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Physical environments, especially the sound environments of ILSs on a university campus, have become increasingly important in satisfying the diverse needs of students. Poor sound environments are widely acknowledged to lead to inefficient and underutilised spaces and to negatively influence students' learning outcomes. This study proposes two hypotheses to explore whether students' sound environment perceptions are related to their individual characteristics and whether students' preferences for the type of ILS are related to their sound environment sensitivities.

An investigation through a questionnaire survey has been conducted on both students' individual characteristics affecting their sound environment perceptions in informal learning spaces (ILSs) of a university campus and their sensitivities to the sound environments in ILSs affecting their preferences for the type of ILSs.

The research findings indicate that students' sound environment perceptions are associated with some of their individual characteristics. In addition, the results show that students' sound environment sensitivities affect their preferences for the type of ILS they occupy.

This study could help architects and managers of university learning spaces to provide better sound environments for students, thereby improving their learning outcomes. The article contributes valuable insights into the correlation between students' individual characteristics, sound environment perceptions and preferences for ILSs. The research findings add to the existing knowledge in this field and offer practical implications for enhancing design and management of university learning environments.

The purpose of this paper is to analyse privacy and interaction preferences in the social dimension of individual learning students and how the spatial configuration affects individual learners’ choices of learning spaces.

This empirical survey study was conducted in an Australian university’s informal learning spaces. Space syntax theories are applied to construct a four-quadrant theoretical framework.

The research findings indicate that based on the differences between students in their individual characteristics, there are significant differences in their needs for privacy and interaction. This study reveals that the spatial configuration affects individual learners’ choices of learning spaces.

This study could assist universities in providing students with more effective and diverse informal learning spaces.

This study aims to develop a finite element method based co-simulation platform for the numerical analysis of motor drive system. With the rising requirement of industry, the comprehensive design of motor drive systems has attracted increasing attentions. An accurate model, which considers the coupling between motor and its drive system, is vital for the analysis and design of motor drive system.

Considering the coupling relationship between motor and its drive system, a flexible and extensible co-simulation platform of motor drive system is developed with the C++ language and finite element machine model to carry out the comprehensive analysis of motor drive system. The control system simulation program developed with C++ language adopts the same discrete form as the single-chip microcomputer and can simulate the interrupt mechanism, making the simulation closer to the actual control system. With the finite element analysis results of current step, the winding input voltage of next step is calculated by the executable program of control system and is fed into the finite element analysis, forming the two-way coupling analysis of drive system.

Preliminary studies, such as calculation of machine core losses fed by inverters, and control parameters optimization, are conducted with this platform, which shows the flexibility and expansibility of this platform.

The power inverter circuit along with the controller is modeled using the C++ language, and embedded into the finite element machine model to achieve more realistic motor drive system simulation and complex functions.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.