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The purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the dynamic nature of building systems.

This paper proposes a reinforcing machine learning (ML) approach based on transfer learning (TL) to address these challenges. The proposed approach dynamically incorporates the data captured by the building management systems into the model to improve its accuracy.

It was shown that the proposed approach could improve the accuracy of the energy performance prediction compared to the conventional TL (non-reinforcing) approach by 19 percentage points in mean absolute percentage error.

The case study results confirm the practicality of the proposed approach and show that it outperforms the standard ML approach (with no transferred knowledge) when little data is available.

This approach contributes to the body of knowledge by addressing the limited data availability in the building sector using TL; and accounting for the dynamics of buildings’ energy performance by the reinforcing architecture. The proposed approach is implemented in a case study project based in London, UK.

Scholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.

To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.

The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.

Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.

The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.

This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.

The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.

The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.

The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.

This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

This paper aims to explore how parasocial relationships with Korean celebrity endorsers on social media result in brand credibility and loyalty.

The participants were identified through a purposive sampling approach, and they were composed of consumers who purchased Korean-celebrity-endorsed products and services of a telecommunications company. The hypothesized relationships were gauged using a predictive approach as a research design via partial least squares (PLS) path modeling.

The findings show that all hypothesized relationships are supported. In particular, social media interaction was found to have a substantial, positive and significant effect on self-disclosure. Moreover, self-disclosure has a considerably significant and direct effect on parasocial relationships and was found to indirectly affect the link between social media interactions and parasocial relationships. The results further reveal that social media interactions and parasocial relationships predict source trustworthiness, leading to brand credibility and loyalty.

To the best of the authors’ knowledge, the present undertaking is the only study that examined how parasocial relationships on social media are built when foreign celebrities, in this case, the well-known Korean popular group BTS, endorse telecommunications products and services.

La presente investigación explora cómo las relaciones parasociales con celebridades coreanas en las redes sociales generan credibilidad de marca y lealtad.

Los participantes se identificaron mediante un muestreo intencional y estaban compuestos por consumidores que compraban productos y servicios de una empresa de telecomunicaciones avalados por famosos coreanos. Las relaciones hipotetizadas se midieron utilizando un enfoque predictivo como diseño de investigación mediante un modelo de mínimos cuadrados parciales (PLS).

Los resultados muestran que todas las relaciones hipotetizadas se confirman. En particular, la interacción con los medios sociales tiene un efecto sustancial, positivo y significativo en la autodivulgación. Además, la autodivulgación tiene un efecto considerablemente significativo y directo en las relaciones parasociales y se descubrió que afecta indirectamente al vínculo entre las interacciones en los medios sociales y las relaciones parasociales. Los resultados revelan además que las interacciones en los medios sociales y las relaciones parasociales predicen la fiabilidad de la fuente, lo que conduce a la credibilidad de la marca y a la lealtad.

El presente trabajo es el único estudio que examina cómo se construyen las relaciones parasociales en los medios sociales cuando celebridades extranjeras, en este caso, el conocido grupo popular coreano BTS, promocionan productos y servicios de telecomunicaciones.

本研究探讨了在社交媒体上与韩国名人的寄生关系如何建立品牌可信度和忠诚度。

通过目的性抽样确定参与者, 包括购买韩国名人代言的电信公司产品和服务的消费者。研究设计使用偏最小二乘法（PLS）模型对假设关系进行预测测量。

研究结果表明, 所有假设关系都得到了证实。特别是, 社交媒体互动对自我披露具有实质性的、积极的和显著的影响。此外, 自我披露对寄生关系也有明显的直接影响, 并被发现间接影响社交媒体互动与寄生关系之间的联系。研究结果进一步揭示了社会化媒体互动和寄生关系能够预测来源的可信度, 从而提高品牌可信度和忠诚度。

研究结果表明, 所有假设的关系都得到了证实。特别是, 社交媒体互动对自我披露具有实质性的、积极的和显著的影响。此外, 自我披露对寄生关系也有明显的直接影响, 并被发现间接影响社交媒体互动和寄生关系之间的联系。研究结果进一步揭示了社会化媒体互动和寄生关系能够预测来源的可信度, 从而提高品牌可信度和忠诚度。

本文是唯一一篇研究外国名人在社交媒体上推广电信产品和服务时如何建立寄生社会关系的研究。

Construction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.

The paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.

The results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.

While previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.

Consumers are increasingly spending more time on social media platforms to cope with anxiety and loneliness resulting from the recent pandemic. The extraordinary times, characterised by isolation and lockdown, has placed increasing dependence on online technology as a coping mechanism in which social media influencers (SMIs) served as the human face of brands, helping both consumers and brands maintain human connection. How should practitioners navigate their social media campaigns in this changing environment?

To answer this question, the Source Connectedness Pyramid is proposed to help explain and compare the interactions between consumers and SMIs in ordinary and extraordinary times.

In their interactions with influencers during ordinary times, consumers are satiated with influencer source characteristics of attractiveness, trustworthiness and expertise. However, during extraordinary times, consumers substitute their usual preference to focus on connectedness, characterised by relatedness, belongingness and attachment.

The empirical study within this paper lends support to this proposition and offers additional insights. The proposed Source Connectedness Pyramid contributes to influencer communication theoretically and has strategic implications for practitioners when navigating their social media campaigns in these extraordinary times.

The purpose of this study is to identify the process of virtual influencer stickiness in the age of influencer marketing, which has received little attention in the literature. This is essential because the research creates a theoretical model of follower loyalty/stickiness to virtual influencer techniques from the standpoint of influencer marketing, which has a substantial effect on the evolution of the global marketing world.

In 2022, 302 people who currently follow an Instafamous virtual influencer took part in an Instagram self-administered online survey.

The findings show that both expertise and trustworthiness have a positive and significant influence on parasocial interaction, which in turn has a significant influence on virtual engagement and stickiness.

This research will specifically assist international readers in understanding how to harness and increase the efficiency and efficacy of interactive marketing strategies and methods to engage and retain followers of Instafamous virtual influencer. Moreover, the findings will be beneficial to opinion leaders, brand managers, company investors, entrepreneurs and service designers.

1. The study pioneers a holistic virtual follower stickiness mechanism that comprises the role of source credibility, parasocial interaction, informational influence and virtual follower’s engagement and their interrelationship to each other.

2. This study is based on parasocial interaction theory and source credibility theory to understand the relationship between virtual followers and influencers stickiness process at social media platforms.

3. In addition, the study examined the subsequent effects of sources of credibility components on parasocial interaction; as well as, on virtual follower engagement and stickiness.

4. This study also categorized and examined the moderating effects exerted by the genres of informative influence of virtual influencer.

The study pioneers a holistic virtual follower stickiness mechanism that comprises the role of source credibility, parasocial interaction, informational influence and virtual follower’s engagement and their interrelationship to each other.

This study is based on parasocial interaction theory and source credibility theory to understand the relationship between virtual followers and influencers stickiness process at social media platforms.

In addition, the study examined the subsequent effects of sources of credibility components on parasocial interaction; as well as, on virtual follower engagement and stickiness.

This study also categorized and examined the moderating effects exerted by the genres of informative influence of virtual influencer.

Cultivating loyal customers is a pressing concern for streamers. The present study investigates how to build interpersonal relationships with streamers and whether different interpersonal relationship factors lead to repurchase intention and WOM intention in live streaming commerce. The moderating effect of gender is also examined.

A self-administered questionnaire was completed by 429 live streaming commerce users in mainland China. Partial least squares structural equation modeling was used to test the research hypotheses.

The results indicate that all four streamer attributes (expertise, authenticity, attractiveness, and homophily) have a positive influence on swift guanxi, and swift guanxi is effective in predicting both calculative commitment and affective commitment. In addition, all interpersonal relationship factors (swift guanxi, calculative commitment, and affective commitment) significantly affect repurchase intentions, with only affective commitment being linked to WOM intention. Also, the moderating role of gender was confirmed in expertise – swift guanxi, attractiveness – swift guanxi, cognitive commitment – repurchase intention and affective commitment – repurchase intention linkages.

This paper contributes to the live streaming commerce literature by integrating swift guanxi, calculative commitment, and affective commitment to understand the repurchase intention and WOM intention from the relationship-building process perspective. In addition, this paper enriches the source credibility and source attractiveness models by identifying gender boundaries on the effectiveness of these models in predicting swift guanxi.

This study aims to explore the diverse sources of electricity generation (coal, natural gas, oil and hydroelectricity) and their respective associations with economic growth and environmental quality.

This study uses static panel data analysis with a random effects model for six selected ASEAN countries (Indonesia, Malaysia, Filipina, Thailand, Vietnam and Myanmar) from 1994 to 2014.

This study reveals that economic growth in six selected ASEAN countries is enhanced by electricity generation from all sources, while the contribution of electricity production from hydroelectricity remains the largest and strongest. There is no environmental impact of electricity production from hydroelectric, whereas fossil fuel-based electricity production emits carbon dioxide, with coal sources being the largest contributor, followed by natural gas and oil.

Based on the results, these six ASEAN countries should invest more in hydropower projects, reduce the coal mix in power generation and promote clean coal technology to improve economic efficiency and environmental sustainability.

To the best of the authors’ knowledge, no research has examined the relationship between electricity production, environmental quality and economic growth in Southeast Asian nations. Therefore, the outcome of this study is expected to provide insightful results to supplement the framing and implementation of national and collective regional strategies for sustainable electricity generation in ASEAN countries.

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.