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With rapid urbanization, cities are facing various ecological and environmental problems. Living in harmony with nature is more important than ever. This paper aims to evaluate the ecosystem and ecological features of Azheke village, a key component of the Hani Rice Terraces World Cultural Heritage in China. The focus is on exploring effective ways to improve the relationship between humans and the natural environment through urban design in order to create a livable and sustainable city that can promote the development of sustainable smart urban ecology design.

This study conducted a systematic literature review to answer the following research questions: (1) How does Azheke design achieve harmony between humans and nature? (2) What are the effective approaches to improve the relationship between humans and nature within urban ecosystems? (3) How can urban design learn and integrate from Azheke’s ecological features to improve the relationship between humans and nature?

Azheke sustains long-term human-nature harmony through traditional ecological knowledge (TEK) and efficient natural resource use. By incorporating biophilic design and nature-based solutions from Azheke, along with biodiversity-friendly urban planning, we can boost urban ecosystem health and create unique Azheke-inspired urban designs.

This research primarily focuses on the human-nature relationship, exploring design strategies based on biodiversity without delving into the interactions between other components of urban ecosystems, such as social-cultural and economic components.

This paper provides a new perspective and strategies for developing sustainable and smart urban ecology design. These findings can provide theoretical references for urban planners, designers and decision-makers.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

This paper aims to investigate the impact of combining grain-oriented electrical steel (GOES) grades on specific iron losses and the flux density distribution within a single-phase magnetic core.

This paper presents the results of finite-element method (FEM) simulations investigating the impact of mixing two different GOES grades on losses of a single-phase magnetic core. The authors used different models: a 3D model with a highly detailed geometry including both saturation and anisotropy, as well as a simplified 2D model to save computation time. The behavior of the flux distribution in the mixed magnetic core is analyzed. Finally, the results from the numerical simulations are compared with experimental results.

The specific iron losses of a mixed magnetic core exhibit a nonlinear decrease with respect to the GOES grade with the lowest losses. Analyzing the magnetic core behavior using 2D and 3D FEM shows that the rolling direction of the GOES grades plays a critical role on the nonlinearity variation of the specific losses.

The novelty of this research lies in achieving an optimum trade-off between the manufacturing cost and the core efficiency by combining conventional and high-performance GOES grade in a single-phase magnetic core.

We empirically assessed the antecedents of subjective well-being at work for French permanent employees.

The methodology includes qualitative and quantitative data analyses. In the first phase, interviews elicited the antecedents of subjective well-being at work among permanent French employees. In the second phase, a questionnaire survey was used to confirm the relevance of the antecedents uncovered in the first phase.

We found 14 distinct elements that influence French employees’ subjective well-being at work: corporate culture, job dissonance, relationships with colleagues, achievement, professional development, relationships with superiors, status, workload, perks, feedback, workspace, diversity and pay. Moreover, we identified discrete antecedents for the three components of subjective well-being at work: work achievement and relationships with superiors and colleagues for positive emotions at work, job dissonance and workload for negative emotions at work and organizational culture and professional development for satisfaction with one’s work.

The original contribution of this study is to have unpacked the black box of the antecedents of subjective well-being in the French workplace and to have uncovered discriminant predictors for each of the three components of subjective well-being at work. Furthermore, we specifically linked each of these three components with their most significant antecedents.

The purpose of this paper is to develop a comprehensive overview of green innovation (GI) in China, which is carried out by reviewing the evolution of GI from 2000 to 2019, and the main type of technology, actors and localizations. When appropriate, GI is compared to non-GI.

The study uses patent data from the European Patent Office database (PATSTAT); these data are processed to map trends and identify the main contributors to GI and the location of such innovation. The findings are then discussed and complemented with academic literature.

Key findings reveal an increasing divergence between GI and nongreen innovation after the 2008 crisis. It is also observed that solar energy appears to be the main component of GI in China, with a shift from photovoltaic thermal energy to solar photovoltaic energy after 2008. Other areas, such as waste management, greenhouse gases capture and climate change adaptation, are less innovative. Companies play an essential role in the development of all types of innovation. In terms of location, green patents are mainly filed in China’s three main megacities. The study also highlights the significant role of the Chinese state, which led policies shaping the trajectories and forms of GI.

This study expands knowledge on GI in China, highlighting its main specificities and the role of key actors. It provides to the reader a comprehensive picture of China’s green policies and innovation realities. The results can therefore be used to improve the understanding of GI evolution in China and facilitate the formulation of new research questions.

The perpetration of sexual coercion is a complex public health problem associated with many kinds of deficits. The literature has shown that women also perpetrate sexually coercive behaviours. Recent work has suggested that this kind of behaviour could be explained by two distinct developmental pathways. However, this model does not allow the authors to identify how the individual processes social information in situ and may decide to resort to coercive behaviours. This study aimed to investigate the role of social information processing in women’s sexual coercion.

A sample of 125 French-speaking women from the general population were recruited to complete online questionnaires pertaining to dark triad personality traits, emotion abilities, alexithymia and antecedents of sexual coercion.

Results revealed that women with a history of sexual coercion had a significantly higher narcissistic traits score and more emotion regulation (ER) deficits than those without a history. For women with a history of sexual coercion perpetration, correlational analyses showed positive correlations, respectively, between psychopathic traits and alexithymia and between Machiavellianism and deficits in ER.

These results contribute to identifying the deficits relating to SIP in terms of sexual coercion perpetrated by women. Women with a history of sexual coercion perpetration appear to endorse more dark triad traits and to have ER issues. Certain level of these deficits could be a trigger and affect the SIP of women and increase the likelihood behaving in a sexually coercive manner.

The purpose of this study is to examine the Nigerian Extractive Industry Transparency Initiative’s (NEITI) ineffectiveness in delivering public accountability to Nigerian citizens. Although this failure is recognised in prior literature, the authors contend that NEITI’s role is obscured by one-sided links to external factors.

The conceptual framework presented in this study is built around Dillard and Vinnari’s (2019) distinction between different accountability systems and Brown and Dillard’s (2020) complimentary insights on the technologies of hubris and humility. The analytical framework draws from Grant and Keohane’s (2005) modes of accountability, which the authors use to articulate conflicting accountability demands (to-whom and for-what) of NEITI’s operating relationships. Combined, the authors analyse official documents, media, reports and interview responses from members of NEITI’s National Stakeholders Working Group.

This study surfaces a variety of intersecting interests across NEITI’s operational relationships. Some of these interests are mutually beneficial like that of Donors and the Extractive Industries Transparency Initiative. Others run counter to each other, such as NEITI’s relationship to the Presidency which illustrates a key source of NEITI’s ineffectiveness. In discussing these interests, the authors articulate their connection to NEITI’s design as an accountability system and its embedded limitations.

The authors provide incremental understanding of prior insight regarding NEITI’s ineffectiveness by drawing attention to its fundamental design as an accountability system and its failure to deliver public accountability. To illuminate these failures, the authors also map NEITI’s competing accountability demands – the nexus of accountability – to demonstrate the complex socio-political reality within which NEITI is expected to operate. The authors posit that NEITI’s ineffectiveness has as much to do with NEITI itself, as it does with external factors like the quality of information disclosed and the unique Nigerian context.

We theorize the critical role of angel investors' affective experiences and first impressions in the context of entrepreneurial finance. We develop a model and propositions to illustrate why angel investors make the decision to continue screening, thus explaining why certain investment proposals make it, while others do not.

Drawing on affective events theory and the literature on affective experiences, we theorize how the perceptions of pitches that trigger positive or/and negative physiological arousal, short-lived emotions, and associated thoughts are different, thus allowing us to build new theory of how these different experiences can influence the outcome of the evaluation process in the initial screening stage.

Our model suggests that the initial evaluation unfolds in five stages: perception of an entrepreneurial pitch, physiological arousal, emotions, first impression, and a decision to continue screening. When different manifestations of physiological arousal and subsequent emotions set the tone of first impressions, they can be either a positive, negative, or mixed experience. While positive and mixed first impression can lead to selection, negative first impression can lead to rejection.

We illustrate what is of value for angel investors when they look for new investments, and why certain entrepreneurial pitches lead to the decision to continue screening, while others do not. We propose that what angel investors feel is particularly important in situations where they are not yet making the ultimate decision to invest money but are involved in decisions about whether to continue to spend time to investigate the investment proposal.

Online investment platforms offer an environment that may lead some traders into excessive behaviors akin to gambling. Over the last decade, gambling behaviors associated with the stock market have attracted the attention of many researchers but the literature on the subject remains scarce. This study aims to present the results of live interviews with a sample (N = 100) of retail investors trading online, and contrasts trading habits with gambling behaviors.

Participants are divided in three groups according to their score on an adapted version of the Problem Gambling Severity Index (referred to as the PGSI-Trading), and their trading habits and behaviors are compared.

The authors find that traders with higher PGSI-Trading scores are more likely to display gambling-related behaviors such as trading within a short timeframe, being motivated by making money quickly and experiencing high sensations when trading.

The sample is small but the authors proceeded this way in order to gather some qualitative data that would be helpful to clinicians in the Province of Quebec. The questionnaire used to classify traders at risk of being gamblers (PGSI-Trading) has not been validated.

The findings of this study will be helpful to clinicians who hwork with patients suffering from excessive online stock trading habits.

Clinicians observe an increasing number of patients who consult with excessive stock trading habits. This study has brought new information allowing clinicians to better understand how gambling manifests itself on the stock market.

To the authors’ knowledge, this study is the first to investigate the trading habits of individuals classified in terms of their score on an adapted PGSI questionnaire.

The crane plays an essential role in modern construction sites as it supports numerous operations and activities on-site. Additionally, the crane produces a big amount of data that, if analyzed, could significantly affect productivity, progress monitoring and decision-making in construction projects. This paper aims to show the usability of crane data in tracking the progress of activities on-site.

This paper presents a pattern-based recognition method to detect concrete pouring activities on any concrete-based construction sites. A case study is presented to assess the methodology with a real-life example.

The analysis of the data helped build a theoretical pattern for concrete pouring activities and detect the different phases and progress of these activities. Accordingly, the data become useable to track progress and identify problems in concrete pouring activities.

The paper presents an example for construction practitioners and researcher about a practical and easy way to analyze the big data that comes from cranes and how it is used in tracking projects' progress. The current study focuses only on concrete pouring activities; future studies can include other types of activities and can utilize the data with other building methods to improve construction productivity.

The proposed approach is supposed to be simultaneously efficient in terms of concrete pouring detection as well as cost-effective. Construction practitioners could track concrete activities using an already-embedded monitoring device.

While several studies in the literature targeted the optimization of crane operations and of mitigating hazards through automation and sensing, the opportunity of using cranes as progress trackers is yet to be fully exploited.