Analytical Framework Research Articles

Synergistic evaluation of energy security and environmental sustainability in BRICS geo-political entities: An integrated index framework

Research background: The increasing demand for energy, driven by economic growth and population expansion, is a critical driver of societal progress. However, the predominant reliance on fossil fuels to meet this demand presents significant challenges, particularly in the rapidly developing BRICS nations (Brazil, Russia, India, China, and South Africa). These countries are faced with a complex interplay of energy security and environmental sustainability issues, stemming from their substantial fossil fuel reserves and the associated environmental consequences. The challenges manifest as inequalities in access to clean energy, environmental degradation, and heightened vulnerability to the impacts of climate change. Addressing these multifaceted issues requires a comprehensive approach. Metrics-based strategies, which employ aggregated indices derived from a diverse set of energy and environmental indicators, have the potential to provide valuable insights into these complexities. However, the development of a universally applicable energy sustainability index is complicated by the heterogeneity of metrics, disparities between countries, and methodological challenges, emphasizing the need for an innovative and holistic analytical framework. Purpose of the article: This study aims to develop a tailored Energy Security and Environmental Sustainability Index for BRICS economies to evaluate the robustness of their energy systems and the viability of their ecological practices. The index serves as an instrument to assess the progress of these nations in the Energy and Environment domain and identify areas that require targeted interventions and improvements. Methods: The construction of the composite ESESI involves the selection of relevant parameters and the application of a Multi-Criteria Decision Analysis (MCDA) framework in conjunction with the Weighted Product Method (WPM). To ensure objectivity in the determination of optimal and least favorable weights for each indicator, the study employs the Multiplicative Data Envelopment Analysis (MDEA) model. Findings & value added: The ESESI analysis reveals disparities in the progress made by BRICS nations in enhancing energy security, promoting renewable energy deployment, and mitigating environmental impacts. While some countries demonstrate substantial advancements, others face challenges in improving energy efficiency and reducing carbon emissions. The study underscores the necessity for tailored policies and targeted infrastructural enhancements that align with the unique challenges and strengths of each nation. Harnessing the abundant renewable energy potential through advanced energy trade mechanisms and fostering cross-border investments are identified as crucial strategies for ensuring environmental sustainability and long-term energy in the BRICS region. The ESESI provides a valuable tool for policymakers and researchers to evaluate the progress of BRICS nations in achieving sustainable energy goals and to inform evidence-based decision-making processes. By offering a comprehensive and scientifically rigorous assessment framework, this study contributes to the ongoing discourse on sustainable energy transitions and environmental stewardship in the context of rapidly developing economies.

A quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus in irrigation areas based on WEAP-MODFLOW

Agricultural irrigation areas are vital for ensuring food security under climate change conditions with extensive water and energy consumption, which will inevitably exert pressures on ecosystem especially in arid and semi-arid areas normally with higher ecosystem degradation risk. The key to realize sustainable development lies in a quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus. Climate change exacerbates these challenges by impacting food production, water resources utilization, energy consumption for water supply and ecological conditions in irrigation areas. In this work, a quantitative analysis framework to analyze the trade-offs and synergies among various scenarios within the water-food-energy-ecosystem nexus in irrigation area was proposed. Two climate models (BCC and MIR1) were selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and three Shared Socioeconomic Pathway (SSP) scenarios (SSP245, SSP370, and SSP585) were employed to quantitatively evaluate different sustainable development strategies among water, food, energy and ecosystem sectors from five key aspects: irrigation demand, food production, groundwater supply for irrigation, ecological base flow and irrigation water shortage. The results show that the sustainability index (SI) of combined management scenarios CC1-IM, CC5-IM, CC1-WSI and CC5-WSI could be 12% to 25% higher than that of the corresponding single management scenario. Among the four climate change and management scenarios, the SI of the integrated measures scenario (CC1-IM, CC5-IM) was the highest, with values of 1.39 and 1.12 respectively. This work highlights the pomising results of the integrated measures scenario in alleviating food production challenges, protecting ecological base flows, reducing groundwater extraction, alleviating irrigation water shortages and improving water supply efficiency when dealing with climate change challenges.

Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population.

Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9~11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state with the most within-network synchrony and the anticorrelations between networks, especially between the sensory networks and between the cerebellum and other networks, was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a DFC state showing integration of sensory networks and antagonism between default-mode and sensorimotor networks but weak segregation of the cerebellum. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.

Role preferences for ethnic autonomous region government in emergency management in China

China's ethnic autonomous regions, inhabited by diverse ethnic groups and located near national borders or at provincial intersections, confront complex natural, relatively underdeveloped economic, and ethnic harmony challenges. Governments balance central government-assigned tasks and ethnic harmony, showing varied responses: some prioritize stability, others focus on management, and some are indifferent to local emergencies. Given this phenomenon, why do governments in these regions vary in their responses to emergencies? This research, based on government role theory, examines how role preferences influence government behavior in emergency management, identifying two key roles: economic man and social man, and establishes a four-quadrant role analysis framework: : local guardian, political competitor, moral paragon, and indifferent bystander. Grounded theory explains these roles and evaluation indicators, with evaluations from 44 scholars gathered via questionnaire, and the indicator weights determined using the Analytic Hierarchy Process. The weight matrix then calculates economic man (X) and social man (Y) values for 272 cases based on expert scores. The research results reveal that (1) these governments exhibit an overall role preference of “local guardian > political competitor > indifferent bystander > moral paragon” in emergency management. (2) This ranking also reflects their preference for the economic man role. (3) Factors contributing to this ranking include cultural differences, administrative contracting, and performance tournaments within the Chinese system and administrative ethics. These findings provide theoretical insights into emergency management role preferences in multi-ethnic, multi-racial, and even multicultural regions, ultimately aiming to enhance local government emergency management efficiency, optimize policy formulation, and ensure community resilience.

Analysis of image formation in optical palpation.

Optical palpation is an emerging elastography technique that generates two-dimensional images of mechanical stress at the tissue surface, with clinical applications such asintraoperative cancer detection and scar assessment. It has been implemented using various imaging systems, however, an analysis of how deformation of the sample and layer influences image formation has not been performed. Here, an analysis framework is presented, which assesses performance independently of the imaging system used. Optical palpation of varying samples and layers is simulated using finite element analysis and validated with experiments on silicone phantoms, providing a characterization of detectability, feature resolution, and contrast ratio. Using our framework, we demonstrate that computational optical palpation, which incorporates realistic assumptions of layer deformation, improves the feature resolution up to a factor of four. This framework can guide the development of optical palpation and aid in the selection of appropriate imaging system and layer properties for a given application.

Long‐range Ising model for regional‐scale seismic risk analysis

AbstractThis study introduces the long‐range Ising model from statistical mechanics to the performance‐based earthquake engineering (PBEE) framework for regional seismic damage analysis. The application of the PBEE framework at a regional scale involves estimating the damage states () of numerous structures, typically performed using fragility function‐based stochastic simulations. However, these simulations often assume conditional independence or employ simplistic dependency models among the of structures, leading to significant misrepresentation of regional risk. The Ising model addresses this issue by converting the available information on binary (safe or failure) into a joint probability mass function, leveraging the principle of maximum entropy. The Ising model offers two main benefits: (1) it requires only the first‐ and second‐order cross‐moments, enabling seamless integration with the existing PBEE framework, and (2) it provides meaningful physical interpretations of the model parameters, facilitating the uncovering of insights not apparent from data. To demonstrate the proposed method, we applied the Ising model to 156 buildings in Antakya, Turkey, using post‐hazard damage evaluation data, and to 182 buildings in Pacific Heights, San Francisco, using simulated data from the Regional Resilience Determination tool. In both instances, the Ising model accurately reproduces the provided information and generates meaningful insights into regional damage. The study also investigates the change in Ising model parameters under varying earthquake magnitudes, along with the mean‐field approximation, further facilitating the applicability of the proposed approach.

Enhancing healthcare data privacy and security: A comparative study of regulations and best practices in the US and Nigeria

The imperative for robust healthcare data privacy and security is escalating as healthcare systems worldwide are increasingly digitized. This review paper presents a comprehensive comparative analysis of the regulatory frameworks, challenges, and best practices related to healthcare data privacy and security in the United States and Nigeria. By examining the Health Insurance Portability and Accountability Act (HIPAA) in the US and the Nigeria Data Protection Regulation (NDPR) alongside other local regulations, this study highlights the nuances of each country's approach to safeguarding patient data. The analysis extends to the effectiveness of technological solutions like encryption and blockchain, and assesses the role of governance in policy implementation. Case studies from both nations offer insights into successful strategies and underscore the gaps and opportunities for cross-country learning and improvement. The paper concludes with targeted recommendations for policymakers and healthcare providers, aiming to strengthen the security measures and propose areas for further research and development in healthcare data management. This comparative study not only sheds light on current practices but also charts a course for future collaborative efforts to enhance data privacy and security in healthcare on a global scale.

Do educators get it? Examining understanding of compulsory civic education for learners in Zambian secondary schools

This study explores educators' understanding of the rationale for Civic Education to be taught as a compulsory subject to learners in secondary schools in Zambia, as enshrined in the national policy on education. The study was conducted in all secondary schools of Kabwe district, guided by the Advocacy Coalition Framework Theory (ACF). Using a mixed-method approach within a pragmatic research paradigm, data were collected from 239 participants. Quantitative insights were derived from 215 respondents across 32 selected schools, including head teachers, heads of Social Science departments, heads of Civic Education sections, and Civic Education teachers who completed an online questionnaire. Qualitative data involved interviews with 24 educators and 2 Ministry of Education officials, supplemented by document analysis of national policy and curriculum frameworks. Findings underscore a robust awareness among educators of Civic Education's role in fostering informed citizenship and preparing learners for democratic engagement, focusing on four themes: (1) cultivating informed learners, (2) promoting good citizenship and social cohesion, (3) preparing learners for political engagement, and (4) enhancing critical thinking skills. Additionally, findings may inform policy and practice. Recommendations include enhancing professional development through targeted training programs, ensuring curriculum alignment with national policy frameworks, promoting interdisciplinary collaboration among departments, and establishing mechanisms for ongoing program evaluation to ensure effectiveness and continuous improvement. These recommendations aim to strengthen compulsory Civic Education implementation in Kabwe District, Zambia, fostering a knowledgeable and engaged citizenry capable of contributing positively to society.

Challenges and Opportunities for AI in Healthcare

The integration of artificial intelligence (AI) in healthcare presents a dual challenge: maximizing the efficiency of medical processes while safeguarding patient privacy. This comprehensive review examines the delicate balance between leveraging AI's potential in healthcare and preserving individual data privacy. Through analysis of recent literature, case studies, and regulatory frameworks, we explore the current landscape of AI applications in healthcare, associated privacy risks, and emerging solutions. Findings reveal that while AI significantly enhances diagnostic accuracy and treatment planning, it also raises concerns about data security and patient confidentiality. Key challenges include ensuring GDPR and HIPAA compliance, managing large-scale health data, and maintaining transparency in AI decision-making processes. Promising approaches such as federated learning and differential privacy emerge as potential solutions. This review underscores the need for a multidisciplinary approach involving healthcare providers, AI developers, ethicists, and policymakers to create robust, privacy-preserving AI systems in healthcare.

Theorizing “Primal World Beliefs”: A Developmental Account

Clifton and his colleagues have proposed a dimension-based conception of “primal world beliefs” – beliefs about “the overall character of the world”. Citing the potential importance of “primal world beliefs”, Lansford et al. (this volume) have called for research on their development. In this paper, I suggest that the concepts of “primal”, “world” and “belief” rely on unarticulated everyday concepts that are psychologically undertheorized. The model of “primal world belief” is a kind of methodological artifact; that is, it arises as dual product of everyday trait-like thinking and factor analytic methodology. Thus, while the authors are hopeful that “primal world beliefs” may provide an alternative way to explain the sources of stability that are typically attributed to “personality traits”, Clifton and his colleagues have themselves produced a kind of “trait theory of beliefs” – one that is vulnerable to many of the same critiques directed toward trait models of personality. After a critical analysis of the theoretical framework that undergirds the concept “primal world beliefs”, I offer a relational-developmental approach to the empirical study of how children develop beliefs within and about our intersubjectively structured lifeworlds.

Game-based Networked Learning

Designing and analyzing learning experiences in non-formal contexts can be challenging, even for those with educational training. The challenge is more significant if the priorities include educational ideological bases or an specific type of educational vision. The DALI project (Data Literacy for Citizens) has the primary goal of offering Data Literacy learning experiences specifically designed for adults in non-formal educational contexts. With this goal in mind, and considering the social and material realities of the target participants –their needs, diversity, interests and cultures– the project combines two of the most promising pedagogical approaches nowadays: networked learning and playful learning. This short paper outlines the pedagogical vision underpinning our efforts to integrate both approaches into a set of strategies and resources, in other words, the principles and ideas driving the design of what is called in the project: Game-based Networked Learning (GBNL) experiences. In particular, we draw on both the Activity Centred Analysis and Design (ACAD) framework and the Transdisciplinary Model for Developing Game-Based Interventions. After reviewing key aspects of the theoretical grounds that define our understanding of educational uses of technology and game-based learning, the paper addresses critical considerations underpinning the adaptation of the ACAD framework in the planning of playful learning experiences. Thus, the paper outlines the main principles guiding the design of DALI experiences, dividing them into the three design areas established by ACAD: set design, epistemic design and social design. The ambition of this approach is to serve as a pedagogical and educational statement to guide valuable actions to improve other adult learning approaches.

Hybrid learning environments: It’s all about ecology

Technology-enhanced learning has been part of higher education health contexts for nearly three decades, but since the recent Covid-19 pandemic specific challenges emerged, requiring learning design reconfigurations to facilitate continuity of student learning. The pandemic calls for a deeper understanding of how technology can promote connections, active participation and knowledge building in higher education health contexts. Identifying what is currently occurring and considering potential innovations requires a move towards a nuanced understanding of the ecology of complex elements contributing to productive networked learning in these contexts. This multiple-case study doctoral research project includes four undergraduate health courses at a New Zealand university, in the disciplines of nursing, social work, and bioscience. Drawing on the Activity Centred Analysis and Design (ACAD) framework, the research explores the influence of course design elements, such as social arrangements, learning space formats, learning tasks, on learning activity, taking a systematic approach to the analysis. In this paper, we report on initial observations and interviews with students involved in diverse hybrid learning environments, in order to identify how innovative learning designs are supporting students' agency and active engagement.

X-raying educator-related factors hindering sexuality education implementation in rural South African schools: learners’ perspectives

BackgroundAlthough numerous studies have demonstrated the effectiveness of well-implemented school-based sexuality education in promoting young people’s sexual and reproductive health outcomes, the prevalence of teenage pregnancy among school-going adolescents in rural schools located in the King Cetshwayo District of South Africa suggests it fell short of its objectives of reducing learners’ vulnerability, enhancing decision-making abilities, and raising self-efficacy in these schools. This study focused on learners’ experiences with their schools’ sexuality education programs.MethodsThis was a qualitative, interpretative phenomenological study. Data was collected from 84 participants across nine schools in the study setting using focus group interviews. An Interpretative Phenomenological Analysis framework was followed to analyze the data.ResultsThe results of the study showed that educator-centered impediments to effective school-based sexuality education included attitudes, beliefs, age differences, health status, prior experiences, and personal discomfort. The results further indicated that these obstacles have a detrimental effect on the pedagogical effectiveness of sexuality education teachers in implementing the curriculum in the study setting and, consequently, on the sexual well-being of the learners.ConclusionBased on the study’s findings, pre-service sexuality education pedagogy training is recommended for student teachers, as well as ongoing in-service training and re-training interventions and support for sexuality education teachers, to address the identified barriers and improve their pedagogical efficacy.

Non-Linear Impact of Economic Performance on Social Equity in Rail Transit Station Areas

Rail transit station areas (RSAs) are heralded as a transformative approach to urban planning, emphasizing the integration of transportation, housing, and commercial development to foster sustainable and inclusive cities. This study presents a comprehensive exploration of the interplay between transit-oriented development (TOD) economic performance and social equity in RSAs, employing advanced methodologies, like eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAPs), to decipher the complex relationships between TOD characteristics and social equity outcomes. Focused on Dalian’s urban center, this study integrates diverse datasets, including mobile location, geospatial, and economic price data, to construct a nuanced analysis framework within the NPE (node–place–economic) model. The results indicate that economic factors significantly impact overall social equity, particularly influencing key variables, such as weekday and weekend commuter population densities. Local explanatory plots reveal that economic performance variables associated with transportation development exhibit a broad non-linear impact on social equity in RSAs. This study advances equitable urban development through TOD by stressing the importance of factoring in multiple variables in RSA planning. This approach is vital for creating synergistic effects, fostering equitable spatial planning, and has both theoretical and practical benefits for improving residents’ well-being.

Adaptive Synchronization of Fractional-Order Uncertain Complex-Valued Competitive Neural Networks under the Non-Decomposition Method

This paper is devoted to the study of adaptive synchronization for fractional-order uncertain complex-valued competitive neural networks (FOUCVCNNs) using the non-decomposition method. Firstly, a new network model named FOUCVCNNs is proposed, which is not separated into two real-valued subsystems in order to keep its intrinsic speciality. In addition, a novel adaptive controller is designed to reduce the cost of control. Meanwhile, with the help of fractional Lyapunov theory, 1-norm analysis framework and inequality techniques, several effective synchronization criteria for FOUCVCNNs are obtained by constructing an appropriate Lyapunov function. Finally, the reliability of the results obtained is tested through numerical simulation.

A survey on machine learning system for intraductal papillary mucinous neoplasms detection

IPMN cysts, a pre-malignant risk to the pancreas, have the potential to develop into pancreatic cancer. Accurately identifying and evaluating the risk level is crucial for planning an efficient treatment strategy. However, this task is immensely challenging due to the varied and irregular shapes, textures, and sizes of IPMN cysts, as well as those of the pancreas itself. In this study, we introduce a new computer-aided diagnostic approach for classifying IPMN risk levels based on multi-contrast MRI scans. The proposed analysis framework comprises an efficient volumetric self-adapting segmentation strategy for delineating the pancreas, followed by a newly developed deep learning-based classification scheme incorporating a radiomics-based predictive approach. To evaluate the proposed decision-fusion model, we use multi-centre datasets and multi-contrast MRI scans, aiming to achieve superior performance compared to the current state of the art in this field. The ablation studies illustrate the importance of both radiomics and deep learning modules in achieving a new state-of-the-art (SOTA) performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). These key findings carry significant implications for clinical decision-making, potentially revolutionizing the way IPMN risk levels are classified. Through a series of rigorous experiments on multi-centre datasets (involving more MRI scans from five centers), we attained unprecedented performance levels with moderate accuracy. The code will be made available upon publication.

How to correctly fit an SIR model to data from an SEIR model?

In epidemiology, realistic disease dynamics often require Susceptible-Exposed-Infected-Recovered (SEIR)-like models because they account for incubation periods before individuals become infectious. However, for the sake of analytical tractability, simpler Susceptible-Infected-Recovered (SIR) models are commonly used, despite their lack of biological realism. Bridging these models is crucial for accurately estimating parameters and fitting models to observed data, particularly in population-level studies of infectious diseases.This paper investigates stochastic versions of the SEIR and SIR frameworks and demonstrates that the SEIR model can be effectively approximated by a SIR model with time-dependent infection and recovery rates. The validity of this approximation is supported by the derivation of a large-population Functional Law of Large Numbers (FLLN) limit and a finite-population concentration inequality.To apply this approximation in practice, the paper introduces a parameter inference methodology based on the Dynamic Survival Analysis (DSA) survival analysis framework. This method enables the fitting of the SIR model to data simulated from the more complex SEIR dynamics, as illustrated through simulated experiments.

Values in drug policy documents: applying Schwartz’s values theory to the report of the Special Commission of Inquiry into the Drug ‘Ice’ (NSW)

Background Documents are influential in shaping drug policy debates and are valuable data for drug policy research. In this article we conduct a values analysis of one drug policy document. The objectives were to assess the values within a policy document; assess the utility of Schwartz’s values framework in documentary analysis; and consider the relationship between values and policy positions. Methods We chose the report foreword of a Special Commission in Australia for analysis. We developed deductive, theoretically-informed codes following Schwartz’s values framework, and undertook reflexive analysis of the coding process. Individual and cultural values were coded at the paragraph level and summed to analyze dominant values. Results Values related to structural context and drug policy; drug policy values were primarily security and universalism values; policy positions did not neatly map onto values positions; and researcher interpretations of motivations shaped the value identified. Conclusion Policy documents can reveal conflicting values; these should be interpreted through inflection and emphasis specific to each occurrence. This work provides a step to future analyses that can differentiate multiple contrasting values informing a single policy position.

Face-work in North American Presidential Debates: A Corpus-based Multi-theoretical Approach

As inherently confrontational contexts, North American presidential debates compel politicians to engage in image restoration efforts when targeted by a face-threatening act. Since the first televised debates in 1960, presidential campaigns have become increasingly mediatized and the need to defend against face-damage has increased, for there is still a need to uphold a favorable presidential image despite the prominence of impoliteness in these events. Although a longstanding tradition, there is still no framework for the combined analysis of threatening and defensive face-work in presidential debates. This paper presents a corpus-based three-fold analysis blending qualitative, quantitative and statistical methodologies to observe the evolution of facework in this context, focusing on impoliteness and image restoration manifestations. The selected corpus comprises the presidential debates of seven North American elections —one per decade—, providing an overview of the evolution of both phenomena. This methodology also allows insight into the frequency of specific strategies. The analysis adopts a multi-theoretical approach to demonstrate the effectiveness of integrating Impoliteness theory (Culpeper, 1996) and Image restoration theory (Benoit and Wells, 1996) to create a comprehensive new approach to the study of face-work in presidential debates. Supported by results from Pearson’s Chi-square test, the combination of these theories was shown to be possible. The recurrence of attack-defense pairings integrating strategies from both theories also pointed to their necessary combination. While the recent incorporation of a female presidential candidate hinted at the potential gender variation of face-work in the debates, further data is needed to obtain conclusive results.

NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis

The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.