Development Of Modeling Approach Research Articles

Light gradient boost tree classifier predictions on appendicitis with periodontal disease from biochemical and clinical parameters

IntroductionUntreated periodontitis significantly increases the risk of tooth loss, often delaying treatment due to asymptomatic phases. Recent studies have increasingly associated poor dental health with conditions such as rheumatoid arthritis, diabetes, obesity, pneumonia, cardiovascular disease, and renal illness. Despite these connections, limited research has investigated the relationship between appendicitis and periodontal disease. This study aims to predict appendicitis in patients with periodontal disease using biochemical and clinical parameters through the application of a light gradient boost tree classifier.MethodsData from 125 patient records at Saveetha Institute of Dental College and Medical College were pre-processed and analyzed. We utilized data preprocessing techniques, feature selection methods, and model development approaches to estimate the risk of appendicitis in patients with periodontitis. Both Random Forest and Light Gradient Boosting algorithms were evaluated for accuracy using confusion matrices to assess their predictive performance.ResultsThe Random Forest model achieved an accuracy of 94%, demonstrating robust predictive capability in this context. In contrast, the Light Gradient Boost algorithms achieved a significantly higher accuracy of 98%, underscoring their superior predictive efficiency. This substantial difference highlights the importance of algorithm selection and optimization in developing reliable predictive models. The higher accuracy of Light Gradient Boost algorithms suggests effective minimization of prediction errors and improved differentiation between appendicitis with periodontitis and healthy states. Our study identifies age, white blood cell count, and symptom duration as pivotal predictors for detecting concurrent periodontitis in acute appendicitis cases.ConclusionsThe newly developed prediction model introduces a novel and promising approach, providing valuable insights into distinguishing between periodontitis and acute appendicitis. These findings highlight the potential to improve diagnostic accuracy and support informed clinical decision-making in patients presenting with both conditions, offering new avenues for optimizing patient care strategies.

Data-driven visual model development and 3D visual analytics framework for underground mining

Data visualisation is broadly utilised to aid in presenting complex information, facilitating decision-making, and enhancing operational efficiency in the mining sector. However, due to the extensive utilisation of CAD files and the complexity and difficulty of converting 2D drawings into 3D models, it is challenging to realise (near) real-time 3D data visualisation in mining applications. The difficulties are including site drawing complexity, lack of standardisation, time-consuming cleaning, and imperative manual intervention. This paper addresses these challenges by focusing on an automatic, data-driven approach for 3D visual model development in mining, with an emphasis on lifetime support and sustainability. Therefore, we introduce standardised visual model data diagram and proposed conversion workflows to leverage digitised 2D drawings in automatic 3D visual model development. Moreover, to improve visualisation efficiency and facilitate visual analytics construction in mining, this paper also proposes a 3D tile index methodology for efficient visual model reconstruction and generic production data integration. To ensure lifetime support and sustainable development in visualisation, we propose two model expansion schemes: one based on dxf file-driven model development, and the other one is interaction-oriented 3D model design and development, which the two schemes share the same data exchange and conversion workflows. Furthermore, to facilitate data fusion among various datasets, we also provide insights into revealing spatial–temporal patterns among multimodality data by real-time visualisation. These advancements aim to propel the field of underground mining visualisation, offering significant insights into enhancing the efficiency and safety of mining operations.

DEVELOPMENT OF INFORMATION SYSTEM STUDENT PERIOD COMPLETION MOBILE-BASED

This research aims to develop a mobile-based student study period completion information system in JPTE. The system developed uses the fountain development model approach with 5 stages, namely analysis, subsystem requirements, design, implementation, and testing. The purpose of choosing the fountain model is to ensure flexibility in development and adaptation to user needs. Black Box testing is carried out to ensure that each main feature, such as login, thesis/final project upload, journal upload notification, input and display of graduation dates, as well as management of student and KAPRODI data, functions according to the specifications set. The test results show that all 100% of the features successfully meet the expected functional criteria, with a system that is able to provide easy access, flexibility, and responsiveness. Overall, the Black Box test results show that the system is ready to be used by users and meets all the functional requirements that have been set. This mobile-based information system is expected to improve the efficiency of academic administration management and support the smooth process of completing the student study period.

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

BackgroundRapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance.ResultsWe develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model.ConclusionsWe demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development.

Common data models provide a standardized way to represent data used in federated learning tasks. The aim of this review was to explore the development and use of common data models to harmonize electronic health record data in health research. The data search yielded 724 records, of which 19 were included for this study. None of the research focused on nursing specific topics. All studies either utilized the Observational Medical Outcomes Partnership (OMOP) common data model, or developed a model partly based on the OMOP. A roadmap to guide research for the development of common data models for federated learning are warranted.

Drivers and epidemiological patterns of West Nile virus in Serbia.

West Nile virus (WNV) is an emerging mosquito-borne pathogen in Serbia, where it has been detected as a cause of infection in humans since 2012. We analyzed and modelled WNV transmission patterns in the country between 2012 and 2023. We applied a previously developed modelling approach to quantify epidemiological parameters of interest and to identify the most important environmental drivers of the force of infection (FOI) by means of statistical analysis in the human population in the country. During the study period, 1,387 human cases were recorded, with substantial heterogeneity across years. We found that spring temperature is of paramount importance for WNV transmission, as FOI magnitude and peak timing are positively associated with it. Furthermore, FOI is also estimated to be greater in regions with a larger fraction of older adult people, who are at higher risk to develop severe infections. Our results highlight that temperature plays a key role in shaping WNV outbreak magnitude in Serbia, confirming the association between spring climatic conditions and WNV human transmission risk and thus pointing out the importance of this factor as a potential early warning predictor for timely application of preventive and control measures.

Development of an Online Nursing Leadership Program for Global Health in the Western Pacific Region.

Global health highlights the transnational determinants, issues, and possible solutions for improving health outcomes. Addressing global health issues requires population-based approaches coupled with individualized healthcare across settings, including strategies for achieving health equity. Designing and implementing global health solutions requires competent nurse leaders who can facilitate multi-disciplinary collaborations, engage in policy development, and lead advocacies that support the global health agenda. Thus, this paper examined the stakeholder perspectives that informed the development of a leadership program on global health for nurse leaders in the Western Pacific Region (WPR). The study used a descriptive qualitative approach to generate key recommendations for a context-appropriate, multi-country capacity-building program for nurse leaders on global health. The Analysis Design, Development, Implementation, and Evaluation (ADDIE) model and reflexive approach were employed to guide the development of the training design. Twenty-five nurse leaders from World Health Organization Collaborating Centers (WHO-CC) in the Western Pacific participated. Qualitative data were collected from participant feedback and post-activity sessions throughout program implementation. Thematic analysis of stakeholder data revealed that the program (1) capitalized on equipping nurse leaders with the concepts related to global health and developing competencies in leadership, policy engagement, and advocacy; (2) ensured learning strategies by having participants from varied contexts and experiences; and (3) reflected on the strengths and limitations of the use of the online platform. This paper contributed to the growing literature on global health and programs that support addressing global health issues. The findings underscored the urgent need to capacitate nurses in leadership positions who contribute to addressing emerging issues in global health. The paper recommended improvements in the design and implementation of the Global Health Nursing Leadership Program to engage more nurse leaders across the region and enhance content and delivery.

Design, Fabrication, and Dynamic Analysis of a MEMS Ring Resonator Supported by Twin Circular Curve Beams.

In this paper, we present a compressive study on the design and development of a MEMS ring resonator and its dynamic behavior under electrostatic force when supported by twin circular curve beams. Finite element analysis (FEA)-based modeling techniques are used to simulate and refine the resonator geometry and transduction. In proper FEA or analytical modeling, the explicit description and accurate values of the effective mass and stiffness of the resonator structure are needed. Therefore, here we outlined an analytical model approach to calculate those values using the first principles of kinetic and potential energy analyses. The natural frequencies of the structure were then calculated using those parameters and compared with those that were simulated using the FEA tool ANSYS. Dynamic analysis was performed to calculate the pull-in voltage, shift of resonance frequency, and harmonic analyses of the ring to understand how the ring resonator is affected by the applied voltage. Additional analysis was performed for different orientations of silicon and assessing the frequency response and frequency shifts. The prototype was fabricated using the standard silicon-on-insulator (SOI)-based MEMS fabrication process and the experimental results for resonances showed good agreement with the developed model approach. The model approach presented in this paper can be used to provide valuable insights for the optimization of MEMS resonators for various operating conditions.

Models, Models Everywhere! A practitioners view on the reality of modeling

AbstractThis paper addresses three of the most common real‐world challenges to modeling. The INCOSE Vision 2035 correctly states that the future of SE is Digital and Model Based. Achieving this requires significant development of the state of the art in Digital Engineering and Modeling. The focus of this paper is, however, the significant gap between the current potential of today's models and the actual practice. As practitioners each with over 20+ years of industrial experience, the authors describe problems that plague current modeling practice. The paper: describes the different types of models and their uses; provides a high‐level generic model development approach; and addresses some of the real‐world challenges that modelers and their managers need to address.

Investigation the effects of operational parameters and multi-factor optimization of butene metathesis process via statistical approach

In this study, the production of propylene from 2-butene and 1-butene via the metathesis process was investigated and simulated. The effects of operational parameters, including reactor temperature, residence time, and 1-butene/2-butene molar ratio, on reactor performance were examined, and statistical models were developed. The design of experiment (DoE) was performed using the response surface methodology (RSM)-central composite design (CCD) approach for efficient experimentation and model development as well as optimization. The study's findings indicate a high conversion rate for 2-butene. By statistically analyzing the obtained data and performing analysis of variance (ANOVA), the influential parameters and their degree of importance were determined. Sensitivity analysis was conducted based on the developed models. Finally, using these models and desirability function as a powerful optimization algorithm, optimal conditions for the process were obtained. The results of the optimization showed that the maximum propylene molar fraction (32.5 %) was obtained at a temperature of 508.4 °C, a residence time of 20.7 s, and a 1-butene/2-butene molar ratio of 0.1.

Spillover effects in transit networks: A parameterized weight matrix spatial lagged approach

Public Transit (PT) systems aim to provide social, economic, and environmental benefits to modern cities offering reliable transportation service to users and ultimately reducing the problems related to traffic externalities due to car dependency. With the growing trend of urbanization and the associated phenomenon of cities’ sprawl being increasingly evident, significant attention should be given to understanding PT systems’ performance and then improving their efficiency. Considering the spatial characteristics of the PT system's performance promotes the environmentally friendly transport “character” that every city endeavors. This paper aims to incorporate the spatial spillover effects of a realistic PT system by augmenting information about the service network along with socio-economic variables, in a spatial demand-supply econometric framework. In detail, geographically separated demand and supply information on bus stops and lines was analyzed by a spatial econometric model, namely, the Spatial Lagged X (SLX) model which may be formatted so that can soundly handle, spatial and –in particular- network data like those coming from the General Transit Feed Specification (GTFS) protocol widely used in PT systems. The novelty and the importance of the proposed model rely on the ability to introduce transit network structure within the framework of spatial econometric regression, fostering the explanatory statistical analysis over networked information. The developed modeling approach was applied to the urban and rural PT system of Nicosia (Cyprus), incorporating the spatial spillover effects of the system over more than 1,500 bus stops, 40 lines, and 252 different but spatially connected postcode areas. The results of the SLX model were compared with other demand models of this form typically used, namely the Ordinary Least Square model and standard Spatial Autoregressive models, providing solid evidence of the benefits of incorporating the network structure in spatial demand modeling, giving valuable input for further planning purposes.

Effects of solubility of Supercritical-CO2 solvent and mass transfer property on extraction of vitamin E from canola seeds

This study is to explore the solubility of the targeted Vitamin E from canola seeds in supercritical-CO2 (SC–CO2)solvent and their intricate relationship through the developed modelling approach. The study investigated the impact of pressure and temperature on the efficiency of Vitamin E extraction through the regulation of Vitamin E solubility and yield in SC-CO2 solvent. These parameters played a crucial role in determining extraction efficiency. Pressures enhanced the solubility and density of Vitamin E, while elevated temperatures facilitated total Vitamin E extraction yield by promoting solvent diffusion and mass transfer through viscosity reduction. The optimal separation process of Vitamin E was achieved at 75ׄ°C, 18 MPa. The Vitamin E concentration in extracted oil is 12 times higher than Vitamin E contents in regular canola seed oil. The mathematical modeling revealed fundamental mechanisms governing the extraction process and offers useful information for optimizing extraction protocols in industrial applications.

A supervised learning tool for heatwave predictions using daily high summer temperatures

AbstractGlobal temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions.

Scheduling stochastic distributed flexible job shops using an multi-objective evolutionary algorithm with simulation evaluation

The trend of reverse globalisation prompts manufacturing enterprises to adopt distributed structures with multiple factories for improving production efficiency, meeting customer requirements, and responding disturbance events. This study focuses on scheduling a distributed flexible job shop with random job processing time to achieve minimal makespan and minimal total tardiness. First, a stochastic programming model is established to formulate the concerned problems. Second, in accordance with the natures of two objectives and randomness, an evolutionary algorithm incorporating an evaluation method is designed. In it, population-based and external archive-based search processes are developed for searching candidate solutions, and the evaluation method integrates stochastic simulation and discrete event simulation to calculate objective values of acquired solutions. Finally, a mathematical optimisation solver, CPLEX, is employed to validate the developed model and optimisation approach. A set of cases is solved to verify the performance of the proposed method. The comparisons and discussions show the superiority of the proposed method for handling the problems under study.

Identifying patients with undiagnosed small intestinal neuroendocrine tumours in primary care using statistical and machine learning: model development and validation study

BackgroundNeuroendocrine tumours (NETs) are increasing in incidence, often diagnosed at advanced stages, and individuals may experience years of diagnostic delay, particularly when arising from the small intestine (SI). Clinical prediction models could present novel opportunities for case finding in primary care.MethodsAn open cohort of adults (18+ years) contributing data to the Optimum Patient Care Research Database between 1st Jan 2000 and 30th March 2023 was identified. This database collects de-identified data from general practices in the UK. Model development approaches comprised logistic regression, penalised regression, and XGBoost. Performance (discrimination and calibration) was assessed using internal-external cross-validation. Decision analysis curves compared clinical utility.ResultsOf 11.7 million individuals, 382 had recorded SI NET diagnoses (0.003%). The XGBoost model had the highest AUC (0.869, 95% confidence interval [CI]: 0.841–0.898) but was mildly miscalibrated (slope 1.165, 95% CI: 1.088–1.243; calibration-in-the-large 0.010, 95% CI: −0.164 to 0.185). Clinical utility was similar across all models.DiscussionMultivariable prediction models may have clinical utility in identifying individuals with undiagnosed SI NETs using information in their primary care records. Further evaluation including external validation and health economics modelling may identify cost-effective strategies for case finding for this uncommon tumour.

Child-friendly school-based learning management model for health and physical education

The development of learning management models is crucial to address the diverse needs of effective learning while promoting active participation, preventing discrimination, and safeguarding students' rights and responsibilities in line with their growth. This article aims to create a child-friendly school-based learning model for health and physical education in elementary schools and validate it through expert assessments. Following Brog and Gall's model development approach, data collection involved assessment questionnaires administered to various stakeholders, including school supervisors, principals, teachers, students, and committees. The findings underscore the importance of incorporating diverse learning methods, adequate infrastructure, gender sensitivity, and meaningful learning experiences into the model. The three sequential stages' assessment results demonstrate the proposed model's high feasibility, scoring consistently high across stages. Referred to as a model, this innovative model showcases significant promise, suggesting further refinement to ensure its efficacy in addressing multifaceted learning requirements, fostering inclusive participation, and upholding students' rights and obligations per their developmental stage. This study highlights the need for evolving learning management models to cater to diverse educational needs effectively.

Simultaneous Water Sorption and Crystallization in ASDs 2: Modeling Long-Term Stabilities.

The physical stability of amorphous solid dispersions (ASDs) is a major topic in the formulation research of oral dosage forms. To minimize the effort of investigating the long-term stability using cost- and time-consuming experiments, we developed a thermodynamic and kinetic modeling framework to predict and understand the crystallization kinetics of ASDs during long-term storage below the glass transition. Since crystallization of the active phrarmaceutical ingredients (APIs) in ASDs largely depends on the amount of water absorbed by the ASDs, water-sorption kinetics and API-crystallization kinetics were considered simultaneously. The developed modeling approach allows prediction of the time evolution of viscosity, supersaturation, and crystallinity as a function of drug load, relative humidity, and temperature. It was applied and evaluated against two-year-lasting crystallization experiments of ASDs containing nifedipine and copovidone or HPMCAS measured in part I of this work. We could show that the proposed modeling approach is able to describe the interplay between water sorption and API crystallization and to predict long-term stabilities of ASDs just based on short-term measurements. Most importantly, it enables explaining and understanding the reasons for different and sometimes even unexpected crystallization behaviors of ASDs.

Development of SAC Media Integrated with Project Based Learning Model on Reaction Rate Material

This research aims to determine the analysis of the feasibility level and response of students to SAC (Smart Apps Creator) media integrated with project based learning on reaction rate material. The research method used is Research and Development with a 4D development model approach (define, design, development and disseminate). Research data collection is carried out by interviews and questionnaires or questionnaires that are distributed to teachers and students. This research was conducted at SMAS PAB 8 Saentis. The population and samples in this study are chemistry lecturers, teachers and students of grade XI 2 for the 2023/2024 academic year taken by purposive sampling of 30 people. Based on validity, it shows that the SAC learning media developed was declared valid by validators with the criteria of "Very Feasible" with an average validation score of 89.86%, then based on the results of student responses, it was obtained that the SAC learning media developed as a whole received an average percentage result of 83.81% with the criteria of "Strongly Agree". Therefore, SAC learning media integrated with the project-based learning model on the reaction rate material is suitable for use as a learning medium.

PENGEMBANGAN KOMIK EDUKASI BERBASIS CREATIVE PROBLEM SOLVING BERBANTUAN KVISOFT FLIPBOOK MAKER PRO UNTUK SEKOLAH DASAR

This research aims to develop and produce the effectiveness of an educational comic based on Creative Problem Solving (CPS) using Kvisoft Flipbook Maker Pro as interactive media for elementary school students. This research uses the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model development approach. The main innovation of this research is the use of the Creative Problem Solving approach in designing educational comic content. In addition, Kvisoft Flipbook Maker Pro is used to present comics interactively, adding a more interesting visual dimension. The research results include educational comics developed and evaluation results from field trials. Evaluation involves responses from students, teachers, and the appropriateness, attractiveness, and effectiveness of comics in increasing understanding of educational concepts. It is hoped that this research can contribute to the development of innovative teaching methods and increase student involvement in the learning process. The results obtained were that the teaching materials developed obtained a validity percentage of 89.06% (Content Validation), 80% (Graphic Validation), and 83.3% (Language Validation). The practicality of the teacher's response was obtained at 3.7 (very practical), while the student's response was 3.5 (very practical. Effectiveness can be seen from student learning outcomes, namely an N-Gain Score of 0.71 (High) in the quite effective category. This means, The teaching materials developed are valid, practical and effective for grade 3 elementary school students

Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach

Aerosol jet printing (AJP) is a newly-developed technology for printed electronics in additive manufacturing (AM) industry. Compared to conventional printed electronics, AJP technology has the capability of fabricating high-resolution patterns (∼ 10 µm) with multiple materials and high degrees of freedom. However, insufficiently optimizing the printing process, including reliance on traditional trial-and-error methods and theoretical models, may lead to a decline in production quality, consequently limiting the widespread adoption of AJP technology. In this paper, an integrated machine learning approach is developed for autonomous optimization of process parameters and in-situ anomaly detection in AJP. In the developed approach, a coaxial camera is incorporated with a stepwise data-driven modeling approach for optimal operating window identification and process model development of AJP, thereby systematically optimizing the main process parameters prior to printing. Subsequently, a convolutional neural network (CNN) model is developed and integrated with an alignment camera for in-situ process monitoring and printing status evaluation, enabling the anomaly detection of the AJP process. Afterwards, leveraging process similarity through transfer learning allows rapid calibration of detected system drifts or variations in process stability, thereby enhancing the product consistency of AJP. Generally, by utilizing state-of-the-art machine learning techniques, the autonomous optimization of process parameters and anomaly detection in AJP achieve accuracies of 95.3% and 92.7%, respectively. This indicates the effectiveness and systematic nature of the developed approach compared to traditional trial-and-error methods and theoretical models. Furthermore, due to its data-driven characteristics, this approach has the potential to extend its applicability to other noncontact ink writing techniques, facilitating further research for process optimization in AM.