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Parents or primary caregivers of children with Autism Spectrum Disorder (ASD) have important role in early recognition of the disorder as well as in the management of the disorder. Knowledge, attitude and practice of primary caregivers towards children with ASD are important factors in promoting health and wellness of such children. The purpose of this paper is to evaluate the effectiveness of video-assisted teaching regarding care of children with ASD on knowledge, attitude and practice among primary caregivers.

Quasi-experimental research design with one group pre-test post-test was adopted. A total of 60 primary caregivers were selected through stratified random sampling technique. Video-assisted teaching was given to 60 primary caregivers. Data collection was done before and after the video-assisted teaching programme using structured questionnaire which consists of 57 questions.

The result of the study showed that the post-test level of knowledge attitude and practice among primary caregivers of children with ASD was significantly high (p<0.0001) when compared to pre-test level by using Wilcoxon Signed Rank Test. The study finding revealed that video-assisted teaching was effective in enhancing the knowledge, developing a positive attitude and good practice among primary caregivers regarding care of children with ASD.

Findings of this study will help mental health nurses, psychologists, intellectual disability nurses, teachers, public health, social workers, etc. to know the importance of video-assisted teaching programme regarding care of children with ASD and to motivate the primary caregivers to participate in such teaching programme. The community mental health nurse can plan video-assisted teaching programme in a community regarding care of children with ASD.

Results of this study indicate that the video-assisted teaching is effective and helps the primary caregivers to enhance the knowledge, attitude and practice regarding care of children with ASD. So continuous awareness in primary health centre and community area is necessary to improve the knowledge, attitude and practice of primary caregivers.

The performance-based utilisation of labour resources is a decisive function for developing characteristics of the next normal in the construction industry. Based on the industry's needs, this study aims to develop a framework for the systematic process of labour performance evaluations and labour grading towards achieving higher productivity in construction operations.

Both qualitative and quantitative methods were applied through a comprehensive literature review, surveys and experts' discussions to develop a set of labour training elements of outcomes. Problem-focused and action-oriented communication approaches were used throughout the study to produce detailed steps for the systematic evaluations of labour performance.

The study has presented a framework consisting of a set of labour training elements of outcomes with the relative weights, as well as the detailed procedures to assess labour competencies towards the calculations of labour performance score values and labour grading.

Although the scope of the study is limited to the Sri Lankan context, the findings may be tested in other countries for upgrading their performance improvement practices of labour operations.

The study outcomes will strengthen the construction management practices for the successful implementation of labour training and performance evaluations at construction sites, and also contribute to upgrading the vocational training programmes in the country towards the next sustainable normal.

The developed framework can be a functional tool for the construction industry that provides a mechanism to show the detailed cross-section of each labourer's performance and values, and also displays their roles and responsibilities to accelerate the next normal in the construction industry.

This study aims to develop a curriculum guide model to upgrade the construction supervision practices, considering evolving challenges and thereby the next normal of the industry.

Both qualitative and quantitative research designs were adopted to identify productivity-related challenges that can be addressed through effective supervision in construction. Meetings, discussions, workshops and surveys were conducted among construction experts to systematically develop the competencies of construction supervision. The necessary mapping models were used to identify the level of outcomes for each competency element along with the learning domains and programme outcomes.

A curriculum guide model consisting of 64 competency elements has been introduced with corresponding assessment weightages and mapping outcomes. Using this model, a new training programme has been designed and tested with weightage percentages on learning domains.

Although the scope of the study is limited to Sri Lanka, the findings can be interpreted for critical learning in other developing countries too.

The study outcomes are expected to make a high impact on improving the standards of vocational training education in the country, thereby upgrading the current industry practices.

The developed guide model is expected to be a valuable tool for training providers/organisations in upgrading their programmes/practices with the scope of productivity improvement. The obtained mapping outcomes are significant for the evolving next normal situations in teaching, learning and assessment methods with regard to construction supervision practices.

This research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection.

Existing studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam).

Analysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%.

This research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.

The increase in autism prevalence and presentation in the media suggests a rise in public awareness. This paper aims to explore what factors (contact, knowledge and ethnicity) may be associated to positive attitudes towards individuals with autism spectrum disorder (ASD).

An online survey examined contact with and knowledge of ASD among Black, Asian and White ethnic groups to predict public attitudes to people with ASD.

In multiple regression models, the results suggested that the level of contact predicts positive attitudes towards autism when demographic factors were accounted. The level of knowledge about autism were significantly associated to attitudes, but not consistently when demographic factors were accounted. However, differences in knowledge and attitudes to people with ASD were identified amongst Black, Asian and White ethnic groups.

These findings have implications for policy and public health and education campaigns, including ensuring contact and knowledge of autism among the public.

These findings have implications for policy and public health and education campaigns, including ensuring contact and knowledge of autism among the public. Additionally, further effort is required to target public knowledge and attitudes to autism, particularly among ethnic groups. Institutional support tailored to encourage structured and unstructured contact across public domains such as education, health, social and care practices could effectively reduce prejudice between the public and people with ASD over time.

Internet of Things (IoT) is an up-and-coming conception that intends to link multiple devices with each other. The aim of this study is to provide a significant analysis of Green IoT. The IoT devices sense, gather and send out significant data from their ambiance. This exchange of huge data among billions of devices demands enormous energy. Green IoT visualizes the concept of minimizing the energy consumption of IoT devices and keeping the environment safe.

This paper attempts to analyze diverse techniques associated with energy-efficient protocols in green IoT pertaining to machine-to-machine (M2M) communication. Here, it reviews 73 research papers and states a significant analysis. Initially, the analysis focuses on different contributions related to green energy constraints, especially energy efficiency, and different hierarchical routing protocols. Moreover, the contributions of different optimization algorithms in different state-of-the-art works are also observed and reviewed. Later the performance measures computed in entire contributions along with the energy constraints are also checked to validate the effectiveness of entire contributions. As the number of contributions to energy-efficient protocols in IoT is low, the research gap will focus on the development of intelligent energy-efficient protocols to build up green IoT.

The analysis was mainly focused on the green energy constraints and the different robust protocols and also gives information on a few powerful optimization algorithms. The parameters considered by the previous research works for improving the performance were also analyzed in this paper to get an idea for future works. Finally, the paper gives some brief description of the research gaps and challenges for future consideration that helps during the development of an energy-efficient green IoT pertaining to M2M communication.

To the best of the authors’ knowledge, this is the first work that reviews 65 research papers and states the significant analysis of green IoT.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

The purpose of this paper is to investigate the momentum, heat and mass transfer characteristics of magnetic-nanofluid flow past a vertical plate embedded in a porous medium filled with ferrous nanoparticles. The analysis is carried out in the presence of pertinent physical parameters such as aligned magnetic field, thermal radiation, chemical reaction, radiation absorption, heat source/sink.

The flow governing PDEs are transformed into ODEs using appropriate conversions. Further, the set of ODEs is solved analytically using the perturbation technique. The flow quantities such as velocity, thermal and concentration fields are discussed under the influence of above-mentioned pertinent physical parameters with the assistance of graphical depictions. Moreover, the friction factor, local Nusselt and Sherwood number are discussed in tabular form.

The results indicate that flow and thermal transport phenomenon is more effective in the case of the aligned magnetic field as compared with the transverse magnetic field. Also, the nanoparticle volume fraction plays a vital role in controlling the wall friction and heat transfer performance. The validation of the obtained results is done by comparing them with the results of various numerical techniques, and hence found them in excellent agreement.

In present days, the external magnetic fields are very effective to set the thermal and physical properties of magnetic-nanofluids and regulate the flow and heat transfer characteristics. The strength of the applied magnetic field affects the thermal conductivity of magnetic-nanofluids and makes it aeolotropic. With this incentive, the authors investigated the flow and heat transfer characteristics of electrically conducting magnetic-nanofluids over a vertical surface embedded in a porous medium. The authors discussed the dual nature of ferrous-water nanofluid in the presence of aligned magnetic field and transverse magnetic field cases. The influence of several physical parameters on velocity, thermal and concentration field converses with the succour of graphs.

The purpose of this paper is to study the flow, heat and mass transfer of MHD Casson nanofluid due to an inclined stretching sheet using similarity transformation, the governing PDE’S equations of flow, heat and mass transfer are converted into ODE’S. The resulting non-linear ODE’S are solved numerically using an implicit finite difference method, which is known as Kellor-box method. The effects of various governing parameters on velocity, temperature and concentration are plotted for both Newtonian and non-Newtonian cases. The numerical values of skin friction, Nusselt number and Sherwood number are calculated and tabulated in various tables for different values of physical parameters. It is noticed that the effect of angle of inclination enhances the temperature and concentration profile whereas velocity decreases. The temperature decreases due to the increase in the parametric values of Pr and Gr due to thickening in the boundary layer.

Numerical method is applied to find the results.

Flow and heat transfer analysis w.r.t various flow and temperature are analyzed for different values of the physical parameters.

The numerical values of skin friction, Nusselt number and Sherwood number are calculated and tabulated in various tables for different values of physical parameters.

The study of the boundary layer flow, heat and mass transfer is important due to its applications in industries and many manufacturing processes such as aerodynamic extrusion of plastic sheets and cooling of metallic sheets in a cooling bath.

Here in this paper the authors have investigated the MHD boundary layer flow of a Casson nanofluid over an inclined stretching sheet along with the Newtonian nanofluid as a limited.

The tremendous growth of wireless applications and the demand for high data rate, the spectrum utilization improvement has been the most crucial challenges for wireless communication. Adapting cognitive radio with orthogonal frequency division multiplexing or offset quadrature amplitude modulation (OFDM/OQAM) improves the spectrum and energy efficiencies.

Thus, it overcomes the spectral leakage problem at the transmitter side and leads to less interference from secondary user (SUs) to primary user (PUs) and between the SUs in cognitive radio technology. The benefit of exploiting pulse shape filtering in the OFDM/OQAM is to not only eliminate the requirement of the guard bands but also reduce the out of band energy transmission, which also improves the spectral isolation from the neighboring systems. But the high peak to average power ratio (PAPR) phenomenon is a common issue in the majority of the multicarrier modulation systems and thus OFDM/OQAM is no exception in this case.

Therefore, this paper aims to examine the effect of integrating the Walsh–Hadamard Transform (WHT) on the power spectral density and investigates the problem of PAPR in the WHT/OQAM system.

Thus, it overcomes the spectral leakage problem at the transmitter side and leads to less interference from SUs to PUs and between the SUs in cognitive radio technology. The benefit of exploiting pulse shape filtering in the OFDM/OQAM is to not only eliminate the requirement of the guard bands but also reduce the out of band energy transmission, which also improves the spectral isolation from the neighboring systems. But the high PAPR phenomenon is a common issue in the majority of the multicarrier modulation systems thus OFDM/OQAM is no exception in this case.