Established Knowledge Research Articles

Multi-state sequential Boolean networks: An analysis of its combinatorial dynamics

Multi-state Boolean networks model the behavior of interrelated (non-binary) Boolean entities over time. These networks can be used in several contexts, and extend the usual (simpler) binary-state Boolean networks. Leveraging the established knowledge of binary-state Boolean networks, we explore the dynamics of sequential multi-state Boolean networks, where their entities or nodes have states which belong to a (general) Boolean algebra with more than two elements and whose updates are determined in an specific order. This study contributes to the advancement of multi-state Boolean networks, enriching their theoretical knowledge and practical utility.

Machine learning - assisted prediction of yield strength in irradiated type 316 stainless steels

The investigation into irradiation hardening and embrittlement is of critical importance in nuclear material subject. This work explores the applications of machine learning (ML) to predict the yield strength of irradiated type 316 stainless steels. A dataset comprising 354 samples is compiled through an extensive review of prior experimental studies. Each sample has 23 potentially influential features. Five distinct machine learning models are trained and evaluated. Among these models, the Gradient Boosting (GB) model demonstrates superior prediction performance and robust stability. The prominent factors identified by the GB model are in reasonable agreement with established knowledge regarding the determinants of yield strength in irradiated type 316 stainless steels. These findings provide critical insights into the mechanical properties of irradiated type 316 stainless steels.

Development of a Flexible Information Security Risk Model Using Machine Learning Methods and Ontologies

This paper presents a significant advancement in information security risk assessment by introducing a flexible and comprehensive model. The research integrates established standards, expert knowledge, machine learning, and ontological modeling to create a multifaceted approach for understanding and managing information security risks. The combination of standards and expert insights forms a robust foundation, ensuring a holistic grasp of the intricate risk landscape. The use of cluster analysis, specifically applying k-means on information security standards, expands the data-driven approach, uncovering patterns not discernible through traditional methods. The integration of machine learning algorithms in the creation of information security risk dendrogram demonstrates effective computational techniques for enhanced risk discovery. The introduction of a heat map as a visualization tool adds innovation, facilitating an intuitive understanding of risk interconnections and prioritization for decision makers. Additionally, a thesaurus optimizes risk descriptions, ensuring comprehensiveness and relevance despite evolving terminologies in the dynamic field of information security. The development of an ontological model for structured risk classification is a significant stride forward, offering an effective means of categorizing information security risks based on ontological relationships. These collective innovations enhance understanding and management of information security risks, paving the way for more effective approaches in the ever-evolving technological landscape.

Needs Assessment for Primary Care for Best Practice Implementation: The CIRCE Joint Action

Abstract Introduction CIRCE is a Joint Action (JA) supporting the transfer of six best practices (BPs) to new primary care settings in Europe. High-quality needs assessment can set the foundation for strong planning and implementation of the CIRCE-JA best practices in these new primary care settings. Methods A needs assessment tool was developed based on the TO-REACH knowledge transfer framework and learnings from a scoping review of international knowledge transfer frameworks. The needs assessment provides structured tool for local sites to self-assess the alignment of the BP with local priorities, readiness of the local site to successfully implement the BP, the availability of necessary leadership, resources and partnerships, and an examination of barriers to local BP implementation and mitigation strategies. Results Twenty-six CIRCE-JA teams completed the needs assessments. Local teams identified the CIRCE-JA BPs were value-adding and aligned with local priorities. Cross-cutting barriers were identified which were common among the six CIRCE-JA best practices across five thematic areas: (1) provider level, (2) intervention level, (3) health system/policy level, (4) partnership and physical resources and (5) information technology. The needs assessment supported primary care sites with a structured approach to planning for transfer of BPs to their local setting that is based on established international best practices and knowledge transfer frameworks including early identification of barriers and mitigating strategies. Conclusions The needs assessment has supported teams with conducting a self-assessment and informing initial planning for adoption of the BP in their local primary care practice. Given the importance of the local needs assessment to high quality implementation of BP the CIRCE-JA experience with local needs assessment may provide learnings for future BP transfer initiatives. Key messages • Local needs assessment supports the transfer of best practices to primary care settings, which is a critical component of service transformation during the post-COVID-19 period. • For successful transfer of best practices in primary care, it is vital to give to examine such as provider capacity, available human resources, health policy, and information technology.

AI in Prosthodontics: A Narrative Review Bridging Established Knowledge and Innovation Gaps Across Regions and Emerging Frontiers

As the discipline of prosthodontics evolves, it encounters a dynamic landscape characterized by innovation and improvement. This comprehensive analysis underscores future developments and transformative solutions across its various subspecialties: fixed, removable, implant, and maxillofacial prosthodontics. The narrative review examines the latest advancements in prosthetic technology, focusing on several critical areas. The integration of artificial intelligence and machine learning into prosthetic design and fitting processes is revolutionizing the field, serving as a common thread that links these innovative technologies across all subspecialties. This includes advancements in automated diagnostics, predictive analysis, and treatment planning. Furthermore, the review offers a forward-looking perspective on how these innovations are influencing each prosthetic dentistry domain, patient outcomes, and current clinical practices. By thoroughly analyzing contemporary research and emerging technologies, the study illustrates how these advancements represent a growing focal point of interest in developing countries, such as Romania, with the potential to redefine the trajectory of prosthetic rehabilitation and enhance patient care not only within this country but also beyond.

Dermoscopy of melanocytoma-groundbreaking or established knowledge

Dermoscopy of melanocytoma-groundbreaking or established knowledge

Stories from the Botanical Underground

Living simultaneously in worlds of subsurface darkness and cosmic light, plants transfer knowledge across time and place thresholds. They communicate through their interactions in plain sight and beyond view, often escaping the reaches of human imagination. Medicinal plants, in particular, offer the intimacy of embodied sensory knowledge. As we consume them, ecosystem functions are reenacted inside our bodies through physiological actions. Drawing on botanically centered practices incorporating field work, herbalism, and writing, this project expands creative methods within geography. Through botanical stories, I build on established knowledge adding insights and interpretations derived from ecological observations and botanical medicine practices. These stories feature ecologically and culturally important species that serve as guides for navigating emerging and ongoing environmental and social issues. Through interconnectivity with Betony (Pedicularis spp.), Globemallow (Sphaeralcea spp.), and Vervain (Verbena spp.), I explore plant perspectives for living relationally with environmental change.

Effects of Mixtures of Emerging Pollutants and Drugs on Modulation of Biomarkers Related to Toxicity, Oxidative Stress, and Cancer.

Background/Objectives: Over time, the scientific community has developed a growing interest in the effects of mixtures of different compounds, for which there is currently no established evidence or knowledge, in relation to certain categories of xenobiotics. It is well known that exposure to pollutants causes oxidative stress, resulting in the overproduction of reactive oxygen species (ROS), which can affect signaling pathways that regulate the cell cycle, apoptosis, energy balance, and cellular metabolism. The aim of this study was to investigate the effects of sub-lethal concentrations of mixtures of emerging pollutants and pharmaceuticals on the modulation of biomarkers related to toxicity, oxidative stress, and cancer. Methods: In this study, the hepatoma cell line HepG2 was exposed to increasing concentrations of polybrominated diphenyl ether 47 (BDE-47), cadmium chloride (CdCl2), and carbamazepine (CBZ), both individually and in mixtures, for 72 h to assess cytotoxicity using the MTT assay. The subsequent step, following the identification of the sub-lethal concentration, was to investigate the effects of exposure at the gene expression level, through the evaluation of molecular markers related to cell cycle and apoptosis (p53), oxidative stress (NRF2), conjugation and detoxification of xenobiotics (CYP2C9 and GST), DNA damage (RAD51 and γH2AFX), and SUMOylation processes (SUMO1 and UBC9) in order to identify any potential alterations in pathways that are normally activated at the cellular level. Results: The results showed that contaminants tend to affect the enzymatic detoxification and antioxidant system, influencing DNA repair defense mechanisms involved in resistance to oxidative stress. The combined effect of the compounds at sub-lethal doses results in a greater activation of these pathways compared to exposure to each compound alone, thereby exacerbating their cytotoxicity. Conclusions: The biomarkers analyzed could contribute to the definition of early warning markers useful for environmental monitoring, while simultaneously providing insight into the toxicity and hazard levels of these substances in the environment and associated health risks.

General Machine Learning Approaches for Lithium-Ion Battery Capacity Fade Compared to Empirical Models

Today’s growing demand for lithium-ion batteries across various industrial sectors has introduced a new concern: battery aging. This issue necessitates the development of tools and models that can accurately predict battery aging. This study proposes a general framework for constructing battery aging models using machine learning techniques and compares these models with two existing empirical models, including a commercial one. To build the models, the databases produced by EVERLASTING and Bills et al. were utilized. The aim is to create universally applicable models that can address any battery-aging scenario. In this study, three types of models were developed: a vanilla neural network, a neural network inspired by extreme learning machines, and an encoder coupled with a neural network. The inputs for these models are derived from established knowledge in battery science, allowing the models to capture aging effects across different use cases. The models were trained on cells subjected to specific aging conditions and they were tested on other cells from the same database that experienced different aging conditions. The results obtained during the test for the vanilla neural network showed an RMSE of 1.3% on the Bills et al. test data and an RMSE of 2.7% on the EVERLASTING data, demonstrating similar or superior performance compared to the empirical models and proving the ability of the models to capture battery aging.

Nature's Contributions to People: Systematic Mapping of Studies and Inventory of Methodologies and Indicators for Quantifying Hydrological Ecosystem Services

Objective: Evaluate the estimation of nature's contributions to people - NCP (i.e. ecosystem services - ES) through bibliographic mapping and systematization of methodologies, tools and indicators of hydrological ecosystem services (HES). Theoretical Framework: Predatory exploitation of ecosystems is causing increasingly severe impacts on humanity. Accelerated by population growth, changes in land use and the decoupling of measures recommended by science from those adopted by nations, climate change is triggering alterations in the global hydrological cycle. Adaptation strategies depend on the production of information on the supply and flow of HES. Method: Consultation of the Web of Science, Scopus and Science Direct databases, and systematic bibliographic mapping (2014-2024), with bibliometric analysis in the VOSviewer software, and recording of information on NCP, ES, metrics, methodologies for quantifying HES and respective indicators. Results and Discussion: A total of 743 abstracts were analyzed and 196 articles were selected. Of these, 103 estimated HES, with a predominance of studies on a river basin scale. Seventy-nine indicators were mapped, mostly biophysical, associated with water flow regulation and water quality. China was the most represented country in terms of mapped area. The most widely used models were InVEST and SWAT. Research Implications: Systematization of information for users interested in mapping and quantifying HES, with an indication of established aspects, methods, and knowledge gaps. Originality/Value: Mapping based on HES indicators, aggregation of studies under different ES frameworks and provision of a dynamic results panel, with spatialization of studies and various data filtering possibilities.

Gender differences in the patterns and consequences of changing research directions in scientific careers

Abstract Changes of research direction in scientific careers are related to the so-called “essential tension” between the exploration of new knowledge and the exploitation of established knowledge in research and innovation. Changes of research direction are thereby assumed to influence the evolution of science in general. Research has shown that such changes may also affect the success of individual scientists in their careers. However, the gender dimension of this aspect of career development is so far understudied. There is also a need for more dynamic indicators to record and interpret career developments in macro data. This study combines the gender perspective with the introduction of new indicators. We selected more than 29,000 scientists in Physics & Astronomy and studied them over six decades using a bibliographic data set from Scopus. We find that women are less likely to change research direction than their men counterparts, and that the research performance of women is less negatively affected by changing research direction. We discuss the policy implications of these findings as well as the methodological advancement related to the new indicators of career development.

Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics.

Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell-cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low-dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial-to-mesenchymal transition and PI3K/AKT signaling in specific sub-regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder-based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biologicalprocesses.

Preprocessing Text Data for the Doc2Vec Model in the Automatic Classification of Tickets in Support Centers of Software Providers

Leading software providers typically implement customer technical support functions, which are crucial for promoting and enhancing the competitiveness of their products and services in global markets. The high volume and heterogeneity of support tickets (functional, temporal, linguistic, etc.) highlight the importance of efficient classification systems. Effective classification optimizes the distribution of these tickets among support center specialists and automates their processing using an established knowledge base. However, classifying these tickets is a loosely formalized task. For companies that have accumulated substantial data on customer requests, automating classification through machine learning methods and natural language processing models, such as Word2Vec, FastText, BERT, and GPT, becomes feasible. It is generally accepted that classification effectiveness primarily depends on the model employed. Nevertheless, the quality of these models is significantly influenced by the nature of the training data. Literature review of the reveals significant research interest in methods for the automatic classification of tickets specifically tailored to the operational conditions of software provider support centers. However, there is a noticeable gap in the literature regarding the impact of data preprocessing on the quality of these models. The article aims to clarify the techniques of data preprocessing and analyze their impact on the effectiveness of text classification, considering the specificity of software provider support centers. This study examines the stages of the automatic classification process for tickets, accounting for the unique characteristics of the data (customer text requests). A relevant set of specified methodological and instrumental tools was developed and tested using open data from a global software provider (DevExpress). The testing involved a database of 165,000 tickets. The study's results indicate that preprocessing can improve classification metrics such as F-measure, Precision, and Recall from 77% to 79%. Additionally, preprocessing significantly reduces the dimensionality of text data (by 48.2%) and increases model training speed (by 26.5%) without loss of accuracy, ensuring cost-efficiency and operational efficiency in the use of computational resources.

Leading by example from high-status individuals: exploring a crucial missing link in climate change mitigation

Behaviour change has great potential to reduce greenhouse gas emissions quickly, helping to prevent dangerous global warming. Some of the most impactful changes are: flying less, eating less meat, driving electric cars, improving home energy efficiency, increased use of public transport and active travel. However, these choices have proved elusive at scale and are rarely encouraged or modelled by high-status individuals (“leaders”), despite established knowledge about the influence of leaders as role models. Applying theories of embodied leadership and credibility enhancing displays, our novel pre-registered survey experiment (n = 1267) reveals that visible leading by example from politicians and celebrities significantly increases the willingness of members of the UK public to make these high-impact low-carbon choices. In addition, leading by example greatly increases perceptions of leader credibility, trustworthiness, competence, and favourability. We find no significant effects of leading by example on people’s wider perceptions of climate change, but a strong “appetite for leadership” among the public is revealed. In light of these findings, we discuss how embodied leadership by way of visible low-carbon behaviour from leaders may provide a crucial “missing link” for climate change mitigation.

Unlocking the Biological Enigma: Influence of Host Length and Infection Site on Parasite Abundance in Ompok bimaculatus.

The influence of two key factors, host length and infection site, on the host-parasite interaction in Ompok bimaculatus (Butter catfish) from Mukutmanipur Dam Lake, were investigated. Present study involved 192 specimens of Ompok bimaculatus with varying body lengths, subjected to diverse statistical analyses. One-way analysis of variance (ANOVA) was performed for the parasite numbers for three groups (cestode, nematode and trematode). Subsequently, we conducted one-way permutational multivariate analysis of variance (PERMANOVA) followed by pairwise test to assess parasite numbers across three body sites (intestine, mesentery, and bodycavity), employing the Bray-Curtis index. Additionally, Principal Coordinate Analysis (PCoA) for the same dataset was performed using the same index. Linear regression analysis was performed for the fish length-cestode number, fish length-nematode number, fish length-trematode number and fish length-total parasite number. One-way ANOVA revealed no significant differences in parasite numbers among the three endo-helminth groups (cestode, nematode, and trematode). The results of PERMANOVA revealed significant differences in parasite numbers across the three body sites of the host fishes (groups) (F = 9.41, p = 0.0001). Pairwise tests further demonstrated significant differences between the intestine-mesentery, intestine-body-cavity, and mesentery-body-cavity. Additionally, Principal Coordinate Analysis (PCoA) unveiled a significant relationship between infection site and parasite number. However, linear regression analysis examining the relationship between fish length and parasite abundance indicated no significant associations. Through a detailed exploration of the statistical analyses, we provide insights into the host-parasite interaction, elucidating both established knowledge and novel findings in fish parasitology.

Evolution, structure, and drug-metabolizing activity of mammalian prenylcysteine oxidases

Prenylcysteine oxidases (PCYOXs) metabolize prenylated cysteines produced by protein degradation. They utilize oxygen as a co-substrate to produce free cysteine, an aldehyde, and hydrogen peroxide through the unusual oxidation of a thioether bond. In this study, we explore the evolution, structure, and mechanism of the two mammalian PCYOXs. A gene duplication event in jawed vertebrates originated in these two paralogs. Both enzymes are active on farnesyl- and geranylgeranylcysteine, but inactive on molecules with shorter prenyl groups. Kinetics experiments outline a mechanism where flavin reduction and re-oxidation occur rapidly without any detectable intermediates, with the overall reaction rate limited by product release. The experimentally determined three-dimensional structure of PCYOX1 reveals long and wide tunnels leading from the surface to the flavin. They allow the isoprene substrate to curl up within the protein and position its reactive cysteine group close to the flavin. A hydrophobic patch on the surface mediates membrane association, enabling direct substrate and product exchange with the lipid bilayer. Leveraging established knowledge of flavoenzyme inhibition, we designed sub-micromolar PCYOX inhibitors. Additionally, we discovered that PCYOXs bind and slowly degrade salisirab, an anti-RAS compound. This activity suggests potential and previously unknown roles of PCYOXs in drug metabolism.

Exploring AI Applications for Addressing Alternative Conceptions in Teaching Physics: Focus on Electrical Circuits at the Secondary Level

This article introduces our initial foray into a comprehensive, long-term study on the learning practices of high school students using chatbots as educational tools. In this section, we explore the correlation between the logic underlying the responses generated by two chatbots for problems related to electrical circuits and the well-known alternative conceptions prevalent in the field of science education. To achieve this, we employed a methodology involving the presentation of ten questions to the chatbots, followed by an analysis of their answers in conjunction with established knowledge in physics and the Theory of Conceptual Fields. The objective was to bridge the gap between AI-generated responses and human responses. Our primary findings reveal a close resemblance between these two groups. This initial endeavor lays the foundation for developing an investigative methodology that will facilitate a comprehensive understanding and categorization of the various forms of interaction between students and chatbots.

A knowledge graph-based recommender system for dementia care: Design and evaluation study

BackgroundCaring for people with dementia is perceived as one of the most challenging caring roles, so effective and practical support is essential. One such innovative approach to internet-based tailored health intervention is the use of recommender system. ObjectiveThis study develops a dementia care intelligent recommender system (DCIRS) that can provide personalized and timely care recommendations for caregivers when they encounter difficult various care problems in people with dementia. MethodsThe development process was divided into 3 stages. In stage 1, we complete the construction of the domain knowledge graph of dementia care. In stage 2, the established domain knowledge graph of dementia care was introduced into the recommendation model by the way of graph embedding to form a recommendation model composed of graph embedding module and recommendation module. In stage 3, on the basis of the application of knowledge graph and recommendation mode, DCIRS was developed, for practical use. In addition, DCIRS has been validated for accuracy for assessing the correctness of the profiling and recommendation, by enrolling 56 caregivers. ResultsThe proposed DCIRS has functions of knowledge graph management and dementia care decision support. Experiments on 56 caregivers in single class recommendation task; the value of accuracy is equals to 98.92% and indicates the high capability of DCIRS. ConclusionsThis study was a pioneering research to develop a more comprehensive DCIRS for caregivers of people with dementia. According to the evaluation results, our DCIRS showing high specificity and accuracy. This system can provide a novel perspective for patient-centered and needs-based support of caregivers of people with dementia.

Rethinking polarization in wurtzite semiconductors

Polarization arising from non-centrosymmetric wurtzite lattice underpins the physics and functionality of gallium nitride (GaN)—the most produced semiconductor materials second only to silicon. However, recent direct experimental measurements unveiled remanent polarization of unexpectedly large magnitudes and opposite orientations to traditionally anticipated. This significant discrepancy not only poses a formidable challenge to our existing theoretical paradigms but also accentuates the need for a critical rethinking and methodological refinement to integrate these emerging observations with established knowledge, mitigating potential misunderstandings and misconceptions in this rapidly evolving field.

Baseline anemia and its associated factors among adult cancer patients at Northwest Amhara Regional State Referral Hospitals, Northwest Ethiopia, 2021.

Currently, the problem of cancer has been increasing around the world, predominantly in middle- and low-income countries. Anemia, a major and often overwhelming health burden for cancer patients, significantly distorts their quality of life. It is well-established that the length of treatment increases the frequency of anemia, with hematological malignancies experiencing nearly double the rate compared to solid tumors. Despite this established knowledge, data on the prevalence of anemia among cancer patients in Ethiopia remains scarce, according to the investigators. This study aimed to assess the prevalence of baseline anemia and associated factors among adult cancer patients at Northwest Amhara Comprehensive Specialized Hospitals, oncology treatment units, Northwest Ethiopia, in 2021. This study employed an institutional-based cross-sectional design and was conducted in Northwest Amhara Comprehensive Specialized Hospitals. A systematic random sampling technique was used to select 315 participants. The data were collected using interviewer-administered questionnaires and chart reviews of existing medical records using a structured and pretested questionnaire format. The data were entered into Epi. Data version 4.6 and analyzed using Stata version 14.0. Bivariable and multivariable logistic regression were carried out to identify factors associated with anemia. Adjusted odds ratios with a 95% confidence interval and variables with a p-value of < 0.05 were considered significantly associated with anemia. The prevalence of baseline anemia among adult patients with cancer was found to be 34.84%. Being a woman (AOR = 1.97; 95% CI: 1.00-3.87), being underweight (AOR = 1.96; 95% CI: 1.09-3.52), and having stage III cancer (AOR = 2.35; 95% CI: 1.12-3.01) were significantly associated with anemia. The prevalence of baseline anemia among adult cancer patients was significant. Women, cancer patients with an underweight body mass index, and those diagnosed with clinical-stage III cancer were more likely to have baseline anemia. For health policymakers and healthcare providers, it is better to give special attention to female patients, patients who are underweight, and patients with advanced-stage cancer to reduce the risk of developing the outcome. This would allow for timely intervention to manage anemia and potentially improve treatment tolerance and quality of life for cancer patients.