Multi-perspective Analysis Research Articles

Exploring the complexity of multilingual spaces: embracing diverse perspectives of linguistic non-understanding

Abstract In our article, we investigate the complex dynamics of linguistic understandings and mis- or non-understanding within multilingual contexts. Through the lens of sociolinguistic exploration, we navigate the multifaceted landscapes of language use, applying a multi-perspective approach and the triangulation method to explore the depths of linguistic non-understanding (LNU). Drawing on ethnographic data collection and multi-perspective analysis, we highlight the challenges and opportunities inherent in incorporating LNUs into research practice. Embracing a non-positivist stance, we acknowledge the role of being a ‘non-knower’ in our pursuit of understanding. Our methodology section briefly demonstrates the application of the triangulation method for researching multilingualism, while our empirical findings unveil various instances of LNU across everyday language use. We emphasise the importance of researchers’ reflexivity and the recognition of repertoire users as experts in their linguistic practices. Ultimately, our study offers insights into the analytical advantages of adopting an in-depth and multi-perspective approach to research in multilingual environments.

Family Support for Older adults’ Media Use: Implications for COVID-19 Research

ABSTRACT The article focuses on family support networks with regard to media-related learning processes of older adults. The article examines the conditions under which media-related support for older people took place within the family before the pandemic. For this purpose, a multi-perspective study on intergenerational media-related learning of older adults in families is presented. Based on multi-perspective analyses of 10 qualitative interviews with family support dyads (five younger supportive family members and five older adults each), it is shown that intergenerational media-related support in the family guides action in three different dimensions: direct and general reciprocity, creation and/or maintenance of options for action and emotional recognition. The results provide evidence for assessing the conditions for media-related support processes during the COVID-19 pandemic and point to implications for COVID-19 research.

Effects of habitat fragmentation on ecosystem services and their trade-offs in Southwest China: A multi-perspective analysis

Exploring the relationships between habitat fragmentation on ecosystem services (ESs) is important for ecological conservation and spatial pattern optimisation. However, analyzing the influence of different habitat fragmentation processes on ESs and their trade-offs from a multiple perspective is still relatively lacking. Therefore, taking Southwest (SW) China as an example, this paper evaluated the dynamics and trade-off relationship of four main types of ESs (i.e., carbon storage (CS), grain production (GP), water retention (WR), soil retention (SR)) during 2000–2020, and analyzed the effects of four habitat fragmentation processes (i.e., decreasing habitat area, increasing habitat density, increasing habitat shape complexity and decreasing habitat cohesion) on ESs and their trade-offs by using geographically and temporally weighted regression model. The results showed that ESs showed a deterioration trend during 2000–2020, with GP, WR, CS, SR, and total ecosystem services (TES) declined by 66.45 %, 54.33 %, 41.68 %, 76.25 % and 65.83 %, respectively. The GP-SR, GP-CS and GP-WR pairs exhibited trade-offs, while other ES types pairs exhibited synergies with each other. Increasing habitat density and increasing in habitat shape complexity were the two strongest fragmentation processes influencing ESs and their trade-offs. The effects of various habitat fragmentation processes on ESs and their trade-offs have obvious spatial heterogeneity. With changes in terrain relief, proportion of karst area, habitat quality, and urbanization level, the association of habitat fragmentation-ESs and habitat fragmentation-ESs trade-offs existed different sensitivities. The results can provide effective guidance for landscape planning and ecosystem management in the SW.

Unraveling the underlying causes and consequences of construction safety neglect: a multiperspective analysis of the Bangladeshi construction industry

Unraveling the underlying causes and consequences of construction safety neglect: a multiperspective analysis of the Bangladeshi construction industry

A data-driven multi-perspective approach to cybersecurity knowledge discovery through topic modelling

Cybersecurity is crucial for protecting the privacy of digital systems, maintaining economic stability, and ensuring national security. This study presents a comprehensive approach to cybersecurity knowledge discovery through topic modelling, using a multi-perspective analysis of academic and industry sources. The datasets include 15,751 articles from the Web of Science (WoS) database and 5,831 articles from Security Magazine, spanning from 2011 to 2023. We employed BERTopic for topic modelling, UMAP for dimensionality reduction, and HDBSCAN clustering algorithm for grouping and analysing distinct article clusters to uncover latent topics, enhancing the understanding of the evolving cybersecurity landscape. This study found 12 micro-clusters and three macro-clusters, namely technology, smart city and education, from the WoS database and 12 more micro-clusters and four macro-clusters, including organization, public security, governance, and education, from magazines. This study reveals key cybersecurity research and practice trends, such as the increasing focus on malware, ransomware, and cyber-attack mitigation. Additionally, temporal analysis indicates a significant rise in cybersecurity interest around 2020, followed by a diversification of topics. The results highlight the importance of integrating diverse data sources to capture a holistic view of cybersecurity developments. Future work will aim to refine the clustering algorithms to further improve topic extraction and analysis and expand the datasets to include more diverse sources and perspectives. This approach helps discover current cybersecurity trends and provides a foundation for more targeted and effective cybersecurity strategies.

Multi-perspective analysis of adiabatic compressed air energy storage system with cascaded packed bed latent heat storage under variable conditions

Adiabatic compressed air energy storage (A-CAES) with advanced thermal energy storage systems has enormous potential in applications. In particular, the extent of thermal energy utilization determines the comprehensive performance of an A-CAES system. In this paper, a cascaded latent heat packed bed storage system is used as a thermal energy storage under variable conditions of A-CAES. The transient thermal characteristic of the packed bed under variable conditions are further studied. Moreover, the key parameters that affect the thermal energy utilization of the system are also parametrically investigated. The results show that the proposed system has a roundtrip efficiency of 63.42 %, an exergy efficiency of 66.58 %, and a payback period of 4.63 years. The economic analysis shows that the proposed system is more advantageous in comparison with A-CAES with heat transfer oil as heat storage system.

Customer Citizenship Behaviour: A Multi-Perspective Analysis

Customer Citizenship Behaviour: A Multi-Perspective Analysis

Analysis of consumer preferences for new electric vehicle technologies: Can future vehicle steering system steer consumer's purchase intention?

As the mainstream of vehicle industry is moving from internal combustion engine vehicles to electric vehicles (EVs), a multi-perspective analysis of consumer preference for the new EV technology is required. Therefore, this study proposes a new framework for consumer preference analysis—a two-stage choice experiment—and conducts an empirical analysis of the four-wheel independent steering system (4-WISS). In the first stage, we analyzed consumer preference for the availability of 4-WISS in EVs. In the second stage, we investigated consumer preference for the specific technology of 4-WISS in EVs. Stated preference data were collected through a choice experiment survey, and a mixed logit model was used to quantitatively analyze consumer preference and willingness-to-pay. Results show that consumers are willing to pay KRW 19,045,098.6 (USD 13,513.9) to use 4-WISS in high-end EVs. Furthermore, consumers who are female, older, and have more knowledge of autonomous vehicles prefer 4-WISS more. We found that consumers prefer the parallel movement among 4-WISS modes, the touch display for selecting 4-WISS, the steering wheel for operating 4-WISS mode, and dashboard for displaying information. We finally discuss the new business model in the vehicle industry and suggest automakers to establish an effective strategy for the initial market for EVs with 4-WISS.

Enhanced heat transfer and thermal storage performance of molten K2CO3 by ZnO nanoparticles: A molecular dynamics study

Molten salt serves as a popular medium for thermal storage and transfer in concentrated solar power (CSP) systems. The existing experimental studies have found the capacity of enhancing the heat storage property of the molten carbonate by doping ZnO nanoparticles. However, it has been rarely reported to conduct simulations of ZnO/molten carbonate nanofluids, and the underlying mechanism of how ZnO nanoparticles enhance the heat transfer and storage capacity has less been explored. In this study, molecular dynamics (MD) simulation has been used to investigate the heat transfer and storage properties of a molten K2CO3 nanofluid containing ZnO nanoparticles. It was found that as the nanoparticle mass ratio in molten salt based nanofluids rises, their specific heat capacity initially increases but subsequently diminishes. The specific heat capacity peaks when the ZnO mass ratio is at 1 wt%, resulting in an increase of 17.83 % compared with the base salt. The thermal conductivity in nanofluids based on molten salt increases with the increasing of ZnO mass ratio. Multi-perspective analysis of the mechanism how ZnO nanoparticles enhance the thermal energy storage performance of molten salt, including the microstructural analysis, number density, vibrational density of state, and heat flow decomposition, is conducted. In addition, results of the number density illustrates that a compressed layer of K+ is formed surrounding the ZnO nanoparticles, and the change in the potential energy leads to an increasing heat capacity. Analysis of the heat flow decomposition suggests that the enhanced nonbonded interaction is the main reason for the improvement of thermal conductive performance in the nanofluids based on molten salt. The findings in the present study provide a new perspective of how ZnO nanoparticles enhance the thermophysical properties of molten salts.

A spatiotemporal attention-augmented ConvLSTM model for ocean remote sensing reflectance prediction

Remote sensing reflectance (Rrs) is an essential parameter in ocean color remote sensing and a fundamental input for the estimation of ocean color elements. Predicting Rrs has the potential to enable simultaneous prediction of multiple marine environmental parameters, facilitating multi-perspective analysis of marine environmental changes. This paper proposes a spatiotemporal attention-augmented ConvLSTM-based model for ocean Rrs prediction. The developed model can predict Rrs for up to seven days by simultaneously learning spatiotemporal features from time series Rrs and auxiliary environmental variables. According to the experiments, the proposed model achieves optimal performances on Rrs predictions at 443, 488, and 555 nm, with Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) for the first four prediction days less than 5.6*10-4 sr-1 and 8.6 %, respectively, which are better than the performance of the convolutional neural network (CNN), the LSTM, CNN-LSTM, and the ConvLSTM. The spatial and temporal variations of Rrs are also compared to evaluate the effectiveness of the model, presenting a consistent spatiotemporal pattern between predicted and observed Rrs. We also found that integrating sea surface temperature (SST), photosynthetically available radiation (PAR), and aerosol optical thickness at 869 nm (AOT869) into the model can improve the prediction accuracy in various degrees. This work suggests the proposed deep learning model can predict Rrs for 7 days with a convincing performance, providing critical data and technical support for ocean-related applications, such as algae bloom monitoring.

Healthy, Inclusive and Sustainable Catering in Secondary Schools-An Analysis of a Transformation Process with Multiple Tensions.

Interest in catering for public sector schools is increasing due to its potential role in addressing the prevailing problems of malnutrition, food insecurity and non-sustainable food habits. Based on the case of secondary schools in Berlin, this study aims to explore this potential by focusing on the process of transformation towards healthy, inclusive and sustainable school catering. It employs a multi-perspective analysis based on the two concepts of food environment and social cohesion. Results are based on quantitative and qualitative data collected via an online survey of pupils from 25 secondary schools in Berlin as well as field notes from six stakeholder events. The survey findings were analyzed by descriptive means and provide explanations for the fact that most of the pupils (66.7%) never eat lunch at school. Based on the qualitative analysis of the stakeholder events, key tensions between actors from the federal state, municipal, school and private levels could be identified. Major areas of conflict arise due to (1) a lack of public funding and catering standards, (2) incompatible demands and preferences, (3) a lack of resources and opportunities for complementary education and participation, and (4) peer and parental influence. Transforming school food environments requires integrative strategies with interventions introduced by multiple actors operating on different levels.

Gender Inequality: A Multi-Perspective Analysis

Gender equality is a fundamental human right that has long been denied, and despite progress in some areas, significant challenges still remain in achieving true gender equality. The research motivation of this paper mainly stems from the pursuit of the universal value of social justice and human rights. The aim of gender equality studies is to understand and address the problems caused by gender inequality and gender discrimination in society. In this paper, the author analyzes gender inequality from three perspectives, namely gender inequality in the workplace, education, and health care. It can be summarized that gender inequality at the workplace mainly refers to the situation that women are put at a disadvantage in their workplace, gender inequality in education mainly refers to the unfair treatment of girls in school, and gender inequality in health care mainly refers to the large differences in maternal mortality between low- and high-income countries.

Multi-perspective analysis on data augmentation in knowledge distillation

Knowledge distillation stands as a capable technique for transferring knowledge from a larger to a smaller model, thereby notably enhancing the smaller model’s performance. In the recent past, data augmentation has been employed in contrastive learning based knowledge distillation techniques yielding superior results. Despite the significant role of data augmentation, its value remains underappreciated within the domain of knowledge distillation, with no in-depth analysis in the literature thus far. To make up for this oversight, we conduct a multi-perspective theoretical and experimental analysis on the role that data augmentation can play in knowledge distillation. We summarize the properties of data augmentation and list the core findings as follows. (a) Our investigations validate that data augmentation significantly boosts the performance of knowledge distillation on the tasks of image classification and object detection. And this holds true even if the teacher model lacks comprehensive information about the augmented samples. Moreover, our novel Joint Data Augmentation (JDA) approach outperforms single data augmentation in knowledge distillation. (b) The pivotal role of data augmentation in knowledge distillation can be theoretically explained via Sharpness-Aware Minimization. (c) The compatibility of data augmentation with various knowledge distillation methods can enhance their performance. In light of these observations, we propose a new method called Cosine Confidence Distillation (CCD) for more reasonable knowledge transfer from augmented samples. Experimental results not only demonstrate that CCD becomes the state-of-the-art method with less storage requirement on CIFAR-100 and ImageNet-1k, but also validate the superiority of CCD over DIST on the object detection benchmark dataset, MS-COCO.

Multi-perspective analysis of sustainability metrics characterising the debris environment

To assess the effectiveness of mitigation measures, long-term simulations of the debris environment are often analysed and evaluated using simple metrics such as the total number of objects or the frequency of fragmentation events. In addition, more complex criticality metrics have been developed in the past to rank objects according to their environmental impact and evaluate the overall state of the debris environment. In this context, three different perspectives are particularly worth highlighting: object-specific metrics that characterise the criticality of individual objects and orbits, spatial metrics that can be used to identify particularly vulnerable orbital regions, and global metrics which describe the sustainability of the environment as a whole. Typically, the criticality metrics are specific to one of the respective perspectives, but unification across and convertibility between perspectives would allow for a more comprehensive assessment of sustainability. In this context, a methodology is developed to transform object-specific and spatial metrics into one another. The conversion concept enables a comparison of two structurally different metrics from their respective initial object-specific and spatial domain. Furthermore, the third perspective is taken into account by using different aggregation methods to retrieve a global metric from the initial object-specific and spatial description. The three perspectives then are compared by conducting environment evolution simulations for various baseline scenarios using a common space debris population in low Earth orbit. The comparative analysis identifies commonalities and differences of the chosen metrics in the three different perspectives and how the conversion between them affects their quantification of sustainability. The multi-perspective convertibility and aggregation of single-perspective metrics represents a contribution to the generalisation of sustainability metrics for different scopes of application.

Standards of care and determinants of women’s satisfaction with delivery services in Nepal: a multi-perspective analysis using data from a health facility-based survey

BackgroundCompliance with standards of care is required for sustained improvement in the quality of delivery services. It thus represents a key challenge to improving maternal survival and meeting the Sustainable Development Goal (SDG) target of reducing the maternal mortality ratio to 70 deaths per 100,000 live births. This study examines the extent to which normal low-risk health facility deliveries in Nepal meet the standards of quality of care and assesses the effect of the standards of quality of care and various contextual factors on women’s satisfaction with the services they receive.MethodsDrawing on the 2021 Nepal Health Facility Survey, the sample comprised 320 women who used health facilities for normal, low-risk delivery services. A weighted one-sample t-test was applied to examine the proportion of deliveries meeting the eight standards of care. Women’s overall satisfaction level was computed from seven satisfaction variables measured on a Likert scale, using principal component analysis. The composite measure was then dichotomized. Binary logistic regression was used to analyze the determinants of women’s satisfaction with delivery care services.ResultsDeliveries complying with the eight standards of care and its 53 indicators varied widely; output indicators were more frequently met than input indicators. Of the eight standards of care, the “functional referral system” performed highest (92.0%), while “competent, motivated human resources” performed the least (52.4%). Women who were attended by a provider when they called for support (AOR: 5.29; CI: 1.18, 23.64), who delivered in health facilities that displayed health statistics (AOR 3.16; CI: 1.87, 5.33), who experienced caring behaviors from providers (AOR: 2.59; CI: 1.06, 6.30) and who enjoyed audio-visual privacy (AOR 2.13; CI: 1.04, 4.38) had higher satisfaction levels compared to their counterparts. The implementation of the Maternity Incentive Scheme and presence of a maternal waiting room in health facilities, however, were associated with lower satisfaction levels.ConclusionsNepal performed moderately well in meeting the standards of care for normal, low-risk deliveries. To meet the SDG target Nepal must accelerate progress. It needs to focus on people-centered quality improvement to routinely assess the standards of care, mobilize available resources, improve coordination among the three tiers of government, and implement high-impact programs.

Intellectual dark web, alt-lite and alt-right: Are they really that different? a multi-perspective analysis of the textual content produced by contrarians

Intellectual dark web, alt-lite and alt-right: Are they really that different? a multi-perspective analysis of the textual content produced by contrarians

Human-computer Interaction (HCI) Approach to Artificial Intelligence in Education (AIEd) in Architectural Design

The field of artificial intelligence (AI) in architectural design (AIEd) has experienced significant growth, and there is great potential for the application of Generative AI (GAI) in architectural design education. However, addressing challenges associated with AI is important. These include overhyped speculation, hidden inherent drawbacks such as fairness and ethics, and a trend of lacking human interaction when AI is involved. To tackle these issues, this paper introduces a triangulated research framework encompassing three key perspectives: vision, technology, and user acceptance. This framework aligns with Human-computer Interaction (HCI) principles, AI technology development, and past experiences in AIEd. By adopting this multi-perspective analysis approach, the paper aims to comprehensively understand the phenomena surrounding AI in architectural design. Furthermore, the research presented in this paper goes beyond theoretical discussions and illustrates how the research findings are applied in practice. It showcases the design of an ongoing architectural design course that incorporates the insights gained from the research. Three key observations from the ongoing designed modules indicate the need to shift the focus towards integrating multi-modal AIs and existing parametric tools. Secondly, it is essential to emphasise AIs as design partners rather than making assumptions about AIs’ specific uses at different stages. Lastly, providing user-friendly tools and theoretical foundations motivates students to explore beyond the design process, expanding their research and design boundaries.

Navigating the Road to Safer Travel in Pakistan: A Multi-Perspective Analysis of Road Safety Challenges and Solutions

With the growing global population, the demand for mobility and well-managed modes of transportation is increasing day by day. However, the rapid growth in motorization is a double-edged sword. On one side, it brings social and economic development; on the other, it results in severe consequences, such as road traffic crashes. Pakistan, a developing country, is also dealing with the same issue. Therefore, this paper aims to explore the road safety issues in Pakistan. The analysis is carried out from five perspectives: infrastructural, institutional, socio-economic, socio-cultural/behavioral, and educational. The study highlights that inadequate road infrastructure, lack of road safety institutions and policies, absence of a credible crash reporting system, poor and ineffective enforcement, road users’ aberrant behaviors, and lack of road safety awareness all contribute to road safety problems in Pakistan. To address these issues, the study proposes a set of key measures, such as expanding road infrastructure, establishing dedicated road safety institutions, centralizing data collection efforts, implementing comprehensive safety tests for vehicles, and organizing road safety seminars to educate the youth. These recommendations aim to enhance road safety, ultimately reducing fatalities and economic losses caused by road crashes in Pakistan.

Exploring Intercultural Insights Through the Critical Incident Research Method

The Critical Incident Technique (CIT) has become a renowned approach in qualitative research, offering a systematic framework for exploring interactions, revealing human perception and conduct in different scenarios. The technique analyses significant incidents that emerge from either observations or narrations within specific contexts and helps to scrutinise thoughts, emotions, attitudes or actions. CIT uncovers cultural discrepancies within intercultural communication and has been implemented in various disciplines. Originating from aviation research, CIT has proved to be adaptable to other areas and has thus been successfully integrated into intercultural training and research. Regardless of its utility and popularity, some critics suggest it can oversimplify complexity. Nevertheless, recent publications have demonstrated the ongoing significance of CIT. These publications use linguistic approaches that broaden its horizons, encompassing narrative methodologies and multi-perspective analyses. While CIT provides significant insights, it is important to acknowledge its limitations in terms of subjectivity and generalisability. In conclusion, CIT continues to be a valuable qualitative research approach that enhances our understanding of human behaviour and experiences through the systematic analysis of key events.

Environmental protection tax on enterprise environmental, social and governance performance: A multi-perspective analysis based on financing constraints

This article fills the research gaps of existing studies on the mechanism between environmental policy and corporate performance in environmental, social, and governance (ESG). The study finds that implementing environmental protection tax can improve the ESG performance of heavily polluting firms. By examining the mediating effects of factors based on financing constraints, the study further explores the link between environmental protection tax introduction and ESG performance of companies. In addition, the heterogeneity analysis in terms of tax preference, total operating cost, and monopoly power, shows that heavily polluting firms with higher tax preferences, higher total operating costs, or weaker monopoly power are more likely to enhance their performance on ESG issues after introducing the environmental tax. This research provides essential theoretical and practical implications for revealing the connection that exists between environmental tax policy and the sustainability of companies.