Modeling Research Research Articles

Reform and Ideological Practice of Operations Research Course in Management and Engineering with The Integration of CDIO and PYTHON

It is pointed out in the National Education Conference and the National Conference on Ideological and Political Work in Colleges and Universities that colleges and universities should apply for ideological-political courses throughout the whole process of higher education, which provides incentives for deepening the reform and ideological-political practice of management engineering and operations research courses. Based on the integration of the CDIO teaching concept and Python and the immersive teaching process of the operations research course, this paper designs the teaching reform and practice of student-oriented management engineering and operations research course, which leads students to realize the algorithm design, program implementation, and visualization of operations research modeling and machine learning step by step. On this basis, based on the inventory management and its expansion model in operational research, taking grain supply chain management as the case object, this paper explores the strategic deployment of the CPC Central Committee and the state to realize the dynamic balance of grain supply and demand at a high level and deal with the uncertainty of the external environment with the security and stability of grain supply, from the perspective of variable cost, supply capacity, consumption demand, holding cost and shortage cost in the model. The research results are conducive to strengthening students' problem analysis and modeling ability, programming, and in-depth analysis, and have theoretical and practical value.

An Assessment of the Coupled Weather Research and Forecasting Hydrological Model on Streamflow Simulations over the Source Region of the Yellow River

The Source Region of the Yellow River (SRYR), renowned as the “Water Tower of the Yellow River”, serves as an important water conservation domain in the upper reaches of the Yellow River, significantly influencing water resources within the basin. Based on the Weather Research and Forecasting (WRF) Model Hydrological modeling system (WRF-Hydro), the key variables of the atmosphere–land–hydrology coupling processes over the SRYR during the 2013 rainy season are analyzed. The investigation involves a comparative analysis between the coupled WRF-Hydro and the standalone WRF simulations, focusing on the hydrological response to the atmosphere. The results reveal the WRF-Hydro model’s proficiency in depicting streamflow variations over the SRYR, yielding Nash Efficiency Coefficient (NSE) values of 0.44 and 0.61 during the calibration and validation periods, respectively. Compared to the standalone WRF simulations, the coupled WRF-Hydro model demonstrates enhanced performance in soil heat flux simulations, reducing the Root Mean Square Error (RMSE) of surface soil temperature by 0.96 K and of soil moisture by 0.01 m3/m3. Furthermore, the coupled model adeptly captures the streamflow variation characteristics with an NSE of 0.33. This underscores the significant potential of the coupled WRF-Hydro model for describing atmosphere–land–hydrology coupling processes in regions characterized by cold climates and intricate topography.

Fundamental experimental and thermodynamic modelling study in support of the lead battery recycling slag

Antimony, tin, and other higher value metals, as well as elements such as arsenic and lead, can be found in the slags produced during the lead battery recycling process. Gopher Resources operates lead battery recycling furnaces that use sodium slag systems for which there has been less thermodynamic research than more common slag systems. Better thermodynamic information could help improve the process efficiency and control of these furnaces, as well as the recovery of higher value metals. The present study focuses on experimental research and thermodynamic modeling of slags belonging to the Na-Si-Fe-O system, with minor elements including S, Sb, Sn, Pb, and As. Examples of important systems studied extensively for the first time include Fe-Sb-Si-O and Na-Sn-Si-O. Phase equilibria methods are used to determine all the interaction parameters between the impurity metals and the main components of the slag. The experimental methodology involves equilibration, quenching, and electron-probe X-ray microanalysis of the samples. The modified Quasichemical model is used to describe the thermodynamics of the slags. The model also takes into account possible formation of matte/metal/speiss liquids, and numerous solid phases, which is important for understanding of fundamentals operation of various process units.

Mental Health Prediction Using Catboost Algorithm

Abstract: This study investigates the application of the CatBoost algorithm in predicting mental health outcomes using Python programming language. Mental health prediction is a critical area of research due to its significant impact on individuals and society. Traditional predictive modeling techniques often encounter challenges in handling complex and highdimensional data inherent in mental health datasets. CatBoost , a state- of-the-art gradient boosting algorithm, has shown promise in effectively addressing these challenges by handling categorical variables seamlessly and exhibiting robust performance in various domains. Leveraging its powerful capabilities, this study aims to develop predictive models for mental health outcomes utilizing a comprehensive dataset encompassing diverse socio- demographic, behavioural , and clinical factors. The predictive performance of the CatBoost algorithm will be evaluated and compared against other commonly used machine learning algorithms, demonstrating its effectiveness in accurately predicting mental health outcomes. This research contributes to the advancement of predictive modeling in mental health research and holds potential implications for personalized interventions and resource allocation in mental healthcare systems

Revolutionizing Pancreatic Cancer Diagnosis and Prognosis through Machine Learning Technology

Abstract: Pancreatic cancer presents significant challenges in terms of early detection and treatment, resulting in poor patient outcomes. This research article explores the application of random forest, a powerful machine learning algorithm, to enhance predictive modeling in pancreatic cancer research. Leveraging a dataset comprising clinical and molecular features, we trained and evaluated random forest models to predict key outcomes such as tumor progression, treatment response, and overall survival. Our findings demonstrate the effectiveness of random forest in accurately stratifying patients based on their prognosis and treatment outcomes. Furthermore, we identified key biomarkers and clinical variables contributing to predictive accuracy, providing valuable insights into the underlying biological mechanisms of pancreatic cancer progression. The integration of random forest into pancreatic cancer research holds promise for improving patient stratification, guiding treatment decisions, and ultimately, advancing personalized medicine approaches in the management of this challenging disease

Development of climate informed management scenarios for fisheries in the eastern Bering Sea

Abstract In recognition of the impact of climate change on marine ecosystems worldwide, integrated research teams have coupled climate change projections with social-ecological models to inform management and evaluate adaptation strategies for the fishing industry and fishing communities. A key step in this process is the selection of scenarios that enable improved adaptation strategies and decision-making through engagements with constituents with diverse interests in the future use of marine resources. This paper presents an approach to selecting and refining climate-informed social-ecological scenarios for groundfish and crab fisheries in the eastern Bering Sea. The approach involved: (a) initial model development to provide worked examples; (b) engagement with stakeholders to seek input on climate-related concerns, priorities, and adaptation options; and (c) establishment of pathways for uptake of climate-informed decision support information into existing management systems. This approach narrowed the range of candidate scenarios, identified pressing climate concerns of constituents, and clarified timelines for scheduling modeling projects to address these concerns. Separating evaluation of management strategies (research modeling) from proposed changes to Fishery Management Plans preserved opportunities for public debate of proposed changes through a well-established regulatory review process. Collectively, these outcomes help to advance the development of a regionally relevant climate-ready harvest policy.

Measuring Alliance and Symptom Severity in Psychotherapy Transcripts Using Bert Topic Modeling

We aim to use topic modeling, an approach for discovering clusters of related words (“topics”), to predict symptom severity and therapeutic alliance in psychotherapy transcripts, while also identifying the most important topics and overarching themes for prediction. We analyzed 552 psychotherapy transcripts from 124 patients. Using BERTopic (Grootendorst, 2022), we extracted 250 topics each for patient and therapist speech. These topics were used to predict symptom severity and alliance with various competing machine-learning methods. Sensitivity analyses were calculated for a model based on 50 topics, LDA-based topic modeling, and a bigram model. Additionally, we grouped topics into themes using qualitative analysis and identified key topics and themes with eXplainable Artificial Intelligence (XAI). Symptom severity could be predicted with highest accuracy by patient topics (r\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r$$\\end{document}=0.45, 95%-CI 0.40, 0.51), whereas alliance was better predicted by therapist topics (r\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r$$\\end{document}=0.20, 95%-CI 0.16, 0.24). Drivers for symptom severity were themes related to health and negative experiences. Lower alliance was correlated with various themes, especially psychotherapy framework, income, and everyday life. This analysis shows the potential of using topic modeling in psychotherapy research allowing to predict several treatment-relevant metrics with reasonable accuracy. Further, the use of XAI allows for an analysis of the individual predictive value of topics and themes. Limitations entail heterogeneity across different topic modeling hyperparameters and a relatively small sample size.

Genetics of gambling disorder and related phenotypes: The potential uses of polygenic and multifactorial risk models to enable early detection and improve clinical outcomes.

Gambling Disorder (GD) is an impactful behavioural addiction for which there appear to be underpinning genetic contributors. Twin studies show significant GD heritability results and intergenerational transmission show high rates of transmission. Recent developments in polygenic and multifactorial risk prediction modelling provide promising opportunities to enable early identification and intervention for at risk individuals. People with GD often have significant delays in diagnosis and subsequent help-seeking that can compromise their recovery. In this paper we advocate for more research into the utility of polygenic and multifactorial risk modelling in GD research and treatment programs and rigorous evaluation of its costs and benefits.

Optimization and Predictive Modelling of Gravimetric Corrosion Characteristic of Irvingia Gabonensis Leaves Extracts as Anti Corrosion Inhibitor of Mild Steel in HCl Solution

In this research, the optimization and predictive modeling of gravimetric corrosion characteristics of Irvingia gabonensis leaf extracts (IGLE) as an anti-corrosion inhibitor of mild steel in hydrochloric acid were investigated. Design expert software version 11 were used to analyze the corrosion inhibition-related process characteristics, such as inhibition efficiency, corrosion rate, and weight loss, and their relationships. An effort were made to obtain the optimal conditions for these corrosion inhibition-related process characteristics. Weight loss measurement and design methodology were used for the evaluation of the inhibition efficiency of IGLE for mild steel in HCl. The corrosion inhibition process variables were optimized and predictive regression models were developed using Box-Behnken tool of the Response Surface Methodology (RSM). The findings showed that there were a good fit between the model predictions and the experimental results. The quadratic models developed were significant with P value less than 0.05. The research established an inhibition efficiency of 88.9%, a corrosion rate of 0.143mm/yr, and a weight loss of 0.02 g, which were obtained at the optimum conditions of an extract concentration of 0.6 g/L, an immersion time of 16 hrs, and a temperature of 298K.Therefore, the models were considered ideal for prediction with a confidence level of 95%, and the optimal combination is suitable for the corrosion inhibition process design. Hence these models can be recommended for applications such as oil well acidizing and pickling pipelines.

Causal Effects of Time-Varying Exposures: A Comparison of Structural Equation Modeling and Marginal Structural Models in Cross-Lagged Panel Research

The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of marginal structural models (MSMs). To better understand this criticism, we describe three phases of causal research. We explain (differences in) the assumptions that are made throughout these phases for structural equation modeling (SEM) and IPW-MSM approaches using an empirical example. Second, using simulations we compare the finite sample performance of SEM and IPW-MSM for the estimation of time-varying exposure effects on an end-of-study outcome under violations of parametric assumptions. Although increased reliance on parametric assumptions does not always translate to increased bias (even under model misspecification), researchers are still well-advised to acquaint themselves with causal methods from the potential outcomes framework.

Deciphering SO2 poisoning mechanisms for passive NOx adsorption: A kinetic modeling approach and development of a high-resistance catalyst

Passive NOx adsorption (PNA) is a promising technology aimed at reducing NOx emissions from vehicles during the cold start phase of the engine. This work investigated the SO2 poisoning mechanism of PNA through a combination of experimental research and kinetic modeling, leading to the development of a novel PNA sample with high resistance to SO2 poisoning. Pd/SSZ-13 samples were synthesized using different drying conditions, revealing that samples dried at room temperature showed lower degradation (10 %) compared to those dried at 80 °C (26 %). Investigation into the degradation revealed that ion-exchanged Pd sites with a hydroxyl group were more resistant to SO2 poisoning than other Pd sites. It is also found that SO2 aids in NOx storage on Pd sites, enhancing the PNA performance. A kinetic model was developed to describe the SO2 poisoning behavior and its influence on NOx storage. The model, which was verified under various conditions, effectively simulated the PNA behavior and SO2 poisoning of Pd/SSZ-13.

Trend mining with Orange – using topic modeling in futures research with the example of urban mobility

Today, assumptions about probable future developments (at least as far as they make use of quantifiable scientific methods and are not pure speculation) are generally based on data from the past. An interesting way to analyze the future through this type of data is text mining or individual methods out of the spectrum of text mining, such as topic modeling. Topic Modeling itself is a combination of quantitative and qualitative methodology and is based on the full spectrum of social science methodology. Therefore, the method is an interesting way for futures research to analyze futures. This publication addresses the question of how a combination of different methods can contribute to trend monitoring or trend mining. For this purpose, a set of scientific publications was first generated with the help of a search query in the Web of Science (WoS), which is the basis for all evaluations and statements and topics. In essence, the method considered here should be more fully integrated into the scientific practice of futures research because it can make a valuable contribution to estimating future development based on past development.

A bi-objective optimization mathematical model integrated with bulk arrival Markovian queueing system and machine learning in publishing industries

ABSTRACT In the publishing industry, it is vital to comprehend customers’ needs and take their pleasure into account. Thus, publishing houses (PHs) must effectively address the necessity to increase profitability as well as minimize the waiting time of the printing requests in the queue. Although some researchers have studied publishing industries’ management, there is still a lack of research modeling PHs as a practical queueing system that reflects their performance precisely. Moreover, most models have not considered the machine learning technique for predicting the circulation of books integrated with a queueing model and mathematical programming. Furthermore, no study has modeled the PH as a M[X]/M/1 queueing system to practically improve the system. To fill these gaps, this paper develops a novel bi-objective mathematical model with a bulk arrival queueing system and a machine learning technique to minimize the total cost and maximize the resource utilization rate. Also, different machine learning classifier algorithms are implemented to estimate the circulation for each publishing product. Owing to the model’s bi-objective character, an enhanced LP-metrics method is presented. We have also examined a real-world case study to validate our approach and demonstrate its applicability to real-world issues. A 25% increase in profitability served as proof of the model’s effectiveness.

Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients.

The objective of the present study is to evaluate the association between longitudinal and survival outcomes in the presence of competing risk events. To illustrate the application of joint modeling in clinical research, we assessed the blood oxygen saturation (SPO2) and its association with survival outcomes in coronavirus disease (COVID-19). In this prospective cohort study, we followed 300 COVID-19 patients, who were diagnosed with severe COVID-19 in the Rohani Hospital in Babol, the north of Iran from October 22, 2020 to March 5, 2021, where death was the event of interest, surviving was the competing risk event and SPO2 was the longitudinal outcome. Joint modeling analyses were compared to separate analyses for these data. The estimation of the association parameter in the joint modeling verified the association between longitudinal outcome SPO2 with survival outcome of death (Hazard Ratio (HR) = 0.33, P = 0.001) and the competing risk outcome of surviving (HR = 4.18, P < 0.001). Based on the joint modeling, longitudinal outcome (SPO2) decreased in hypertension patients (β = -0.28, P = 0.581) and increased in those with a high level of SPO2 on admission (β = 0.75, P = 0.03). Also, in the survival submodel in the joint model, the risk of death survival outcome increased in patients with diabetes comorbidity (HR = 4.38, P = 0.026). The association between longitudinal measurements of SPO2 and survival outcomes of COVID-19 confirms that SPO2 is an important indicator in this disease. Thus, the application of this joint model can provide useful clinical evidence in the different areas of medical sciences.

Моделирование работы газотурбинной установки SGT5-4000F с исследованием влияния режимных и климатических факторов на устойчивость горения в камере сгорания

When regulating gas turbine units, it is necessary to be confident in the stable combustion process in the combustion chambers, considering the dynamics of the processes and changes of external climatic factors. Many scientific studies are devoted to the problems of experimental research and mathematical modeling of processes in combustion chambers. The possibilities of flame failure, expansion of the lower limit of combustion, determination of the stable position of the flame front and other issues have been assessed. Even though the problem of design of mathematical models and their use remains relevant. The purpose of this research is to model and study the influence of operation and climatic factors on the stability of combustion in the combustion chamber of the SGT5-4000F gas turbine unit. The subject of the research is the SGT5-4000F gas turbine unit with a low-emission combustion chamber. The simulation model of the gas turbine unit has been developed in the environment of dynamic modeling of technical systems SimInTech. The research is carried out using theoretical methods of calculating fuel combustion, thermodynamic foundations of the theory of gas turbine engines, methods of mathematical and simulation modeling, as well as data from the archive of automated process control systems. A simulation model of SGT5-4000F gas turbine unit has been developed. It is distinguished by the ability to assess the proximity of the compressor operating mode to the stability limit and determine the boundaries of stable combustion procedure in the combustion chamber, considering environmental climatic factors. The authors have studied the stability of the compressor and combustion chamber. The results of the experimental studies of a gas turbine unit in the operating load range from 113 to 282 MW and in the range of outside air temperature from –12 to 30 °C have shown that the combustion process in the combustion chamber is unvarying over the entire operational stress spectrum. The results obtained are verified by comparing model values with technological parameters of a gas turbine unit taken from the archive of the power plant control system. The authors have assessed the influence of electrical load on the performance indicators of a gas turbine unit at outdoor temperatures of +30, +15 and –12 °C. According to the results of simulation modeling, the model of SGT5-4000F gas turbine unit is found to be adequate. The developed model can be used as a tool at the stage of functional and technological design of automated control systems for the development of effective automatic control systems.

Особенности разработки программного продукта по физической подготовленности студентов «ПНИПУ»

Data processing is the leading direction in mathematical modeling. Increasing the volume of information, storage, accessibility in messengers, translation into related languages, biometric data have become a necessary introduction of new programs in the educational field. Physical fitness of students is one of the indicators of learning curricula. An increase in the number of students and the introduction of TRP standards requires rapid data processing. Search for research methods and modeling of information processes and technologies and design of information technologies for data processing in the field of physical culture, as well as design of automation and control systems and database processing.

Recent Advances in Group-Based Trajectory Modeling for Clinical Research.

Group-based trajectory modeling (GBTM) identifies groups of individuals following similar trajectories of one or more repeated measures. The categorical nature of GBTM is particularly well suited to clinical psychology and medicine, where patients are often classified into discrete diagnostic categories. This review highlights recent advances in GBTM and key capabilities that remain underappreciated in clinical research. These include accounting for nonrandom subject attrition, joint trajectory and multitrajectory modeling, the addition of the beta distribution to modeling options, associating trajectories with future outcomes, and estimating the probability of future outcomes. Also discussed is an approach to selecting the number of trajectory groups.

Same yet different

Abstract The article compares the distributional differences in the use of the partitive object cases in Estonian and Finnish via multifactorial modeling in contrastive research using the European Parliament parallel text corpus. Based on previous contrastive research on Finnic, we expected the principles of object case marking to be similar for Estonian and Finnish (confirmed), and the partitive objects to be more numerous in Estonian than in Finnish (not confirmed, as countable objects with scalar verbs proved less likely to be partitive in Estonian). We hypothesized that multifactorial modeling in contrastive research design could help identify the causes for variation and unfold subtle differences between related language systems. Since preferences related to grammatical voice and constituent order revealed subtle differences between the systems, this hypothesis was confirmed.

Adjunctive interventions: change methods directed at recipients that support uptake and use of health innovations

BackgroundImplementation science groups change methods into two categories: (1) clinical, behavioral, or biomedical intervention targeting recipient’s health outcomes and (2) implementation strategies targeting the delivery system. Differentiating interventions from strategies based on their intended functions is critical to accurately attributing their effects to health or implementation outcomes. However, in coordinating 200+ HIV implementation research projects and conducting systematic reviews, we identified change methods that had characteristics of both interventions and strategies that were inconsistently categorized. To alleviate confusion and improve change method specification, we propose that implementation science should adopt an extant but rarely used term—adjunctive interventions—to classify change methods that are distinct from the common intervention/strategy taxonomy.Main textAdjunctive interventions as change methods that target recipients (e.g., patients, participants) of a health intervention but are designed to increase recipients’ motivation, self-efficacy, or capacity for initiating, adhering to, complying with, or engaging with the health intervention over time. In two of our published reviews on implementation of HIV interventions, 25 out of 45 coded change methods fell into this gray area between strategy and intervention. We also noted instances in which the same change method was labelled as the intervention (“the thing”), as an adjunctive intervention, or an implementation strategy in different studies—further muddying the waters. Adjunctive interventions are distinguished from other change methods by their intended targets, desired outcomes, and theory of action and causal processes. Whereas health interventions target recipients and have a direct, causal effect on the health outcome, adjunctive interventions enhance recipients’ attitudes and behaviors to engage with the intervention and have an indirect causal link to the health outcome via increasing the probability of recipients’ utilization and adherence to the intervention. Adjunctive interventions are incapable of directly producing the health outcome and will themselves require implementation strategies to effectively impact sustained uptake, utilization, and adherence. Case examples, logic modeling, and considerations (e.g., relationship to consumer engagement strategies) for adjunctive intervention research are provided.ConclusionConceptualizing adjunctive interventions as a separate type of change method will advance implementation research by improving tests of effectiveness, and the specification of mechanisms and outcomes.

Exploring reliable photogrammetry techniques for 3D modeling in anatomical research and education.

In anatomical research and education, three-dimensional visualization of anatomical structures is crucial for understanding spatial relationships in diagnostics, surgical planning, and teaching. While computed tomography (CT) and magnetic resonance imaging (MRI) offer valuable insights, they are often expensive and require specialized resources. This study explores photogrammetry as an affordable and accessible approach for 3D modeling in anatomical contexts. Two photogrammetry methods were compared: conventional open-source software (Colmap) and Apple's RealityKit Object Capture. Human C3 vertebrae were imaged with a 24 MP camera, with and without a cross-polarization filter. Reconstruction times, vertex distances, surface area, and volume measurements were compared to CT scans. Results revealed that the Object Capture method surpassed the conventional approach in reconstruction speed and user-friendliness. Both methods exhibited similar vertex distance from reference mesh and volume measurements, although the conventional approach produced larger surface areas compared to CT-based models. Cross-polarization filters eliminated the need for pre-processing and improved outcomes in challenging lighting conditions. This study demonstrates that photogrammetry, especially Object Capture, as a reliable and time-efficient tool for 3D modeling in anatomical research and education. It offers accessible alternatives to traditional techniques with advantages in texture mapping. While further validation of various anatomical structures is required, the accessibility and cost-effectiveness of photogrammetry make it a valuable asset for the field. In summary, photogrammetry would have the potential to revolutionize anatomical research and education by providing cost-effective, accessible, and accurate 3D modeling. The study underscores the promise of advancing anatomical research and education through the integration of photogrammetry with ongoing improvements in user-friendliness and accessibility.