Cross-disciplinary Research Research Articles

Visualization Analysis of Artificial Intelligence Literature in Forensic Research.

To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database, to explore research hotspots and developmental trends. A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the literature measuring tool CiteSpace. The authors, institution, country (region), title, journal, keywords, cited references and other information of relevant literatures were analyzed. A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries (regions) were identified, with the number of articles published showing an increasing trend year by year. Among them, the United States had the highest number of publications and China ranked the second. Academy of Forensic Science had the highest number of publications among the institutions. Forensic Science International, Journal of Forensic Sciences, International Journal of Legal Medicine ranked high in publication and citation frequency. Through the analysis of keywords, it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technology for sex and age estimation, cause of death analysis, postmortem interval estimation, individual identification and so on. It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research. Exploring the combination of advanced artificial intelligence technologies with forensic research will be a hotspot and direction for future research.

What Is a Society? Building an Interdisciplinary Perspective and Why That's Important.

I submit the need to establish a comparative study of societies, namely groups beyond a simple, immediate family that have the potential to endure for generations, whose constituent individuals recognize one another as members, and that maintain control over access to a physical space. This definition, with refinements and ramifications I explore, serves for cross-disciplinary research since it applies not just to nations but to diverse hunter-gatherer and tribal groups with a pedigree that likely traces back to the societies of our common ancestor with the chimpanzees. It also applies to groups among other species for which comparison to humans can be instructive. Notably, it describes societies in terms of shared group identification rather than social interactions. An expansive treatment of the topic is overdue given that the concept of a society (even the use of such synonyms as primate "troop") has fallen out of favor among biologists, resulting in a semantic mess; while sociologists rarely consider societies beyond nations, and social psychologists predominantly focus on ethnicities and other component groups of societies. I examine the relevance of societies across realms of inquiry, discussing the ways member recognition is achieved; how societies compare to other organizational tiers; and their permeability, territoriality, relation to social networks and kinship, and impermanence.We have diverged from our ancestors in generating numerous affiliations within and between societies while straining the expectation of society memberships by assimilating diverse populations. Nevertheless, if, as I propose, societies were the first, and thereafter the primary, groups of prehistory, how we came to register society boundaries may be foundational to all human "groupiness." A discipline-spanning approach to societies should further our understanding of what keeps societies together and what tear them apart.

From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

The human digital twin (HDT) emerges as a promising human-centric technology in Industry 5.0, but challenges remain in human modeling and simulation. Digital human modeling (DHM) provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis. However, it has limitations in real-time data usage, personalized services, and timely interaction. The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment. Hence, this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective. The framework comprises the physical twin, the virtual twin, and the linkage between these two. The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation. HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis, timely feedback, and bidirectional interactions. Finally, the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed. In general, this study outlines a human factors perspective on HDT for the first time, which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

Individual sleep need is flexible and dynamically related to cognitive function.

Given that sleep deprivation studies consistently show that short sleep causes neurocognitive deficits, the effects of insufficient sleep on brain health and cognition are of great interest and concern. Here we argue that experimentally restricted sleep is not a good model for understanding the normal functions of sleep in naturalistic settings. Cross-disciplinary research suggests that human sleep is remarkably dependent on environmental conditions and social norms, thus escaping universally applicable rules. Sleep need varies over time and differs between individuals, showing a complex relationship with neurocognitive function. This aspect of sleep is rarely addressed in experimental work and is not reflected in expert recommendations about sleep duration. We recommend focusing on the role of individual and environmental factors to improve our understanding of the relationship between human sleep and cognition.

How Should a One Health Perspective Promote Cross-Disciplinary Research About Bat-Associated Viruses in Uganda?

Bats are diverse mammals that are globally distributed and ecologically critical, yet some bat species are associated with disease agents that have severe consequences for human health. Disease outbreak responses require interdisciplinary knowledge of bat-associated pathogens and microbial transmission patterns. Health promotion requires close, collaborative attention to the needs, vulnerabilities, and interests of diverse stakeholders, including the public and professionals in public health, conservation, ecology, social science, communication, and policy. This article describes a successful One Health engagement among such stakeholders and partners looking to motivate both bat-human ecology preservation and viral disease management in Uganda.

Systematising clustering techniques through cross-disciplinary research, leading to the development of new methods

Clustering algorithms can help scientists gain valuable insights from data. Thereâ–™s a variety of clustering methods in use, which means there are gaps between the methods used in different fields. Associate Professor Kohei Inoue, Department of Media Design, Kyushu University, Japan, wants to bridge these gaps by investigating the relationships among various clustering methods developed in different fields, in order to systematise the world of clustering. He is bringing two decades of research activities in pattern recognition and image processing to this work. In order to clarify the relationships between different clustering methods, Inoue and the team are conducting an interdisciplinary survey. First, the researchers are working to clarify the relationship between the technologies used across different fields. So far, they have successfully clarified the relationship between the rolling guidance filter and the local mode filter. In a previous study, Inoue and his collaborators proposed a robust K-means clustering al-algorithm. The researchers demonstrated the effectiveness of their technique utilising a BBC dataset originating from BBC News. In their work, the team is collaborating with a laboratory at a university in Japan that is studying non-photorealistic rendering. They have so far published several co-authored papers, as well as having obtained results from their joint research. Ultimately, by systemising clustering technology, Inoue believes that the characteristics of each method, as well as the interrelationships between each method, can be explained and clustering technology enhanced, as well as new clustering techniques developed.

The whiteness of ‘sex discrimination’: theorising white feminist ideology in politics

The year 2025 will mark 50 years since the prohibition of gender discrimination in employment in Europe. Feminist narratives of this time have often been ones of progress, despite Black feminist critique of how such legislation effaced racially minoritised women’s experiences. Why was gender equality legislation designed to mainly benefit white privileged women? What discourses were involved? Meanwhile, while the term ‘white feminism’ has recently been popularised, there has been relatively little investigation of what it comprises in politics in Europe. Drawing on cross-disciplinary empirical research on UK gender equality legislation involving analysis of parliamentary debate, archival research and oral history, this article contributes to theorisation of the tenets of white feminism, namely, as a political ideology wherein gender is constructed as the most important marker of social inequality and, specifically, as being more important than racial inequality. Ultimately, such specification aims to make white feminism in politics impossible to render neutral.

Dancing in Movements, Movements in Sports: a Comparative Approach Toward a Metaphysical Realist Ontology

ABSTRACT Ontological approaches to the arts have neglected art forms such as dance. This hinders analysis of the metaphysical similarities and differences between different art forms. In this paper, I develop a metaphysical realist ontological approach to dance and sport that is grounded in embodiment. I first examine the debate between descriptivism and metaontological realism in the philosophy of arts in the context of Thomasson’s descriptive approach and Dodd’s metaontological approach of folk-theoretic modesty. Following Dodd, I adopt a realist metaontological approach to dance as an art form, drawing on philosophical debates within dance. I then review metaphysical approaches to sport and argue that most such approaches are based on abstract, top-down models. I therefore propose an ontological account of sport and dance based on metaontological realism and grounded in embodied movement knowledge. Drawing on Schaffer’s concept of ground functionalism, I use this approach to compare the aesthetic and bodily aspects of sport and dance in terms of elite-level movement knowledge. I describe how the embodiment of such movement knowledge through practice permits dancers and athletes to create expert movements. Drawing on findings from neuroscientific research, I argue that dancers and athletes, through the process of physiological plasticity, acquire embodied memories that are transformed into new bodily and behavioral dispositions. I believe that the metaphysical realist ontological approach presented here could help to broaden the focus in arts ontology to encompass movement disciplines such as sport and dance. This account may also engender cross-disciplinary research and encourage philosophers to explore the role of movement in arts such as music.

Heading towards carbon neutrality: how do marine carbon sinks serve as important handle for promoting marine ecological civilization construction?

As an efficient long-term carbon sink, marine carbon sinks and the associated carbon sink effects, technology, accounting and trading market construction warrant investigation across various disciplines. However, information on the interrelationships and their development over time with respect to the research conducted in China is limited, affecting the ability to drive research directions and optimize continued advancement in this field. Therefore, in this study, we aimed to understand the current situation of marine carbon sink research in China to promote a deeper level of scientific development based on the research literature related to marine blue carbon sinks in the core databases of the China National Knowledge Internet (CNKI) and Web of Science (WOS). We used bibliometric tools in the Citespace software to quantitatively compare and analyse the main characteristics of marine blue carbon sink research including publication volume, time, journals, authors and institutions. We also explored the popular research topics, frontier areas, and theme evolution trends through keyword clustering and emergent and co-occurring knowledge maps. The key recommended research directions for ocean carbon sinks are: (1) to promote the unified carbon sink market research of land and sea integration through multidisciplinary and cross-disciplinary research; (2) to achieve new breakthroughs in ocean carbon sinks with the support of coastal wetlands and seawater offshore aquaculture environments; (3) to explore the protection provided by ocean carbon sinks with a comprehensive eco-compensation mechanism; (4) to improve the application of marine carbon sinks by taking the theory and technological innovation research related to marine carbon sinks as the guide. Ultimately, our work helps characterise the current situation of marine carbon sink research, promote the research in this field to a deeper level of development and provide reference for subsequent scholars to carry out related research.

How Can We Use Mathematical Modeling of Amyloid-β in Alzheimer's Disease Research and Clinical Practices?

The accumulation of amyloid-β (Aβ) plaques in the brain is considered a hallmark of Alzheimer's disease (AD). Mathematical modeling, capable of predicting the motion and accumulation of Aβ, has obtained increasing interest as a potential alternative to aid the diagnosis of AD and predict disease prognosis. These mathematical models have provided insights into the pathogenesis and progression of AD that are difficult to obtain through experimental studies alone. Mathematical modeling can also simulate the effects of therapeutics on brain Aβ levels, thereby holding potential for drug efficacy simulation and the optimization of personalized treatment approaches. In this review, we provide an overview of the mathematical models that have been used to simulate brain levels of Aβ (oligomers, protofibrils, and/or plaques). We classify the models into five categories: the general ordinary differential equation models, the general partial differential equation models, the network models, the linear optimal ordinary differential equation models, and the modified partial differential equation models (i.e., Smoluchowski equation models). The assumptions, advantages and limitations of these models are discussed. Given the popularity of using the Smoluchowski equation models to simulate brain levels of Aβ, our review summarizes the history and major advancements in these models (e.g., their application to predict the onset of AD and their combined use with network models). This review is intended to bring mathematical modeling to the attention of more scientists and clinical researchers working on AD to promote cross-disciplinary research.

Pandemic preparedness: why humanities and social sciences matter.

Whilst many lessons were learned from the COVID-19 pandemic, ongoing reflection is needed to develop and maintain preparedness for future outbreaks. Within the field of infectious disease and public health there remain silos and hierarchies in interdisciplinary work, with the risk that humanities and social sciences remain on the epistemological peripheries. However, these disciplines offer insights, expertise and tools that contribute to understanding responses to disease and uptake of interventions for prevention and treatment. In this Perspective, using examples from our own cross-disciplinary research and engagement programme on vaccine hesitancy in South Africa and the United Kingdom (UK), we propose closer integration of expertise, research and methods from humanities and social sciences into pandemic preparedness.

SWEBoK-based Bibliometric Analysis of Software Engineering

In our research study spanning 2015-2021, we analyzed Software Engineering (SE) across 15 Knowledge Areas (KAs) outlined by SWEBoK. Using Web of Science (WoS) database, we examined 32,232 articles (2015-2017), 31,459 articles (2018-2020), and 6,919 articles (2021). Collaborative and co-word networks were generated using VOS Viewer. Our findings reveal SE's influence in 81 research areas. English dominated, but Spanish and Portuguese were notable. Europe led in publications, with USA and China prominent. Stronger collaboration networks, especially in the USA and China, correlated with increased research output. Asian countries' rise was evident from 2018-2021. Top 10 highly cited papers and co-word analysis were included, aiding researchers and educators. SE's impact crossed domains, enabling cross-disciplinary research and informing educators' course designs. Institutional and country productivity influenced partnerships and academia-industry collaborations. Multinational companies gauged research potential through active countries. Analyzing co-words depicted trends, and highly cited papers set industry standards. Bibliometric analysis offered qualitative and quantitative research evaluation.

Links Between Risk Source Identification and Resilience Capability Building in Agri-Food Supply Chains: A Comprehensive Analysis

Agri-food supply chain (AFSC) resilience is receiving increasing attention as AFSC stakeholders perceive its benefits in recovering from unexpected disruptions. However, which resilience capabilities are more effective in mitigating AFSC risks remains unclear. To address this gap, this article presents a novel AFSC risk and resilience analysis based on a systematic literature review (SLR). In total, 95 journal articles on AFSC risk and resilience management published between 2004 and 2020 are analyzed to identify key risks and resilience capabilities in AFSCs, the relationships, correlations and causalities between them, and research gaps and future research directions in the field. Our SLR reveals eight types of AFSC risk and seven types of AFSC resilience capability, and enables us to develop a one-to-one resilience-risk correspondence model. Suggestions for future research include: cross-country comparative analysis to gain a deeper understanding of risk and resilience management; identification of risk and resilience strengthening strategies through a multi-sectoral approach; longitudinal studies to determine the long-term effects of resilience capabilities; research to understand resilience from the perspectives of supply-chain collaboration, traceability, redundancy, knowledge management, innovation, leadership, and flexibility; investigations of the positive effects of AFSC risks in triggering resilience capabilities; and cross-disciplinary research to understand the relationships between resilience and other disciplines.

Gel transition of active triblock copolymers

The self-propulsion of active matter leads to many non-equilibrium self-organization phenomena, and the conformational freedom of polymer chains can produce unique equilibrium self-assembly behaviors, which stimulates cross-disciplinary research between active matter and polymer physics. In this work, we use molecular dynamics simulations to investigate the modulation of self-propulsion activity on the gel transition of ABA triblock copolymers. The research results indicate that under different active forces and attractive strengths, the gel states formed by ABA copolymers can be divided into three types: stable polymer gels with stable percolation paths and uniform spatial distribution, dynamic polymer gels with constantly changing percolation path and strand conformation, and collapsed polymer gels aggregating into large percolating clusters. The spatial uniformity of active gels is related not only to the concentration fluctuation during the formation of the network, but also to the inconsistent movement of the network chains caused by the activity, which is manifested in the rotation of crosslinking points in the flexible system and the directional movement of the bundles along their contour directions in the semi-flexible and rigid systems. In terms of topological conformation of polymer networks, when the attractive strength between A blocks is strong, the proportion of loop increases with the active force increasing. When attractive strength is weak, inter- and intra-chain binding are unstable, and the conformation is easily changed by the activity drive, noise and other chain collisions, so the proportion of loop decreases with the active force increasing. The branching number of crosslinking points varies with active force, which is not only affected by the attraction strength, but also related to the rigidity of the network chain. Generally, the branch number of crosslinking points in semi-flexible networks is larger than that in flexible and rigid networks. In addition, the directional motion of active polymers induces anomalous diffusion in stable polymer gels. This study contributes to the understanding of the collective behavior of active polymers and serves as a guide for designing and implementing active polymeric materials.

Fundamental statistics of higher-order networks: a survey

Complex networks serve as indispensable instruments for characterizing and understanding intricate real-world systems. Recently, researchers have delved into the realm of higher-order networks, seeking to delineate interactions within these networks with greater precision or analyze traditional pairwise networks from a higher-dimensional perspective. This effort has unearthed some new phenomena different from those observed in the traditional pairwise networks. However, despite the importance of higher-order networks, research in this area is still in its infancy. In addition, the complexity of higher-order interactions and the lack of standardized definitions for structure-based statistical indicators, also pose challenges to the investigation of higher-order networks. In recognition of these challenges, this paper presents a comprehensive survey of commonly employed statistics and their underlying physical significance in two prevalent types of higher-order networks: hypergraphs and simplicial complex networks. This paper not only outlines the specific calculation methods and application scenarios of these statistical indicators, but also provides a glimpse into future research trends. This comprehensive overview serves as a valuable resource for beginners or cross-disciplinary researchers interested in higher-order networks, enabling them to swiftly grasp the fundamental statistics pertaining to these advanced structures. By promoting a deeper understanding of higher-order networks, this paper facilitates quantitative analysis of their structural characteristics and provides guidance for researchers who aim to develop new statistical methods for higher-order networks.

PlantBiCNet: A new paradigm in plant science with bi-directional cascade neural network for detection and counting

Deep learning is increasingly popular for precise plant detection and counting in computer vision applications. Despite the rising cross-disciplinary research in this field, an important issue has been neglected: due to the inherent constraints imposed by the natural growth patterns of plants, the costly and time-consuming process of collecting plant image data, resulting in a scarcity of real-world available data. Consequently, researchers are often constrained to exploring large-scale data representations or limited applications. Currently, still no generalized network architecture excels in demanding environments. To address this, we introduce two new bi-directional cascade neural network paradigms, PlantBiCNet and its lightweight variant, PlantBiCNet-Lite. Unlike previous research, we place greater emphasis on the network decoding process itself rather than the encoder. Our bi-directional cascade and weight fusion decoding approach maximizes high-level semantics and low-level spatial data, enhancing the localization signal in a way that respects gradient diversity. We evaluate PlantBiCNet and PlantBiCNet-Lite on five public plant detection and counting datasets and a novel plant dataset. The average precision AP50 on these six small-scale datasets reached 0.905, with a high coefficient of determination R2 of 0.935, while the reported frame rate was 196 FPS. These results significantly outperform state-of-the-art computer vision methods. Moreover, we found that the introduction of the Dropout mechanism markedly enhances the model's performance and generalization ability. Further analysis supports our experiments and provides valuable insights into network optimization. Overall, our organization's open datasets and network modeling details provide a solid foundation for future research in the field of plant science.

Introduction: New Geographies of Scandinavian Studies. Moving maps, reciprocal images, emerging communities

This article provides an introduction to the Research Network “New Geographies of Scandinavian Studies” while at the same time discussing some of its main concerns and questions: the position of the Nordic countries and the role of Scandinavian Studies in the changing geopolitical landscape of post-Cold War Europe. The collapse of the Eastern bloc in 1989–1991 led to a reconfiguration of the European political map. This situation also entailed new possibilities for international and cross-disciplinary research: A new understanding of Nordic and Baltic studies was institutionalized and new regional concepts were developed as alternatives to Cold War geopolitics. The network “New Geographies of Scandinavian Studies” is rooted in this ongoing reorientation of the field. The article discusses some of the potentials and challenges of this new agenda of Scandinavian Studies in the context of the new geopolitical confrontation between Russia and the West after Russia’s military attack on Ukraine in February 2022.

How does motor performance change with increasing stress doses? A tutorial on dose-response profiles applied to crew rowing

Many models of motor performance acknowledge that adapting to stressors plays a major role in how we move. However, most models lack a precise conceptualization of the way in which stress-response dynamics unfold. To fill this void, we first present popular models from the domain of biology and psychology which argue that the impact of a stressor depends on its dose. Next, we provide a tutorial using the example of crew rowing to demonstrate how these models can be scaled to human motor performance. In this example, the dose of the stressor is varied by target times for several 500 m races and the response variable represents the crew coordination. Specifically, we discuss how the necessary parameters can be determined a priori and how the data can be analyzed to pinpoint the exact dose-response relationship. These strides are necessary for developing more comprehensive theories of motor performance and engage in cross-disciplinary research on the impact of stressors.

Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts

Abstract. Tropical cirrus clouds, i.e., any type of ice cloud with tops above 400 hPa, play a critical role in the climate system and are a major source of uncertainty in our understanding of global warming. Tropical cirrus clouds involve processes spanning a wide range of spatial and temporal scales, from ice microphysics on cloud scales to mesoscale convective organization and planetary wave dynamics. This complexity makes tropical cirrus clouds notoriously difficult to model and has left many important questions stubbornly unanswered. At the same time, their multi-scale nature makes them well-positioned to benefit from the rise of global, high-resolution simulations of Earth's atmosphere and a growing abundance of remotely sensed and in situ observations. Rapid progress on our understanding of tropical cirrus requires coordinated efforts to take advantage of these modern computational and observational abilities. In this opinion paper, we review recent progress in cirrus studies, highlight important unanswered questions, and discuss promising paths forward. Significant progress has been made in understanding the life cycle of convectively generated “anvil” cirrus and the response of their macrophysical properties to large-scale controls. On the other hand, much work remains to be done to fully understand how small-scale anvil processes and the climatological anvil radiative effect will respond to global warming. Thin, in situ formed cirrus clouds are now known to be closely tied to the thermal structure and humidity of the tropical tropopause layer, but microphysical uncertainties prevent a full understanding of this link, as well as the precise amount of water vapor entering the stratosphere. Model representation of ice-nucleating particles, water vapor supersaturation, and ice depositional growth continue to pose great challenges to cirrus modeling. We believe that major advances in the understanding of tropical cirrus can be made through a combination of cross-tool synthesis and cross-scale studies conducted by cross-disciplinary research teams.

Prospects in the field of learning and individual differences: Examining the past to forecast the future using bibliometrics

Over two centuries, research has delved into individual differences in learning across educational and professional contexts. This commentary conducts a bibliometric analysis of 6556 articles, identifying key research keywords, topics and themes, and their historical evolution. The findings revealed a longstanding emphasis on educational psychology, particularly motivation and achievement, rather than cross-curricular competencies and learner's well-being and socio-economic background. Notably, self-regulated learning (SRL) emerged as an overarching research subject in terms of motivation and achievement, but, surprisingly, not (meta)cognition. Prospects for the field build on cross-disciplinary research, theoretical refinement, and methodological advances. Further, the field is expected to maintain academic rigor, address diversity among learners, foster global collaboration, and focus on underprivileged populations.