Intersection Of Science Research Articles

Anterior pelvic organ prolapse repair: the intersection of science and art

Anterior pelvic organ prolapse repair: the intersection of science and art

Social Justice, Community Engagement, and Undergraduate STEM Education: Participatory Science as a Teaching Tool.

Social justice is increasingly being seen as relevant to the science curriculum. We examine the intersection of participatory science, social justice, and higher education in the United States to investigate how instructors can teach about social justice and enhance collaborations to work toward enacting social justice. Participatory science approaches, like those that collect data over large geographic areas, can be particularly useful for teaching students about social justice. Conversely, local-scale approaches that integrate students into community efforts can create powerful collaborations to help facilitate social justice. We suggest a variety of large-scale databases, platforms, and portals that could be used as starting points to address a set of learning objectives about social justice. We also describe local-scale participatory science approaches with a social justice focus, developed through academic and community partnerships. Considerations for implementing participatory science with undergraduates are discussed, including cautions about the necessary time investment, cultural competence, and institutional support. These approaches are not always appropriate but can provide compelling learning experiences in the correct circumstances.

Guiding prevention initiatives by applying network analysis to systems maps of adverse childhood experiences and adolescent suicide

Abstract Suicide is a leading cause of death in the United States, particularly among adolescents. In recent years, suicidal ideation, attempts, and fatalities have increased. Systems maps can effectively represent complex issues such as suicide, thus providing decision-support tools for policymakers to identify and evaluate interventions. While network science has served to examine systems maps in fields such as obesity, there is limited research at the intersection of suicidology and network science. In this paper, we apply network science to a large causal map of adverse childhood experiences (ACEs) and suicide to address this gap. The National Center for Injury Prevention and Control (NCIPC) within the Centers for Disease Control and Prevention recently created a causal map that encapsulates ACEs and adolescent suicide in 361 concept nodes and 946 directed relationships. In this study, we examine this map and three similar models through three related questions: (Q1) how do existing network-based models of suicide differ in terms of node- and network-level characteristics? (Q2) Using the NCIPC model as a unifying framework, how do current suicide intervention strategies align with prevailing theories of suicide? (Q3) How can the use of network science on the NCIPC model guide suicide interventions?

Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy.

NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.

AI ASSISTANCE FOR HEALTHCARE USING NLP

Artificial Intelligence (AI) integration into healthcare has the potential to transform patient care, diagnostics, and treatment. This paper provides a detailed overview of AI support in healthcare, focusing on the intersection of (NLP) Natural Language Processing, (ML) Machine Learning, and (DS) Data Science. By employing the power of these slice- edge technologies, AI can offer intelligent, data- driven results to ameliorate healthcare delivery. Natural Language Processing( NLP) is employed to prize precious perceptivity from medical textbooks, clinical notes, and case records. This enables healthcare providers to more understand patient histories and make informed opinions. Machine literacy ways are abused to prognosticate complaint issues, identify anomalies, and epitomize treatment plans. also, data wisdom plays a vital part in aggregating and analysing large healthcare datasets, icing data security, and maintaining compliance with nonsupervisory norms. The paper explores colorful AI operations in healthcare, similar as automated opinion and triage, medical image analysis, medicine discovery, and patient monitoring. These operations have the eventuality to enhance clinical decision- timber, reduce medical crimes, and ameliorate patientoutcomes.AI backing in healthcare isn't without its challenges, including data sequestration enterprises, the need for robust model interpretability, and ethical considerations. The paper discusses these issues and presents strategies to address them. In conclusion, the integration of AI, NLP, Machine literacy, and Data Science in healthcare has the implicit to marshal in a new period of perfection drug and case- centred care. Then we used SVM Algorithm for training the data set and the delicacy position is 97 percent. This technology confluence is poised to revise healthcare by perfecting opinion delicacy, treatment efficacy-ity, and patient issues while icing data security and ethical use.

CRIMINAL LIABILITY FOR ANALYSING GENOMIC DATA WITHOUT OWNER'S CONSENT: A COMPARATIVE STUDY

Background: In the rapidly evolving landscape of genomics and biotechnology, the United Arab Emirates (UAE) has launched the Emirates Genome Program to harness the potential of genomic technologies for advancing healthcare services. Central to this initiative is the informed and voluntary participation of citizens in genetic research aimed at contributing to national health objectives through genetic data utilisation. Notably, the enactment of UAE Decree-Law No. (49) of 2023 underscores the importance of safeguarding genomic privacy as a foundational element for data security and individual rights. This study addresses the challenge of striking a delicate balance between individual genetic rights and the pressing scientific and medical needs of genomic research. It aims to analyse the right to genomic privacy and examine crimes associated with the unauthorised analysis of biological samples. Methods: This research employed an analytical legal methodology and a comparative approach to explore the crime of disclosing genomic data. By analysing Decree-Law No. (49) of 2023 and other relevant Emirati legislation, we examined the legal framework governing genomic research and data protection in the UAE. Comparative legal analysis was then conducted between Emirati and French laws to identify similarities and differences in approaches to genomic data disclosure crimes. The study also considered international standards and ethical principles to provide a comprehensive, multidisciplinary understanding of the intersection of law, ethics, and science in genomic privacy. Results and conclusions: This study’s findings underscore the necessity of establishing a robust legal framework that safeguards individual rights and ensures the confidentiality of genetic data. Such measures are crucial for fostering public trust in genomic research and aligning the UAE's genomic endeavours with rigorous ethical and legal standards. Ultimately, Decree-Law No. (49) of 2023 exemplifies the state's commitment to promoting ethical and legal practices in genomic research, thereby facilitating sustainable advancements in medical science.

Art, science and inclusion: multisensory Sciart of immunology for blind, low-vision and diverse-needs audiences.

Art is a powerful tool for conveying scientific discovery. Despite the perceived gap between art and science, as highlighted by CP Snow and others, examples of art communicating science can be found in the ancient world, the Renaissance and contemporary data visualization, demonstrating an enduring and historic connection. However, much of science relies on visual elements, excluding those with blindness, low vision and diverse needs, resulting in their low representation in STEM discourse. This paper introduces a novel science and art collaboration in the form of an exhibition program exploring the concepts of Immunology and Biomedicine to blind and vision-impaired audiences, capitalizing on the lived experience of a legally blind artist. Employing multisensory design, art and co-creation methodologies, it transcends traditional vision-based science communication, showcasing the potential for multisensory art to bridge the gap at the intersection of science and inclusion.

A Fit of Anger

From the intersection of computational science and technological speculation, with boundaries limited only by our ability to imagine what could be. An egg-sized, flying droid makes an apartment dweller's life miserable.

Feedback thought at the intersection of systems and design science

AbstractThis paper explores the interplay of feedback principles in design and systems science. From their roots in engineering, biology, and economics, it investigates intersections between design, cybernetics and servomechanisms. The synthesis emphasizes the need for considering feedback in anticipating unintended consequences and proposes an integrative view reconciling fundamental assumptions from the different fields through simulation. This holistic approach underscores the pivotal role of feedback in understanding and addressing complex phenomena, such as rebound effects, in design science.

What is infodemiology? An overview and its role in public health.

The exponential increase in internet use and the consequent surge in data generation present both opportunities and challenges for public health. Infodemiology, an emerging field at the intersection of information science and public health, seeks to harness the vast amounts of health-related data generated online for public health benefits. This paper provides a comprehensive overview of infodemiology, examining its development, methodologies, and potential to address public health challenges. We discuss the role of infodemiology in identifying and mitigating the spread of misinformation, especially in the context of the COVID-19 pandemic, which underscored the dangers of the "infodemic" - an overabundance of information, both accurate and not, that complicates public health responses. Through both demand and supply-based studies, infodemiology offers unique insights into health trends, misinformation dynamics, and the digital behaviors of health information seekers. Tools such as sentiment analysis are highlighted as essential in navigating the vast digital landscape for real-time health data analysis. Despite the potential of infodemiology, challenges such as data overload or misinformation. The paper concludes by emphasizing the importance of interdisciplinary collaboration, the development of advanced analytical tools, and the need for guidelines to maximize the field's impact on public health policy and practice.

Future-making through eventing human-machine listening

Reverb-Resonate: Sounding the Affective Frequencies of Migration operates at the intersection of art, science, and technology to articulate an emotional landscape of migration and exile. Rooted in the methodology of research-creation (RC) and grounded in the interdisciplinary field of Art, Science, and Technology Studies (ASTS), the project transcends conventional disciplinary boundaries to offer speculative possibilities through human-machine listening. Drawing on the body is an already augmented site, the project makes audible physiological sensors that capture micro-level intricacies responsible for stress regulation. Listening, is, thus, foregrounded as the core public engagement strategy, creating a layered sound collage that interweaves somatic registers of recorded breathing samples and physiological sensor values with machine listening to recreate new forms of sound. Engagement with ASTS is, hence, in the form of a method that traverses transforming sensor application, generating technicized sound and composing an acoustic experience capable of affective engagement. Through machine learning—a subfield of artificial intelligence—the notion of ‘machine listening’ extends beyond human hearing limitations, introducing non-normative structures to challenge and expand habitual forms of human listening. Reverb-Resonate, hence, leverages artistic strategies and techno-augmentations to address the crisis of imagination that hinders opening up to realities far from the familiar and the personal to imagine ‘what could be’ as a way of future-making. It underscores the critical edge of RC and ASTS in addressing complex critical issues, proposing a speculative space where the human-machine hybrid puts forth a socio-technical assemblage of listening to understand 'otherly' experiences. The project, thus, advances a critical inquiry into the mediation and augmentation of listening to imagine new possibilities for embodied engagement with unfamiliar emotional spaces and experiences.

Synthesis and biophysical characterization of porous bi-phase calcium phosphate/gelatin-PVA scaffold: Enhanced antibacterial, osteoconductivity and anticancer activity via silver nanoparticles and methotrexate incorporation

In our relentless pursuit of advancing bone tissue regeneration, we have meticulously crafted multifunctional scaffolds that transcend traditional approaches. These freeze-dried scaffolds, composed of biphasic calcium phosphate (BCP) intricately woven into a gelatin-polyvinyl alcohol (PVA) matrix, harbor the potential to revolutionize regenerative medicine. The scaffolds are characterized with XRD, FTIR, SEM, 3D images of the SEM, mechanical properties, swelling and degradation tests, drug release, cell viability, antibacterial and anticancer tests. Our in-situ synthesis of BCP within the PVA framework yielded a biphasic structure comprising hydroxyapatite (HAp) and beta-tricalcium phosphate. These scaffolds exhibit good mechanical compressive strength (ranging from 28 to 173 kPa) and boast high porosity levels (approaching 80 %) enhance the processes of nutrition and waste removal. The incorporation of silver nanoparticles (Ag) introduces antimicrobial prowess, resulting in a complex scaffold texture characterized by well-defined pores and rugged walls. The interconnectivity within this fibrous network contributes to enhanced mechanical properties. Additionally, our scaffolds facilitate the sustained release of methotrexate (MTX), a potent anticancer agent. Within the first day, MTX is promptly delivered, potentially impacting cancer cells. These Ag-containing scaffolds effectively combat common pathogens, demonstrating significant antibacterial activity as an essential property for preventing infections at the implantation site. Notably, our scaffolds, whether infused with Ag or MTX (at a concentration of 0.25 wt%), exhibit no toxic effects on Vero and MG-63 cells within a 24-hour window. However, higher Ag content (2.0 wt%) shows cytotoxicity. In summary, our multifaceted scaffolds, poised at the intersection of science and creativity, hold immense promise for bone tissue engineering. Further investigations will explore long-term effects, broader microbial contexts, and in vivo models.

No recognised ethical standards, no broad consent: navigating the quandary in computational social science research

Recital 33 GDPR has often been interpreted as referring to ‘broad consent’. This version of informed consent was intended to allow data subjects to provide their consent for certain areas of research, or parts of research projects, conditional to the research being in line with ‘recognised ethical standards’. In this article, we argue that broad consent is applicable in the emerging field of Computational Social Science (CSS), which lies at the intersection of data science and social science. However, the lack of recognised ethical standards specific to CSS poses a practical barrier to the use of broad consent in this field and other fields that lack recognised ethical standards. Upon examining existing research ethics standards in social science and data science, we argue that they are insufficient for CSS. We further contend that the fragmentation of European Union (EU) law and research ethics sources makes it challenging to establish universally recognised ethical standards for scientific research. As a result, CSS researchers and other researchers in emerging fields that lack recognised ethical standards are left without sufficient guidance on the use of broad consent as provided for in the GDPR. We conclude that responsible EU bodies should provide additional guidance to facilitate the use of broad consent in CSS research.

Key Issues of Private Law Transformation under Influence of Behavioural Economics and Data Science

Corporations are now increasingly embracing the advances of Data Science and behavioural economics. It will undoubtedly have far-reaching implications for many areas of legal regulation . In author’s opinion, private law institutions aimed at regulating relations between business and customers will be the first to deal with transformation. The paper outlines the main questions and issues that lawyers will face in the next five to ten years as the ideas of behavioural economy and Data Science spread to private law, and offers some thoughts addressing these issues. In the beginning the author briefly reviews the progress of behavioural economy and how its achievements help to attain the aims of legal regulation. In particular, the author surveys private law tools such as discretionary rules and information disclosure for “pushing” individuals to a more rational behaviour. The author then analyses how the current level of Big Data collection, processing and use can affect the discretionary rules and information disclosure in corporate contracts with consumers, including the possibility of private law institution “personalisation” with account of the individual features of the parties to the transactions. Further on, the asks and attempts to answer the key question of the article: What regulatory environment should be in place to enable behaviourally informed personalisation of private law institutions using Big Data? In responding to this question, the author analyses three related problems arising at the intersection of law, Data Science, psychology, and economics: How to ensure freedom of choice and autonomy of will of individuals while using information and behavioural innovations? How much information should legal actors be able to receive in order to make the best decision? How to find a balance between private law “personalisation” and personal data protection? In conclusion, the author summarises the results of the study and concludes that to date there are no universal rules and algorithms for private law personalisation, and the introduction of Data Science and behavioural economics into law is still taking place in individual legal relations on case-by-case basis.

Painting with Metal: When History and Art Meet Thermal Spray

Abstract Cold spray technology provides a multidisciplinary approach to additive manufacturing at the intersection of science and art. This article describes the novel use of cold dynamic spray technology to create a large and complex 3D artwork that incorporates six different metals.

Public science and environmental sustainability: a national culture framework for innovation ecosystems en route to net zero

Despite the proliferation of national environmental science worldwide, the erosion of environmental sustainability presents a problem for advanced economies with a substantial volume of scientific output. The worsening state of the natural environment presents a profound conundrum at the intersection of science and sustainability, characterised by conflicting pathways for the world’s nations. In this study, I confront this predicament by examining whether the influence of national culture moderates the transition of domestic scientific research into tangible CO2 emissions reduction. Drawing on a dataset spanning 30 nations renowned for their high scientific productivity over a 24-year period, I use a panel data model that incorporates lag time to analyse the nuanced impact of national cultures on sustainability. My findings reveal distinctive outcomes: those cultures characterised by high Power Distance (e.g., Eastern European) contribute to increased CO2 emissions via an industrial innovation pathway that prioritises economic growth, while those with high Individualism and high Uncertainty Avoidance (e.g., Western European) facilitate a reduction in CO2 emissions through the translation of scientific knowledge into public science that stimulates a societal innovation pathway and sustainability. In addition to these moderating effects, my investigation exposes that Gross Domestic Product (GDP) per capita exerts a direct positive influence on CO2 emissions, while an increase of GDP allocated to military expenditure (e.g., USA, China, Israel, South Korea) has detrimental effects on CO2 emissions, potentially hampering Net Zero aspirations. These findings hold significant implications for both theory and policymaking in the environmental arena.

LINGUISTIC ECONOMY AND SYSTEMATIC CONSTRUCTION OF LANGUAGE

The article provides general information about language phenomena, artificial intelligence and their connection with knowledge, thinking, memory, etc. In addition, the approaches and opinions of scientists on these topics are presented and explained with the help of examples. The author states that the movement of thinking and the formation of thought is associated with cognitive activity, a process that ensures the reflection of reality directly in the mind. It is known that the formation of speech units and their understanding are the result of mental activity. The performance of this activity is directly related to the condition of obtaining linguistic knowledge. There is no way to know reality without linguistic knowledge, to lyrically express logical structures that reflect information about the current event-events. However, at the intersection of cognitive science, six fields of science are adjacent, and a single scientific goal is to be occupied with the search for a solution to the problems of concentration, processing and application of knowledge in natural and artificial systems. It seems-that interrelation between these areas does not take place in the same way, whereas, Artificial Intelligence and anthropological philosophy are directly related.Meanwhile, human cognitive activity should be considered not as a reflection of being as a mirror, but as a phenomenon that reflects the inner or outer essence of objects. The mental structure formed in the process of cognition –the content of the concept is occupied by character-features that have a clear appearance of exactly this type, and the same features lead in the linguistic reality of the concept.

ENTROPY-RANDOMIZED ESTIMATION OF NONLINEAR DYNAMICAL MODEL PARAMETERS ON OBSERVATION OF DEPENDENT PROCESS

The work is devoted to the development of the method of randomized machine learning in the direction of estimating dynamic models of related processes using real data, one of which is considered as the main one, and the other as a dependent one. The model of the main process in this concept is implemented by a dynamic model based on differential equations with parameters, which in turn are implemented by a static model in a different time scale. Randomized machine learning is a new theory at the intersection of data science, machine learning and data mining, based on the concept of entropy to estimate the probabilistic characteristics of model parameters. Such characteristics are the probability distributions of the corresponding objects, the estimates of which are the distributions implemented by the probability density functions or discrete distributions. Achieving this goal becomes possible thanks to the idea of moving from models with deterministic parameters to models with random parameters and, additionally, measured at the output with random noise, which achieves the consideration of the stochastic nature, which is obviously present in any natural phenomenon. As a demonstration of the proposed method, we consider the problem of predicting the total number of infected people based on the SIR dynamic epidemiological model, in which one of the parameters is considered as the state of the associated process implemented by the static model. Its evaluation is carried out according to the observations of the main process, and forecasting is carried out using the model of the associated one. Conducted experiment using real data on COVID-19 cases in Germany shows the efficiency of the proposed approach. The forecast obtained by the classical least squares method leads to an underestimation of the model output compared to the real observed data, while the proposed approach demonstrates more flexibility and potentially allows you to obtain forecasts that are more adequate to the real data, which confirms its effectiveness and adequacy under conditions a small amount of data with a high level of uncertainty.

Sonic branding: A narrative review at the intersection of art and science

AbstractThe field of sonic branding/marketing has grown rapidly in recent years, as has commercial interest in more science‐based approaches to the practice, particularly as more brands and agencies look for qualitative and quantitative evidence to support the efficacy of sonic branding. In this research note, we explore the ways in which the design and execution (i.e., the “art”) of sonic branding initiatives can be informed by overlapping approaches and techniques drawn from the sciences, namely psychoacoustics, semiotics, music/auditory cognition, and crossmodal research. We explore whether the rapid growth of generative AI may represent the next major evolution in the design, creation, and assessment of sonic assets, where science and art are used to train AI tools that could one day augment (and potentially disrupt) the work of human sound designers and composers. These developments notwithstanding, it is argued that sonic branding will likely remain as much an art as a science, though basing one's approach on the emerging scientific literature ought to at least tilt the odds of success in the creative's favor.

Photonic data analysis in 2050

Photonic data analysis is a field at the intersection of imaging, spectroscopy, machine learning, and computer science. The diversity of both data types and application scenarios requires flexibility in the methods applied, combining a full range of computational methods, from classical chemometric techniques to state-of-the-art deep learning solutions. Interdisciplinary and international collaborations are needed to accelerate the progress of photonic data science. An underlying data infrastructure and standardization will be needed to provide collaborative platforms for research on data comparability, enabling the integration of novel photonic techniques into routine applications. The increasing complexity of the questions being investigated requires the application of more sophisticated data-driven models, which may only be optimized for large data sets. Unfortunately, novel techniques in the early stages of development can rarely provide a variability of measured samples sufficient to build a generalizable complex model. To overcome this problem, state-of-the-art methods will emerge for working with extremely limited or unbalanced data, as well as for dealing with device-to-device variations. Further developments are also foreseen in computable artificial intelligence methods, which will allow the validation of models of any architecture by comparing them with the knowledge of the researchers.