Formulation Of Model Research Articles

Governance in Universities: the Mechanism of Leadership and the Experience of the Federal University of Sergipe

Objective: To analyze the need to consolidate governance systems in Federal Universities, highlighting leadership as an essential pillar and using the Federal University of Sergipe (UFS) as a reference. Theoretical Framework: The research is grounded in the concepts of public governance as described in Decree No. 9,203/2017 and the guidelines of the Federal Court of Accounts (TCU). Leadership is emphasized as a central element. Within the context of federal universities, the study examines the impact of governance on the interaction with institutional autonomy, while addressing challenges and innovations in the field, such as the experience of the Federal University of Sergipe (UFS). Method: A document analysis of key governance and management documents from Brazilian federal universities was conducted, with a focus on the Federal University of Sergipe. The UFS document analysis covered a time frame of eight years. The results were organized into categories. Results and Discussion: The research identified significant advances in the governance of the Federal University of Sergipe (UFS). The findings show improvements in performance indicators, aligned with the guidelines of Decree No. 9,203/2017 and the TCU. Nevertheless, challenges remain. The proposal of a research agenda underscores the need to deepen the discussion and explore new solutions. Research Implications: The practical and theoretical implications of this research include consolidating governance systems as a reference for strengthening public university management. Originality/Value: The research contributes to the formulation of guidelines and replicable models for other federal universities, fostering greater institutional cohesion and alignment with public policies.

Machine learning of the dynamics of strain hardening based on contact transformations

Abstract Dislocation density-based models offer a physically grounded approach to modeling strain hardening in metal forming. Since these models are typically defined by Ordinary Differential Equations (ODEs), their accuracy is constrained by both, the model formulation and the parameter identification process. Machine Learning (ML) provides an alternative by allowing models to be constructed directly from experimental data, bypassing the accuracy limitations of explicitly defined models. However, applying ML to ODEs introduces the need for novel training techniques. This work presents a new approach for developing neural ODE models for flow curve description, utilizing a contact transformation to simplify the problem of learning an ODE into a learning a multivariate function. Graphical abstract

Evaluating the Impacts of Parameter Uncertainty in a Practical Transportation Demand Model

The inherent uncertainty in travel forecasting models—arising from potential and unknown errors in input data, parameter estimation, or model formulation—is receiving increasing attention from both the scholarly and practicing communities. In this research, we investigate the variance in forecasted traffic volumes resulting from varying the mode and destination choice parameters in an advanced trip-based travel demand model. Using Latin hypercube sampling to construct several hundred combinations of parameters across the plausible parameter space, we introduce substantial changes to implied travel impedances and modal utilities, on the order of a 10 percent variation. However, the aggregate effects of these changes on forecasted traffic volumes are small, with a variation of approximately 1 percent on high-volume facilities. It is likely that in this example—and perhaps in others—the network assignment places constraints on the possible volume solutions and limits the practical impacts of parameter uncertainty. Nevertheless, parameter uncertainty may not be the largest contributor to error in practical travel forecasts. Further research should examine the robustness of this finding across other less constrained networks and within activity-based travel model frameworks.

Simulation-based high-speed elongational rheometer for Carreau-type materials

Abstract For the simulation-based design of fiber melt spinning processes, the accurate modeling of the processed polymer with regard to its material behavior is crucial. In this work, we develop a high-speed elongational rheometer for Carreau-type materials, making use of process simulations and fiber diameter measurements. The procedure is based on a unified formulation of the fiber spinning model for all material types (Newtonian and quasi-Newtonian), whose material laws are strictly monotone in the strain rate. The parametrically described material law for the elongational viscosity implies a nonlinear optimization problem for the parameter identification, for which we propose an efficient, robust gradient-based method. The work can be understood as a proof of concept, a generalization to other, more complex materials is possible.

Mathematical Modeling of Host-Parasitoid Dynamics for Sustainable Pest Management

Aims: The management of agricultural pest populations represents a critical challenge in sustainable crop production, particularly for challenging species like the tomato fruit borer, Helicoverpa armigera. The research work aimed in developing and analysing a novel mathematical model describing the interaction between the tomato fruit borer and its egg parasitoid, Trichogramma chilonis. The main objective of this study to analyze the stability of the proposed system of differential equations. Model Formulation: The model incorporates stage-structured population dynamics and impulsive control measures, providing insights into biological pest control strategies. In this model the tomato borer is represented by the egg and larval stages, and the parasitoid is considered in terms of the parasitized eggs. This model assumes uniform spatial distribution of both host and parasitoid populations, constant environmental conditions affecting parameters like reproduction and mortality rates, and idealized parasitoid release conditions where the released amount p remains consistent across interventions. Methodology: We used existing secondary data from research articles' instead of conducting primary field experiments for parameter identification for this mathematical model. The use of secondary data from peer-reviewed literature not only provides validated parameter values that have been rigorously tested and verified by multiple researchers, but also allows for the incorporation of findings from diverse geographical locations and climatic conditions. Results: Stability analysis identified three equilibrium points: the trivial, host-only E0,coexistence equilibria E1, and E2. The coexistence equilibirium E2 found to be locally stable under optimal parasitoid release intervals and quantities. The equilibrium point E2= (9.14,0.02,5.71) represents a coexistence state in the host-parasitoid dynamics, where the tomato fruit borer's egg density stabilizes at 9.14, the density of parasitized eggs at 0.02, and the larval density stabilizes at 5.71. This equilibrium is analyzed through its associated eigenvalues. This stability provides a reliable basis for implementing successful biological pest control strategies in tomato cultivation, ensuring a resilient and sustainable ecosystem.

Truck Appointment Scheduling: A Review of Models and Algorithms

This paper provides a comprehensive review of truck appointment scheduling models and algorithms that support truck appointment systems (TASs) at container terminals. TASs have become essential tools for minimizing congestion, reducing wait times, and improving operational efficiency at the port and maritime industry. This review systematically categorizes and evaluates existing models and optimization algorithms, highlighting their strengths, limitations, and applicability in various operational contexts. We explore deterministic, stochastic, and hybrid models, as well as exact, heuristic, and metaheuristic algorithms. By synthesizing the latest advancements and identifying research gaps, this paper offers valuable insights for academics and practitioners aiming to enhance TAS efficiency and effectiveness. Future research directions and potential improvements in model formulation are also discussed.

Dynamics Modeling for Key Performance Indicators in Higher Education Through Optimization Methods

Dynamic models of higher education Key Performance Indicators (KPI) help in understanding how internal and external factors affect future KPI achievement. This study aims to construct a dynamic model of university KPI variables and estimate model parameters value. Several used steps to achieve goals are problem definition, variables, model formulation, prerequisite estimation, and conformity analysis. This model involves eight KPIs and three types of funding. Three optimization methods used are Type I constrained optimization, Type II constrained optimization, and unconstrained optimization. The results showed that the percentage of graduates getting decent jobs (KPI 1) in the future year is strongly influenced by two KPIs, namely the work of lecturers (KPI 5) and study programs with international accreditation (KPI 8). The existence of active practitioners (KPI 4) opens opportunities for international cooperation (KPI 6) and collaborative learning (KPI 7). Significant investment in improving the quality of lecturers and the right allocation of funds has proven to impact achieving the goals of higher education positively.

Effective macropore diffusivity of carbon dioxide on binderless pellets of Y-type zeolites

The adsorption kinetics of carbon dioxide (CO2) in three cationic forms of binderless pellets of Y-types zeolites (H-Y, Na-Y, and TMA exchanged Na-Y) are studied using the zero-length column (ZLC) technique. The measurements were carried out at and using different flowrates and an initial CO2 partial pressure of – conditions representative of post-combustion CO2 capture applications. The mass transport within the adsorbent pellets was described using a 1-D Fickian diffusion model accounting for intra- and inter-crystalline mass transport. For the latter, the parallel pore model formulation was used to explicitly account for the adsorbent’s macropore size distribution in estimating the volume-averaged diffusivity of the gas. Experiments carried out using different carrier gases, namely helium and nitrogen, were used (i) to determine that these systems are macropore diffusion limited and (ii) to simplify the parameter estimation to a single parameter - the macropore tortuosity. The latter () was in good agreement with independent measurements using MIP (). The associated diffusion coefficient, , was found to vary due to differences in the materials’ macropore size distributions and overall porosity. Upon combining the parallel pore model formulation with the temperature dependencies for the pore diffusivities derived from molecular theories of gases, we predict with depending on the macropore size distribution. Notably, for the range of temperature tested in this study, varies approximately linearly with temperature ()– in contrast to the commonly reported correlation of , which may be more appropriate for systems where molecular diffusion dominates and Knudsen diffusion is negligible. The binderless pellets of Y-type zeolites studied exhibit generally higher values for the effective macropore diffusivity of CO2 compared to previously reported results on commercial FAU zeolites.

Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model.

Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establish a machine learning-based predictive model for POF following surgery of oral cancer. A total of seven hundred and twenty-seven consecutive patients undergoing radical resection of oral cancer were retrospectively investigated. The analysis encompassed 34 parameters, incorporating demographic and clinical characteristics, biochemical and hematological assay results, surgical-related data, hospitalization costs and stay in hospital. Six machine learning models were compared by the area under the receiver operating characteristic curve (AUC). The best-performing models were selected for further analyze, including feature importance evaluation and nomogram analysis, identifying key POF risk factors, and establish a comprehensive prediction model. A total of 466 patients with surgery for oral cancer met the criteria, with an average age of (54.2 ± 11.1) years, including an POF group (n = 197) and a non-POF group (n = 269). The fever group has greater hospitalization costs, longer lengths of stay, and higher infection biochemical indicators (leucocyte ratio and erythrocyte sedimentation rate). Furthermore, Among the 6 machine learning models, logistic regression models performed best, with the higher AUC and accuracy. In univariate and multivariate logistic analysis showed that age, sex, reoperation, Charlson Comorbidity Index score (CCI), leukocyte, bleeding and blood transfusion were independent risk factors for POF of patients following surgery in oral cancer. Then seven variables were selected to establish the nomogram for predict the probability of POF by nomogram algorithm. Postoperative fever patients following radical resection of oral cancer have greater burden. Machine learning algorithms can be effectively used to identify potential risk factors of POF, which may enhance individualized treatment plans in oral cancer patient during perioperative period.

Inverse Eigenmode Modeling and Multiscale Seismic Analysis of Irregular Frame Structure by Shear Spring–Multifloor Mass System

ABSTRACTA modeling method of an elastic shear spring–multifloor mass system (MF system) based on the fundamental eigenmode and natural circular frequency of a full‐scale structural frame is presented. MF system has plural lumped masses as representative of single floor, and the lumped masses are connected by interfloor shear springs. MF system focuses on both global and local responses of the original frame, namely, the total displacement, the non‐uniform displacements of an identical floor and the member force distributions. Therefore, the alternative use of MF system to the original full‐scale frame leads to the accurate and rapid evaluation of the responses of the frame. The introduction of the coupled shear springs to the modeling formulation provides the coupled horizontal responses in the two orthogonal directions. In addition, the interstory shear force distribution of the original frame and that of the equivalent MF system correspond. The accuracy of the proposed method is investigated through numerical examples for in‐plan irregular frames.

Towards contention resolution model to enhance rate of interference during dynamic spectrum utilization in cognitive radio network environment

In cognitive radio networks (CRNs), contention resolution is a fundamental challenge for optimizing dynamic spectrum access among secondary users (SUs) without causing interference to primary users (PUs). This study introduces a contention resolution model within cognitive radio networks. The queuing theory model of M/G/1/K with a First Come First Serve (FCFS) algorithm was employed. This paper proposes a novel contention resolution model using the First-Come-First-Served (FCFS) strategy, framed within a Markov Decision Process (MDP). The model dynamically allocates spectrum to SUs based on their arrival time. It leverages on M/G/1/K queuing model, providing a theoretical foundation for optimizing contention resolution. Through the FCFS algorithm, the model ensures equitable access to the spectrum, prioritizing the first arriving users. As a proof of concept and in order to validate the effectiveness of the model, simulations were performed using MATLAB and Minitab software. MATLAB was employed to simulate the dynamic behavior of the network under varying conditions, providing insights into throughput, delay, and collision metrics while Minitab was used for statistical analysis and validation of the simulation data, ensuring accuracy and reliability of the results. The results obtained from the simulations indicates that the MDP-based FCFS model significantly improves spectrum efficiency, reduces access delays, and minimizes collision rates compared to traditional contention resolution techniques. Another main contribution of this model formulation and evaluation is to help Nigerian Communication Commission (NCC) and other Dynamic Spectrum Usage functionaries in analyzing and optimizing the contention resolution predominant challenge, for the effective utilization of available spectrum resources in dynamic cognitive radio environment.

P-686. Risk of COPD Exacerbations, Asthma Exacerbations, and Hospitalizations for Heart Failure after RSV Hospitalization Among US Adults Aged at Least 50 Years

Abstract Background Older adults and adults with comorbidities are at increased risk of severe respiratory syncytial virus (RSV) disease. Limited data are available on the long-term impact of RSV on comorbid diseases. This study evaluated the association between RSV hospitalization and the risk of future acute disease worsening in adults aged ≥ 50 years. Figure 1 Methods This retrospective cohort study analyzed Optum’s de-identified Clinformatics Data Mart Database (CDM) healthcare claims from October 2015 to June 2023. Adults aged ≥ 50 years with ≥ 12 months of continuous enrollment were assigned to cohorts based on RSV hospitalization (RSV cohort), influenza hospitalization (flu cohort), or not having a recent acute respiratory illness (ARI) (controls). Index date was the start of an ARI episode that included hospitalization and an RSV or influenza diagnosis for the RSV and flu cohorts, respectively. Controls were matched 5:1 with the RSV cohort. Outcomes included acute exacerbations of (AE) chronic obstructive pulmonary disease (COPD), AE asthma, and hospitalizations for heart failure (HHF), were measured during the follow-up period (Fig. 1), and analyzed as recurrent events. AE COPD, AE asthma, or HHF risks were compared between the RSV vs influenza and RSV vs control cohorts using the Andersen-Gill formulation of Cox proportional hazards models in the overall population and subgroups. Results The study included 14,759, 77,468, and 73,795 adults in the RSV, flu, and control cohorts, respectively (Table 1). Mean cumulative event counts were highest in the RSV cohort (Table 2). In adjusted analyses, adults with an RSV hospitalization had a higher risk of AE COPD (for those with COPD: hazard ratio [HR] = 1.15 vs flu cohort, p < 0.001; HR = 2.78 vs control cohort, p < 0.001), AE asthma (for those with asthma: HR = 1.53 vs flu cohort, p < 0.001; HR = 6.51 vs control cohort, p < 0.001), and HHF (for those with heart failure: HR = 1.08 vs flu cohort, p < 0.001; HR = 3.07 vs control cohort, p < 0.001) (Table 3). Conclusion Increased risk of AE COPD, AE asthma, and HHF was observed among adults with RSV hospitalization compared to influenza hospitalization and compared to adults without a recent ARI. These findings build on previous research demonstrating the importance of RSV prevention in older and comorbid adults. FUNDING: GSK (VEO-000616) Disclosures David Singer, PharmD, MS, GSK: employee|GSK: Stocks/Bonds (Public Company) Yan Wang, ScD, Analysis Group: Employee Aozhou Wu, PhD, Analysis Group: Employee Elizabeth M. La, PhD, GSK: employee|GSK: Stocks/Bonds (Private Company) Susan Gerber, MD, GSK: Employee Lydia Lee, PharmD, MS, GSK: Fellowship Sponsorship Hongjiao Liu, PhD, Analysis Group: Employee Keith A. Betts, PhD, Analysis Group: Employee

Optimal multi-objective design of plastic injection molds for clinical vials

A critical review was made of previous research works, dedicated to the topic of injection molds design with different approaches, including the use of simulation and optimization procedures and tools. The work responds to the problem of selecting the most appropriate design and technology for plastic injection molding to obtain vials. A methodological approach to the analysis and synthesis of engineering systems was used, which led to the realization of a systemic analysis of the associated task, obtaining its conceptual mathematical model, its parameterization and, as a result of the subsequent internal analysis, the detailed mathematical model of the decision-making process was obtained, including the formulation of the multi-objective optimization model for obtaining the fundamental design parameters as a multi-criteria decision-making task. In conclusion, this model was adapted to the particularities of the Excel Solver application and solved with its help. Finally, the decision-making procedure was carried out with the help of this application and proposed blister manufacturing technology.

Statistical flaws of the fitness-fatigue sports performance prediction model

Optimizing athletic training programs with the support of predictive models is an active research topic, fuelled by a consistent data collection. The Fitness-Fatigue Model (FFM) is a pioneer for modelling responses to training on performance based on training load exclusively. It has been subject to several extensions and its methodology has been questioned. In this article, we leveraged a Bayesian framework involving biologically meaningful priors to diagnose the fit and predictive ability of the FFM. We used cross-validation to draw a clear distinction between goodness-of-fit and predictive ability. The FFM showed major statistical flaws. On the one hand, the model was ill-conditioned, and we illustrated the poor identifiability of fitness and fatigue parameters using Markov chains in the Bayesian framework. On the other hand, the model exhibited an overfitting pattern, as adding the fatigue-related parameters did not significantly improve the model’s predictive ability (p-value > 0.40). We confirmed these results with 2 independent datasets. Both results question the relevance of the fatigue part of the model formulation, hence the biological relevance of the fatigue component of the FFM. Modelling sport performance through biologically meaningful and interpretable models remains a statistical challenge.

3D printing driven business model innovation and supply chain operations: a perspective of strategic alignment

PurposeThe dissemination of 3D printing is contingent upon the strategic formulation of innovative business models that are integrated with supply chain management. While 3D printing is considered a disruptive innovation that has simultaneously transformed business models and supply chain operations, previous research has often treated these two aspects separately. To bridge this gap, this paper aims to explore the alignment between business model and supply chain operations that enhance the proliferation of 3D printing technology.Design/methodology/approachThis study integrates manufacturer- and customer-centric business models through 3D printing’s capacity to support both centralized and decentralized supply chains, conducts a strategic evaluation to align business model innovations with supply chain operations and employs fsQCA analysis with Chinese 3D printing provider cases.FindingsOur research reveals that business models focusing on efficiency benefit from aligning with centralized supply chain strategies, while those emphasizing novelty are enhanced by decentralized supply chains, both positively influencing the adoption of 3D printing technology innovation. The research highlights the benefits of combining hybrid manufacturing and customization in centralized environments, as well as the significance of embracing supercenter and marketplace models in decentralized supply chains to foster 3D printing innovation.Practical implicationsThe findings of our research provide valuable insights for practitioners looking to assess their supply chains and explore how their business innovations can be effectively implemented within the supply chain structure. This examination can help them recognize the transformative potential of 3D printing and how it can revolutionize their industry by enabling new business models and processes.Originality/valueThe contribution of this paper is (a) to analyze business model innovation considering the strategic alignment between the business model and supply chain strategy driven by 3D printing; (b) to empirically testify this aligned performance in 3DP manufacturer for the first time.

FGF-based drug discovery: advances and challenges.

The fibroblast growth factor (FGF) family comprises 15 paracrine-acting and 3 endocrine-acting polypeptides, which govern a multitude of processes in human development, metabolism and tissue homeostasis. Therapeutic endocrine FGFs have recently advanced in clinical trials, with FGF19 and FGF21-based therapies on the cusp of approval for the treatment of primary sclerosing cholangitis and metabolic syndrome-associated steatohepatitis, respectively. By contrast, while paracrine FGFs were once thought to be promising drug candidates for wound healing, burns, tissue repair and ischaemic ailments based on their potent mitogenic and angiogenic properties, repeated failures in clinical trials have led to the widespread perception that the development of paracrine FGF-based drugs is not feasible. However, the observation that paracrine FGFs can exert FGF hormone-like metabolic activities has restored interest in these FGFs. The recent structural elucidation of the FGF cell surface signalling machinery and the formulation of a new threshold model for FGF signalling specificity have paved the way for therapeutically harnessing paracrine FGFs for the treatment of a range of metabolic diseases.

How Microeconomics Works Within a Firm

While it is a widespread practice for business majors to take the microeconomics course in the early years of their college education, for most students it is followed by courses in other disciplines such as accounting, finance, and marketing. In turn, in these disciplines, the formulation of theory, models and examples involve use of nomenclature specific to that discipline such as accounting and financial performances. Sometimes the word “economic” measure does appear in these discipline specific courses. Many of these concepts and models with resulting measures though perceived to be developed in different business disciplines, they originate from or developed utilizing the micro economic theory. In this study, we plan to link together the microeconomic theory and its applications in development of concepts and models in other disciplines so that student and practitioners will be able tie in a seamless manner, and if necessary, modify these concepts to apply and solve business issues in a firm.

Method of Collaboration between Humans and Generative Artificial Intelligence in the Development of Information Systems

We propose a method for information system (IS) design with a focus on the applicability of intelligent chatbots. An analysis of existing chatbots shows that current solutions do not have the ability to formulate IS requirements during designing without human intervention. The study proposes to integrate ChatGPT (GPT4 model) as a chatbot to solve the problem of writing requirements elicitation for IS. The proposed method for solving the problem is based on a gradual reduction in entropy of chatbot response, which can be achieved by controlling such basic query parameters as the form, depth and breadth of the prompt. Based on the proposed method, an algorithm for controling prompts during a dialogue with a chatbot was developed, regulating the form, depth and breadth of the question to obtain the required level of content in the answer. The following results were obtained during the study: a method for obtaining new knowledge based on entropy of chatbot response reduction; algorithm for prompts control; the second phase of IS development is automated (including identification of business requirements and system objectives; analysis of existing solutions and technologies; determination of functional requirements and system capabilities; development of system architecture, including service definition and interaction; creation of interfaces for service interaction; establishment of a data model and database and formulation of business process logic). The method was tested to design an IS to support strategic decision making in the forestry industry. The design results received a positive assessment from experts. Analysis of the obtained results revealed the advantages and limitations of using chatbots as co-pilots in the design of IS and outlined directions for future research.

LES combustion model for premixed turbulent hydrogen flames with thermodiffusive instabilities: a priori and a posteriori analysis

Premixed hydrogen flames are prone to thermodiffusive instabilities due to strong differential diffusion effects. Reproducing these instabilities in large eddy simulations (LES), where their effects are only partially resolved, is challenging. Combustion models that account for differential diffusion effects have been developed for laminar flames, but to use them in LES, models for the turbulence/flame subfilter interactions are required. Modelling of the subfilter interactions is particularly challenging as instabilities synergistically interact with turbulence resulting in a strong enhancement of the turbulent flame speed. In this work, a combustion model for LES, which accounts for thermodiffusive instabilities and their interactions with turbulence, is presented. In the first part, an a priori analysis based on a direct numerical simulation (DNS) of a turbulent hydrogen/air jet flame is discussed. Progress variable, progress variable variance and mixture fraction are rigorously identified as suitable model input parameters, and an LES combustion model based on pre-tabulated unstretched premixed flamelets with varying equivalence ratio is formulated. Subfilter closure is achieved via a presumed probability density function and a significant reduction of modelling errors is achieved with the presented model. In the second part, LES of the DNS configuration are performed for an a posteriori analysis. The presented combustion model shows significant improvements in predicting the flame length and local phenomena, such as super-adiabatic temperature, compared with combustion models that either neglect differential diffusion effects or consider these effects but neglect the subfilter closure. Two variants of the model formulation with a water- or hydrogen-based progress variable have been tested, yielding overall similar predictions.

Reformulating the meta-analytical random effects model of the standardized mean difference as a mixture model

The classical meta-analytical random effects model (REM) has some weaknesses when applied to the standardized mean difference, g. Essentially, the variance of the studies involved is taken as the conditional variance, given a δ value, instead of the unconditional variance. As a consequence, the estimators of the variances involve a dependency between the g values and their variances that distorts the estimates. The classical REM is expressed as a linear model and the variance of g is obtained through a framework of components of variance. Although the weaknesses of the REM are negligible in practical terms in a wide range of realistic scenarios, all together, they make up an approximate, simplified version of the meta-analytical random effects model. We present an alternative formulation, as a mixture model, and provide formulas for the expected value, variance and skewness of the marginal distribution of g. A Monte Carlo simulation supports the accuracy of the formulas. Then, unbiased estimators of both the mean and the variance of the true effects are proposed, and assessed through Monte Carlo simulations. The advantages of the mixture model formulation over the “classical” formulation are discussed.