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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.

The purpose of this paper is to argue in favor of the open hardware philosophy (open-source hardware – OSH) as a technological innovation and academic entrepreneurshipmodel in Brazil.

This argument is based on three pillars. The first one refers to a bibliographic review of theoretical frameworks related to academic entrepreneurship and technological innovation to emphasize the disruptive innovation capacity of academia. Second, a few elements related to the Brazilian political, economic and structural scenario, which entail a (more) favorable environment to technological innovation and academic entrepreneurship, is presented. Finally, concepts related to OSH and its business model are approached to demonstrate the facilitating effect toward the whole process.

As a result of the argument made herein, it is possible to perceive the viability of the OSH model in terms of entrepreneurship and technological innovation in the academic sphere, and to perceive its benefits before social and economic needs in areas such as health and education.

There are no empirical or quantifiable data in the literature that enable comparison between OSH and traditional technological innovation models.

The considerations on the philosophical value of OSH and its business models are scarcely explored in international literature. As far as we know, relating OSH to technological innovation entrepreneurship in the academic sphere, as well as its singularities in Brazil regarding the innovation national system and social and economic demands, is a unique approach in literature.

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。

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.

Managing components' resources plays a critical role in the success of systems' architectures designed for cyber–physical systems (CPS). Performing the selection of candidate components to pursue a specific application's needs also involves identifying the relationships among architectural components, the network and the physical process, as the system characteristics and properties are related.

Using a Model-Driven Engineering (MDE) approach is a valuable asset therefore. Within this context, the authors present the so-called Systems Architecture Ontology (SAO), which allows the representation of a system architecture (SA), as well as the relationships, characteristics and properties of a CPS application.

SAO uses a common vocabulary inspired by the Architecture Analysis and Design Language (AADL) standard. To demonstrate SAO's applicability, this paper presents its use as an MDE approach combined with ontology-based modeling through the Ontology Web Language (OWL). From OWL models based on SAO, the authors propose a model transformation tool to extract data related to architectural modeling in AADL code, allowing the creation of a components' library and a property set model. Besides saving design time by automatically generating many lines of code, such code is less error-prone, that is, without inconsistencies.

To illustrate the proposal, the authors present a case study in the aerospace domain with the application of SAO and its transformation tool. As result, a library containing 74 components and a related set of properties are automatically generated to support architectural design and evaluation.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

Business model innovation is a key element for firms' competitiveness. Its development can be supported by the establishment of an actor-oriented scheme to overcome hierarchical structures. The actor-oriented scheme is characterized by intra-organizational networks of relationships that can be established and dissolved between individuals. However, we lack an empirical perspective about its establishment; therefore, the purpose of this research is to advance our understanding of intra-organizational networks for supporting business model innovation.

Individuals create and manage knowledge aimed to innovate the business model through cognitive search and experiential learning mechanisms. Knowledge is spread within organizations by using intra-organizational advice networks, whose patterns reflect the presence of an actor-oriented scheme. This work applies social network analysis to network data from a multi-unit organization specializing in personal care services. We use a Logistic Regression-Quadratic Assignment Procedure to analyze intra-organizational network data on managers' advice exchange related to the learning modes of cognitive search and experiential learning.

Our research empirically identifies the main elements of an actor-oriented scheme in a business model innovation process. We find that managers are able to self-organize, because they are not influenced by their organizational roles, and that commons for sharing resources and protocols, processes and infrastructures enable advice exchange, thus showing the presence of an actor-oriented scheme in business model innovation process.

This research is based on a cross-sectional database. A longitudinal study would provide a better understanding of the network evolution characterizing the innovation process.

The results of our study support organizational decision-making for business model innovation.

This study provides empirical evidence of how an actor-oriented scheme emerges in a business model innovation process.

The ongoing business dynamics show two aspects for generating innovation: first, high-impact innovations are developed jointly by several actors, such as universities, enterprises, and governments. Second, startups are better suited to develop innovation during crises or periods of low growth as experienced at the moment. Based on these aspects and drawing on the constructs of the triple helix, this study analyzes the influence between the characteristics of the actors on the quality of the startup ecosystem from a global view.

The study examines the cross-section data of 35 countries between 2017 and 2018 and applies the partial least squares structural equation modeling (PLS-SEM) for assessing the relationships between the triple helix on the quality of the startup ecosystem on a country-level.

The findings suggest that each actor of the triple helix individually does not positively affect the quality of the startup ecosystem. Yet, when analyzing the actors jointly by creating a second-order latent variable (i.e. triple helix), the study found out that in this way, the triple helix construct has a positive effect on the quality of the startup ecosystem.

Although a large body of prior literature indicates the importance of generating interrelationships among the different entities involved in ecosystems, few studies provide empirical evidence from a global perspective of the need for these entities to act in an overlapping manner. The present study supports previous research and reinforces the importance of the triple helix for a more innovative environment.

Evaluating the economic effects of climate change is a pivotal step for planning adaptation in developing countries. For Bangladesh, global warming has put it among the most vulnerable countries in the world to climate change, with increasing temperatures and sea-level rise. Hence, the purpose of this paper is to examine how climate change impacts the economy in Bangladesh in the case of climate scenarios.

Using a dynamic computable general equilibrium (CGE) model and three climate change scenarios, this paper assesses the economy-wide implications of climate change on Bangladesh’s economy and agriculture. It is clear from the examination of the CGE model that the impacts of climate change on agricultural sectors were felt more sharply, reducing output by −3.25% and −3.70%, respectively, and increasing imports by 1.22% and 1.53% in 2030 and 2050, compared to the baseline.

The findings reveal that, relative to baseline, agricultural output will decline by a range of −3.1% to −3.6% under the high climate scenario (higher temperatures and lower yields). A decrease in agricultural output results in declines in agricultural labor and household income. Household income falls in all categories, although it drops the most in urban less educated households with a range of −3.1% to −3.4%. On the other hand, consumption of commodities will fall by −0.11% to −0.13%, according to the findings. Although climate change impacts had a relatively small effect on gross domestic product, reducing it by −0.059% and −0.098% in 2030 and 2050, respectively.

As agricultural output, household consumption and income decline, it will impact the majority of the population’s health in Bangladesh by increasing malnutrition, hidden hunger, poverty, changing food environment, changing physical and mental health status and a changing health-care environment. Therefore, population health and food security will be a top socioeconomic and political concern for Bangladesh Government.

The examination of the dynamic CGE model is its originality. In conclusion, the evidence generated here can provide important information to policymakers and guide government policies that contribute to national development and the achievement of food security targets. It is also necessary to put more emphasis on climate change issues and address potential risks in the following years.