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This paper aims at understanding the dynamics underlying toleration as a complex social phenomenon and its pattern on Facebook during the June 30th revolution in Egypt. Thanks to the huge advances in ICT, internet-mediated research (IMR) has become one of the most prominent research methodologies in social sciences. Discussions on social network sites cannot be neglected in studying the dynamics complex and emerging social phenomena such as changes in public opinion, culture, attitudes and virtues.

To fulfill this aim, the researchers used web content analysis as a method inside IMR paradigm to analyze the discussions on Tamarrod’s Facebook page in the period from June 30th to July 5th and to examine the emerging overall pattern of toleration.

The results show indications that toleration is inherent in the Egyptian culture, and that the Egyptian society still keeps its reputation as a highly tolerant society, even in crises periods where tensions are witnessed everywhere. Moreover, the results also show that the web content analysis process proposed in this study is highly reliable and valid.

The importance of the study lies in introducing a computational and empirical approach to analyze web content in a semi-automated way and proving its validity and reliability to study social phenomena such as toleration.

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

Domestic abuse (DA) is a major issue with serious psychological, social, societal and economic impacts. Consequently, there has been an increased focus by policymakers and multiple statutory and third-sector agencies on addressing harms associated with DA and fostering healthy intimate and domestic relationships. This paper details the development and implementation of a whole family approach to DA set within a community social services setting.

A detailed description of the development and implementation of a new whole family approach is provided. This includes a focus on the equilibrium programme, an accredited strengths-based, solution-focused group element that has been devised and established for those engaging in harmful behaviours.

The importance of governance, programme support and practitioner supervision are discussed along with the ways these are used by the service. The evaluation framework presented will enable the impact of the programme to be determined over the coming years.

There is clear need to address the significant problem of DA/intimate partner violence. This paper provides a model and accredited treatment approach to implementing a whole family approach to DA set within a community social services setting. This provides an opportunity for early intervention based on a strengths-based, solution focussed approach to addressing harmful behaviours and building skills and resilience.

This paper details a whole system approach to early intervention with families in which there is DA. Providing input via social care child and family support services prior to legal involvement provides an opportunity to avoid an escalation of harms. It also enables solutions to conflict to be found which take account of the relationship between parents and children.

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

The study investigates the factors that impact the adoption of learning management systems (LMSs) among educators for effective implementation of open and distance learning (ODL) environment in advanced technological institutes (ATIs).

This study uses the extended technology acceptance model (TAM) and analyses data using the partial least square–based structural equation modelling approach to validate the construct and test proposed hypotheses. Data were collected through an online questionnaire from the respondents.

This study reveals that perceived self-efficacy and job relevance significantly impact perceived usefulness (PU) and perceived ease of use (PEU). PU, PEU and service quality significantly impact attitudes of educators, which impact their behavioural intention and actual use of LMS as a chain reaction.

The management should organise hands-on training sessions to improve educators' computer self-efficacy and explain the importance of the LMS and its features to offer an effective ODL environment for delivering high-quality education.

The previous studies focused on LMS use from the students' point of view rather than educators. This study investigates educators' LMS adoption in ATIs using the extended TAM. The findings may be helpful for management to implement an effective ODL environment that offers fully integrated distance learning and e-learning during the prevailing COVID-19 pandemic.