Conceptual Modeling Research Articles

Multiscale fatigue modelling for riveted railway bridges: A case study for real damage validation

Fatigue is a concerning process of structural degradation that impacts the maintenance in service of existing railway bridges. Analysing the corresponding damage is challenging in large structures, due to the scale difference between the full size of bridges and the millimetric or smaller areas affected by fatigue. In order to understand all the local properties that influence fatigue in riveted details, a parametric analysis is conducted using a representative case study, advising admissible values with appropriate literature review. The influence of modelling hypotheses is also investigated. These analyses contribute to guiding further studies, filling a gap in the state of the art. From the results obtained, a three-scale modelling concept is proposed, integrating an innovative modelling approach for rivets that bridges the global and local scales, focusing on the intermediate member scale, which has received less attention in past research. Such global-to-local modelling process is applied to a real riveted railway bridge, with numerical results being confronted with actual damage data, allowing for a validation process that is scarce in the literature.

Revisiting the tension water storage capacity distribution in conceptual rainfall-runoff modeling: A large-sample approach

Accurately characterizing the spatial variability of tension water storage capacity (TWC) within a catchment is challenging due to limited in-situ hydrologic data availability. Conventional conceptual rainfall-runoff models typically rely on an empirically specified TWC distribution. However, this empirical distribution lacks a physical foundation and fails to effectively redistribute critical hydrologic components, such as local capacity and contributing area, to real-world contexts. To overcome this limitation, the topographic wetness index (TWI) and its generalized form (GTWI) are introduced to bridge local topographic information with hydrologic components. Four TWC distribution curves are contrived based on the empirical parabolic distribution, empirical linear distribution, TWI, and GTWI, respectively. The effects of these alternate TWC distributions on streamflow are investigated within the framework of the HYdrologic MODel (HYMOD) across 460 Australian catchments. The results illustrate that the GTWI-based HYMOD (GTHYMOD) outperforms other models in terms of daily streamflow, with high Kling-Gupta Efficiency (KGE) attained in 74.8% of the study catchments during the validation period. The eastern coast of Australian catchments presents a superior streamflow performance compared to that in the western coast. GTHYMOD demonstrates its superiority in characterizing spatial variability, an aspect HYMOD lacked. This study has the potential to refine the empirical TWC distribution from a physical perspective and advance our comprehension of underlying hydrologic behaviors.

Development of A Man-Machine Interface for Managing Photovoltaic Energy: Applied Study

Objective: Develop and evaluate a human-machine interface (HMI) that integrates advanced monitoring, forecasting, and management functionalities for photovoltaic solar energy systems, aiming to optimize energy production and operational efficiency. Theoretical Framework: This study is based on concepts of modeling and simulation, solar energy management, and problem-solving methodologies such as Soft System Methodology (SSM). Method: An applied approach was adopted using modeling, simulation, and statistical analysis techniques. The research included a bibliographic review in scientific databases, a case study, and SSM to organize and solve complex problems. 121 digital solar energy platforms in Brazil were analyzed to define the interface requirements. The interface was developed with React JS, Axios, Bootstrap v5, Apache Echarts, HTML, CSS, JavaScript, and Python libraries for forecasting models. Results and Discussion: The interface, named "Solar Smart Manager," enables efficient monitoring and management of energy production using critical data such as temperature, time of day, and solar irradiation. Tests in a real operational environment demonstrated improvements in energy management, incident response, and preventive maintenance. The functionality of validating solar radiation incidence data represents a significant contribution to the energy sector, promoting sustainability and innovation. Research Implications: The practical and theoretical implications of this research provide insights into the efficient and optimized management of photovoltaic solar energy systems, contributing to a better understanding and optimization of available solar resources. Originality/Value: This study contributes to the literature by developing an innovative interface that improves operational efficiency and solar energy management. The relevance and value of this research are evidenced by its positive impact on the energy sector, promoting sustainability and innovation.

Expanding Models for Physics Teaching: A Framework for the Integration of Computational Modeling

Teaching computation in science courses can enhance science education, but doing so requires that teachers expand the vision of their discipline beyond the traditional view of science presented in most curricula. This article describes a design-based research (DBR) program that included collaboration among high school teachers and professional development leaders in physics and computer science education. Through three years of professional development and teacher-led development, field testing, and refinement of integrated curricular resources, we have combined instructional modeling practices, physical lab materials, and computer programming activities. One of the outcomes is a co-created framework for the integration of computational modeling into physics that is sensitive to teachers’ interests and expressed needs in addition to learning goals. This framework merges two evidence-based approaches to teaching: Bootstrap:Algebra, a web-based computing curriculum that emphasizes using multiple representations of functions and scaffolds that make the programming process explicit, and Modeling Instruction in physics, an approach that emphasizes the use of conceptual models, modeling practices and representational tools. In doing so, we uncover the need to balance teachers’ visions for integration opportunities with practical instructional needs and emphasize that frameworks for integration need to reflect teachers’ values and goals.

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models

This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizophrenia prediction using genotype data. It introduces an innovative three-step approach leveraging neural networks' capabilities to efficiently handle genetic interactions. A locally connected network initially routes input data from variants to their corresponding genes. The second step employs an Encoder-Decoder to capture relationships among identified genes. The final model integrates knowledge from the first two and incorporates a parallel component to consider the effects of additional genes. This expansion enhances prediction scores by considering a larger number of genes. Trained models achieved an average AUC of 0.83, surpassing other genotype-trained models and matching gene expression dataset-based approaches. Additionally, tests on held-out sets reported an average sensitivity of 0.72 and an accuracy of 0.76, aligning with schizophrenia heritability predictions. Moreover, the study addresses genetic heterogeneity challenges by considering diverse population subsets.

Categorical analysis of logic in conceptual modeling of subject areas for semantic integrity of information resources

Categorical analysis of logic in conceptual modeling of subject areas for semantic integrity of information resources

Microdrop InkJet printed supercapacitors of graphene/graphene oxide ink for flexible electronics

The inkjet printing (IJP) technique is a highly promising technology for fabricating flexible electronics for wearable applications. The latest Microdrop IJP technology was introduced to produce flexible supercapacitors using a readily prepared graphene/graphene oxide ink. Molecular modelling concepts were conducted prior to ink formulation to investigate the interaction between graphene and GO via adsorption interactions to investigate the feasibility of adding GO to G for energy storage processes. Calculations validate that their interaction yields stable, highly reactive, and conductive structures. Molecular electrostatic potential maps reveal excellent charge distribution proposing G/GO as a potential candidate for electrochemical processes. Subsequently, GNPs/GO blend was prepared, leveraging the electric conductivity and high surface area of graphene, and the functional groups in GO to enhance graphene dispersion. Simple and interdigitated supercapacitor electrodes were fabricated without further post-printing complexities. The highly flexible interdigitated supercapacitor (100L) demonstrated notable performance with areal capacitance of 195.1 F m−2 at 0.4 A.m−2, power density of 1199.34 mW m−2, energy density of 24.91 mWh.m−2, and 80.65 % capacitance retention after 5000 cycles. The outstanding electrocapacitive performance was ascribed to GO's abundant functional groups and its 3D open structure. These factors contribute to adding a pseudocapacitive effect and enhancing charge transfer within the electrode channel-like networks.

Care coordination models for transition and long-term follow-up among childhood cancer survivors: a scoping review

ObjectivesChildhood cancer survivors may experience complex health issues during transition and long-term follow-up (LTFU); therefore, high-quality healthcare is warranted. Care coordination is one of the essential concepts in advanced healthcare....

Research on the construction and mapping model of knowledge organization system driven by standards

With the rapid development of artificial intelligence and enterprise digital transformation, the standardization organization, storage and management of semantic knowledge in computers have become the current research focus. As the core theory of knowledge system construction, knowledge organization (KO) provides theoretical support for the study of semantic knowledge organization and representation, among which knowledge organization system (KOS) is the important tool of semantic organization. At present, many scholars have carried out research from different perspectives of KOS based on theory, which provides the direction for the sustainable development of KOS. However, most of these studies focus on some aspects of KOS, which are in a "scattered" state, lacking systematic analysis of the basic principles of KOS construction and semantic organization based on theories and international standards. Therefore, this paper firstly constructs KOS theoretical models in the conceptual world and computer world respectively through a comprehensive study of multi-disciplinary basic theories such as semantics, logic, system theory, and international standards such as ISO 1087:2019, ISO 25964:2013, and ISO 11179:2023, and traces the iterative construction, organization and mapping process from "concept" in the conceptual world to "metadata" knowledge and semantics in the computer world. The semantic organization based on metadata is realized in computer. Secondly, on this basis, in order to realize ontology representation of domain knowledge, the ontology construction method based on MDR metadata is proposed. Finally, taking the semantic organization and ontology construction of Epicentre model in petroleum field as an example, the feasibility of the ideas and methods proposed in this paper is verified. The model and method proposed in this paper is independent of the specific type of KOS, so it is innovative and universal. The methodology is also applicable to other fields of conceptual system modeling, metadata standard construction, and data model modeling.

Application of a Data Quality Framework to Ductal Carcinoma In Situ Using Electronic Health Record Data From the All of Us Research Program.

The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using All of Us Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework. We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older. Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality. This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the All of Us Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.

Research on maintenance decision-making approach based on dynamic opportunistic window

We have developed a maintenance decision-making approach based on the dynamic opportunistic window, utilizing algorithms such as k-mean clustering, expected maximum, and parameter estimation to address the lack of a reasonable basis for the duration and divided number of opportunistic windows in current maintenance decision-making. Firstly, we have comprehensively summarized the multi-stage opportunistic maintenance decision-making approach, focusing on its current strengths and limitations. Secondly, the modeling concept of the dynamic opportunistic window is analyzed, and the underlying assumptions are established. Furthermore, it elaborates on the theoretical foundation of the proposed approach through a detailed modeling process. Finally, we validate the proposed model by conducting experiments on tandem components from the main combustion chamber in an aero engine. The experimental results demonstrate the significant value of the proposed approach in enhancing equipment reliability and optimizing maintenance support resources.

Cloud Computing and Its Mechanisms: A Comprehensive Study

Abstract: Cloud computing has emerged as a transformative technology, revolutionizing how computational resources are accessed and managed. This paper explores the fundamental concepts of cloud computing, its architecture, service models, deployment models, and key mechanisms that enable its functionality. We also examine the benefits and challenges associated with cloud computing, along with its applications across various domains. The paper concludes with a discussion on future trends and potential developments in cloud computing.

Editorial Introduction to Issue 39: Managing Complexity and Knowledge in Enterprise Projects

This issue of CSIMQ includes five articles selected on the topics of complexity management and knowledge management in enterprises from various perspectives and towards diverse goals: the economic and societal perspective seeking strategy fulfillment with the help of enterprise architecture management, the operational performance perspective, which requires grounding management decisions on data insights, and the human aspect perspective to facilitate collaboration, creativity and innovation. Methodologically, the issue reports a mix of conceptual modeling and analytics approaches, suggesting an emerging requirement to balance or alternate, in complexity management, abstraction-based analysis, and data-oriented analytics.

Statistical Framework for Modeling Asymmetrical Data with Dual Peaks

Objectives: To create a comprehensive framework that effectively identifies the most suitable model for asymmetrical data based on its unique characteristics. Methods: This study proposed a new model named Gompertz-Gumbel distribution (GGD) based on the results from the framework which utilizes various statistical tools, as well as information criteria. A dip test is used to check the modality of the data. To propose a new model, the finite mixture model concept was employed. The location, scale, shape, and weight parameters of the GGD were estimated using the maximum likelihood estimation method. Findings: The suggested framework exhibits superior performance in developing a suitable model for the asymmetrical data with dual peaks, resulting in the best fit for the data. To validate the effectiveness of the proposed model, it has been compared with various models like Gaussian models and two-component mixture models. The GGD's properties have also been determined. The various shapes of the GGD were also analyzed. Novelty: A novel framework is proposed to identify the appropriate model for the asymmetrical data with dual peaks that outperform the existing models. It shows the significance of the framework. Keywords: Lifetime distributions, Mixture models, Information Criteria, Goodness of fit, Asymmetrical data

Guiding attention in flow-based conceptual models through consistent flow and pattern visibility

A critical part of flow-based conceptual modeling, such as process modeling, is visualizing the logical and temporal sequence in which activities in a process should be completed. While there are established standards and recommendations, there is limited empirical research examining the influence of process model layout on model comprehension. To address this research gap, we conducted a controlled eye-tracking experiment with 70 participants comparing different layouts. The experimental results confirm that the visibility of control flow patterns is critical for assisting users with visual processing, particularly attentional allocation, when comprehending process models for both local comprehension tasks and tasks requiring cognitive integration of model components. In models with more directional changes, users’ visual attention is more drawn to irrelevant regions, but comprehension is less affected as long as patterns remain visible. Our findings not only elucidate how cognitive fit between a visual representation and a task can manifest itself and the perceptual benefits it brings, but they can also guide the automated layout of models in tools and complement practical process modeling guidelines.

Retraction Note: Mathematical analysis of COVID-19 pandemic by using the concept of SIR model

Retraction Note: Mathematical analysis of COVID-19 pandemic by using the concept of SIR model

Application of flow-through flaps for replantation after traumatic limb amputation according to the angiosome concept.

This study was performed to explore the treatment of the injury caused by traumatic limb amputation. From October 2002 to October 2021, 30 cases were enrolled in the present study. The reasons for injury were as follows: 8 cases with single hydraulic column crush injury, 12 cases with gear and wire rope stranding, 6 cases with belt avulsion injury, and 4 cases with carbon block smash injury. The present study application of a free or small saphenous vein bypass to reconstruct the injured artery and vein according to the concept of the angiosome model. The defective vessels were bridged with the axial vessels of a flow-through flap, such as a medial calf flap or anterolateral femoral flap, to construct an additional blood supply and drainage vein for the severed limb. The clinical data of 30 cases with traumatic limb amputation of the lower leg and ankle were retrospectively analyzed. In all 30 cases of traumatic limb amputation, the replantation via the adoption of a flow-through flap was successful, and 85.6% of the patients remained in good postoperative condition. There were no symptoms of ischemia in the marginal segment after blood supply reconstruction of the transected limb by axial vascular bridging within the flap. Via the adoption of microsurgical techniques, the blood supply to the transected limb can be reconstructed by bridging the defective vessels with the adoption of the axial vessels of the flow-through flap is a feasible and advanced treatment option.

Patient Flow Analysis with a Custom Simulation Engine

Patient flow simulation and analysis is one of the oldest IT -based methods used to optimize patient care processes and hospital management. During the pandemic, interest in this domain suddenly increased due to the various constraints and recommendations to reduce the likelihood of further infections in the hospital. Suddenly, metrics such as the number of patients waiting in the same area, the maximum time a patient could stay in a single room, and the minimum distance between patients became important issues to monitor and optimize. Using data and modelling concepts from various hospitals, our team developed a simulation tool that used bpmn models to define an emergency department. We then modified a single day's usual patient flow with various real-world inspired edge cases to evaluate how the simulated flow would change and which stations would become bottlenecks, where the quality of patient care would deteriorate and rooms would become overcrowded. To execute the models, we developed our own tool based on the open-source Camunda modeling tool and the Business Process Model Notation (BPMN) file format. To execute the generated models, we use our own Python-based execution environment based on the SpiffWorkflow library, which permits extensive logging and extensive customization of the attributes analysed. In addition, the modelling toolkit of Camunda was narrowed down and compiled so that it could be easily used by researchers who are not programmers. In the paper, we present both the modeling process and the scenario design process, as well as the results obtained through the runs, including the maximum waiting times during the model runs and the maximum number of patients waiting at once, which allowed us to validate the effectiveness of the framework.

Сравнительное исследование бизнес-моделей компаний NIKE и Adidas

In recent years, the business model concept has become a popular tool in business practice for managing intense competitiveness among companies. The purpose of this article is a comparative study of the business models of NIKE and Adidas companies that allow them to maintain leadership in the sporting goods market. The main tasks are to define the concept of “business model”, to reveal strategies, tactics such as branding, marketing in NIKE and Adidas business models that affect the success of companies and also the role of sponsorship in brand promotion. The hypothesis of the study suggests that through business models, companies create value, ensure sustainability, and even ensure their successful development and maintain competitiveness in the conditions of rapid advances in technology. The research used the methods of study, comparison and analysis.

Whole Coffee Cherry Extract Improves Working Memory and Response Inhibition: Acute and Longitudinal Results from a Remote, Randomized, Double-Blind, Placebo-Controlled Clinical Trial.

Earlier laboratory-based evidence has suggested that polyphenol-rich, decaffeinated whole coffee cherry extract (CCE) supports improvements in acute and long-term cognitive performance. To better understand CCE's potential to promote cognitive processing, we conducted a first-of-its-kind remote clinical trial. Participants were randomized into one of two intervention arms: placebo or 200 mg CCE. At the beginning of the study, participants were asked to complete a set of acute cognitive challenges as part of the baseline assessment. Tasks were nearly identical to those used in previous, laboratory-based research. Acute results support that CCE outperformed placebo, reducing omissions and improving accuracy, during working memory and inhibitory control tasks. Long-term results indicate that CCE outperformed placebo on a measure of accuracy. This contributes to the literature in three ways: (1) results improve upon previously reported robust and consistent findings in a real-world setting that a single-dose of CCE acutely improved cognitive performance; (2) results replicate previous laboratory findings but in a real-world setting that long-term CCE supplementation outperformed placebo on measures of accuracy in a working memory task; and (3) it serves as proof of concept of a novel remote clinical trial model that may provide real-world evidence of efficacy while increasing accessibility and cohort diversity.