, reaching a minimum level of maturity, guarantees the security of products and services in cyberspace. This work proposes a cyber defense business ecosystem of ecosystems (BEoE) model with two ecosystems that must be created or fostered, the human resources training ecosystem and the product and service homologation and certification ecosystem. These two cyber defense ecosystems are key to the sustainable growth of an entire chain of production and sourcing of cyber defense goods and services. The proposed model allows the Cyber Defense BEoE to evolve, so that different actors (companies and government agencies) with different levels of maturity in defense and cybersecurity may emerge. In this way, a country's Cyber Defense BEoE may be able to provide products and services at different levels of security for its defense system.
Privacy in Online Social Networks
Authors: Kaur Ramanpreet , Klobučar Tomaž , Gabrijelčič Dušan
Published: September 2020

This chapter is concerned with the identification of the privacy threats to provide a feedback to the users so that they can make an informed decision based on their desired level of privacy. To achieve this goal, Solove's taxonomy of privacy violations is refined to incorporate the modern challenges to the privacy posed by the evolution of social networks. This work emphasizes on the fact that the privacy protection should be a joint effort of social network owners and users, and provides a classification of mitigation strategies according to the party responsible for taking these countermeasures. In addition, it highlights the key research issues to guide the research in the field of privacy preservation. This chapter can serve as a first step to comprehend the privacy requirements of online users and educate the users about their choices and actions in social media.
The Intersection of Cybercrime and the Blockchain
Authors: Venkatesh Vishnu , Gordon Steven
Published: September 2020

The immutability of blockchains and the transparency of their transaction records would appear to limit the benefit of exploiting them for criminal activity. However, blockchains also offer a high degree of anonymity, similar to fiat paper currency; the technology was intended to facilitate trustless transactions. Coupled with a global, borderless reach, blockchains have become an enabler of cybercrime. They are a new class of assets that, like all other assets, possess security risks and become potential targets of attack. In particular, cryptocurrencies, which depend on blockchain technology, provide significant incentives for attack because of their value. The goals of this chapter are to identify and classify blockchain-based cybercrimes and to explore the avenues for protecting against them at individual, organizational, and policy levels.
Molecular Docking Study of Expansin Proteins in Fibers of Medicinal Plants Calotropis Procera
Authors: Basu Anamika
Published: September 2020

Calotropis procera is used in several traditional medicines to treat a variety of diseases. Calotropis is also used as a homeopathic medicine. The bast fiber can substitute cotton wool for surgical or stuffing purposes. This fiber can be used as an excellent model system to study the genes involved in fiber elongation. Expansins are typically 250-275 amino acids long, pH-dependent cell wall-loosening proteins required for cell wall expansion in many developmental processes. Four Expansin A proteins are present in fast growing Calotropis procera fibers. Expansins proteins disrupt the cellulose-hemicellulose network transiently, allowing slippage of cell wall polymers. But the molecular mechanism by which expansin loosens the cellulosic network is not yet established. To understand of the role of protein expansins in the xyloglucan-cellulose, different computational biology techniques are used. Molecular docking analysis of protein expansins with ligand xylose present in xyloglucan shows that arginine and serine are responsible for protein-ligand interactions.
Diabetic Foot
Authors: Trishin Leonid
Published: September 2020

In the article, questions of an etymology, a pathogeny of diagnostics, and treatment of diabetic foot are considered. In addition to a reasonable diet, it is necessary to take medicine, to regularly check sugar level in blood, to train regularly, and to consult constantly with the doctor to control recovery process. Long-term and stable control of blood sugar and bringing its level to norm can considerably reduce the risk of injury of nerves, kidneys, eyes, and blood vessels.
A Multi-Agent Approach to Segment Arabic Handwritten Text Lines
Authors: Elkhayati Mohsine , Elkettani Youssfi , Mourchid Mohammed
Published: September 2020

In text line segmentation, there are three classes of methods: either by sorting physical units such as pixels or connected components (CC) constituting a line or by searching for the baseline of each word and grouping together those who participate in the same line. The third class analyzes the separation locations between the lines. After an overview of lines segmentation approaches, the authors introduced a new method emphasizing its simplicity, speed, and originality. The proposed approach detects the starting components of the lines in the first step. In the second step, it defines a number of agents that start the segmentation process from their starting points between the starting components of lines. Each agent aims to reach the left edge of the document through the correct path. The algorithm used by the agents is based on the morphological process, characteristics of the Arabic manuscript and a communication system. The experimental results on an Arabic dataset show that this approach is an effective solution for the segmentation of lines from different Arabic manuscripts.
WMN-PSODGA - An Intelligent Hybrid Simulation System for WMNs Considering Load Balancing
Authors: Ohara Seiji , Qafzezi Ermioni , Barolli Admir , Sakamoto Shinji , Liu Yi , Barolli Leonard
Published: September 2020

Wireless mesh networks (WMNs) are becoming an important networking infrastructure because they have many advantages, such as low cost and increased high-speed wireless Internet connectivity. In the authors' previous work, they implemented a hybrid simulation system based on particle swarm optimization (PSO) and distributed genetic algorithm (DGA), called WMN-PSODGA. Moreover, they added to the fitness function a new parameter for mesh router load balancing a number of covered mesh clients per router (NCMCpR). In this article, the authors consider Exponential, Weibull, and Normal distributions of mesh clients and carry out a comparison study. The simulation results show that the performance of the Exponential, Weibull and Normal distributions was improved by considering load balancing when using WMN-PSODGA. For the same number of mesh clients, the Normal distribution behaves better than the other distributions. This is because all mesh clients are covered by a smaller number of mesh routers and the standard deviation is improved by effectively using NCMCpR.
An Automatic Centroid Image Selection Method Based on Fuzzy Logic Reasoning in Image Deduplication
Authors: Chen Ming , Yan Jinghua , Gao Tieliang , Ma Huan , Duan Li , Tang Qiguang
Published: September 2020

Centroid selection plays a key role in image deduplication. It means selecting an optimal solution as a centroid image in a duplicate image set. Meanwhile, it will delete other image copies and establish pointers to point to the centroid image in the original position. At present, there is not a mature centroid selection scheme. Centroid selection mainly relies on users to manually complete according to experience. In a massive data environment, it will consume a lot of human resources, and it is easy to make mistakes by subjective judgment. Therefore, in order to solve this problem, this article proposes an automatic centroid image selection method based on fuzzy logic reasoning. In a duplicate image set, the image attribute information is used to automatically infer comprehensive quantized values to represent images, and the centroid image is selected by comparing the quantized values. The experimental results showed that the scheme not only could meet the visual perception characteristics, but also meet the purpose of image deduplication.
Hypertension Prediction Using Machine Learning Technique
Authors: Choi Youngkeun , Choi Jae
Published: September 2020

Machine learning technology is used in advanced data analysis and optimization approaches for different kinds of medical problems. Hypertension is complicated, and every year it causes a lot of many severe illnesses such as stroke and heart disease. This study essentially had two primary goals. Firstly, this paper intends to understand the role of variables in hypertension modeling better. Secondly, the study seeks to evaluate the predictive performance of the decision trees. Based on these results, first, age, BMI, and average glucose level influence hypertension significantly, while other variables have an influence. Second, for the full model, the accuracy rate is 0.905, which implies that the error rate is 0.095. Among the patients who were predicted not to have hypertension, the accuracy that would not have hypertension was 90.51%, and the accuracy that had strike was 30.77% among the patients who were predicted to have hypertension.
All-Terrain Cyclocopter Capable of Aerial, Terrestrial, and Aquatic Modes
Authors: Shrestha Elena , Davis Brian , Hrishikeshavan Vikram , Chopra Inderjit
Published: September 2020

This paper describes the design and experimental validation of an all-terrain cyclocopter micro air vehicle capable of power-efficient aerial, terrestrial, and aquatic locomotion with seamless transition between the modes. The vehicle has a mass of 1010 g and solely relies on its four
cycloidal rotors (cyclorotors) to achieve all modes of locomotion. The cyclorotor rotational speeds and thrust vectors are individually modulated to sustain stable hover in aerial mode. A similar control strategy using aerodynamic forces generated by cyclorotors is also implemented for aquatic
locomotion. The wheels are efficiently integrated into the carbon fiber rotor endplates since cyclorotors rotate about the horizontal axis. As a result, the cyclocopter maneuvers in terrestrial mode by directly relying on motor torque. Seamless transition is accomplished using a retractable
landing gear system equipped with polystyrene foam pontoons. In aerial mode, the cyclorotors operate at 1550 rpm and consume 232 W to sustain hover. Forward translation at 2 m/s in terrestrial mode requires 28 W, which is a 88% reduction from hover. In aquatic mode, the cyclorotors operate
at 348 rpm and consume 19 W, which is a 92% reduction from hover. Overall, a versatile platform capable of multimodal operation is successfully demonstrated with only a modest addition in total mass.
The need to combat stigmatizing dogmas in the midst of the novel coronavirus disease (COVID-19) pandemic
Authors: Adefuye Anthonio A.O. , Butler Markes , Adeola Henry A.
Published: September 2020

No abstract available.
Malaria and HIV sero-positivity: study on selected individuals at a tertiary healthcare centre in Port Harcourt, Nigeria
Authors: Okonko Iheanyi O. , Osadebe Anwuli U. , Okoli Eberechukwu M. , Eke Ego D.
Published: September 2020

Over 50% of the global population is vulnerable to malaria infection. An estimated 300 million malaria cases occur annually in the tropics with 90% of these in the sub-Sahara, a region that already suffers the greatest burden of HIV-1 infection. This study assessed the prevalence of HIV and malaria infections in a cohort of 200 undergraduates, consisting of 100 females and 100 males, attending the Health Centre at the University of Port Harcourt, Nigeria. Blood samples were screened for the presence of malaria and HIV, using SD Boline malaria Antigen P.F. test kit and Determine® HIV one step rapid Kit. The overall prevalence for HIV in this study was 2.5% while the overall prevalence of malaria was 1.5%. The prevalence of HIV based on gender was 2.0% and 3 % for females and males, respectively. For malaria, these values were 2.0% and 1.0% for females and males, respectively. No incident of co-infection was recorded in this study. The findings did not indicate any relationship between HIV seropositivity and malaria infection but underscored the low incidence of both malaria and HIV within the 17-26 age range and amongst relatively well-educated individuals. In spite of the low prevalence observed, there is still the need for awareness of HIV and Malaria prevention amongst university students. Further studies could be undertaken to investigate other relevant epidemiology parameters with regards to HIV and malaria in the tropics.
First utilization of BioFire FilmArray® Respiratory Panel 2 Plus in post-mortem diagnosis of Influenza A H1N1
Authors: D'Aleo Francesco , Gatto Valentina , Chisari Mattia , Marcuccio Daniela , Cuzzola Maria , Conte Marco , Farina Claudio
Published: September 2020

Post-mortem microbiology is an important tool in legal medicine and forensic pathology; this case report shows the importance of microbiological testing to establish the cause of death in infection suspected death. We refer a case of fatal Influenza A (H1N1) diagnosed only after dead with post-mortem microbiology tool.
Reduction of response time for the assessment of invasive bacterial disease in bacteriology by using h24 molecular tests at low operational impact
Authors: Corbo Nicoletta , Comi Cristina
Published: September 2020

In February 2017, the Lombardy Region (Italy) provided some indications for the surveillance of invasive bacterial diseases, including the need to perform molecular tests together with culture and other diagnostic tests. Starting from September 2017, the Microbiology and Virology Unit of ASST Lecco has acquired the FilmArray system (BioMérieux, Marcy-l’Étoile, France) which includes the Meningitis/Encephalitis (ME) panel, able to identify 14 common pathogens responsible for community acquired meningitis or encephalitis including virus, bacteria and yeast with molecular technology directly from cerebrospinal fluid (CFS). In 2018, the organizational model implemented has provided to use of the FilmArray system by the TSLB dedicated to microbiology during routine hours (8.00-16.30) and by the TSLB of Clinical Pathology on active guard from 16.30 to 8.00 with validation of the analysis by the Manager available. The new organization, which involved the use of the FilmArray ME h 24 panel by the TSLB who works belonging to Clinical Pathology and Microbiology and Virology, has allowed a considerable reduction of response times in case of suspected meningitis.
The colours of bacteria and fungi
Authors: Fiscarelli Ersilia Vita
Published: September 2020

Talking about colours, a bacteriology laboratory can be compared to a painter’s studio: culture media are like canvasses, bacteria and fungi like paints and sterile loops look like extraordinary paintbrushes. Even Sir Alexander Fleming, the father of penicillin, before making his discovery, took pleasure in painting [...].