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Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.

The purpose of this study was to assess knowledge, attitudes and behaviors about COVID-19 among Portuguese higher education students.

In May 2020, all students from a Portuguese University were invited to participate in completing an online questionnaire. A total of 262 students participated. COVID-19 related knowledge, attitudes toward COVID-19 and preventive behaviors were assessed. Differences between outcomes and sociodemographics were analyzed through independent t-tests and the ANOVA. A generalized linear model was calculated to determine the predictive variables of preventive behaviors.

Students revealed good knowledge about COVID-19, correctly answering 13.06 (SD = 1.25) questions in a total of 14 and favorable attitudes toward preventive behaviors (M = 32.73, SD = 2.88). Students reported always engaging in, on average, 5.81 (SD = 2.61) of the 12 behavior analyzed. Females presented higher levels of knowledge, more positive attitudes and engaged in more preventive behaviors than males. Being a bachelor's (Exp (β) = 8.213, 95% CI: 1.791–37.670, p < 0.01) or a master's degree student (Exp (β) = 7.568, 95% CI: 1.598–35.835, p < 0.05) and having positive attitudes toward preventive behavior of COVID-19 predicted the adoption of those preventive behavior (Exp (β) = 1.340, 95% CI: 1.189–1.510, p < 0.001).

This study provides useful data to plan health education programs about COVID-19 among higher education students. The continuous investment by universities in preventive campaigns is essential to promote good preventive behaviors in the next academic year.

The purpose of this paper is to investigate the design method of partial observer canonical form (POCF), which is one of the important research tools for industrial plants.

Motivated by the two-steps method proposed in Xu et al. (2020), this paper extends this method to the case of Multi-Input Multi-Output (MIMO) nonlinear system. It decomposes the original system into two subsystems by observable decomposition theorem first and then transforms the observable subsystem into OCF. Furthermore, the necessary and sufficient conditions for the existing of POCF are proved.

The proposed method has a wide range of applications including completely observable nonlinear system, noncompletely observable nonlinear system, autonomous nonlinear system and forced nonlinear system. Besides, comparing to the existing results (Saadi et al., 2016), the method requires less verified conditions.

The new method concerning design POCF has better plants compatibility and less validation conditions.

This study aims to investigate whether temperature affects the product quality of exporters and whether the effect is non-linear. More specifically, whether the impact of high temperatures differs from the impact of low temperatures, and whether different types of companies or industries are affected differently.

The paper uses detailed data covering all Chinese exporters from 2000 to 2016 to estimate the effects of temperature on the product quality of export firms. To clarify the relationship between them, the authors use a semi-parametric regression method, trying to test whether there is a non-linear relationship between temperature and the export quality of firms.

The increase in the number of high temperature days significantly reduces the quality of exported products, and this negative effect increases as the temperature rises. High temperature has the most significant negative impact on export quality for firms with low technical complexity, private firms and firms with no intermediate imports and located in historical hot cities. Product quality of both labor-intensive and capital-intensive firms will be affected by heat. High temperatures have the greatest negative impact on the export quality of newly entering products, followed by exiting products, with the least negative impact on persisting product.

To the best of the authors’ knowledge, this paper is the first to examine the impact of temperature on the quality of economic development. The findings of this paper again show that the potential economic impacts of global warming are huge. In addition to some potentially devastating impacts in the future, global warming is already causing imperceptible impacts in the present. Public and economic agents need to fully understand the possible adverse impacts of climate change and take corresponding adaptation measures to cope with global warming.

The purpose of this study is to investigate the effectiveness of university-driven knowledge transfer initiatives in enhancing the capabilities and performance of local suppliers in the garment industry. By focusing on the impact of UDIs in Myanmar, this research aims to provide empirical evidence on how these initiatives can foster supplier development and performance improvement through targeted capability enhancement strategies.

This study utilizes a combination of surveys and an experimental design to evaluate the impact of university-driven supplier development interventions (UDIs) based on Lean principles in Myanmar’s garment industry. Nine garment suppliers were assessed before and after the UDI program. The research employed partial least squares structural equation modeling (PLS-SEM) to analyze the direct, indirect and mediating effects of UDIs on supplier performance, focusing on the role of supplier capability enhancement as a mediating factor.

The study found that the UDI program significantly improved supplier capabilities, which in turn led to enhanced performance. The analysis revealed partial mediation, indicating that while UDIs directly impact supplier performance, their effect is significantly amplified through the enhancement of supplier capabilities. These findings highlight the critical role of targeted capability development in achieving substantial performance improvements among local suppliers.

This research contributes to the literature by providing empirical evidence on the effectiveness of university-driven supplier development initiatives in a developing country context. It validates the indirect role of UDIs in boosting supplier performance via capability enhancement, emphasizing the importance of industry-specific and capability-focused development strategies. The findings underscore the value of structured knowledge transfer programs in supporting local suppliers, offering practical insights for policymakers and educational institutions aiming to enhance industrial performance through strategic interventions.

This study presents evidence of the role of performance measurement and management (PMM) in sustainable supply chain governance. This study tests a model hypothesizing whether it is the PMM itself or the mediating effect of supply chain governance that is essential for both business and sustainability performance.

This study builds on a survey of 274 SMEs in Finland.

The findings indicate that PMM does not directly contribute to SMEs’ business or sustainability performance. Supply chain governance mediates the relationship between PMM and business performance. Business performance also enhances sustainability.

These findings can guide managers in managing company relationships with customers and suppliers. The mediating role of supply chain governance highlights the potential of PMM to enhance performance. Without supply chain governance, the PMM, while efficient in traditional business practices, may lose its effectiveness because of the pressure to advance sustainability values within firm operations.

The role of PMM in enhancing supply chain sustainability is frequently overlooked in the existing research, necessitating an empirical evaluation of PMM’s impact on supply chain sustainability. This study addresses this gap by focusing on the SME context, where the pressure to adopt sustainable practices is increasing, yet SMEs employ PMM less frequently than larger firms.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.