bluuebunny/crossref_metadata_2025 huggingface dataset:
Jardines luminiscentes en las salinas de las Islas Canarias
Authors: Cabrera García Víctor Manuel
Published: September 2021

Recuperar el patrimonio salinero canario en desuso mediante la incorporación de nuevos usos que sean compatibles con las salinas supone un reto importante para nuestra sociedad. Ante el manifiesto estado de abandono que tienen actualmente las salinas de las Islas Canarias se propone revitalizarlas desde la óptica del paisaje creando jardines luminiscentes en aquellas salinas que han quedado desnaturalizadas en la trama urbana atendiendo al fenómeno natural de la bioluminiscencia. Este novedoso concepto de jardines podría convertirse en nuevos polos de atracción para el turismo cultural, lo que permitiría generar nuevas experiencias sensoriales que aportan valores añadidos respecto a las diversas rutas culturales y patrimoniales que se realizan en unos paisajes marinos tan singulares como son las salinas.
Imaginando estructuras. Una investigación histórica sobre las obras del ingeniero y arquitecto Enrico Castiglioni en la Italia de los años cincuenta
Authors: Giannetti I.
Published: September 2021

Durante la década de 1950, una “tendencia estructural” afectó a la arquitectura moderna internacional a través de las geometrías de vanguardia del hormigón armado. En el contexto especial de Italia, mientras que las grandes obras de hormigón armado de la ingeniería estructural recibieron la atención internacional, se estableció una profunda afinidad entre la concepción estructural y la arquitectónica en la arquitectura italiana. En este marco, el ingeniero y arquitecto Enrico Castiglioni (1914-2000) desarrolló su lenguaje arquitectónico caracterizado por estructuras imaginativas en hormigón armado. Su obra, ampliamente difundida en la escena internacional a través del relato de las revistas de arquitectura de los años cincuenta y sesenta, está hoy completamente olvidada. Este artículo presenta los principales trabajos de Castiglioni a través de una investigación en el campo de la historia de la construcción.
La force des dispositions
Authors: Pichonnaz David
Published: September 2021

Cet article étudie la manière dont des dispositions incorporées avant l’entrée dans la police contribuent à façonner la manière dont les nouvelles recrues dans ce métier acquièrent leurs manières de le penser et de le pratiquer. Il montre, d’une part, que les pertes de statut ou les stratégies d’ascension sociale échouées antérieurement ont pour effet de rendre les nouvelles recrues davantage sensibles au pouvoir de coercition auquel elles sont habilitées, alors perçu comme pourvoyeur de statut. Par ailleurs, les policières interrogées ont un rapport plus distant avec les outils coercitifs, et ont moins tendance à aligner leurs pratiques sur celles des hommes que dans d’autres contextes nationaux, phénomène que l’on peut mettre sur le compte de l’élévation de leur niveau de diplôme. La socialisation de genre se matérialise en outre par des constructions différenciées de la masculinité parmi les policiers, qui sont associées à des visions du métier également différentes.
Artificial Neural Network Models for Large-Scale Data
Authors: Phu Vo Ngoc , Tran Vo Thi Ngoc
Published: September 2021

Artificial intelligence (ARTINT) and information have been famous fields for many years. A reason has been that many different areas have been promoted quickly based on the ARTINT and information, and they have created many significant values for many years. These crucial values have certainly been used more and more for many economies of the countries in the world, other sciences, companies, organizations, etc. Many massive corporations, big organizations, etc. have been established rapidly because these economies have been developed in the strongest way. Unsurprisingly, lots of information and large-scale data sets have been created clearly from these corporations, organizations, etc. This has been the major challenges for many commercial applications, studies, etc. to process and store them successfully. To handle this problem, many algorithms have been proposed for processing these big data sets.
Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN
Authors: Karegowda Asha Gowda , G. Devika
Published: September 2021

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.
Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms
Authors: Leema N.  , Nehemiah Khanna H. , Elgin Christo V. R.  , Kannan A.
Published: September 2021

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.
Artificial Neural Network for PWM Rectifier Direct Power Control and DC Voltage Control
Authors: Fekik Arezki , Denoun Hakim , Azar Ahmad Taher , Zaouia Mustapha , Benyahia Nabil , Hamida Mohamed Lamine , Benamrouche Nacereddine , Vaidyanathan Sundarapandian
Published: September 2021

In this chapter, a new technique has been proposed for reducing the harmonic content of a three-phase PWM rectifier connected to the networks with a unit power factor and also providing decoupled control of the active and reactive instantaneous power. This technique called direct power control (DPC) is based on artificial neural network (ANN) controller, without line voltage sensors. The control technique is based on well-known direct torque control (DTC) ideas for the induction motor, which is applied to eliminate the harmonic of the line current and compensate for the reactive power. The main idea of this control is based on active and reactive power control loops. The DC voltage capacitor is regulated by the ANN controller to keep it constant and also provides a stable active power exchange. The simulation results are very satisfactory in the terms of stability and total harmonic distortion (THD) of the line current and the unit power factor.
A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks
Authors: Onur Tuğba Özge , Onur Yusuf Aytaç
Published: September 2021

Steel wire ropes are frequently subjected to dynamic reciprocal bending movement over sheaves or drums in cranes, elevators, mine hoists, and aerial ropeways. This kind of movement initiates fatigue damage on the ropes. It is a quite significant case to know bending cycles to failure of rope in service, which is also known as bending over sheave fatigue lifetime. It helps to take precautions in the plant in advance and eliminate catastrophic accidents due to the usage of rope when allowable bending cycles are exceeded. To determine the bending fatigue lifetime of ropes, experimental studies are conducted. However, bending over sheave fatigue testing in laboratory environments require high initial preparation cost and a long time to finalize the experiments. Due to those reasons, this chapter focuses on a novel prediction perspective to the bending over sheave fatigue lifetime of steel wire ropes by means of artificial neural networks.
Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network
Authors: Rao Gummadi Srinivasa , Obulesh Y. P. , Rao B. Venkateswara
Published: September 2021

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.
Optimizing Material Removal Rate Using Artificial Neural Network for Micro-EDM
Authors: Upadhyay Ananya , Prakash Ved , Sharma Vinay
Published: September 2021

Machining can be classified into conventional and unconventional processes. Unconventional Machining Process attracts researchers as it has many processes whose physics is still not that clear and they are highly in market-demand. To predict and understand the physics behind these processes soft computing is being used. Soft computing is an approach of computing which is based on the way a human brain learns and get trained to deal with different situations. Scope of this chapter is limited to one of the soft computing optimizing techniques that is artificial neural network (ANN) and to one of the unconventional machining processes, electrical discharge machining process. This chapter discusses about micromachining on Electric Discharge Machining, its working principle and problems associated with it. Solution to those problems is suggested with the addition of powder in dielectric fluid. The optimization of Material Removal Rate (MRR) is done with the help of ANN toolbox in MATLAB.
Applications of ANN for Agriculture Using Remote Sensed Data
Authors: M. Geetha , Karegowda Asha Gowda , Rudrappa Nandeesha , G. Devika
Published: September 2021

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defense, intelligence, commerce, economics, and administrative planning. Remote sensing is used in science and technology, and through it, an object can be identified, measured, and analyzed without physical presence for interpretation. In India remote sensing has been using since 1970s. One among these applications is the crop classification and yield estimation. Using remote sensing in agriculture for crop mapping, and yield estimation provides efficient information, which is mainly used in many government organizations and the private sector. The pivotal sector for ensuring food security is a major concern of interest in these days. In time, availability of information on agricultural crops is vital for making well-versed decisions on food security issues.
Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS)
Authors: Chanda Mriganka Mohan , Banerjee Neelotpaul , Bandyopadhyay Gautam
Published: September 2021

Agriculture is an important sector of the Indian economy. In the present paper an attempt has been made to theoretically explore the development of an agricultural knowledge management system (KMS) in respect of various micro irrigation techniques for agriculture, as well as relevant crop-/region-specific agricultural practices in different regions of the country, as the same has been observed to be very much necessary for the overall benefits of wider cross section of farmers, agricultural scientists, economists, and other stakeholders in the domain. It is further observed that artificial neural networks (ANNs), which are a part of soft computing techniques, can be used as a KMS tool for effective management of various sub sectors of agriculture. In this context, it has been shown that use of ANNs as a KMS tool can improve the effectiveness of applications of the above mentioned agricultural KMS by accurately forecasting the year-wise estimated yield of food grains of India with the help of past data of various relevant parameters.
Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods
Authors: Abdelkader Eslam Mohammed , Marzouk Mohamed , Zayed Tarek
Published: September 2021

Bridges are aging and deteriorating. Thus, the development of Bridge Management Systems (BMSs) became imperative nowadays. Condition assessment is one of the most critical and vital components of BMSs. Ground Penetrating Radar (GPR) is one of the non-destructive techniques (NDTs) that are used to evaluate the condition of bridge decks which are subjected to the rebar corrosion. The objective of the proposed method is to develop standardized amplitude scale for bridge decks based on a hybrid optimization-decision making model. Shuffled frog leaping algorithm is employed to compute the optimum thresholds. Then, polynomial regression and artificial neural network models are designed to predict the prioritizing index based on a set of multi-criteria decision-making methods. The weibull distribution is utilized to capture the stochastic nature of deterioration of concrete bridge decks. Lastly, a case study is presented to demonstrate the capabilities of the proposed method.
An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter
Authors: Jena Bibekananda , Patel Punyaban , Sinha G.R.
Published: September 2021

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.
Classifying Diabetes Disease Using Feedforward MLP Neural Networks
Authors: Al-Khasawneh Ahmad , Hijazi Haneen
Published: September 2021

Diagnosing chronic diseases is about making accurate and quick decisions based on contradictory information and constantly evolving knowledge. Hence, there has been a persistent need to help health practitioners in making correct decisions. Diabetes is a common chronic disease. It is a global healthcare threat and the eighth leading cause of death in the world. Modern artificial intelligence techniques are being used in diagnosing chronic diseases including artificial neural networks. In this chapter, a feedforward multilayer-perceptron neural network has been implemented to help health practitioners in classifying diabetes. Through the work, an algorithm was proposed in purpose of determining the number of hidden layers and neurons in a MLP. Based on the algorithm, two topologies have been introduced. Both topologies exhibited good classification accuracies with a slightly higher accuracy for the MLP with only one hidden layer. The data set was obtained from King Abdullah University Hospital in Jordan.
Modelling and Forecasting Portfolio Inflows
Authors: Rapoo Mogari I. , Munapo Elias , Chanza Martin M. , Ewemooje Olusegun Sunday
Published: September 2021

This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.
Technology for Gifted Students
Authors: Anderson Cindy L. , Anderson Kevin M.
Published: September 2021

This chapter proposes that software design using software with programming capabilities, such as Classroom Suite, is a good activity for gifted students to increase their science, technology, engineering, and mathematics (STEM) skills while designing appropriate learning activities for all students, including those with disabilities. The chapter describes design techniques that gifted students can apply during the development, offers as an example the design process for one of the authors' own Classroom Suite instructional games, and concludes with a description of several other games designed by the authors as further examples of the kinds of instructional software that gifted students can develop for students with disabilities using Classroom Suite.
Strategies for Teaching Math to Middle and High School Students With Special Needs
Authors: Epler Pam L.
Published: September 2021

This chapter is designed to inform and educate 6th- through 12th-grade teachers on how to provide math activities for students with an identified learning disability as defined by the Individuals with Disabilities Education Act. The chapter provides an introduction to the topic, background information on teaching math at the secondary level, research-based instructional strategies that can be used for teaching math to students with identified special needs, and specific manipulatives that can be created and utilized to teach the Common Core State Standards in Mathematics to this population. Additional resources and readings are included as well.
Using Ethnographic Approaches to Co-Construct Strategies for Success With Students With Dyslexia
Authors: Fogarty-Perry Barbara , McKenzie Margaret
Published: September 2021

This chapter takes a dual focus/lens to applying and performing research methods in inclusive and special education in a tertiary/post-secondary setting with a group of students with dyslexia. It links the core ideas of United Nations Convention on the rights of persons with disabilities Article 24, Inclusive Education to describing the development and implementation of a co-constructed, collaborative project to identify success strategies for students with dyslexia, utilizing both student and teacher perspectives and experiences, and examines the fit of utilizing ethnographic approaches in this. As practitioner –researchers, authors provide a narrative case study of a project which was developed/co-constructed to respond to a set of identified needs for a group of students presenting with dyslexia. Authors then examine how the project practices link to research methodology, particularly ethnography, and produce both a set of strategies to work inclusively in the future with students with dyslexia and also construct a methodological approach within the lens of new ethnography. This serves to theories both the project work and guidelines for future use of ethnographic approaches for research in the field of inclusive and special education.
How Can Accessibility for Deaf and Hearing-Impaired Players be Improved in Video Games?
Authors: Costello Robert , Lambert Murray , Kern Florian
Published: September 2021

This research investigates how the accessibility of video games can be improved for deaf and hearing-impaired players. The journal is divided into several areas, first, examining the use of subtitles and closed captions used in video games; and second, how visual cues can be used to provide better accessibility for deaf and hearing-impaired gamers. This includes effectively creating suitable atmospheres and mood in games through lighting as well as having a varied environment that prevents the players from getting bored with the setting of a game and finally exploring current best practices within the gaming industry. Through this research data the issues with accessibility can be found as well as how a lack of accessibility affects deaf and hearing-impaired gamers. Research from this investigation supports some of the evidence from other researchers in the field that accessibility features for deaf and hearing-impaired can be considered and implemented.
Comparisons of Speech Parameterisation Techniques for Classification of Intellectual Disability Using Machine Learning
Authors: Aggarwal Gaurav , Singh Latika
Published: September 2021

Classification of intellectually disabled children through manual assessment of speech at an early age is inconsistent, subjective, time-consuming and prone to error. This study attempts to classify the children with intellectual disabilities using two speech feature extraction techniques: Linear Predictive Coding (LPC) based cepstral parameters, and Mel-frequency cepstral coefficients (MFCC). Four different classification models: k-nearest neighbour (k-NN), support vector machine (SVM), linear discriminant analysis (LDA) and radial basis function neural network (RBFNN) are employed for classification purposes. 48 speech samples of each group are taken for analysis, from subjects with a similar age and socio-economic background. The effect of the different frame length with the number of filterbanks in the MFCC and different frame length with the order in the LPC is also examined for better accuracy. The experimental outcomes show that the projected technique can be used to help speech pathologists in estimating intellectual disability at early ages.
OUPIP
Authors: Kourtiche Ali , Benslimane Sidi mohamed , Hacene Sofiane Boukli
Published: September 2021

This article aims to propose an ontological user model called OUPIP (Ontology-Based User Profile for Impairment Person), that extends existing ontologies to help designers and developers to adapt applications and devices according to the user's profile, disability and dynamic context. Besides, the approach has been applied in a typical real-life scenario in which personalized services are provided to impairment person through a mobile phone.
Improving Social and Economic Mobility for People With Disabilities Through Online Education
Authors: Love Jessica D. K.
Published: September 2021

Students with disabilities face many roadblocks to graduation, including but not limited to campus buildings that are difficult to manage, rigid class schedules that do not accommodate a disability, and unnecessarily complex process for obtaining ADA accommodations. Online education is comparable to traditional university programs in quality and accreditation standards. Online education could improve graduation rates and thus provide a higher probability that a graduate with disabilities will find a job and have greater opportunities for economic mobility.
Educated Physically Disabled Women in the Information and Communication Technology Sector
Authors: Mononen Jukka , Halonen Raija
Published: September 2021

The purpose of this study was to analyze how highly educated women with physical disability (WwPD) are employed and integrated in the information and communication technology (ICT) field as experienced by themselves. This question is important due to the lack of knowledge regarding the employment of highly educated WwPD in the ICT sector. An empirical study was carried out with the help of a qualitative research method that involved eight interviews. The study showed that disability has a significant impact when women choose the ICT sector for their studies. However, one of the significant findings was that the individuals characters matter the most.
Awareness Towards Intervention for Individuals With Intellectual Disability
Authors: Upadhyay Palak , Mishra Jyoti
Published: September 2021

Intellectual disability is sub-average intellectual functioning with impaired adaptive functioning. The limitations in their adaptive skill hampers the ability to cope with stressful life events. There are many issues which require attention while dealing with them like parental level, peer groups, schools, presence of comorbid conditions, etc. Interventions at right stage under efficient supervision can lead to healthy and smooth functioning of cases with ID and with good outcomes. Difficulties like lack of human resources, quality training of clinical psychologists in India, scarcity of effective clinical guidelines, conflicts among the team approach, acceptance in the family and society requires attention in current scenario. There are many things that can be offered but what we need is their acceptance in our society and awareness towards Intervention. This chapter aims at acknowledging these issues to create awareness amongst the responsible caregivers. Timely recognition of treatable causes of intellectual disability can be very rewarding and prevent a lifelong disability.
The State of Education and Employment of People With Visual Impairment in Malaysia
Authors: Amzat Ismail Hussein , Li Chen Ann
Published: September 2021

This chapter is an in-depth qualitative study using a phenomenological approach in understanding the state of education and employment of people with visual impairment in the Klang Valley, Malaysia to identify the challenges they encounter and to discover the strategies they use in the employment sector. The results showed that people with visual impairment had a positive self-concept towards themselves, family, and friends; perceived that there was still a lot for the society to improve in terms of their awareness; acceptance and daily predicament faced by people with visual impairment. They faced challenges such as insufficiency of facilities provided in schools and employers, personal skills, limited mode of transportation in rural areas, job choice, and too many expectations are required from them. Therefore, proactive recommendations are also made to the respective parties in order to be fully knowledgeable concerning what further supportive and accommodative actions need to be taken to aid those who are visually impaired for better employability and education.
Emotional Intelligence in Individuals With Intellectual disability
Authors: Sivasubramanian Priyadharshini
Published: September 2021

This chapter is aimed to draw attention to the integrated understanding of an individual with intellectual disability. It is very well known that intellectual disability is described as significant inefficiency in cognitive skills like learning, reasoning, problem solving, and in adaptive behavior, but this is not all when the individual is considered as a whole. This is where emotional intelligence comes in to help the individual cope with the cognitive disability. This chapter will discuss emotional intelligence in people with intellectual disability and how understanding of this will lead to better management plans for individual with intellectual disability, which will in turn lead to better empowerment. Further, this chapter will discuss the research gaps in this area and future study scope.
Accessibility, Self-Advocacy, and Self-Efficacy of Students With Disabilities in the 21st Century University
Authors: Orr Lynne , Brillante Pamela , Weekley Linda
Published: September 2021

Few studies have addressed the challenging transition that occurs when students with disabilities graduate from the K-12 system and enter the world of higher education. Once in college, students with disabilities no longer have, among other federally-mandated supports, a child-study team to represent them, and thus must develop strong self-advocacy and self-efficacy skills in order to receive the accommodations and modifications they need to succeed academically. This chapter discusses the issues facing students with disabilities during this transition, details the services and support offered by colleges to guide students with disabilities, and shares recommended best practices for instructional strategies higher education can employ to ensure that these students flourish in the classroom and as self-assured, independent adults in society.
Attitudes of Society Towards People With Neurodevelopmental Disorders
Authors: Khanna Pallavi
Published: September 2021

Social acceptance and inclusion of people with neuro-developmental disorders is challenging. Though each human being is unique, the world population can be segregated into two groups: neuro-typical and neuro-diverse people who have conditions such as autism spectrum disorder, cerebral palsy, learning disabilities, ADHD, and several others. The neuro-typical population claims to be accepting of the neuro-diverse population, but some case studies show that it may sadly not be true. Life can be bewildering and confusing for a person with disabilities. Dealing with so many aspects can be daunting and frustrating for them and their families. Social attitude is a significant factor as the Pwd navigates the harsh world of discrimination and social abandonment and faces barriers where support, guidance, and services are required. Many go through painful journeys and come out stronger and wiser, but bitter as well. Others have had better support. This chapter is a collection of experiences of some Pwd and their families and a list of solutions to the different challenges they encounter.
Evaluating the Accessibility of Computer Laboratories, Libraries, and Websites in Jordanian Universities and Colleges
Authors: Abu Doush Iyad , Alhami Ikdam
Published: September 2021

This article evaluates the accessibility computer laboratories, libraries, and web sites of five different institutions of higher education in Jordan. The evaluation is performed using a questionnaire and by expert visiting these institutions of higher education to check their accessibility according to a set of universal standard collected from the literature. The analysis of the results revealed a serious weakness in understanding, adopting and implementing web accessibility guidelines throughout nearly all evaluated Jordanian universities and colleges. On the other hand, the accessibility of computer laboratories and libraries the questionnaire answers show that there is environmental barrier and technological barrier for persons with disabilities to access such services. The article points out the importance of improving awareness, training staff and developers, and developing formal guidelines to improve the accessibility of universities and colleges services.
Assessing Disruptive Innovation From the Perspectives of New Entrants and Incumbent Firms
Authors: Abdul-Kareem Aleem , Attar Melis
Published: September 2021

Disruptive innovation (DI) is evolving as a strategically significant innovation type in this current dynamic, volatile, and global competitive business environment. The theory of DI is critical in comprehending entrant-incumbent relationships and elucidating how their interplay affects the nature of the market. The fundamental aim of this study is to assess disruptive innovation from the perspectives of new entrants and incumbents using the empirical reviews approach. The chapter contends that disruption is a phenomenon whose forces are always at work, and incumbents can elude it by putting in place the prerequisite measures and having the right caliber of human resources to pre-empt and avert genuine potential disruptive threats. The study demonstrates the steps taken by upstarts to displace leading firms and the responses that can be provided by the latter against disruption. It extends its scope to significance of DI, reasons for failure of incumbents in DI battle, and barriers to effective implementation of disruptive innovation.
Female Managers in the Healthcare Organizations
Authors: Üstgörül Sema
Published: September 2021

In a globalized and complicated international economy, executives believe that management culture will be given new direction by management teams comprised of both men and women, and that these mixed teams will contribute to the diversity of ideas and perspectives in management. Despite the fact that there are as many female graduates as male graduates today, women continue to be significantly underrepresented in executive roles in the business world, particularly in top management (executive board, supervisory board). The most important factor preventing women from being managers is work-family balance. While women indeed tend to climb the ladder step by step, men make several jumps in their careers, and they meet with success. It makes no difference whether a candidate for a position as a board member, director, or divisional manager is male or female. The goal of this study is to try to demonstrate that female managers may be more effective in management because of their personalities and areas of competence in the healthcare sector.
Muscle Fatigue Analysis During Welding Tasks Using sEMG and Recurrence Quantification Analysis
Authors: Panahi Ali Keshavarz , Cho Sohyung , Gordon Chris
Published: September 2021

The main goal of this study was to detect muscle fatigue and to identify muscles vulnerable to musculoskeletal disorders by evaluating muscle activation of subjects during welding tasks. In this study, six subjects performed two different welding tasks for a total of three hours. Surface electromyography (sEMG) was used to record the muscle activation of sixteen different muscles. Recurrence Quantification Analysis (RQA) was then used to analyze the EMG data. In addition, a subjective fatigue assessment was conducted to draw comparisons with the RQA results. According to the RQA results, twelve of the tested muscles experienced fatigue by showing significant difference in RQA values (p-value < 0.05) between the first and last 10 minutes of the experiment. Moreover, time-to-fatigue results obtained from RQA and subjective analysis were closely correlated for seven muscle groups. This study showed that RQA can be used in ergonomic studies for evaluating muscle activation during construction tasks.
Violence Detection With Two-Stream Neural Network Based on C3D
Authors: zanzan Lu  , Xia Xuewen , Wu Hongrun , Yang Chen
Published: September 2021

In recent years, violence detection has gradually turned into an important research area in computer vision, and have proposed many models with high accuracy. However, the unsatisfactory generalization ability of these methods over different datasets. In this paper, the authors propose a violence detection method based on C3D two-stream network for spatiotemporal features. Firstly, the authors preprocess the video data of RGB stream and optical stream respectively. Secondly, the authors feed the data into two C3D networks to extract features from the RGB flow and the optical flow respectively. Third, the authors fuse the features extracted by the two networks to obtain a final prediction result. To testify the performance of the proposed model, four different datasets (two public datasets and two self-built datasets) are selected in this paper. The experimental results show that our model has good generalization ability compared to state-of-the-art methods, since it not only has good ability on large-scale datasets, but also performs well on small-scale datasets.
A Novel Approach to Enhance Image Security using Hyperchaos with Elliptic Curve Cryptography
Authors: Ganavi M  , Prabhudeva S
Published: September 2021

Information security dominate the world. All the time we connect to the internet for social media, banking, and online shopping through various applications. Our priceless data may be hacked by attackers. There is a necessity for a better encryption method to enhance information security. The distinctive features of Elliptic Curve Cryptography (ECC) in particular the key atomity, speedy ciphering and preserving bandwidth captivating its use in multimedia encipher. An encryption method is proposed by incorporating ECC, Secure Hash Algorithm – 256 (SHA-256), Arnold transform, and hyperchaos. Randomly generated salt values are concatenated with each pixel of an image. SHA-256 hash is imposed which produces a hash value of 32-bit, later used to generate the key in ECC. Stronger ciphering is done by applying Arnold’s transformation and hyperchaos thereby achieved more randomness in image. Simulation outcomes and analysis show that the proposed approach provides more confidentiality for color images.
Factors Influencing Donation Intention to Personal Medical Crowdfunding Projects Appearing on MSNS
Authors: Liu Qihua , Wang Li , Zhou Jingyi , Wu Wei , Li Yiran
Published: September 2021

This purpose of this study is to develop a research model by extending the theory of planned behavior in a new application context, and applies it to investigate the extrinsic factors influencing people’s attitude towards donating to medical crowdfunding projects appearing on mobile social networking sites (MSNS) and their intention to donate. A survey of 356 Chinese users was conducted and structural equation modeling was used to validate the proposed model and hypotheses. The results indicate that project information, retweeter information and MSNS information all have the significant effect on the general attitude towards donating to medical crowdfunding projects, and general attitude positively affects people’s donation intention. In addition, perceived behavioral control also has positive effect on people’s donation intention, while experienced donating to medical crowdfunding projects has negative effect on people’s donation intention. The research findings provide important theoretical and practical implications.
An Application of Deep Belief Networks in Early Warning for Cerebrovascular Disease Risk
Authors: Qin Qiuli , Yang Xing , Zhang Runtong , Liu Manlu , Ma Yuhan
Published: September 2021

To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention, and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.
Antecedents of Customer Adoption on Digital Banking with Special Reference to Non-Banking Financial Institutes in Sri Lanka
Authors: Madusanka K. A. E. , Kumari D. A. T.
Published: September 2021

Purpose: The principal destinations of this examination are to contemplate and recognize the variables affecting the appropriation of digital banking among non-bank clients in Sri Lanka. Design: A survey was carried out by using structured self-administered questionnaire. As the study is mainly focused on exploring the antecedents of adopting to the digital banking of non-banking organizations of the country, the target population were all the customers who are using digital banking services provided by non-banking organizations in the Sri Lankan context. Accordingly, the sample was based on 300 customers of the digital banking services provided by main players of the industry. The data was analyzed by using descriptive and inferential statistical tools and PLS based SEM was adopted to test the hypotheses. Findings: The researcher has identified the factors; perceived usefulness, perceived ease of use, perceived risk, customer trust, compatibility and information quality affect customer adoption on digital banking among non-bank clients. Originality: The study attempts to distinguish and examine the most significant and practical predecessors that can impact the advanced financial appropriation of non-monetary establishments considering the client's perspective. The study has chosen the TAM model for examining exact discoveries due to its nearby pertinence to the examination question. In view of the chosen model (TAM), however, numerous studies demonstrate that web ease of use, security, data quality, trust, administration quality, comfort, and protection are the main components in the reception of advanced banking by clients.
The PD-1 Regulatory Axis Inhibits T Cell–Independent B Cell Memory Generation and Reactivation
Authors: Spurrier M Ariel , Jennings-Gee Jamie E , Daly Christina A , Haas Karen M
Published: September 2021

The inability of T cell–independent type 2 (TI-2) Ags to induce recall responses is a poorly understood facet of humoral immunity, yet critically important for improving vaccines. Using normal and VHB1–8 transgenic mice, we demonstrate that B cell–intrinsic PD-1 expression negatively regulates TI-2 memory B cell (Bmem) generation and reactivation in part through interacting with PDL1 and PDL2 on non–Ag-specific cells. We also identified a significant role for PDL2 expression on Bmems in inhibiting reactivation and Ab production, thereby revealing a novel self-regulatory mechanism exists for TI-2 Bmems. This regulation impacts responses to clinically relevant vaccines, because PD-1 deficiency was associated with significantly increased Ab boosting to the pneumococcal vaccine after both vaccination and infection. Notably, we found a B cell–activating adjuvant enabled even greater boosting of protective pneumococcal polysaccharide-specific IgG responses when PD-1 inhibition was relieved. This work highlights unique self-regulation by TI-2 Bmems and reveals new opportunities for significantly improving TI-2 Ag-based vaccine responses.
The Development and Survival of Thymic Epithelial Cells Require TSC1-Dependent Negative Regulation of mTORC1 Activity
Authors: Liang Zhanfeng , Zhang Qian , Zhang Zhaoqi , Sun Lina , Dong Xue , Li Tianxiu , Tan Liang , Xie Xubiao , Sun Liguang , Zhao Yong
Published: September 2021

Thymic epithelial cells (TECs) are critical for the development and generation of functionally competent T cells. Until now, the mechanism that regulates the survival of TECs is poorly understood. In the current study, we found that Tsc1 controls the homeostasis of medullary TECs (mTECs) by inhibiting lysosomal-mediated apoptosis pathway in mice. TEC-specific deletion of Tsc1 predominately decreased the cell number of mTECs and, to a lesser content, affected the development cortical TECs. The defect of mTECs caused by Tsc1 deficiency in mice impaired thymocyte development and peripheral T cell homeostasis. Mechanistically, Tsc1 deficiency did not affect the cell proliferation of mTECs but increased the apoptosis of mTECs significantly. RNA-sequencing analysis showed that pathways involved in lysosomal biogenesis, cell metabolism, and apoptosis were remarkably elevated in Tsc1-deficient mTECs compared with their wild-type counterparts. Tsc1-deficient mTECs exhibited overproduction of reactive oxygen species and malfunction of lysosome, with lysosome membrane permeabilization and the release of cathepsin B and cathepsin L to the cytosol, which then lead to Bid cleaved into active truncated Bid and subsequently intrinsic apoptosis. Finally, we showed that the impaired development of mTECs could be partially reversed by decreasing mTORC1 activity via haploinsufficiency of Raptor. Thus, Tsc1 is essential for the homeostasis of mTECs by inhibiting lysosomal-mediated apoptosis through mTORC1-dependent pathways.
A revised model for mitochondrial dysfunction in Duchenne muscular dystrophy
Authors: Vu Hong Ai , Sanson Mathilde , Richard Isabelle , Israeli David
Published: September 2021

We recently identified a signaling pathway that links the upregulation of miR-379 with a mitochondrial response in dystrophic muscle. In the present commentary, we explain the significance that this pathway may have in mitochondrial dysfunction in Duchenne muscular dystrophy (DMD). We identified the upregulation of miR-379 in the serum and muscles of DMD animal models and patients. We found that miR-379 is one of very few miRNAs whose expression was normalized in DMD patients treated with glucocorticoid. We identified EIF4G2 as a miR-379 target, which may promote mitochondrial oxidative phosphorylation (OxPhos) in the skeletal muscle. We found enriched EIF4G2 expression in oxidative fibers, and identified the mitochondrial ATP synthase subunit DAPIT as a translational target of EIF4G2. The identified signaling cascade, which comprises miR-379, EIF4G2 and DAPIT, may link the glucocorticoid treatment in DMD to a recovered mitochondrial ATP synthesis rate. We propose an updated model of mitochondrial dysfunction in DMD.