Dynamic Modeling Approach Research Articles

Investigating new drugs from marine seaweed metabolites for cervical cancer therapy by molecular dynamic modeling approach

The etiology of cervical cancer in women is attributed to the continuous infection of the human papillomavirus (HPV). The high costs and side effects of standard treatments and the limited efficacy of HPV vaccination have led to a quest for novel, cost-effective cervical cancer treatments, particularly in middle- and low-income countries. Therefore, our objective was to evaluate the capacity of marine seaweed compounds to hinder the activity of the cervical cancer E6 Oncoprotein. The Seaweed Metabolite Database was evaluated for its ability to inhibit E6 Oncoprotein functions by high throughput virtual screening. The investigations included molecular docking, ADMET test, MD simulation, and MM/GBSA analysis to identify three lead seaweed drug-like compounds: BC008 (-8.9 kcal/mol), RL379 (-8.9 kcal/mol), and BC014 (-8.7 kcal/mol). All of the leading candidates had positive characteristics in terms of pharmacokinetics, pharmacodynamics, and toxicity. The molecular dynamics simulation of the apoprotein, control drug, and lead compounds revealed the superior structural stability and uniformity of the main drug candidates. The MM/GBSA study revealed that the BC008-protein complex exhibited the most significant free binding energy, with a value of -57.41 kcal/mol. In the end, the findings derived from this investigation might provide a basis for developing innovative anticancer treatments.

Simulation of the fluid flow pattern and property estimation for bio-diesel plant design

The fluid flow pattern that occurs in the bio-diesel production process is related to viscosity, flow velocity, and pressure. Fluid flow is a paramount cause of failure in pipes in several industries. The heat transfer cause across pipes needs to be analyzed to avoid failure during operation. The simulation and visualization of fluid flow in the plant pipes were developed for the pressure drop along with the length of the pipe. Flow patterns was analyzed using a Computational Fluid Dynamics model approach (CFD). The CFD within the ANSYS environment has three important stages, namely pre-processing, finding solutions, and post-processing. Mesh was generated and the material properties were assigned. The boundary conditions were stipulated, and the process analyzed to ensure convergence of the solution in a stable state. High density of the blend was observed for 40% bio-diesel blend and low density at 5% bio-diesel blend. The results of the correlation analysis shows that density is directly proportional to bio-diesel content. The density-composition depict a uniform increasing density value with percentage bio-diesel mixture content. The viscosity slightly increases with bio-diesel content. There is a clear trend of viscosity increasing proportional to bio-diesel content. The simulation ensures that the parameters for the design and fabrication of the bio-diesel reactor as obtained from the simulation results is optimal.

Evaluating circular economy performance in the global chemical sector: a dynamic network Data Envelopment Analysis model approach

This study evaluates the circular economy performance of global chemical companies from 2013 to 2022, focusing on production efficiency and circular efficiency. Dynamic network Data Envelopment Analysis model is employed to assess the overall, production and circular performance of firms. Additionally, management decision matrix is presented, offering firms a clearer understanding of their efficiency levels and a detailed projection analysis on the optimization adjustments needed for both resource inputs and outputs. The findings reveal varying levels of overall efficiency among the observed firms. While some companies have managed to maintain or improve their efficiency over the study period, others exhibit significant inefficiencies. Opportunities for operational streamlining and resource optimization are identified, particularly in reducing resource consumption without compromising production capacity. The analysis of circular efficiency further highlights substantial potential for improving sustainability practices, particularly through enhanced waste reuse and a reduction in environmental impact. This study provides practical implication of circular economy performance in the chemical industry, offering a new way to measure and improve resource efficiency and sustainability practices.

Dynamic to Static Model Comparison and Hybrid Metaheuristic Optimization in Induction Motor Parameter Estimation

This paper presents a comprehensive study of parameter estimation for three-phase induction motors (IMs) using hybrid optimization methods and a comparative evaluation of static and dynamic modeling approaches. A hybrid metaheuristic combining the Sine Cosine Algorithm (SCA) and Particle Swarm Optimization (PSO) is developed to identify optimal motor parameters efficiently. The approach utilizes a static model for rapid estimation, with final parameter values validated against a dynamic model to ensure accuracy in operational predictions. Results confirm that the static model provides robust parameter estimates for key performance metrics, including torque, power factor, and current, aligning well with experimental results from real-motor no-load tests. Parameters estimated by the proposed method demonstrate a high adherence with the motor real measurements. Comparisons also reveal the limitations of static models in scenarios requiring state-space accuracy, such as observer-based control applications. This study concludes by recommending further exploration of alternative motor modeling structures and the hybrid optimization algorithm for parameter estimation.

Dynamical models reveal anatomically reliable attractor landscapes embedded in resting-state brain networks

Abstract Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings of FC and RSNs are still not well-understood. It remains debated whether resting-state activity is best characterized as reflecting noise-driven fluctuations around a single stable state, or instead, as a nonlinear dynamical system with nontrivial attractors embedded in the RSNs. Here, we provide evidence for the latter, by constructing whole-brain dynamical systems models from individual resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized NeuroDynamic (MINDy) framework. The MINDy models consist of hundreds of neural masses representing brain parcels, connected by fully trainable, individualized weights. We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles. However, when projected into anatomical space, these attractors mapped onto a limited set of canonical RSNs, including the default mode network (DMN) and frontoparietal control network (FPN), which were reliable at the individual level. Further, by creating convex combinations of models, bifurcations were induced that recapitulated the full spectrum of dynamics found via fitting. These findings suggest that the resting brain traverses a diverse set of dynamics, which generate several distinct but anatomically overlapping attractor landscapes. Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss critical attractor properties and structure within the resting brain. Instead, these may be better captured through neural dynamical modeling and analytic approaches. The results provide new insights into the generative mechanisms and intrinsic spatiotemporal organization of brain networks.

Noncatalytic Reduction of Nitrogen Oxide and Soot Inhibition during Cocombustion with Biotar: The Molecular Dynamics Modeling Approach.

Cocombustion with biomass tar is a potential method for NO reduction during fossil fuel combustion. In this work, the molecular dynamic method based on the reactive force field was used to study the NO reduction by phenol, which is a typical tar model compound. Results indicate that phenol undergoes significant decomposition at 3000 K, resulting in the formation of small molecular fragments accompanied by the generation of large molecular, network-structured soot particles. At higher temperatures (3500 K), the morphology of the soot produced from phenol decomposition undergoes a certain degree of change. It evolves from a two-dimensional network structure to a three-dimensional particulate structure. Soot particles are significant products of the thermal decomposition process of phenol. NO acts as an oxidant during the phenol decomposition process, significantly inhibiting the formation of soot particles during this process. Phenol also promotes the reduction of NO. The corresponding NO reduction ratios of NO are 52%, 76%, and 89% for 1500, 2000, and 2500 K respectively. CO, H2O, and N2 were the three most important reaction products during phenol-NO interaction. HNO is an important intermediate during the reduction of NO by phenol. The combination of the H radical and NO is the main route for HNO. It can be seen that HNO is quickly produced at the initial stage. The calculated activated energies for NO reduction and phenol oxidation are 30.47 and 50.85 kJ/mol, respectively.

From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium and ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence of mental health disorders like depression and schizophrenia, which necessitate precise, innovative approaches. Emerging technologies like artificial intelligence, induced pluripotent stem cells, and multi-omics have the potential to transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies such as animal models and single-variable analyses continue to be used, frequently failing to capture the complexities of human neuropsychiatric conditions. Summary: This review critically evaluates the transition from serendipity to precision-based methodologies in neuropsychiatric research. It focuses on key innovations such as dynamic systems modeling and network-based approaches that use genetic, molecular, and environmental data to identify new therapeutic targets. Furthermore, it emphasizes the importance of interdisciplinary collaboration and human-specific models in overcoming the limitations of traditional approaches. Conclusions: We highlight precision psychiatry's transformative potential for revolutionizing mental health care. This paradigm shift, which combines cutting-edge technologies with systematic frameworks, promises increased diagnostic accuracy, reproducibility, and efficiency, paving the way for tailored treatments and better patient outcomes in neuropsychiatric care.

Spillovers on the mean and tails: a semiparametric dynamic panel modeling approach

Abstract Recently, there has been a surge in interest in exploring how common macroeconomic factors impact different economic results. We propose a semiparametric dynamic panel model to analyze the impact of common regressors on the conditional distribution of the dependent variable (global output growth distribution in our case). Our model allows conditional mean, variance, and skewness to be influenced by common regressors, whose effects can be nonlinear and time-varying driven by contextual variables. By incorporating dynamic structures and individual unobserved heterogeneity, we propose a consistent two-step estimator and showcase its attractive theoretical and numerical properties. We apply our model to investigate the impact of US financial uncertainty on the global output growth distribution. We find that an increase in US financial uncertainty significantly shifts the output growth distribution leftward during periods of market pessimism. In contrast, during periods of market optimism, the increased uncertainty in the US financial markets expands the spread of the output growth distribution without a significant location change, indicating increased future uncertainty.

Assessment of Vaccination Impact in PPR-Control Program Implemented in Southern States of India: A System Dynamics Model Approach.

Mass vaccination against peste des petits ruminants (PPR) in two southern states of India, namely Andhra Pradesh and Karnataka, has reduced disease outbreaks significantly. The sporadic outbreaks reported now can be attributed in part to the recurring movement of sheep and goats between these contiguous states. This study assessed the present level of economic burden and impact of vaccination on the local system (one state), considering the exposure from the external system (neighboring state) using a system dynamic (SD) model. The SD model relies on interdependence, interaction, information feedback, and circular causality and captures potential feedback between disease control interventions and their impact on various epidemiological and economic outcomes. The data for parameterization of the model were collected through surveys, expert elicitation, and literature review. The sporadic outbreaks reported in recent years (<10 outbreaks/year during 2022) were due to continuous "mass vaccination" for more than a decade. During 2021-2022, the PPR incidence was less in both the states, with an estimated loss of USD 26.30 and USD 22.86 million in Andhra Pradesh and Karnataka, respectively. The SD model results showed a systemic increase in flock size and offtakes and a decline in the number of infected and death cases under high vaccination coverage (75% and 100% coverage) compared to the low-coverage scenario. Hence, the coordinated inter-state vaccination efforts offer better prospects, as efforts in one state have positive externalities in terms of fewer outbreaks in a neighboring state.

Hierarchical Bayesian continuous‐time dynamic modeling approach to investigate the dynamic interplay between aerobic exercise and time‐locked cognitive performance in older adults with AD and metabolic risk

AbstractBackgroundTraining studies report beneficial effects of physical (PP) on cognitive performance (COG) in older adults, but are often accompanied by potentially biased parameters, conclusions, and lack of directionality. To address these issues, we used a dynamic Bayesian approach to analyse the dynamic session‐to‐session change and coupling of PP and COG over time.MethodsWe used two studies (N = 17 each): Study 1 contained 24‐weeks (72 sessions) of training of older adults with suspected Alzheimer’s disease (AD). Study 2 included four months (40 sessions) of training of older adults at metabolic risk. The hierarchical Bayesian continuous‐time dynamic modeling approach (R package ctsem) comprised: (i) A subject‐level latent dynamic model, (ii) subject‐level measurement model, and (iii) population model. The dynamic model was specified with two fully connected state variables enabling bidirectional coupling between PP and COG using default priors and starting values (4 chains and 8.000 iterations). Intercept and drift parameters were set to vary freely. Population and individual level parameters are estimated simultaneously using all data from all subjects. Second‐level models included MMSE, SF12 (physical health questionnaire) in study 1 and whole hippocampal volumes (wHCV: T2‐ASHS, longitudinal) and white matter hyperintensities (SAMSEG, longitudinal) in study 2 as time independent covariates.ResultsStudy 1: Higher PP was dynamically linked to COG (‐1.335, 95%‐BCI [‐1.725, ‐0.954]). The effect was short‐term, lasting up to five days (‐0.368, 95%‐BCI [‐0.479, ‐0.266]). Study 2: PP improved COG in subsequent sessions (‐1.11, 95%‐BCI[‐1.22, ‐0.99]), which lasted for up to 15 days. Higher wHCV was associated with a stronger coupling‐effect of PP on COG (‐0.15, 95%‐BCI[‐0.18, ‐0.11] ) and lower persistence of COG (‐0.145, 95%‐BCI[‐0.21, ‐0.08]).DiscussionOur results show immediate exercise‐induced improvements in COG, with a longer‐lasting effect for participants at metabolic risk compared to older adults with suspected AD. Despite impaired cognitive performance, the cognitive system was still able to fluctuate and change favorably. Physical exercise seems to mobilize capacities that would otherwise remain unused. Utilizing a Bayesian approach to examine time‐locked effects can provide insights for implementing individualized training approaches and guiding clinical decision‐making.

Hierarchical Bayesian continuous‐time dynamic modeling approach to investigate the dynamic interplay between aerobic exercise and time‐locked cognitive performance in older adults with AD and metabolic risk

Hierarchical Bayesian continuous‐time dynamic modeling approach to investigate the dynamic interplay between aerobic exercise and time‐locked cognitive performance in older adults with AD and metabolic risk

Real-Time Environmental Design Parameters and their Impact on the Performance of Passive Basin Solar Still: A Dynamic CFD Modeling Approach

A computational fluid dynamics (CFD) approach was used in this work to investigate various design and environmental factors and their impact on the performance of passive basin solar stills (PBSS). This requires using the ANSYS software to build a sophisticated geometrical solar still model with the associated solar cells. The impact of solar cell configuration on energy optimization was determined by analyzing the developed system in both series and parallel configurations. Accuracy of simulation results was ensured in the model by introducing a meshing technique that consisted of&gt;2 million components. The results (for static pressure analysis) indicated the absence of any high-pressure-induced strains after evaluating the performance metrics (static pressure, velocity, and energy distribution) for impact on the system using &gt; 200 iterations; this suggests that the system design is balanced. Hence, the performance of solar stills can be improved by using new materials and design configurations as shown in this work. This study has improved knowledge of solar water desalination and other forms of renewable energy sources for related applications.

On the Numerical Investigation of Two-Phase Evaporative Spray Cooling Technology for Data Centre Applications

Two-phase evaporative spray cooling technology can significantly reduce power consumption in data centre cooling applications. However, the literature lacks an established methodology for assessing the overall performance of such evaporation systems in terms of the water-energy nexus. The current study develops a Lagrangian–Eulerian computational fluid dynamics (CFD) modelling approach to examine the functionality of these two-phase evaporative spray cooling systems. To replicate a modular system, a hollow spray cone nozzle with Rosin–Rammler droplet size distribution is simulated in a turbulent convective natural-air environment. The model was validated against the available experimental data from the literature. Parametric studies on geometric, flow, and climatic conditions, namely, domain length, droplet size, water mass flow rate, temperature, and humidity, were performed. The findings indicate that at elevated temperatures and low humidity, evaporation results in a bulk temperature reduction of up to 12 °C. A specific focus on the climatic conditions of Dublin, Ireland, was used as an example to optimize the evaporative system. A new formulation for the coefficient of performance (COP) is established to assess the performance of the system. Results showed that doubling the injector water mass flow rate improved the evaporated mass flow rate by 188% but reduced the evaporation percentage by 28%, thus reducing the COP. Doubling the domain length improved the temperature drop by 175% and increased the relative humidity by 160%, thus improving the COP. The COP of the evaporation system showed a systematic improvement with a reduction in the droplet size and the mass flow rate for a fixed domain length. The evaporated system COP improves by two orders of magnitude (~90 to 9500) with the reduction in spray Sauter mean diameter (SMD) from 292 μm to 8–15 μm. Under this reduction, close to 100% evaporation rate was achieved in comparison to only a 1% evaporation rate for the largest SMD. It was concluded that the utilization of a fine droplet spray nozzle provides an effective solution for the reduction in water consumption (97% in our case) for data centres, whilst concomitantly augmenting the proportion of evaporation.

Integration of dynamic CHPP and gas boiler behaviour into the convex planning problem for the optimised operation of multimodal microgrids

Integration of dynamic CHPP and gas boiler behaviour into the convex planning problem for the optimised operation of multimodal microgrids

Effects of parietal iTBS on resting-state effective connectivity within the frontoparietal network in patients with schizophrenia: An fMRI study

Although intermittent theta burst stimulation (iTBS) has shown effectiveness in addressing working memory (WM) deficits in individuals with schizophrenia (SZ), the current body of evidence is limited and the specific mechanisms involved remain unclear. Therefore, this pilot fMRI study aimed to examine the efficacy of parietal iTBS in ameliorating WM impairments and explore its influence on the resting-state effective connectivity within the frontoparietal network in patients with SZ. A total of 48 patients diagnosed with SZ were randomly assigned to an active or sham iTBS group and underwent 20 sessions of active or sham iTBS over 4weeks. Subsequently, all patients underwent cognitive tests, clinical symptom assessments, and resting-state functional MRI (rs-fMRI) scans. The effective connectivity between the frontal and parietal brain regions during the rs-fMRI scans was analyzed using a spectral dynamic causal modeling approach. Additionally, this trial was registered at the Chinese Clinical Trial Registry in November 2022 (registry number: ChiCTR2200057286). iTBS treatment improved the positive symptoms, negative symptoms, general psychopathology, and WM deficits. Following the iTBS intervention, the active group demonstrated a significant increase in connectivity strengths from the right MFG to the right SPL (p=0.031) and from the left SPL to the left MFG (p=0.010) compared to the pre-treatment levels. Additionally, compared to the sham group, the active group displayed a significantly higher connectivity strength from the right MFG to the right SPL (p=0.042) after iTBS treatment. All these findings suggest that iTBS targeting the parietal region may influence the resting-state effective connectivity within the frontoparietal network, thereby offering promising therapeutic implications for alleviating the cognitive deficits in SZ.

Parametric global mode method for dynamical modeling and response analysis of a rotating and length-varying flexible manipulator

Parametric global mode method for dynamical modeling and response analysis of a rotating and length-varying flexible manipulator

Predicting COD and TN in A2O+AO Process Considering Influent and Reactor Variability: A Dynamic Ensemble Model Approach

The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH3-N, and NO3-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future.

Hierarchical dynamic wake modeling of wind turbine based on physics-informed generative deep learning

Hierarchical dynamic wake modeling of wind turbine based on physics-informed generative deep learning

A dynamic modelling approach to explore zero emission building stock opportunities towards 2050 – Case study of a university campus

A dynamic modelling approach to explore zero emission building stock opportunities towards 2050 – Case study of a university campus

The modern synthesis and "Progress" in evolution: a view from the journal literature.

The concept of "progress" in evolutionary theory and its relationship to a putative notion of "Progress" in a global, normatively loaded sense of "change for the better" have been the subject of debate since Darwin admonished himself in a marginal note to avoid using the terms 'higher' and 'lower.' While an increase in some kind of complexity in the natural world might seem self-evident, efforts to explicate this trend meet notorious philosophical difficulties. Numerous historians pin the Modern Synthesis as a pivotal moment in this history; Michael Ruse even provocatively hypothesizes that Ernst Mayr and other "architects" of the Synthesis worked actively to eliminate Progress from evolutionary biology's scientific purview. I evaluate these claims here with a textual analysis of the journals Evolution and Proceedings of the Royal Society B (a corpus of 27,762 documents), using a dynamic topic modeling approach to track the fate of the term 'progress' across the Modern Synthesis. The claim that this term declines in importance for evolutionary theorizing over this period can, indeed, be supported; more tentative evidence is also provided that the discussion of 'progress' is largely absent from the British context, emphasizing the role of American paleontology in the rise and fall of 'progress' in 20th-century evolutionary biology.