Search results

The governance of our towns and cities requires an approach that connects people with nature and places. Digital technology can be the glue that does this, if it serves the needs of the various stakeholders, including urban communities. It means identifying the potential connections across people, digital, and place themes, examining successful approaches, and exploring some of the current practice (or lack of it) in spatial planning and smart cities. This can be considered using a range of Internet of Things (IoT) technologies with other methodologies which combine the use of socioeconomic and environmental data about the urban environment. This ambient domain sensing can provide the ecological and other data to show how digital connectivity is addressing the placemaking challenges alongside providing implications for urban governance and communities.

In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.

The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.

In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

This paper aims to address third actor introductions to interaction episodes aiming at fast-forwarding the continuous development of business relationships of new firms.

The study is qualitative, collecting data from 30 interviews from 28 informants associated with creation of new ventures and business network development in the context of a novel type of third actor called venture builder. Venture builders are privately owned organizations devoted to new firm creation in a factory-like mode, collaborating with individual entrepreneurs.

The findings suggest that interaction episodes, central to the development of new relationships, may be triggered by introductions managed by third actors using different types of involvement depending on the location and focus of the potential relationship. A framework is presented including four types of introductions to interaction episodes, aiming at saving time by removing the perceived distance between new firms and their counterparts in the initiation of business relationships. The framework describes four types of introductions of interaction episodes: Managed, Advised, Facilitated and Monitored.

Triggers and introductions of interaction episodes for new firms has previously been sparsely addressed. This paper presents how third actor involvement, by the introductions of interaction episodes with internal and external counterparts is managed with an aim of fast-forwarding relationship development.

This paper aims to determine the adaptability of China’s legal system in recognizing and enforcing foreign judgements in China.

Academic articles, case law and books are examined as are relevant reports by various regulatory authorities and organizations.

Historically, Chinese courts have strictly adhered to “de facto reciprocity”, which made it difficult for foreign judgements to be recognized and enforced in China. Fortunately, Chinese courts have since abandoned their rigid adherence to de facto reciprocity, and have instead, used flexible tests of reciprocity such as de jure reciprocity, reciprocal commitment and reciprocal understand/consensus. Accordingly, this would facilitate the recovery of stolen assets, as there is a lower threshold for the recognition and enforcement of a foreign judgement.

There are limited data available in relation to the recognition and enforcement of foreign judgements pertaining to the recovery of stolen assets. Any discussions within this paper are based on the impressionistic observations of this author, which may not reflect the true state of affairs within the Belt and Road Initiative.

Those who are interested in examining the viability in recognizing and enforcing foreign judgements relating to stolen assets will have an interest in this topic.

The value of the paper is to demonstrate the difficulties in recognizing and enforcing foreign judgements in China in relation to stolen assets.

Artificial intelligence (AI) is poised to reshape mental health practices, policies and research in the coming decade. Simultaneously, mental health inequalities persist globally, imposing considerable costs on individuals, communities and economies. This study aims to investigate the impact of AI technologies on future citizenship for individuals with mental health challenges (MHCs).

This research used a community-based participatory approach, engaging peer researchers to explore the perspectives of adults with MHCs from a peer-led mental health organisation. This study evaluated potential threats and opportunities presented by AI technologies for future citizenship through a co-created film, depicting a news broadcast set in 2042. Data were gathered via semi-structured interviews and focus groups and were analysed using a reflexive thematic approach.

The analysis identified four key themes: Who holds the power? The divide, What it means to be human, and Having a voice. The findings indicate that adults with living experiences of MHCs are eager to influence the development of AI technologies that affect their lives. Participants emphasised the importance of activism and co-production while expressing concerns about further marginalisation.

This study provides new insights into the intersection of AI, technology and citizenship, highlighting the critical need for inclusive practices in technological advancement. By incorporating the perspectives of individuals with living experiences, this study advocates for participatory approaches in shaping AI technologies in mental health. This includes the co-creation of machine learning algorithms and fostering citizen engagement to ensure that advancements are equitable and empowering for people with MHCs.

Ball grid array (BGA) package is prone to failure issues in a thermal vibration-coupled environment, such as deformation and fracture of solder joints. To predict the minimum equivalent stress of solder joints more accurately and optimize the solder joint structure, this paper aims to compare the machine learning method with response surface methodology (RSM).

This paper introduced a machine learning algorithm using Grey Wolf Optimization (GWO) Support Vector Regression (SVR) to optimize solder joint parameters. The solder joint height, spacing, solder pad diameter and thickness were the design variables, and minimizing the equivalent stress of solder joint was the optimization objective. The three dimensional finite element model of the printed circuit board assembly was verified by a modal experiment, and simulations were conducted for 25 groups of models with different parameter combinations. The simulation results were employed to train GWO-SVR to build a mathematical model and were analyzed using RSM to obtain a regression equation. Finally, GWO optimized these two methods.

The results show that the optimization results of GWO-SVR are closer to the simulation results than those of RSM. The minimum equivalent stress is decreased by 8.528% that of the original solution.

This study demonstrates that GWO-SVR is more precise and effective than RSM in optimizing the design of solder joints.

Drawing on signaling theory and social capital theory, this study aims to examine the underlying mechanisms and contingencies of the relationships between social capital (SC) and collaboration in buyer-supplier relationships (BSRs). This is achieved by evaluating the mediating effect of psychological contract fulfillment (PCF) and the moderating roles of guanxi orientation and market uncertainty.

The current study used a survey method to collect data from 271 buyers in China. Structural equation modeling (SEM) and moderated regression analysis were applied to examine the hypotheses.

The positive effect of structural and relational SC on buyers’ collaborative behaviors is partially mediated by buyers’ PCF. In contrast, the positive effect of cognitive SC on collaboration is fully mediated by buyers’ PCF. Guanxi orientation strengthens the indirect effect of buyers’ PCF on the cognitive SC-collaboration relationship and relational SC-collaboration relationship. Market uncertainty amplifies the relational SC-collaboration relationship.

Prior studies have presented mixed evidence of the effect of SC on collaboration and have paid little attention to the underlying mechanisms and conditions moderating the effect. This research proposes a theoretical model that integrates signaling theory and social capital theory to explore how three dimensions of SC can enhance buyer collaboration through buyers’ PCF under different levels of guanxi orientation and market uncertainty.

Construction and demolition waste has emerged as a significant challenge for sustainable development globally. Hence, construction waste management (CWM) is considered one of the critical sustainable deliveries stipulated in various green building rating systems (GBRSs), including Homestar in New Zealand (NZ). The 6 Homestar rating is mandated for use by the national public housing provider. However, no empirical study has been conducted on CWM in 6 Homestar dwellings. This study investigates the current practice of CWM in those projects.

Primary data were extracted from 6 Homestar built assessment submissions for 100 public housing projects. The waste reports provided quantitative data to calculate the waste generation rate (WGR), waste diversion rate (WDR) and diverted waste rate (DWR) for descriptive analysis. These findings underwent further exploration by analysing site waste management plans.

With the aid of on- and off-site sorting and the recycling centre, a significant WDR is achieved at an average of 75.6%. However, diverted waste is treated at a low-priority level in the waste management hierarchy, and WGR remains relatively high.

The findings of this study can serve as valuable resources for managers in formulating comprehensive waste management plans and for policymakers in developing strategies and policies towards enhancing CWM practices. The study suggested the need for further focus towards minimising construction waste (CW) from the early design plan to achieve the construction industry's zero-waste target.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

This study aims to understand how product attributes (object stimuli), social influences (social-psychological stimuli) and internal factors (internal stimuli) contribute to Chinese Generation Z’s purchase intentions (responses) for new Chinese style apparel (NCSA) through NCSA attitudes (cognitive state) and cultural pride (affective state) based on the stimulus-organism-response (S-O-R) model.

Data were collected from 989 respondents aged between 18 and 29 years through self-administrated questionnaires via a professional survey panel, Credamo, in China. Data were analyzed using structural equation modeling.

Results showed that among the seven stimuli, NCSA’s design, online social networking communities, cultural identity and personal norms significantly influenced Generation Z’s purchase intentions through both NCSA attitudes and cultural pride. Further, NCSA’s cultural connotations and celebrity influences elicited purchase intentions merely through the affective state – cultural pride. In contrast, NCSA’s functionality did not have a significant influence on NCSA attitudes and adversely affected cultural pride.

This study fills the research gap and extends the application of the S-O-R model within the NCSA context. The findings of this study shed light on the practical implications for marketers, brands and policymakers with regard to a better understanding of Chinese Generation Z’s NCSA consumption.