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The energy performance gap (EPG) in building construction has been one of the major barriers to the realization of environmental and economic sustainability in the built environment. Although there have been a few studies addressing this issue, studying this topic with a special focus on the project delivery process has been almost overlooked. Hence, this study aims to address the EPG in building construction through the lens of collaborative and life cycle-based project delivery.

In order to realize the objective of this study, the development of a theoretical framework based on the literature review was followed by a qualitative study in which 21 semi-structured interviews were conducted with Finnish project professionals representing clients, design/planning experts, constructors and building operation/maintenance experts to explore their views on the topic under study.

The findings reveal the project delivery-related causes of EPG in building construction. Moreover, the obtained results present a collaborative and life cycle-based delivery model that integrates project and product (i.e. building) life cycles, and it is compatible with all types of contractual frameworks in building construction projects.

Although the findings of this study significantly contribute to theory and practice in the field of collaborative and sustainable construction project delivery, it is acknowledged that these findings are based on Finnish professionals’ input, and expanding this research to other regions is a potential area for further studies. Moreover, the developed model, although validated in Finland, needs to be tested in a broader context as well to gain wider generalizability.

The obtained results reveal the significance and impact of collaborative and life cycle-based project development and delivery on the realization of environmentally sustainable building construction.

Night lighting reflects the prosperous development of economic and the increasingly rich and colorful cultural life. Currently, various technical standards, protocols and management specifications have been developed to build a safe, comfortable and economical lighting environment. However, prevailing evaluation systems focus on objective indexes of illumination and have ignored environmental characteristics and subjective feelings and lacked consideration of regional culture, economic benefit, management and maintenance. In this context, a lighting evaluation system combining subjective and objective is proposed for the first time in this study to explore approaches to guide the development of a healthy and comfortable urban night-time environment.

Existing research and relevant lighting standards are analyzed and an evaluation model with a logical hierarchy is constructed by combining with the evaluation theory that is set based on people and the environment. The index weights were scientifically determined on the basis of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. The rationality and validity of the proposed evaluation system is verified in accordance with field projects and case studies.

Taking into account traditional and cultural factors, the evaluation model established has an acceptable accuracy. Evaluation based on subjective-objective combination can provide a scientific basis for the management and optimization of night lighting.

The proposed evaluation system can serve as a guiding reference for other areas of cultural identity and esthetic perspective.

The study examines the relationship between the interaction of indigenous–foreign cultures and public employee performance (PEP) in the Ghanaian public sector due to the perceived unproductive cultures in the public sector.

The study employs a quantitative approach, where cross-sectional survey design is used to collect the data from Ghanaian public employees. The analysis is done using correlation and hierarchical regression techniques.

The results reveal that both indigenous and foreign cultures are pervasive in the Ghanaian public sector, with high power distance and individualism being dominant cultures. Furthermore, while the indigenous cultures have negative significant relationship with PEP, the foreign cultures have positive significant relationship with PEP. The foreign cultures effectively control the relationship between the indigenous cultures and PEP but insignificantly moderate such relationship.

The findings imply that deliberate attempts should be made to encourage the foreign cultures with attractive reward packages to induce workers. This will indirectly control the practice of the inimical cultures and ultimately reduce their negative effect on PEP.

The study contributes significantly to the extant literature by providing empirical evidence of the indigenous–foreign culture fit and PEP from a developing country, Ghana.

The impact of COVID-19 on tourism destinations has been severe, but a future crisis is never far away. How communities can better prepare for disasters to come in the near future continues to be researched. This research aims to understand the tourism community’s responses to the COVID-19 pandemic and present the Tourism Community Resilience Model as a useful instrument to help communities better respond to disasters in the future.

This research uses a qualitative research approach which seeks to understand phenomena, events, social activities, attitudes, beliefs, perceptions and individual and group opinions that are dynamic in character in accordance with the situation in the field. Research primary data is in the form of Kuta Traditional Village local community responses in enduring the COVID-19 pandemic conducted between January and May 2022. These data were obtained through in-depth observations and interviews involving informants based on purposive sampling, including traditional community leaders, village officials, tourism actors (i.e. street vendors, tourist local guides, taxi drivers and art workers) and tourism community members. We selected the informants who are not only directly impacted by the pandemic, but also some of them have to survive during the pandemic because they do not have other job options. The results of previous research and government data concerning the pandemic and community resilience were needed as secondary data, which were obtained through a study of the literature. The data which had been obtained were further analysed based on the Interpretative Phenomenological Analysis (IPA) technique, which seeks to make meaning of something from the participants’ perspective and the researchers’ perspective as a result there occurs a cognition of a central position.

Based on findings from Bali, Indonesia, this resilience model for the tourism community was created in response to the difficulties and fortitude shown by the community during the COVID-19 pandemic. It comprises four key elements, namely the Local Wisdom Foundation, Resource Management, Government Contributions and External Community Support. These elements are all rooted in the concepts of niskala (spirituality) and sekala (real response); it is these elements that give the tourism community in the Kuta Traditional Village a unique approach, which can inspire other tourism destinations in other countries around the world.

A tourism community resilience model based on local community responses has implications for the process of enriching academic research and community management practices in facing future crisis, particularly by involving local wisdom foundation.

A tourism community resilience model based on local community responses has implications for the process of enriching academic research and community management practices in facing future crisis, particularly by involving local wisdom foundation.

The existence of the resilience model strengthens local community social cohesion, which has been made stronger by the bonds of culture and shared faith in facing disaster. This social cohesion then stimulates the strength of sustainable and long-term community collaboration in the post-pandemic period. For tourism businesses, having strong connections with the local communities is an important condition to thrive.

The value of this research is the Tourism Resilience Community Model, which is a helpful tool to optimise and improve future strategies for dealing with disasters. Illustrated by this Balinese example, this paper emphasises the importance of adding social factors such as niskala and sekala to existing community resilience models. Addressing these local characteristics is the innovative aspect of this paper and will help inspire communities around the world to prepare for future disasters better and build more sustainable and resilient tourism destinations elsewhere.

This paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents.

In this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances.

Comparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks.

Reliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.

This study aims to validate a model to assess the level of lean maturity of Brazilian hospitals based on the adaptation of the SAE J4000/2021 standard.

The methodology was divided: (1) adaptation of the standard to the health context, (2) application of the questionnaire through a survey, and (3) comparison of the results of the level of maturity in the standard and by the clustering technique, using Minitab.

The research presents two contributions: (1) validation of a model to assess the level of lean maturity based on the SAE J4000/2021; (2) insights into the level of maturity of Brazilian hospitals. It was observed that only 10% of the sample was classified at maturity level 3 and 75% at level 2. Private and large hospitals showed greater maturity compared to the others.

This includes the limited number of hospitals that participated in the survey, given the difficulty of adherence to due constraints of time and staffing from hospitals, during the COVID-19 pandemic.

This article presents a lean maturity assessment proposal, adapting a consolidated standard in the automobile industry to the health context. Insights on the lean maturity of Brazilian hospitals can contribute to the development of policies to encourage the implementation of the lean philosophy directed to each specific environment.

This study serves as a guide for public agents interested in monitoring the quality of hospital indicators through the SAE J4000/2021 standard. From this lean maturity analysis, hospital managers can understand their opportunities for improvement in both human and organizational aspects. This favors the improvement of service delivery to society that depends on health services.

Due to the lack of research that validates lean maturity level assessment models in Brazilian hospitals, this study can be considered a pioneer in this Brazilian research by validating the SAE J4000/2021 standard in its updated version in the health context.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.