Perception of lean construction implementation barriers in the indian prefabrication sector.

Prefabricated construction, increasingly recognized as a sustainable method, enhances productivity while mitigating the drawbacks of traditional approaches. Lean construction, pivotal for sustainability, targets waste reduction and cost efficiency while delivering value to customers. In India's prefabrication sector, numerous barriers impede the implementation of lean principles, necessitating their identification and resolution to advance lean practices. This study aims to identify and analyze primary barriers to implementing lean principles within India's prefabrication industry, focusing on professionals' perceptions. Employing exploratory factor analysis, it examines these barriers' interconnections and causal relationships, providing actionable recommendations for enhanced lean construction effectiveness. Through a review of the literature, 26 significant barriers were identified and primary data was obtained with the help of a questionnaire. 25 barriers were discerned after pre-exploratory factor analysis, culminating in ten common components. Notably, the study highlights a primary barrier: understanding of lean construction. Drawing from expert insights, substantial recommendations are provided, intending to guide the prefabricated building sector in overcoming barriers to on-site lean construction. These findings and recommendations offer valuable direction for industry stakeholders.

Incentivizing green building technology: A financial perspective on sustainable development in India.

Future viability depends on ensuring a sustainable society because green energy methods may efficiently reduce greenhouse gas emissions. Nevertheless, stakeholders, consumers, and developers continue to be notably ignorant of the financial incentives connected to green technology. Moreover, there is still a dearth of studies on the range of financial incentives offered by different authorities in India. Monetary incentives, such as tax breaks, indirect tax exemptions, and refunds, are crucial in encouraging the use of green technology in the modern world. This study explores the importance of financial incentives for green building technologies in India, which also looks at the wide range of incentives provided by federal, state, and local governments. Furthermore, the study highlights various state government programs such as goods subsidies, exemptions from local taxes, and fee waivers. Notably, several incentives aimed at consumers, developers, and other stakeholders have been implemented by the Indian Green Building Council (IGBC). This review study emphasizes the effectiveness of financial incentives in green construction projects and draws attention to a clear knowledge gap regarding the adoption of green technology. This study also provides insights into potential future directions. Studies and research results emphasize the importance of spreading the word about financial incentives as a key factor in determining the adoption of green technologies. Many parties, including governmental organizations, municipal governments, developers, and clients engaged in green building technology projects, stand to gain increased awareness.

On effectively predicting autism spectrum disorder therapy using an ensemble of classifiers.

An ensemble of classifiers combines several single classifiers to deliver a final prediction or classification decision. An increasingly provoking question is whether such an ensemble can outperform the single best classifier. If so, what form of ensemble learning system (also known as multiple classifier learning systems) yields the most significant benefits in the size or diversity of the ensemble? In this paper, the ability of ensemble learning to predict and identify factors that influence or contribute to autism spectrum disorder therapy (ASDT) for intervention purposes is investigated. Given that most interventions are typically short-term in nature, henceforth, developing a robotic system that will provide the best outcome and measurement of ASDT therapy has never been so critical. In this paper, the performance of five single classifiers against several multiple classifier learning systems in exploring and predicting ASDT is investigated using a dataset of behavioural data and robot-enhanced therapy against standard human treatment based on 3000 sessions and 300 h, recorded from 61 autistic children. Experimental results show statistically significant differences in performance among the single classifiers for ASDT prediction with decision trees as the more accurate classifier. The results further show multiple classifier learning systems (MCLS) achieving better performance for ASDT prediction (especially those ensembles with three core classifiers). Additionally, the results show bagging and boosting ensemble learning as robust when predicting ASDT with multi-stage design as the most dominant architecture. It also appears that eye contact and social interaction are the most critical contributing factors to the ASDT problem among children.

Design science research application in medical radiation science education: A case study on the evaluation of a developed artifact.

Design Science Research (DSR) combines quantitative and qualitative approaches for educational research. One of the critical steps of DSR is the evaluation phase. In this phase, the artifact's utility, fitness, and usefulness are noted and reviewed. Since the DSR applied to health science is limited, this paper aims to present the evaluation phase of a study that developed an artifact for training student radiographers in chest pattern recognition. The artifact which is described in detail elsewhere by Mdletshe et al. [1], was developed as a tailor-made solution in medical radiation sciences education (MRSE), using DSR. During the evaluation of the artifact, the System Usability Scale (SUS) was used for the quantitative evaluation of the artifact. Meanwhile, the qualitative approach was performed using a hierarchy of qualitative criteria based on a review of multiple sources. This study demonstrated the DSR key concepts of the evaluation phase applied to health science. The presented case will help to demonstrate the implementation of the evaluation phase in a research project in health sciences (MRSE).

Energy System 4.0: Digitalization of the Energy Sector with Inclination towards Sustainability.

The United Nations' sustainable development goals have emphasized implementing sustainability to ensure environmental security for the future. Affordable energy, clean energy, and innovation in infrastructure are the relevant sustainable development goals that are applied to the energy sector. At present, digital technologies have a significant capability to realize the target of sustainability in energy. With this motivation, the study aims to discuss the significance of different digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), edge computing, blockchain, and big data and their implementation in the different stages of energy such as generation, distribution, transmission, smart grid, and energy trading. The study also discusses the different architecture that has been implemented by previous studies for smart grid computing. Additionally, we addressed IoT-based microgrids, IoT services in electrical equipment, and blockchain-based energy trading. Finally, the article discusses the challenges and recommendations for the effective implementation of digital technologies in the energy sector for meeting sustainability. Big data for energy analytics, digital twins in smart grid modeling, virtual power plants with Metaverse, and green IoT are the major vital recommendations that are discussed in this study for future enhancement.

Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations.

Parkinson's disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as 'bradykinesia', loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation.

Enhancing medical radiation science education through a design science research methodology.

Recently, there has been a call for research-informed and research-developed practice in health sciences education. This prompts the consideration of alternative suitable research approaches that could be used to enhance health sciences education practice, including medical radiation sciences education (MRSE) practice. In this discussion paper, the authors uphold design science research (DSR) methodology as a suitable research approach to enhance MRSE practice and research. An overview of the DSR methodology and an example of a project that used DSR methodology are presented to demonstrate the application of this methodology in MRSE practice and research. The paper concludes that the use of DSR methodology could be instrumental in addressing practice related challenges while developing a theoretical contribution to the discipline.