Set Of Elements Research Articles

Some properties of a graph associated with a non-quasi-local weakly factorial domain and a supergraph of it

Let [Formula: see text] be a weakly factorial domain which admits at least two maximal ideals. We use [Formula: see text] to denote the set of all primary elements of [Formula: see text] and denote [Formula: see text] by [Formula: see text]. Let [Formula: see text]. We denote the subset of [Formula: see text] consisting of all [Formula: see text] such that [Formula: see text] does not belong to the Jacobson radical of [Formula: see text] by [Formula: see text]. With [Formula: see text], in this paper, we associate an undirected graph denoted by [Formula: see text] whose vertex set is [Formula: see text] and distinct vertices [Formula: see text] and [Formula: see text] are adjacent if and only if [Formula: see text]. In Secs. 2 and 3 of this paper, we discuss results regarding the connectedness, the girth, and the clique number of [Formula: see text] and study the interplay between graph properties of [Formula: see text] and the properties of [Formula: see text]. In Secs. 4 and 5 of this paper, we consider a supergraph of [Formula: see text], denoted by [Formula: see text] whose vertex set is [Formula: see text] and distinct vertices [Formula: see text] and [Formula: see text] are adjacent if and only if [Formula: see text] and study some graph properties of [Formula: see text].

Investigating the Morphology of a Free-Falling Jet with an Accurate Finite Element and Level Set Modeling

This study investigates the morphology of a free-falling liquid jet by using a computational approach with an experimental validation. Numerical simulations are developed by means of the Finite Element Method (FEM) for solving the viscous fluid flow and the level set method in order to track the interface between the fluid and air. Experiments are conducted in order to capture the shape of a free-falling jet of viscous fluid via circular orifice, where the shape is measured optically. The numerical results are found to be in agreement with the experimental data, demonstrating the validity of the proposed approach. Furthermore, we analyze the role of the surface tension by implementing linear as well as nonlinear surface energy models. All computational codes are developed with the aid of open-source packages from FEniCS and made publicly available. The combination of experimental and numerical techniques provides a comprehensive understanding of the morphology of free-falling jets and may be extended to multiphysics problems rather in a straightforward manner.

Adherence to Synoptic Cancer Pathology Reporting Among Pathologists in the National Department of Veterans Affairs Health Care System.

Quality communication between clinicians and pathologists is required for optimal cancer care. The College of American Pathologists provides anatomic site-specific cancer protocols that facilitate synoptic reporting for efficient communication, contributing to accuracy and completeness of cancer staging. To evaluate synoptic cancer pathology reporting across the Department of Veterans Affairs (VA), the largest integrated health system in the United States, for 4 common cancers: melanoma and colon, bladder, and kidney cancer. For each cancer type, we investigated at least 200 biopsy and 200 resection reports from 2019 to 2021. In each report, we determined whether a synoptic format was used. The reports were selected using random sampling across all VA health care facilities. We also identified a set of core elements that were underdocumented. Among 1618 pathology reports, 778 (48%; 95% CI, 46%-50%) were synoptic reports. Synoptic reporting was much more common among resections (621 of 811; 77%; 95% CI, 74%-79%) than among biopsies (157 of 807; 19%; 95% CI, 17%-22%). It was most common in colorectal resections (200 of 206; 97%; 95% CI, 94%-99%) and least common in colon biopsy reports (1 of 200; 0.5%; 95% CI, 0%-3%). Core elements that were underdocumented included procedure and regional lymph nodes for resections of bladder and kidney cancer and of melanoma. Synoptic reporting was used about three-quarters of the time for resections and about 1 in 5 times for biopsies. Future work should develop implementation strategies to improve synoptic reporting, especially for biopsy specimens and core elements that were relatively underdocumented.

Harmonizing data across the accelerating colorectal cancer screening and follow-up through implementation science (ACCSIS) program to enhance data quality and promote data sharing

BackgroundThe purpose of the Accelerating Colorectal Cancer Screening and Follow-up through Implementation Science (ACCSIS) Program, a Cancer Moonshot℠ Initiative, is to support research to build the evidence base on multilevel interventions that increase rates of colorectal cancer screening, follow-up, and referral to care to address disparities in colorectal cancer screening. The National Cancer Institute funded eight Research Projects to implement multilevel interventions to improve colorectal cancer screening among communities who traditionally have been medically underserved. To analyze the impact of ACCSIS across Research Projects, the consortium developed a set of common data elements. The purpose of this paper is to describe the process of developing the common data elements to facilitate analysis of ACCSIS data as well as support and inform implementation science research studies.MethodsThe ACCSIS Data, Design, and Analysis Work Group was tasked with designing common data elements through a review of existing data collection instruments, examination of data elements proposed by the ACCSIS Research Projects, and deliberations on the data required to compare across the Research Projects. ACCSIS Consortium members drafted, revised, and finalized a common data elements document consisting of variables to collect and surveys to administer to evaluate ACCSIS implementation activities and outcomes in a standardized manner across the Research Projects.ResultsThe ACCSIS Consortium decided to collect the following categories of common data elements: characteristics across multiple levels of the multicomponent interventions, implementation climate, and determinants; interventions and strategies; implementation outcome constructs and definitions; colorectal cancer screening episodes (screening, diagnostic testing follow-up, cancer detection, and cancer treatment); and cost measures. To assess implementation climate, the consortium prioritized constructs from five domains of the Consolidated Framework for Implementation Research.ConclusionsThe ACCSIS common data elements offer a set of harmonization data for future implementation efforts. The consortium is conducting a systematic assessment using both quantitative and qualitative approaches to assess data quality and approaches to improve and sustain data collection. Lessons learned from these ongoing activities will offer additional insights to tailor the ACCSIS common data elements and support efforts to increase colorectal cancer screening for populations experiencing disparities.

Content validation of the nursing diagnosis “ineffective protection” in the context of adolescents with cancer

ObjectiveTo analyze the relevance of a set of clinical elements to represent the content domain of the nursing diagnosis Ineffective Protection. Design and methodsThis study employed content validation, using expert judgment to assess the appropriateness of clinical indicators, etiological factors, and conceptual and operational definitions, integrating the collective wisdom framework, the theory of predictive diversity, and the determination of rater skill level. ResultsFrom the middle range theory, 28 clinical indicators and 17 etiological factors were identified, which were rated by 34 raters. The analysis showed that the clinical indicators restlessness and insomnia and the etiological factors ambient temperature and humidity were not considered relevant for the nursing diagnosis of Ineffective Protection, because the median content validity index was lower than 0.9. ConclusionsContent validation verified that most of the components of the nursing diagnosis of Ineffective Protection were considered relevant by judges. Practical implicationsIt is believed that the data obtained can help nurses to more easily evaluate the related factors and clinical indicators of Ineffective Protection presented by adolescents with cancer, which favors the process of diagnostic inference.

Discontinuous galerkin method of the first order parabolic differential equation

The objective of this paper is to propose a modest numerical error analysis by applying DG finite element method for the parabolic differential equation. The DG method is an imperative numerical method with much mass compensation and more flexible meshing than other numerical methods. The DG method starts by discretizing the domain into a set of non-overlapping elements. This study gives a general introduction and discuss about the discontinuous Galerkin Method of first order parabolic problem. The parabolic problem satisfies the condition of the existence and uniqueness of DG solution. The main goal of this study is to theoretically explore the convergence of the solution of the above methods and show the validity of the results.

Increasing the efficiency of operation and management of railroad transport infrastructure based on maximum levels of fault tolerance

This paper considers the optimization of parameters for a railroad transport system. The maximum level of technological reliability and the average time spent by trains on the route are used as optimization criteria. The purpose of the study is to establish the optimal parameters for the operational process of railroad transport systems according to the criterion of the maximum level of technological reliability and the minimum time of trains on the route. Methods of technological reliability research have been proposed. Taking into account that the entire technological process is a sequential set of technological elements, a simulation model of the technological process of the transit transport-technological line along a route direction has been built. A population of agents that simulates the operation of railroad sections of the rotation of train locomotives and is a key subsystem of the simulation model has been developed and configured. The simulation model makes it possible to optimize the parameters of multi-section railroad lines. This approach is provided owing to the agent approach. As a result of the experiments, the optimal parameters of the functioning of railroad lines were established when organizing the passage of transit trains. The coefficient of utilization of the locomotive fleet fluctuates within the optimal range (0.55–0.65), which indicates the sufficiency of traction resources in the railroad system. The optimal parameters of the railroad transport system were established experimentally using the example of a train flow of 85 pairs of trains on a two-track route with five sections. The problem of "abandoned trains" has a solution but, to this end, it is necessary to increase the fleet of train locomotives by 150–200 % relative to existing standards. At the same time, even with an unlimited fleet of train locomotives, there is a fairly high probability (up to 30–50 %) of technological failures

Terra incognita of the uncontrolled manifold (UCM).

The review addresses the central concept of the uncontrolled manifold (UCM) hypothesis, which has become a major framework for analysis of performance-stabilizing motor synergies. The major goals are to summarize the status quo in the field and to ask new questions stimulating new studies. We focus on a few main questions: What is the UCM? What are the likely neural origins of the UCM? How is the UCM reflected in movement patterns? Are properties of the UCM similar in all directions? We contrast experience-based features of movements seen very soon after the movement initiation vs. those based on on-line sensory feedback signals. Further, we address a number of poorly explored issues such as the differences in characteristic times of processes within the UCM and orthogonal to the UCM space, the interplay between movement stability and optimality, the origin of preferred sharing patterns of performance variables across abundant sets of elements and of their inter-trial variability, problems with the UCM-based analysis in different spaces, and likely neurophysiological mechanisms contributing to the UCM formation. In particular, we focus on the UCM in spaces of hypothetical neural control variables, which we associate with the reciprocal and coactivation commands to the effectors. Analysis of performance-stabilizing synergies within the UCM framework in abundant spaces of kinetic, kinematic and electromyographic variables at the selected level of analysis may be practically useful. However, mapping findings in such studies onto neural control mechanisms has been challenging.

The Oncology QCARD Initiative: Fostering efficient evaluation of initial real-world data proposals.

The oncology quality, characterization, and assessment of real-world data (Oncology QCARD) Initiative was formed to develop a set of minimum study design and data elements needed to evaluate the fitness of the real-world data (RWD) source(s) proposed in an initial study concept as part of early interaction with scientific reviewers. A multidisciplinary executive committee (EC) was established to guide the Oncology QCARD Initiative. The EC conducted a landscape review of published literature, guidances, and guidelines to evaluate relevant dimensions of data quality measurement. Guided by the review and informed by expert feedback, the Oncology QCARD Initial Protocol Characterization (IPC) provides a summary of minimum elements needed to adequately describe an initial clinical study concept that involves RWD and is intended to support decision-making. Fit-for-use data and fit-for-purpose design emerged as themes from the landscape analysis. Data that are fit-for-use are both relevant (sufficiently capturing exposure, outcomes, and covariates) and reliable (understanding data accrual and quality control and whether the data represent the underlying concepts they are intended to represent) to answer a specific research question. A fit-for-purpose design takes appropriate steps to ensure internal and external validity and allows for transparency in reporting. The QCARD-IPC focuses on high-level characteristics of RWD sources and study design domains including data temporality, population, medical product exposure, comparators, and covariates, endpoints, statistical analysis, and data quality assurance plans. Evaluation of studies including RWD requires understanding the data source, study design, and potential biases to preliminarily evaluate whether selected RWD are fit-for-use for the research question. The Oncology QCARD-IPC provides a structured, transparent approach to facilitate early review and enhanced communication between study sponsors and scientific reviewers of initial study proposals including RWD.

Conceptual Model on Access to Food in the Favela Food Environment

The inequalities of Brazilian society are amplified in favelas, affecting access to basic sanitation, health, education services, and food. More research is needed to better understand the favela food environment and propose appropriate public food and nutrition policies to increase the availability of and access to healthy food. In this context, this study aimed to develop a conceptual model of the relationship between access to food and the favela food environment and its determinants. In developing the conceptual model, this study undertook a bibliographical survey of the food environment, and a preliminary version was submitted to an expert panel. The model represents a set of dimensions (individual, micro-environment, macro-environment, and decision-making) and elements that interact in a complex manner and help understand access to food in areas subject to multiple social vulnerabilities. This model can guide future research and aid policymakers in designing effective strategies to improve the food security and health of populations in areas of high socio-spatial vulnerability.

Zero-sum partitions of Abelian groups and their applications to magic- and antimagic-type labelings

The following problem has been known since the 80s. Let $\Gamma$ be an Abelian group of order $m$ (denoted $|\Gamma|=m$), and let $t$ and $\{m_i\}_{i=1}^{t}$, be positive integers such that $\sum_{i=1}^t m_i=m-1$. Determine when $\Gamma^*=\Gamma\setminus\{0\}$, the set of non-zero elements of $\Gamma$, can be partitioned into disjoint subsets $\{S_i\}_{i=1}^{t}$ such that $|S_i|=m_i$ and $\sum_{s\in S_i}s=0$ for every $1 \leq i \leq t$. Such a subset partition is called a \textit{zero-sum partition}. $|I(\Gamma)|\neq 1$, where $I(\Gamma)$ is the set of involutions in $\Gamma$, is a necessary condition for the existence of zero-sum partitions. In this paper, we show that the additional condition of $m_i\geq 4$ for every $1 \leq i \leq t$, is sufficient. Moreover, we present some applications of zero-sum partitions to magic- and antimagic-type labelings of graphs.

Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction.

The prediction of the thermodynamic and kinetic properties of elementary reactions has shown rapid improvement due to the implementation of deep learning (DL) methods. While various studies have reported the success in predicting reaction properties, the quantification of prediction uncertainty has seldom been investigated, thus compromising the confidence in using these predicted properties in practical applications. Here, we integrated graph convolutional neural networks (GCNN) with three uncertainty prediction techniques, including deep ensemble, Monte Carlo (MC)-dropout, and evidential learning, to provide insights into the uncertainty quantification and utility. The deep ensemble model outperforms others in accuracy and shows the highest reliability in estimating prediction uncertainty across all elementary reaction property data sets. We also verified that the deep ensemble model showed a satisfactory capability in recognizing epistemic and aleatoric uncertainties. Additionally, we adopted a Monte Carlo Tree Search method for extracting the explainable reaction substructures, providing a chemical explanation for DL predicted properties and corresponding uncertainties. Finally, to demonstrate the utility of uncertainty qualification in practical applications, we performed an uncertainty-guided calibration of the DL-constructed kinetic model, which achieved a 25% higher hit ratio in identifying dominant reaction pathways compared to that of the calibration without uncertainty guidance.

High-throughput screening of the potential alloying elements for high-strength Mg alloys based on solid-solution strengthening

Abstract 49 types of alloy atomic dopants and their effects on the doping stability and micro-mechanical behaviour of magnesium matrix were investigated using density functional theory and high throughput first-principles calculations. Geometry optimization was performed for each doping system, and the ability of atom doping into the magnesium matrix was assessed based on the doping energy and atomic radius. Results show that the transition metal elements have negative or near negative doping energy, especially for the elements with a radius that similar to Mg. The micro-mechanical properties of the doping system were evaluated by computing the fracture energy and theoretical tensile stress. Through a screening on the doping stability and strengthening effect of 49 types of alloy atoms, a set of elements (Re, Os, Ir, Tc and W, etc.) are screened out that could strengthen the magnesium matrix with a good doping stability. The high throughput screen results serve as a theoretical guide for the selection of appropriate alloy elements for designing the high-strength magnesium alloys in the regime of solid solution strengthening effects.

The solution of super-scale DEA models based on preference character

The basic relationships and intrinsic laws in the data envelopment analysis (DEA) are the basis for designing fast solutions for big data DEA models. By delving into these relationships, it becomes feasible to address super-scale DEA problems more swiftly, aligning with the need for timeliness in real-world efficiency evaluations and reducing time costs. This paper leverages the intrinsic characteristics of DEA to formulate a DEA model scale reduction approach, offering reliable theoretical support for solving super- scale DEA problems. Initially, the partially ordered set theory to prove the relationship between the efficient decision making units (DMUs) of DEA and the maximal element of the partially ordered set. Building on this, a DEA model based on maximal element set (DEA-PS) is constructed based on the theory of partially ordered set theory, and the equivalence between DEA-PS model and the original DEA model is proved. Furthermore, it is proved that the dominant theorem exists in the solution of multiple classical DEA models, and a method of super-scale DEA models based on dominant theorem is given. The conclusions of this paper theoretically reveal the relationship between efficient DMUs and maximal elements, from this relationship, two key insights emerge: (1) Regardless of how huge the number of DMU is, the proposed method in this paper can realize the scale reduction of the DEA model quickly, (2) The algorithm has good compatibility with all current big data DEA model algorithms.

Lexical field theory: Key concepts and contemporary approaches

ABSTRACT The article aims to discuss the ideas and principles of Trier’s theory of lexical field and to demonstrate how the concept of field can be fruitfully employed outside of the domain of lexicology. The first part of the article deals with Trier’s main idea that no lexical-semantic element can be considered outside of its systemic relations: a given element (word) can be ascribed a certain significance only depending on the place it occupies in some system called a lexical field (Wortfeld). The second part of the article demonstrates how the field principle can be applied to the analysis of grammatical categories using the ‘functional-semantic field’ approach and the analysis of communicative behaviour using the ‘communicative-pragmatic field’ approach. The former helps to reveal and systematize sets of language elements of a specific language with respect to the coding of a certain categorial function. The latter makes it possible to summarize different possibilities for the realization of speech behaviour patterns and to rank them according to the degree of their usage frequency. These two cases prove that the field concept can be used to solve issues that are deemed unsolvable if one applies the framework of the traditional level-oriented linguistic analysis.

Green Infrastructure and Adaptation to Climate Change in Marginal Areas: A Reference Scheme for Implementation Guidelines in Italy

Marginal areas (MAs) can show scarce disaster resilience in the context of climate change. Proactive adaptation to climate change (ACC) based on green infrastructure (GI) has the potential to increase the disaster resilience of the MAs. The scientific literature has scarcely addressed research on methods and guidelines for promoting ACC and GI to increase the resilience of MAs. No previous research has focused on a method to set a reference scheme for implementation guidelines concerning the use of GI as an ACC approach to deal with the effects of a changing climate in Italian MAs. In this regard, this study aims to provide planners and public administrations with an appropriate scheme to foster the mainstreaming of ACC and GI into the planning of MAs. To do so, we proposed and applied a methodological approach consisting of the scrutiny of the scientific and grey literature with the purpose of distilling a set of key elements (KEs) that need to be considered as a reference scheme for implementation guidelines. As main findings, we identified ten KEs relevant to drafting guidelines for integrating ACC and GI into planning tools, e.g., a clear definition of GI, participative approaches, public–private cooperation, and others, that will be tested in ongoing research.

7194 The Use Of GloBE-Reg As A Global Platform For Performing Safety & Efficacy Studies Of rhGH Therapy - First Year Results

Abstract Disclosure: M. Alimussina: None. J. Bryce: None. M. Chen: None. S. Koley: None. A. Al-Agha: None. N. Amin: None. N. Atapattu: None. J. Chen: None. Y. Deyanova: None. A. Fu: None. C. Gong: None. V. Iotova: None. D. Janus: None. A. Jorge: Consulting Fee; Self; Novo Nordisk. Grant Recipient; Self; BioMarin. R. Markosyan: None. S. Seneviratne: None. M.G. Shaikh: None. S. Shenoy: None. S. Shepherd: None. L. Tack: None. M. Wasniewska: None. S. Chen: Research Investigator; Self; Pfizer, Inc. S.F. Ahmed: Grant Recipient; Self; GenSci, Novo Nordisk, Pfizer, Inc.. Speaker; Self; Ipsen. Introduction: The Global Registry For Novel Therapies In Rare Bone & Endocrine Conditions (GloBE-Reg, https://globe-reg.net/) project was launched in 2022 with the aim of supporting studies that focus on effectiveness and long-term safety of specific therapies. The project’s initial focus has been on recombinant human growth hormone therapy (rhGH). Although safety information on rhGH therapy has now been collected for several years, there are still existing gaps in our knowledge, especially with the introduction of new indications and novel forms of rhGH. Methods: The GloBE-Reg registry has three layers of datasets, with the first dataset consisting of internationally agreed core data elements that can apply to any rare condition, another set of data elements that allows the selection of a specific therapy and diagnosis, a third set of additional data elements that is made up of the therapy and diagnosis-specific minimum dataset (MDS) and an option to have a more extended dataset. The minimum dataset collects information on the diagnosis, therapy, clinician reported outcomes, patient reported outcomes and adverse events. The platform and the registry received ethics/IRB approval in the UK and is now extending internationally. The fields within the MDS are developed following guidance from short-life expert working groups (EWG). Results: Since its launch in November 2022, 51 centres from 29 countries have registered their interest to use the platform and of these, 14 centres from 11 countries in 3 continents have already included 444 (M:F, 282:162) patients with a median age of 11 yr (range <1, 29). Of these, 317 (71%) were on daily rhGH, 126 (28%) were on long-acting rhGH, one was on daily rhGH as an adult and in one, rhGH therapy had been discussed but not started. Nine different brands of rhGH were being used across these centres for 9 groups of indications, whereas long-acting rhGH was being used for only 4 indications. In addition, there were 11 (3%) cases receiving rhGH for other conditions associated with short stature or growth retardation. The largest indication group was GHD (65%) followed by SGA (10%), TS (7%), PWS (7%), ISS (5%) and others. Of the 444 cases, 67 (15%) had also been entered in other disease registries and 18 (27%) of these 67 had been entered by one centre into the GloBE-Reg through its bulk upload facility. A Childhood GHD MDS is now fully operational and MDS for other conditions are under development. Conclusion: Since its launch, GloBE-Reg has shown that a relatively low-cost platform operated through an academic institution can have sufficient stakeholder acceptability and versatility for collecting information that can support the development of long-term safety and effectiveness studies. The preliminary data that have been collected on rhGH show the long-term utility of the platform for collecting global brand and diagnosis-specific data. Presentation: 6/2/2024

Degree-based topological indices of the idempotent graph of the ring [formula omitted]

Let R be a finite commutative ring with a non-zero identity, and Id(R) be the set of idempotent elements of R. The idempotent graph of R, denoted by GId(R), is a simple undirected graph with all elements of R as vertices, and two distinct vertices u, v are adjacent if and only if u+v∈Id(R). In this paper, we consider the idempotent graph of the ring Zn and investigate some degree-based topological indices, such as the general sum-connectivity index, the general Randić index, the general Zagreb index, and the Sombor index of that graph by considering the M-polynomial of the graph.

Massively parallel reporter assays identify enhancer elements in oesophageal Adenocarcinoma.

Cancer is a disease underpinned by aberrant gene expression. Enhancers are regulatory elements that play a major role in transcriptional control and changes in active enhancer function are likely critical in the pathogenesis of oesophageal adenocarcinoma (OAC). Here, we utilise STARR-seq to profile the genome-wide enhancer landscape in OAC and identify hundreds of high-confidence enhancer elements. These regions are enriched in enhancer-associated chromatin marks, are actively transcribed and exhibit high levels of associated gene activity in OAC cells. These characteristics are maintained in human patient samples, demonstrating their disease relevance. This relevance is further underlined by their responsiveness to oncogenic ERBB2 inhibition and increased activity compared to the pre-cancerous Barrett's state. Mechanistically, these enhancers are linked to the core OAC transcriptional network and in particular KLF5 binding is associated with high level activity, providing further support for a role of this transcription factor in defining the OAC transcriptome. Our results therefore uncover a set of enhancer elements with physiological significance, that widen our understanding of the molecular alterations in OAC and point to mechanisms through which response to targeted therapy may occur.

Classifying zircon: A machine-learning approach using zircon geochemistry

This study presented a novel, rapid, and accurate method for determining zircon origin via a comprehensive analysis of a dataset containing 27,818 zircon trace element sets. This method integrated back propagation neural networks with the AdaBoost algorithm. The optimal classifier characterized as a linear combination of a two-layer neural network model, comprised 100 base classifiers and 400 hidden neurons. It was rigorously trained over 1000 iterations, which resulted in an unbiased error rate of 8.31%. To facilitate practical application, the classifier was integrated into a macro-enabled Excel spreadsheet.