Analytical Framework Research Articles

Balancing Acts: Navigating the Ethical and Legal Challenges of Genomic Medicine in Healthcare

This paper delves into the intricate relationship between the evolving field of genomic medicine and healthcare providers' legal and ethical responsibilities, with a particular focus on the duty to warn in the context of genetic risks. Through a critical examination of landmark cases such as Pate v. Threlkel and Safer v. Estate of Pack, this study underscores the expanding scope of healthcare providers' duties to include not just the patient but potentially at-risk family members as well. It highlights the legal, ethical, and practical challenges that arise when balancing patient confidentiality with the need to prevent harm through the disclosure of genetic information. The descriptive portion of the paper outlines the current legal precedents and the ambiguity surrounding healthcare providers' responsibilities. The prescriptive portion proposes the establishment of comprehensive, clear guidelines to support healthcare providers, particularly genomic counselors, in navigating these complexities. This includes recommendations for policy changes and the development of protocols that respect patient autonomy while addressing the preventive potential of genomic medicine. Through an analysis of legal frameworks and ethical considerations, this paper argues for a nuanced approach that equitably balances individual rights with the collective good, ultimately advocating for a future where genomic medicine is integrated into patient care in an ethically responsible and legally sound manner.

Space-time CutFEM on overlapping meshes I: simple continuous mesh motion

AbstractWe present a cut finite element method for the heat equation on two overlapping meshes: a stationary background mesh and an overlapping mesh that moves around inside/“on top” of it. Here the overlapping mesh is prescribed by a simple continuous motion, meaning that its location as a function of time is continuous and piecewise linear. For the discrete function space, we use continuous Galerkin in space and discontinuous Galerkin in time, with the addition of a discontinuity on the boundary between the two meshes. The finite element formulation is based on Nitsche’s method and also includes an integral term over the space-time boundary between the two meshes that mimics the standard discontinuous Galerkin time-jump term. The simple continuous mesh motion results in a space-time discretization for which standard analysis methodologies either fail or are unsuitable. We therefore employ what seems to be a relatively uncommon energy analysis framework for finite element methods for parabolic problems that is general and robust enough to be applicable to the current setting. The energy analysis consists of a stability estimate that is slightly stronger than the standard basic one and an a priori error estimate that is of optimal order with respect to both time step and mesh size. We also present numerical results for a problem in one spatial dimension that verify the analytic error convergence orders.

A framework for leaf disease analysis and estimation using MAML with DeepLabV3

A framework for leaf disease analysis and estimation using MAML with DeepLabV3

Research on Portable Intelligent Terminal and APP Application Analysis and Intelligent Monitoring Method of College Students' Health Status

As a carrier of college students' health status monitoring, portable intelligent terminal APP, the study of its APP application analysis not only provides a new way for college students' extracurricular physical exercise, guides college students to actively participate in extracurricular physical activities using intelligent terminal APP software, but also promotes college students' physical health monitoring and enhancement in various aspects. Aiming at the current portable terminal APP college students' health monitoring application analysis method research exists low precision, real-time poor and other problems, through the analysis of the basic functional framework and functional characteristics of the portable intelligent terminal APP, the establishment of the portable intelligent terminal APP analysis index system applied to college students' health monitoring, combined with the heuristic optimisation algorithm and the improvement of deep learning algorithms, the construction of the marine predator based heuristic optimisation algorithm and the attention mechanism to improve the gating control loop. Combining the heuristic optimisation algorithm and the improved deep learning algorithm, we construct the portable intelligent terminal APP application analysis method for college students' health monitoring based on the marine predator heuristic optimisation algorithm and the attention mechanism improved gated recurrent unit neural network. Through simulation analysis, the results show that the proposed method meets the real-time requirements while improving the prediction accuracy of the portable smart terminal APP application analysis method, and significantly improves the efficiency of portable smart terminal APP analysis.

Cost-effectiveness analysis of a cardio-oncology rehabilitation framework compared to an exercise intervention for cancer survivors with high cardiovascular risk.

A cardio-oncology rehabilitation model among cancer survivors showed superior results comparing to a community-based exercise intervention. However, questions remain about its cost-effectiveness. To assess the cost-effectiveness of a center-based cardiac rehabilitation (CBCR) program when compared to usual care encompassing a community-based exercise training (CBET), among cancer survivors with high cardiovascular risk. The CORE study was a single-center, prospective, randomized controlled trial; 80 adult cancer survivors with previous exposure to cardiotoxic cancer treatment and/or with previous cardiovascular disease were assigned (1:1 ratio) to an 8-week CBCR or CBET, twice/week. Cost-effectiveness was a pre-specified secondary endpoint. Outcomes included healthcare resource use and costs, quality-adjusted life-years (QALYs) and cost-effectiveness; incremental cost-effectiveness ratio (ICER) was computed from a societal perspective. 75 patients completed the study (CBCR N=38; CBET N=37). The CBCR had significantly higher cost per patient (477.76 ± 39.08€) compared to CBET group (339.32 ± 53.88€), with a significant between-group difference 138.44€ (95% CI, 116.82 to 160.05€, p<0.01). A between-group difference by 0.100 points in QALYs was observed, favouring the CBCR (95% CI, -0.163 to -0.037, p=0.002). When CBCR was compared with CBET, the ICER was €1,383.24 per QALY gained; at a willingness-to-pay threshold of €5,000 per QALY, the probability of CBCR being cost-effective was 99.9% (95% CI, 99.4 to 100.0). The CORE trial shows that a CBCR is a cost-effective intervention in the management of cancer survivors with high cardiovascular risk, reinforcing the potential benefits of this multidisciplinary approach in supportive care of this specific subset of cancer patients.

Urban mobility in the future: text analysis of mobility plans

Sustainable Urban Mobility Plans (SUMPs) or similar documents from 17 European capitals (published between 2010 and 2021) and the European SUMP guidelines have been analysed to understand how cities shape mobility and their transport systems. Text analysis is applied to identify development tendencies in a time- and cost-effective manner, without relying on traditional deep semantic analysis techniques. In addition to traditional statistical indicators, we introduce Category Term Frequency (CTF) as a new measure in text analysis. CTF reveals the number and proportion of words belonging to the same content group, namely specific mobility-related categories. The results indicate that categories describing general aspects such as the future, general transport, environment, and society are more prominently represented compared to more forward-looking categories like automation, electromobility, and sharing. The aggregated CTF of categories describing these emerging aspects is highest in the mobility plans of Copenhagen, Helsinki, Luxembourg, and Vienna, which are considered forerunners in their implementation. In general, the analysis concludes that despite recent technological developments and new business models, the examined mobility plans barely mention terms that would imply radical changes by the 2030s. Strategic documents and, thus, urban mobility developments suggest only a slow transition towards the expected levels of sustainable and smart urban mobility. These findings may contribute to understanding and (re)considering urban and transport development strategies in Europe. Furthermore, this text analysis framework provides planners and other experts with a novel tool to identify the focal areas of mobility-related (or other) documents.

Study on the Morphological Analysis and Evolution of the Street Network in the Historic Urban Area of Changsha City from 1872–2023

This study focuses on the streets and spatial networks of the historic urban area in Changsha, the capital of Hunan Province, China, by mainly investigating the evolution of their geometric and topological characteristics. It draws on the theories and methods of urban morphology and space syntax, combines the digital historical maps at different times, and uses methods such as historical map spatial interpretation, geographic information system (GIS), sDNA tools, and urban morphological parameter analysis to explore and sort out the evolutionary process of the street and alley network in Changsha from the early modern period to the present. The paper constructs a parameter system for analyzing the street and alley network of historic urban areas from a geometric and topological perspective. It introduces the indicators of road density, road orientation, intersection density, and approaches such as closeness, betweenness, and intelligibility in space syntax into the parameter analysis framework of street and alley network morphology and spatial characteristics evolution. By comparing the changes in various parameters at different spatial scales, the process of the spatial order evolution of the street and alley network in the historic urban area is analyzed, and the evolutionary rules of the various periods’ morphological characteristics are extracted.

Navigating the decision-making landscape of AI in risk finance: Techno-accountability unveiled.

The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data-centric decision-making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno-stressors) impact both techno-eustress (positive stress) and techno-distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e-survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno-accountability as a significant factor, contribute to the risk analysis domain by improving the failure mode and effect analysis framework to better fit the rapidly evolving landscape of AI-driven innovations.

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.

Deconstructing Digital Rhetoric to Face Geopolitical Challenges: A Social Media Narrative Analysis Framework

Skyrocketing technological advancement along with changes in environment dynamics allowed people, states and governments to influence each other’s diplomacy, security and international relations, using words and images, strings of events, to construct narratives. The rhetoric of power and conflict is distributed in chameleonic volatile environments, targeting members of the society to challenge security status and power balance, while new media instruments feed people`s thirst for information to challenge their beliefs into tailoring attitudes and behaviours. Defined by many as a “portrayal of events surrounding change” (Dunford and Jones, 2000) or as “central feature of modern influencing across borders (Wagnsson and Lundström, 2023) in a continuous “battle”, (Van Noort, 2017), strategic narratives bear multiple meanings, generated by the versatility of the digital dissemination channels. Understanding these meanings enlighten many directions, like international relations, diplomacy and politics, resulting in risk-free engagement, vulnerability- awareness and controlled decision-making policies, just by using narrative analysis as the appropriate instrument. However, the classical analytical models do not provide enough lenses to grasp all the components generating meaning in a digitally disseminated content and many features are thus ignored, mutilating paramount intelligence otherwise needed to complete the picture. That is why, this paper advances a model framework for a digital narrative analysis that looks at components of the textual narrative backed by the features of the environment, the characteristics of the audience and the dynamics of the dissemination, coupled with more recent social media metrics. To this end, we reviewed literature on Russian formalism (Propp, 1928), (Tomashevsky, 1965) structuralist narratology (Todorov 1969, Genette 1988, Bal 1977), poststructuralism (Chatman,1978) and postclassical narratology (Herman, Fludernik, 1999) to the most recent digitally related approaches (Georgakopoulou, 2020), (Bal, 2021) and then advanced our analysis framework that adds specific traits which social media environments enhance. We thus intend to contribute to the large body of literature with an analytical model, to render a mould of the strategic narratives in the security, defence, politics and international relations.. This paper is part of a larger study, the framework will be piloted in further research, where data will be gathered to validate the model.

Copyright Protection of Digital Content in Vietnam’s Internet Age

The digital content sector in Vietnam is viewed as a "new gold mine”, with a multi-billion-dollar industry. We look at Vietnam's legislative framework for copyright protection, which includes both Intellectual Property, and Cybersecurity Laws. The paper contends that while technology can help address some of the issues of preserving digital content, it is not a cure-all and must be used together with legal and educational measures. The significance of international cooperation in preserving digital content is highlighted. Finally, this research presents a complete analysis of Vietnam's current legal framework for copyright protection of digital information, as well as an assessment of the obstacles and limitations in enforcing these laws. It further highlights the importance of international collaboration in digital content protection and asks for increased efforts to deepen cooperation in this area, and is aimed at policymakers, legal professionals, and scholars interested in copyright protection and digital material in Vietnam.

An information-theoretic framework for conditional causality analysis of brain networks

An information-theoretic framework for conditional causality analysis of brain networks

PARALLEL IMPORT: THEORY, PRACTICE AND SPECIFICS IN THE CONTEXT OF SANCTIONS RESTRICTIONS

Parallel import refers to one of the controversial and ambiguous issues actively discussed in scientific literature over the past decades. On the one hand, parallel import is associated with the violation of intellectual property rights of trademark owners, and on the other hand, it is an indirect tool to combat price discrimination on the world market. Parallel import as a forced measure under sanctions is of particular research interest. The legalization on parallel imports in Russia, which allows to freely import certain goods into the country without the copyright holders’ permission, is aimed at countering large-scale anti-Russian sanctions, maintaining stable trade turnover and protecting the domestic industrial sector. The purpose of the work is to study the reasons for parallel import existence, its positive and negative aspects, as well as the specifics of importing goods into the country under sanctions. The methodology of the study is based on the analysis of international and Russian legislative framework, generalization of scientific and practical approaches to parallel import issues, as well as statistical data. The results of the study allows us to conclude that using the term «parallel import» to characterize the processes of importing goods under sanctions is not entirely legitimate due to the absence of the main, authorized import channel in most cases. The work provides a risk map related to the use of a parallel import scheme in the activities of Russia’s enterprises. The findings can be applied by commercial and industrial enterprises in devising a risk minimization strategy in conditions of sanctions restrictions.

Exploring textual–visual strategies in internet-based light food advertising: a study of Taobao advertisements in China

Amidst global health concerns such as COVID-19 and rising obesity rates, the connection between food choices, societal well-being, and cultural shifts has gained attention. In China, the rise of “light food” consumption on platforms like Taobao reflects a growing trend towards health-conscious dietary preferences. With a dataset comprising 633 images and 45,817 words, condensed into 50 images for analysis, this research aims to understand how these advertisements influence perceptions of healthiness and desirability, and whether there is a discrepancy between advertised health benefits and actual nutritional content. This study delves into the advertising strategies employed for light food products on the Taobao platform in China, utilizing a Textual–Visual Thematic Analysis (TVTA) framework. Noteworthy findings include: (1) Internet-based light food advertising adopts a dual textual and visual approach. (2) Identification of prevalent multimodal devices, such as medium shots, varied camera angles, vibrant colors, and large size. (3) Uncovering inconsistencies in presentation that may pose potential consumer misperceptions. These discoveries underscore the nuanced landscape of light food advertising, offering valuable insights for both academia and industry.

Design of Fast Mining Algorithm for Educational Sport Course Data Based on Cluster Analysis

In this age of big data, education researchers are reconceptualizing and re-evaluating the value of education data. Therefore, we need to use educational data mining methods for data analysis to better guide teaching. The informatization level of colleges and universities is improving year by year, and the entire training data of students from enrollment to graduation is stored. These datasets are collected, stored, and kept by different departments, contain a large amount of regular and relevant information, and truly record the growth footprint of students. Traditional educational decision-making has not yet fully explored and used the valuable information hidden in data resources. Although some scholars have carried out research related to campus data mining at this stage, there are still many problems that have not yet been solved in the application of decision-making in colleges and universities. This paper is based on the idea of data-driven decision-making, combined with the data characteristics of campus big data, and establishes a model solution for student behavior analysis and behavior prediction by applying multiple machine learning algorithms. On the basis of the analysis of students’ academic behavior performance in the context of multi-category educational data, we proposed a cluster analysis framework for processing multi-type campus big data, and described the group characteristics of the clustering results. By introducing the K-prototype algorithm, we effectively solved the multi-category problem where traditional clustering algorithms (such as K-Means, etc.) cannot adapt to the attributes of educational data. The research results show that innovative educational decision-making models and methods are based on the idea of “data-prediction-decision”, which promotes the application research of big data science in the area of education.

Deep parallel contextual analysis framework based emotion prediction in community wellness communications on social media

Understanding public emotion on social media about community wellness is crucial for enhancing health awareness and guiding policy-making. In order to more fully mine the deep contextual semantical information of short texts and further enhance the effectiveness of emotion prediction in social media, we propose the Deep Parallel Contextual Analysis Framework (DPCAF) in the community wellness domain, specifically addressing the challenges of limited text length and available semantical features in social media text. Specifically, at the embedding layer, we first utilize two different word embedding techniques to generate high-quality vector representations, aiming to achieve more comprehensive semantical capture, stronger generalization ability, and more robust model performance. Subsequently, in the deep contextual layer, the obtained representations are fused with POS and locational representations, and processed through a deep parallel layer composed of Convolutional Neural Networks and Bidirectional Long Short-Term Memory Network. An attention model is then used to further extract semantical features of social media texts. Finally, these deep parallel contextual representations are post-integrated for emotion prediction. Experiments on a dataset collected from social media regarding community wellness demonstrate that compared to benchmark models, DPCAF achieves at least a 4.81 % increase in Precision, a 3.44 % increase in Recall, and a 10.81 % increase in F1-score. Relative to the most advanced models, DPCAF shows a minimum improvement of 2.65 % in Precision, 3.02 % in Recall, and 2.53 % in F1-score.

EEG motor imagery decoding: a framework for comparative analysis with channel attention mechanisms

Objective. The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact. Approach. We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms. Results. Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture. Significance. Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for electroencephalogram motor imagery decoding within BCIs.

Information transfer pathways: signal propagation in complex global topologies

Abstract In the real world, many dynamic behaviors can be explained by the propagation of perturbations, such as the transfer of chemical signals and the spread of infectious diseases. Previous researchers have achieved excellent results in approximating the global propagation time, revealing the mechanism of signal propagation through multiple paths. However, the known frameworks rely on the extension of physical concepts rather than mathematically rigorous derivations. As a result, they may not perfectly predict time or explain the underlying physical significance in certain specific cases. In this paper, we propose a novel method for decomposing network topology, focusing on two modules: the tree-like module and the path-module. Subsequently, we introduce a new framework for signal propagation analysis, which can be applied to estimate the propagation time for two fundamental global topology modules and provide a rigorous proof for the propagation time in global topology. Compared to previous work, our results are not only more concise, clearly defined, efficient, but also are more powerful in predicting propagation time which outperforms some known results in some cases, for example, biochemical dynamics. Additionally, the proposed framework is based on information transfer pathways, which can be also applied to other physical fields, such as network stability, network controlling and network resilience.

L1 Education in Times of Globalization, Digitalization and Super-Diversity

This introduction first briefly sketches the (history of the) International Mother Tongue Education Network (IMEN). IMEN was founded in 1981 as an information and research network that over the years initiated an empirical-interpretive research program focusing on comparative analyses of the rhetoric and practices of L1 education across a dozen of European countries. Main elements of IMEN’s methodology that will be discussed below were the development of different types of research collaboration, a theoretical framework for comparative analysis, and a method for international triangulation. It then describes how, at the turn of the century, IMEN’s way of doing slow science became increasingly threatened by the requirements of publish or perish mainstream research, and how its research program was confronted with the challenges posed to L1 education by processes of globalization, digitalization, and super-diversity, becoming manifest in the superdiverse nature of student bodies, their sociolinguistic and ethnocultural doings, and the increasingly digitalized modes of teaching and learning they are exposed to. All these elements, in one way or another, also have an impact on the teaching-learning practices in mainstream L1 education and consequently must be addressed by IMEN-like studies. Finally this contribution briefly introduces the contributions to this special issue and suggests that they are a strong argument for a renewed interest in international comparative discussion on and around L1 education and a plea to further collaborative research from an empirical interpretive ethnographic perspective on teachers’ professional practical knowledge and classroom practices that can guide our understanding of the ubiquitous school subject L1 education that aims at preparing students for a global, digital, and super-diverse society.

Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics.

Chronic Kidney Disease (CKD) and Metabolic dysfunction-associated steatohepatitis (MASH) are metabolic fibroinflammatory diseases. Combining single-cell (scRNAseq) and spatial transcriptomics (ST) could give unprecedented molecular disease understanding at single-cell resolution. A more comprehensive analysis of the cell-specific ligand-receptor (L-R) interactions could provide pivotal information about signaling pathways in CKD and MASH. To achieve this, we created an integrative analysis framework in CKD and MASH from two available human cohorts. The analytical framework identified L-R pairs involved in cellular crosstalk in CKD and MASH. Interactions between cell types identified using scRNAseq data were validated by checking the spatial co-presence using the ST data and the co-expression of the communicating targets. Multiple L-R protein pairs identified are known key players in CKD and MASH, while others are novel potential targets previously observed only in animal models. Our study highlights the importance of integrating different modalities of transcriptomic data for a better understanding of the molecular mechanisms. The combination of single-cell resolution from scRNAseq data, combined with tissue slide investigations and visualization of cell-cell interactions obtained through ST, paves the way for the identification of future potential therapeutic targets and developing effective therapies.