Interaction Patterns Research Articles

Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review

With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.

Evaluation and identification of high yielding and stable cowpea genotypes using GGE biplot and joint regression analysis for varietal development in East and Southern Africa

Malawi, Mozambique, and Tanzania's cowpea breeding programs have been collaborating on pre-breeding trials in different locations, focusing on key traits such as grain yield. However, there is a lack of information regarding the nature of genotype-environment interaction patterns among these environments. The study was conducted to assess and identify high-yielding and stable cowpea genotypes for the development of improved varieties in these three countries. Thirty genotypes were evaluated in ten environments using an incomplete block design with three replications. Grain yield data was analyzed using genotype (G) × genotype-environment interaction (GGE) biplot and joint regression analysis. The joint analysis of variance showed highly significant differences (P < 0.001) for the main effects of genotypes and environment, as well as their interaction effects. This suggested that the environments assessed were distinct and that the genotypes exhibited varying performances in response to environmental variations. The environmental indices showed that only four environments were favorable for high grain yield, and overall, Guariba was the topmost yielder (1973.5 kg ha−1) across all environments. The analysis identified IT10K-817-7 and MWcp08 as superior and stable genotypes, making them highly desirable and potentially recommended across test environments for their expected ability to achieve economically viable yields. Overall, Chitedze Research Station in Malawi and Nametil Research Station in Mozambique were both discriminating and representative, indicating that they are good test environments for selecting best-performing genotypes that can perform well across a wide range of conditions. These environments can be recommended as the best environments for future cowpea genotype evaluations targeting the release of varieties in the three countries (Malawi, Mozambique and Tanzania) where the METs were carried out.

Genetic association and computational analysis of MTHFR gene polymorphisms rs1801131 and rs1801133 with breast cancer in the Bangladeshi population

Methylenetetrahydrofolate reductase (MTHFR) plays a crucial role in regulating one-carbon metabolism. Polymorphisms within the MTHFR gene have been found to increase the risk of breast cancer in different populations. In this study, we evaluated the association of polymorphisms of the MTHFR gene (rs1801133 and rs1801131) with the risk of breast cancer in the Bangladeshi population. This case‒control study included 202 breast cancer patients and 104 healthy controls. After the organic extraction of DNA, genotyping was performed via the PCR-RFLP method. Sanger sequencing was performed to validate the RFLP data. Statistical analyses were performed to evaluate the associations of the polymorphisms. Different computational tools were used to predict the structural and functional consequences of the SNPs. Our study revealed that the MTHFR gene polymorphism rs1801131 is associated with an increased risk of developing breast cancer (p < 0.001, OR = 3.85, 95% CI = 2.06–7.25 for the AC genotype and p < 0.001, OR = 7.82, 95% CI = 2.69–22.05 for the CC genotype). An association was also observed in the dominant model (AC + CC) (p < 0.001, OR = 4.19, 95% CI = 2.28–7.78). For rs1801131, premenopausal status was significantly associated with breast cancer risk (p < 0.001). For rs1801133, no significant association was found with breast cancer risk (p > 0.05, OR = 1.57, 95% CI = 0.90–2.74 for the CT genotype; p > 0.05, OR = 1.35, 95% CI = 0.36–4.92 for the TT genotype). Computational analyses predicted rs1801131 to be tolerated and rs1801133 to be deleterious. Structural analyses demonstrated no significant changes in protein structure but revealed alterations in neighboring interactions according to both bond distances and angles. In conclusion, rs1801131 but not rs1801133 is significantly associated with breast cancer risk in the Bangladeshi population. Moreover, in silico analyses demonstrated changes in the interaction pattern of polymorphic residues with adjacent amino acids.

Effects of environmental factors on host-parasite interaction patterns in backyard-tethered goats of Kerala, India

The environment is the most important stratum in the epidemiological triad of a parasitic disease and any variations in the environmental factors may decide the dynamic occurrence and existence of different lifecycle stages of these parasites. The present study investigated the correlations between key biometeorological and demographical parameters with the incidence of different gastrointestinal parasites and hemoparasites among goats. Four hundred and thirty-two goats were screened for parasitic infection in a yearlong survey conducted from July 2022 to June 2023 in the Teaching Veterinary Clinical Complex, Mannuthy, Kerala, India. The weather parameters (Tmax, Tmin, RH, THI, and bright sunshine hours), air quality parameters [AQI, PM2.5, and PM10], and demographic parameters (gender and age) were recorded along with the test positivity of different categories of gastrointestinal parasites and hemoparasites in goats by routine fecal sample examination and blood smear examination, respectively. The infection level was ranked based on the severity of the infection. The mean and daily variations in biometeorological parameters were calculated and the data were statistically analyzed to figure out the pertinent correlations in host-parasite-environment interaction patterns. High levels of parasitic infections with significant month-wise variations and climate-correlated peak infection patterns were noticed. The incidence of parasites was negatively correlated to all parameters except humidity, indicating more severe parasitism during monsoon months. The significant variations in the host-parasite interaction dynamics point towards the need for detailed explorations concerning the lifecycle of each specific parasite with a focus on the possible environment-favourable and resistant lifecycle stages. Future studies may be designed from a biometeorological perspective to develop a crucial understanding of host-parasite-environment interactions in goats ensuring sustainable goat farming.

Exploring collaborative writing among large groups in online distance learning through a public forum and private chat tool

Abstract This study explored how collaborative writing, an often-used instructional strategy in second language (L2) learning, intersects with large-group dynamics, and investigated their potential impact on the quality of writing outcomes in an online distance learning course. Using a mixed-methods approach, the research scrutinized intra-group interaction processes in two large groups undertaking a computer-assisted language learning writing assignment and evaluated the impact of these interaction processes on their writing products. Data from discussions in both a public online forum and a private social communication platform (WeChat) were collected, systematically coded, and analysed quantitatively and qualitatively based on language functions. Data collection also included an assessment of the written products and follow-up group interviews. The findings indicate distinct interaction patterns between high-performing and low-performing groups, characterised by an expert/participant pattern and a dominant/passive pattern, respectively. Additionally, insights from the interviews shed light on these interaction patterns and the potential impact on student learning outcomes. The study suggests practical implications, highlighting the importance of task design in promoting high levels of collaborative knowledge construction to enhance students’ writing skills and L2 language learning in large-group settings.

Microbial Enzymes for the Biodegradation of Polylactic Acid (PLA): Molecular Docking Simulation and Biodegradation Pathway Proposal

Polylactic acid (PLA), as a biodegradable polymer, finds widespread utility across diverse sectors, encompassing transportation, agriculture, biomedicine, textiles, electronics, and food packaging. Nonetheless, its degradation kinetics exhibit significant environmental sensitivity, necessitating the development of novel biotechnological methodologies to investigate the microbial and enzymatic constituents implicated in PLA biodegradation. In this review, molecular docking simulations are discussed to analyze the enzymatic degradation of PLA using specific enzymes: proteinase K, PLA depolymerase, and lipase. The binding energy, binding affinity, and dimensions of PLA-binding sites within the enzyme cavities have been examined for each enzyme. The binding amino acid residues mainly include serine, histidine, and aspartate for all plastics, while the interactions involve Van der Waals forces, conventional hydrogen bonding, carbon-hydrogen bonding, and Pi-interactions. Based on our synthesis of the literature, a theoretical PLA biodegradation pathway that illustrates the interaction pattern between PLA and the enzymes, as well as the degradation mechanism is proposed. Our review indicates that in natural environments, enzymes work synergistically, utilizing their unique properties to facilitate an efficient sequential catalytic process for PLA degradation. This integrative approach elucidates the mechanistic underpinnings and metabolic cascades governing PLA degradation, enriching insights into enzymatic catalysis and microbial enzyme-mediated plastic biotransformation. Consequently, a comprehensive understanding of these processes is paramount in optimizing biodegradation kinetics and enhancing the environmental performance of biodegradable polymers. This review provides a detailed framework for refining PLA management strategies and advancing sustainable polymer utilization.

Sex Differences in Hierarchical and Modular Organization of Functional Brain Networks: Insights from Hierarchical Entropy and Modularity Analysis.

Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functional brain network is a critical feature for assessing these neural mechanisms. But the sex differences in hierarchical organization have not been fully investigated. Here, we explore whether the hierarchical structure of the brain network differs between females and males using resting-state fMRI data. We measure the hierarchical entropy and the maximum modularity of each individual, and identify a significant negative correlation between the complexity of hierarchy and modularity in brain networks. At the mean level, females show higher modularity, whereas males exhibit a more complex hierarchy. At the consensus level, we use a co-classification matrix to perform a detailed investigation of the differences in the hierarchical organization between sexes and observe that the female group and the male group exhibit different interaction patterns of brain regions in the dorsal attention network (DAN) and visual network (VIN). Our findings suggest that the brains of females and males employ different network topologies to carry out brain functions. In addition, the negative correlation between hierarchy and modularity implies a need to balance the complexity in the hierarchical organization of the brain network, which sheds light on future studies of brain functions.

Gender differences in the association between inter-generational interaction and depressive symptoms among Chinese older adults

BackgroundInter-generational interaction is a carrier of Chinese traditional culture, and it can exert important influence on the depressive symptoms on Chinese older adults. The study aims to analyze gender differences in the association between inter-generational interaction and depressive symptoms among Chinese older adults and explore factors contributing to the differences.MethodData from China Family Panel Studies in 2020 were used. Depressive symptoms were assessed using the 8-item Center for Epidemiologic Studies Depression Scale. The latent class analysis was applied to identify patterns of inter-generational interaction of older adults (aged 60 and above). Before the comparison between older man and older woman, we used Coarsened Exact Matching to control confounding factors and improve causal inferences. Multiple linear regression was conducted to explore the association between inter-generational interaction and depression symptoms. Oaxaca-blinder decomposition method was used to analyze the gender difference and the sources.ResultOur study identified three types of inter-generational interaction: detached, nearby but discordant and two-way tight-knit. Analysis indicated that most of older man (54.39%) and older woman (49.78%) were in the type of nearby but discordant. Older man and older woman who in detached type had higher depression scores than other types, and the depression score of two-way tight-knit type accounted for 12.42 and 13.77 respectively. Our findings demonstrated that two-way tight-knit type (-11.89%) significantly decreased the gender differences in the depression symptoms. Other major contributors also included living without spouse (20.56%), primary school and junior middle school (15.95%), higher middle school and above (9.50%) and no illness for two weeks (47.70%).ConclusionOur study highlighted three patterns of inter-generational interaction, and most of older man and older woman were in the pattern of nearby but discordant. In addition, the two-way tight-knit pattern significantly can decrease gender differences in depression symptoms. The contributors should be taken into account in more targeted intervention strategies for narrowing gender differences in the depression symptoms, which could achieve a gender dividend in the era of China's aging population.

Usage of Social Media and Cultural Identity in Chitral: An Exploration through Mix-Method Approach

Social media is a fast-expanding platform, and considered as a hallmark of 21st century communication and cultural patterns. Rapidly changing cultural trends are observable as the ways of communication and patterns of interaction through social media are playing a pivotal role and its profound impact on cultural identity is evident. Framed under mix-method design, the aim of this study was to investigate the usage of social media and its impact on cultural identity in Chitral, Pakistan. A multifaceted sampling approach was utilized by using purposive and simple random sampling techniques. A well-structured interview schedule for quantitative information and semi- structured interview guide for qualitative information were utilized. The data was analyzed statistically and thematically. Findings shows that social media has a significant impact on cultural patterns and cultural identity in Chitral as the social media has provided an environment with enhanced information and communication means. It facilitated sharing opinions, perceptions, digital images, photographs, and knowledge on various issues of human life including but not limited to academic, social- cultural, spiritual, and business and tourism. On the contrary, acculturation and loss of cultural identity has been found at significant level. Moreover, physical interaction in terms of social isolation has caused considerable impact on psychological wellbeing of people. Qualitative explorations are further required to know about micro level cultural impacts of social media on Chitrali culture.

A Data Mining-Based Method to Disclose Usage Behavior Patterns of Fresh Air Systems in Beijing Dwellings during the Heating Season

As the popularity of fresh air systems (FAS) in residential buildings increases, exploring the behavioral characteristics of their use can help to provide a comprehensive understanding of the potential for demand flexibility in residential buildings. However, few studies in the past have focused on the personalized usage behavior of FAS. To fill this gap, this study proposes a method based on data mining techniques to reveal the behavioral patterns of FAS usage and the motivations behind them, including motivational patterns, operation duration patterns, and human–machine interaction patterns, for 13 households in Beijing. The simultaneously obtained behavioral patterns, in turn, form the basis of association rules, which can classify FAS usage behavior into two typical residential user profiles containing user behavioral characteristics. This study can not only provide more accurate assumptions and inputs for behavioral stochastic models but also provide data support for the development and optimization of demand response strategies.

Strategic Identification of Anti-Cancer Compounds Targeting PARP15 in DNA Repair Pathways for Enhanced Therapeutic Efficacy

Cancer is a significant worldwide health concern that requires effective therapies. Addressing this critical issue necessitates innovative treatment strategies concentrating on the fundamental causes of cancer progression. The PARP15 protein is essential in cancer progression by facilitating DNA repair pathways, making it a promising target for anti-cancer therapies. This investigation relies on computational strategies, including virtual screening and molecular dynamics simulations, to identify potential inhibitors of PARP15 target protein. Three potential compounds (26646684, 104224077, and 17505556) with notable binding affinities and interaction patterns were selected for further investigation. Compound 17505556 shows significant interactions, suggesting more stable conformation throughout the simulation against the target protein. Compound 17505556 exhibited the highest binding free energy (-34.07 kcal/mol), significantly outperforming the reference inhibitor I4X (-26.70 kcal/mol). This strong binding affinity suggests that 17505556 forms stable and sustained interactions with PARP15, making it the most promising inhibitor among those studied. Other compounds, 26646684 (-28.92 kcal/mol) and 104224077 (-26.83 kcal/mol), also demonstrated favorable binding energies, indicating their potential as viable inhibitors. The overall result of the investigation suggests the compound 17505556 as a possible drug candidate for the inhibition of DNA repair protein, offering novel avenues for developing an anti-cancer drug candidate.

Speech recognition technology for assessing team debriefing communication and interaction patterns: An algorithmic toolkit for healthcare simulation educators

BackgroundDebriefings are central to effective learning in simulation-based medical education. However, educators often face challenges when conducting debriefings, which are further compounded by the lack of empirically derived knowledge on optimal debriefing processes. The goal of this study was to explore the technical feasibility of audio-based speaker diarization for automatically, objectively, and reliably measuring debriefing interaction patterns among debriefers and participants. Additionally, it aimed to investigate the ability to automatically create statistical analyses and visualizations, such as sociograms, solely from the audio recordings of debriefings among debriefers and participants.MethodsWe used a microphone to record the audio of debriefings conducted during simulation-based team training with third-year medical students. The debriefings were led by two healthcare simulation instructors. We processed the recorded audio file using speaker diarization machine learning algorithms and validated the results manually to showcase its accuracy. We selected two debriefings to compare the speaker diarization results between different sessions, aiming to demonstrate similarities and differences in interaction patterns.ResultsTen debriefings were analyzed, each lasting about 30 min. After data processing, the recorded data enabled speaker diarization, which in turn facilitated the automatic creation of visualized interaction patterns, such as sociograms. The findings and data visualizations demonstrated the technical feasibility of implementing audio-based visualizations of interaction patterns, with an average accuracy of 97.78%.We further analyzed two different debriefing cases to uncover similarities and differences between the sessions. By quantifying the response rate from participants, we were able to determine and quantify the level of interaction patterns triggered by instructors in each debriefing session. In one session, the debriefers triggered 28% of the feedback from students, while in the other session, this percentage increased to 36%.ConclusionOur results indicate that speaker diarization technology can be applied accurately and automatically to provide visualizations of debriefing interactions. This application can be beneficial for the development of simulation educator faculty. These visualizations can support instructors in facilitating and assessing debriefing sessions, ultimately enhancing learning outcomes in simulation-based healthcare education.

Rapid diagnosis and recurrence prediction of choledocholithiasis disease using raw bile with machine learning assisted SERS

Surface-enhanced Raman spectroscopy (SERS) analysis based on body fluids has been widely applied in disease diagnose. Choledocholithiasis is a widespread and often recurrent digestive system disease, with limited data on factors predicting its formation and reappearance. Bile contains many components that could provide valuable diagnostic information; however, the current diagnosis of biliary disease by SERS focuses on detecting specific component in the bile, overlooking the complex interplay and correlations among multiple factors that could be crucial for accurate diagnosis. This work directly obtained multi-component SERS spectral information of raw bile from 46 patients. Characteristic information was extracted from bile SERS spectra using Principal Component Analysis (PCA), revealing variations in the content of characteristic components associated with different choledocholithiasis types and their recurrence frequency. Pearson correlation analysis was also introduced to reveal the interactions of primary active substances pertinent to choledocholithiasis diagnosis. The efficacy of PCA and Support Vector Machine (SVM) models in classifying stone types, presented an accuracy of 99.2 %. Furthermore, the interaction patterns among SERS characteristic components in choledocholithiasis recurrence frequency were revealed, and with the support of SVM, the prediction for different recurrence rates reached an accuracy of 95.2 %. Overall, this work demonstrates that integrating SERS with machine learning can support disease diagnosis and the interpretation of correlations among multiple components, facilitating elucidating the disease mechanisms.

Dynamics of cMOOC learner interactions in different social media: a longitudinal network analysis

ABSTRACT Social media may have an impact on people’s interaction patterns and social relationships. Investigating the interaction patterns of learners across various social media platforms can provide profound insights into the characteristics and mechanisms of connectivist learning. This study explores interaction network characteristics and the dynamic evolution of interaction patterns across multiple social media platforms within a cMOOC by using SNA and SIENA. The results show that cMOOC learners' interaction patterns have evolved with different characteristics on WeChat, blogs, and forums, eventually forming distinct interaction networks on each platform. The interaction networks on WeChat demonstrate superior performance in terms of size, cohesion, and connectivity, followed by the networks on blogs, and lastly, those on forums. WeChat and blog interaction networks exhibit a multi-center structure and modular characteristics, while forum interaction networks display a single-center structure. The evolution of interaction patterns on WeChat reveals more significant effects, such as reciprocity, transitivity, homophily, and preferential attachment, in contrast to blogs and forums, where transitivity and homophily are not prominent. The paper concludes with a discussion on the interaction patterns and network characteristics supported by three types of social media. Additionally, it highlights the significance and implications of these findings for educational practice.

Cyberactivism and Social and Political Participation Regarding the War in Ukraine: Analysis of Social Interactions in Spanish on Twitter

To evaluate and understand the dynamics of citizen participation in social networks concerning the war in Ukraine and delve into the phenomenon of cyberactivism, this study focuses on the conversation generated in Spanish around the conflict on Twitter. The research analyses 1,138,747 original tweets to investigate the general characteristics of the conversation, the user interaction patterns, and the creation and structure of communities and to determine the connecting factors. The study employs machine learning and artificial intelligence techniques via the Graphext data analytics tool. The conversation volume is very high, but the network of interactions is characterized by being unstructured and dominated by white noise, with disjointed interactions setting the tone of the conversation. Verified accounts and emotionality stand out as factors of connection and interconnection in the network. This case study highlights a form of social and political participation oriented towards visibility and information about the cause at a level of deliberation and debate that shows empathy and sympathy in a context close to slacktivism.

Development and Evaluation of an AI-Based Chatbot for Preventing Social Media Addiction: A Waterfall Model Approach

The rapid growth of social media has transformed interaction and communication patterns, but it has also led to the rise of social media addiction, particularly among teenagers and young adults. This addiction, marked by compulsive usage and negative impacts on mental health and daily life, necessitates effective interventions. This research explores the development and evaluation of an AI-based chatbot designed to mitigate social media addiction by employing cognitive and behavioural strategies. The study utilizes the Waterfall model—a structured, sequential approach—in the chatbot’s development, encompassing stages from needs analysis to maintenance. The chatbot’s effectiveness was assessed through rigorous testing and user feedback. The methodology included problem analysis, system design, implementation, testing, and iterative improvements. A comprehensive needs analysis identified the psychological and behavioural factors contributing to social media addiction, leading to the design of a prototype chatbot integrated with AI for dynamic content adaptation and real-time feedback. The implementation phase focused on coding and system integration, followed by rigorous testing using Black Box Testing and the System Usability Scale (SUS) to ensure functionality and user-friendliness. Results indicate that the chatbot significantly reduced social media addiction scores, with a mean decrease from 55.21 to 50.17, supported by a highly significant p-value of &lt;0.0001. User satisfaction was high, particularly regarding ease of use and information quality. However, user engagement declined over time, highlighting the need for ongoing content updates and feature enhancements. This study contributes to the field by providing insights into the application of the Waterfall model in AI chatbot development and offers a scalable solution for addressing social media addiction, with implications for future digital interventions in mental health.

Trends and topics: Characterizing echo chambers’ topological stability and in-group attitudes

Nowadays, online debates focusing on a wide spectrum of topics are often characterized by clashes of polarized communities, each fiercely supporting a specific stance. Such debates are sometimes fueled by the presence of echo chambers, insulated systems whose users’ opinions are exacerbated due to the effect of repetition and by the active exclusion of opposite views. This paper offers a framework to explore how echo chambers evolve through time, considering their users’ interaction patterns and the content/attitude they convey while addressing specific controversial issues. The framework is then tested on three Reddit case studies focused on sociopolitical issues (gun control, American politics, and minority discrimination) during the first two years and a half of Donald Trump’s presidency and on an X/Twitter dataset involving BLM discussion tied to the EURO 2020 football championship. Analytical results unveil that polarized users will likely keep their affiliation to echo chambers in time. Moreover, we observed that the attitudes conveyed by Reddit users who joined risky epistemic enclaves are characterized by a slight inclination toward a more negative or neutral attitude when discussing particularly sensitive issues (e.g., fascism, school shootings, or police violence) while X/Twitter ones often tend to express more positive feelings w.r.t. those involved into less polarized communities.

Membangun Komitmen Aktivitas Karyawan Kelurahan Tegal Sari Mandala III Medan di Era Digital melalui Desain Komunikasi Organisasi yang Efektif

Abstract This research aims to investigate the role of technology and digital communication in building commitment to employee activities in Tegal Sari Mandala III Medan Village in the digital era through effective organizational communication design. Employee activity commitment is a key factor that influences organizational performance and success. However, the digital era brings new challenges in building employee commitment through changes in communication and interaction patterns in the work environment. Using a mixed quantitative and qualitative approach, this research combines surveys, in-depth interviews, and participant observation to collect data. The research results show that the effective use of digital communication technology, such as email, instant messaging applications, and online collaboration platforms, has a significant positive relationship with the level of employee activity commitment. The more effective the use of digital communication technology, the higher the level of affective, sustainable and normative commitment that employees have. This research also identified challenges in implementing digital communication technology and effective communication design, such as varying levels of digital literacy among employees. To overcome these challenges, organizations need to provide training and mentoring to employees, as well as adopt an integrated communication approach. In general, this research confirms that efficient organizational communication design, supported by the appropriate use of digital communication technology, can be a key factor in strengthening employee engagement in the digital era. Organizations that are able to optimize the potential of technology and digital communications well will have a competitive advantage in building a team of human resources that is qualified and has a high level of commitment.

Multilevel actor networks in China’s growing fossil-fuel based role in the global electricity sector

Abstract Chinese investments into fossil fuel-based electricity generation capacity under its Belt and Road Initiative will create lock-in for decades. Despite China’s recent rise as major public finance provider for the electricity sector of the developing world and the related environmental impacts, there is limited knowledge of the extent and characteristics of non-Chinese participation in Chinese-funded projects. We apply complexity theory approaches and network modelling on a new dataset that links funding activities of Developmental Institutions (Chinese Developmental Institutions (CDIs), Western-backed Multilateral Development Banks (MDBs)) and the involvement of companies in different roles (i.e., as direct investors, contractors, equipment suppliers, and other service providers) to power plants around the world at the unit-level (1999-2020). Previous literature suggests that CDI funded projects show preference to Chinese commercial partners, but we find more than 70% include non-Chinese participants. This also applies for fossil-fuel based technologies where we observe increasing shares of international actors that together account for nearly every third commercial linkage. However, involvement levels and interaction patterns not only differ by technologies (fossils, hydro, non-hydro renewable, nuclear) but also by the time period and types of commercial partners and we observe overall convergence between the CDI- and MDB-supported power plant networks over time. The decreasing involvement of Chinese companies in CDI-funded projects, across technologies, in favour of increasing Western involvement, has important implications for development and climate policy on which we elaborate. However, the failure of both MDB and CDI funding to promote domestic company involvement in the recipient countries may be the largest failing of both sets of agencies in the pursuit of development outcomes.

Influences of VR technology on the effect of museum narrative based on embodied cognitive theory: a case study of the opium war museum

Purpose With an increasing array of new technologies and devices emerging as novel approaches to museum narratives, scholars are intrigued by the narrative effect they produce. Within this context, this study aims to conduct a quantitative case study on the influencing mechanisms among technology embodiment, technology attachment, museum narrative effect and perceived museum image based on embodied cognitive theory (ECT). Design/methodology/approach By introducing the Opium War Museum, Dongguan City, Guangdong Province, China, as an empirical case study, the authors collected data, through questionnaires, from 425 visitors who had experienced virtual reality (VR) technology in the museum. The data was then analyzed by using the maximum likelihood estimate for structural equation modeling. Findings Technology embodiment and technology attachment have significant positive correlations with the museum narrative effect. Technology attachment provides a partial mediating effect between technology embodiment and the museum narrative effect. The perceived museum image moderates such mediating effects of technology attachment. Originality/value This study brings the emerging idea of developing visitor experiences with VR through expanding the applications of ECT in the museum scenario. First, the authors identified the underlying interaction patterns between various factors that influence the museum narrative effect. Second, the authors verified that technology can optimize the museum narrative effect and help the creative transformation of cultural relics and culture. This study also provides practical recommendations to improve the technological service experience and strengthen the “audience-centric” management concept of museums