Modeling Of Processes Research Articles

Forward and backward modeling of cultural evolutionary processes

Modern cultural evolution theory adopts a variety of concepts and methods developed in mathematical biology, in particular population genetics theory. In addition to forward-looking approaches such as two-locus models, backward-looking approaches such as coalescent theory, which describe ancestral states of the current population, have played an important role in population genetics. Here, we show how forward and backward approaches can be applied to two examples in cultural evolution. The first example deals with the number of cultural traits, which can be analyzed by a backward approach. Cultural coalescent theory illustrates many unique aspects of cultural processes such as information transfer (i.e., social learning) from many donors (“cultural parents”), including or excluding the biological parents. Theory predicts that many cultural traits of intermediate (or higher) frequency (popularity) can exist that are surprisingly old. Many unsolved issues remain, however, such as how to incorporate social structure as well as natural and/or cultural selection, which we believe to be an urgent agenda. The second example is the punishment of sibling incest, a problem to which cultural coalescent theory cannot currently be usefully applied. By analyzing forward recursions, we show that punishment is ineffective in suppressing incest, unless the incestuous inclination is diminished by the presence of punishers. Based on these examples, we discuss the merits and demerits of forward and backward approaches.

Development and verification of material models in modeling of wave strain hardening and additive synthesis processes

BACKGROUND: The creation of competitive machine parts capable of withstanding standard and increased operational loads is an urgent task in mechanical engineering. Developing additive synthesis technologies together with strengthening technologies make it possible to create such products with high load-bearing capacity. However, to improve the efficiency of these technologies, it is necessary to create theoretical models of the processes taking place. The article presents the results of the first stage of creating complex theoretical models of the combined 3DMP process and wave strain hardening (WSH) required for designing technological processes for manufacturing engine parts and brake systems of automotive equipment. . AIMS: Creation and assessment of the adequacy of material models used in finite element modeling of additive synthesis processes with subsequent hardening. MATERIALS AND METHODS: Theoretical models of the material were created in the ANSYS software package, which allows for multidisciplinary calculations. The experimental data required for preparing the models were obtained by testing tensile samples manufactured using standardized methods. The hardness of materials was studied using a KB 30S automatic hardness tester. The adequacy of additive synthesis modeling was assessed based on the distribution of temperature fields. The adequacy of material models for the VDU process was assessed based on the sizes of individual plastic indentations and the distribution diagrams of the depth and degree of hardening in the surface layer. RESULTS: Theoretical models of the following materials were developed: steel, stainless steel, bronze alloy, titanium alloy, aluminum alloy. The theoretical data obtained from the modeling results have a high level of significance. The studies were conducted for various thermal (in the range from +20ºС to +800ºС) and deformation modes. Graphical results of theoretical and experimental studies allow us to obtain a qualitative assessment of the processes under study with the required accuracy. CONCLUSIONS: As a result of the assessment of the adequacy of the developed models, it was established that the discrepancy between the empirical and theoretical data does not exceed 7.4%. The obtained models of materials are statistically significant and can be correctly applied in further studies.

Modeling the dynamics of addiction relapse via the double-well potential system.

Substance use relapse is difficult to define, and previous work has used one-size-fits-all ad hoc definitions. Researchers have called for a dynamic and personalized understanding of relapse as a concept and model, necessitating novel statistical tools. We aimed to develop and validate a novel statistical model of latent relapse processes: the double-well potential model (DWPM). This model describes posttreatment substance use in terms of a dynamical system with stable equilibria of abstinence and relapse, person-specific dominant equilibria (tilt), the ease of changing between equilibria (steepness), and an overall relapse risk (RR). Using timeline follow-back data from N = 139 adults with a substance use disorder transitioning back to the community after residential treatment, we examined individual differences and the criterion-related validity of DWPM parameters to determine the clinical utility of the double-well model. While nonuse was the predominant stable state across participants, we found significant between-subjects variability steepness and RR. These individual differences were predictable via demographics, baseline psychopathology, treatment history, and treatment condition. Steepness and RR also predicted long-term outcomes, including life satisfaction and criminal behavior, above and beyond traditional metrics of relapse (proportion of days used and time to first use). Thus, the DWPM is a strong theoretical and statistical representation of the underlying relapse processes. Moreover, the parameters show criterion-related validity and may be useful in precision medicine. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Under the magnifying glass: A combined 3D model applied to cloudy warm Saturn-type exoplanets around M dwarfs

Warm Saturn-type exoplanets orbiting M dwarfs are particularly suitable for an in-depth cloud characterisation through transmission spectroscopy because the contrast of their stellar to planetary radius is favourable. The global temperatures of warm Saturns suggest efficient cloud formation in their atmospheres which in return affects the temperature, velocity, and chemical structure. However, a consistent modelling of the formation processes of cloud particles within the 3D atmosphere remains computationally challenging. We explore the combined atmospheric and micro-physical cloud structure and the kinetic gas-phase chemistry of warm Saturn-like exoplanets in the irradiation field of an M dwarf. The combined modelling approach supports the interpretation of observational data from current (e.g. JWST and CHEOPS) and future missions (PLATO, Ariel, and HWO). A combined 3D cloudy atmosphere model for HATS-6b was constructed by iteratively executing the 3D general circulation model (GCM) expeRT/MITgcm and a detailed kinetic cloud formation model, each in its full complexity. The resulting cloud particle number densities, particle sizes, and material compositions were used to derive the local cloud opacity which was then used in the next GCM iteration. The disequilibrium H/C/O/N gas-phase chemistry was calculated for each iteration to assess the resulting transmission spectrum in post-processing. We present the first model atmosphere that iteratively combines cloud formation and 3D GCM simulation and applied it to the warm Saturn HATS-6b. The cloud opacity feedback causes a temperature inversion at the sub-stellar point and at the evening terminator at gas pressures higher than 10$^ $ bar. Furthermore, clouds cool the atmosphere between $10^ $ bar and 10 bar, and they narrow the equatorial wind jet. The transmission spectrum shows muted gas-phase absorption and a cloud particle silicate feature at $ The combined atmosphere-cloud model retains the full physical complexity of each component and therefore enables a detailed physical interpretation with JWST NIRSpec and MIRI LRS observational accuracy. The model shows that warm Saturn-type exoplanets around M dwarfs are ideal candidates for a search for limb asymmetries in clouds and chemistry, for identifying the cloud particle composition by observing their spectral features, and for identifying in particular the cloud-induced strong thermal inversion that arises on these planets.

Power Brownian motion: an Ornstein-Uhlenbeck lookout

Abstract The well-known Ornstein-Uhlenbeck process (OUP) is the central go-to Gaussian model for statistical-equilibrium processes. The recently-introduced power Brownian motion (PBM) is a Gaussian model for diffusive motions, regular and anomalous alike. Using the Lamperti transform, this paper establishes PBM as the ‘diffusion counterpart’ of the OUP. Namely, the paper shows that PBM is for diffusive motions what the OUP is for statistical-equilibrium processes. The intimate parallels between the OUP and PBM are explored and illuminated via four main perspectives. 1) Statistical characterizations. 2) Kernel-integration with respect to Gaussian white noise. 3) Spatio-temporal scaling of the Wiener process. 4) Langevin stochastic dynamics driven by Gaussian white noise. To date, the prominent Gaussian models for anomalous diffusion are fractional Brownian motion (FBM), and scaled Brownian Motion (SBM). Due to its intimate OUP parallels, due to the ‘anomalous features’ it displays, due to the fact that it encompasses SBM, and following a detailed comparison to FBM: this paper argues the case for PBM as a central go-to Gaussian model for regular and anomalous diffusion.

New Groups and Post-Traumatic Growth: Experimental Evidence That Gaining Group Memberships Supports Recovery From Natural Disaster

This paper provides the first experimental test of whether two social identity model of traumatic identity change processes—(i) group membership gain, and (ii) group membership continuity—predict post-traumatic growth (PTG) and post-traumatic stress (PTS) via social identity revitalization. Participants ( N = 210, Mage = 49.59 years) were adult flood survivors who were randomly allocated to one of three conditions: group membership gain, group membership continuity, or a weather control. Participants then completed measures of PTG, PTS, and social identity revitalization. Participants in the group membership gain condition reported greater social identity revitalization, compared to the group membership continuity and control conditions. Social identity revitalization, in turn, predicted PTG, and the indirect effect of group membership gain on PTG was significant. We concluded that gaining new group memberships play a role in supporting natural disaster recovery by fostering positive reappraisals of the disaster via social identity revitalization.

Recombination efficiency in c-plane (In,Ga)N/GaN quantum wells: saturation of localisation sites versus Auger–Meitner recombination

Abstract Light emitting diodes based on c-plane (In,Ga)N/GaN quantum wells (QWs) can have >90% emission efficiency at modest current densities but this drops significantly at higher excitation, an effect known as efficiency droop that limits device efficacy at high brightness. Several explanations for this have been proposed including the saturation of carrier localisation sites at high excitation densities, resulting in a greater exposure of carriers to defects and hence a significant increase in the associated non-radiative recombination processes. Here, power- and temperature-dependent photoluminescence spectroscopy of c-plane (In,Ga)N/GaN QWs is used to investigate the relationship between the saturation of localised states and emission efficiency. For the samples studied, we find that the saturation of localised sites broadly coincides with the onset of efficiency droop. However, it is also found that as the localised states saturate with increasing excitation, the relative contribution of defect-associated non-radiative processes to overall recombination decreases rather than increases. Based on these observations and on modelling of recombination processes in the QW, it is concluded that the saturation of localised states does not significantly contribute to the reduction in emission efficiency at high excitation. Our studies rather suggest that defect-related non-radiative recombination is out-competed by radiative and Auger–Meitner recombination at the carrier densities required for saturation.

The HVAC system engineering method for a new family of unified cabins of combine harvesters

BACKGROUND: Designing a combine harvester cabin climate system is a complex and multi-stage process that includes solving a set of tasks. To solve them, a design engineer must have knowledge and skills in various fields, starting with thermal engineering calculations and ending with experimental research methods and computer modeling, which requires a large amount of intellectual and time resources. Therefore, the task of creating a unified method of engineering of combine harvester cabin HVAC system is highly relevant. AIM: Development of the HVAC system for a unified cabin of a grain harvester and a forage harvester. The designed HVAC system is purposed to create a comfortable microclimate in the cabin of the combine for 2 people in summer and winter operating conditions. METHODS: The methodology of calculation and selection of the combine harvester cabin HVAC system, which includes the development of engineering calculation methods and mathematical modeling of thermodynamic and ventilation processes in the cabin, is considered. RESULTS: The main parameters were determined: heat intakes and heat losses for the grain harvester and forage harvester cabins, which amounted to 2.8 and 2.2 kW for the grain harvester, 2.9 and 2.35 kW for the forage harvester; the required air flow rate supplied to the cabin to ensure a comfortable temperature — 740 m3/h; the percentage of air recirculation regarding the conditions of absence of fogging and creation of excessive pressure in the cabin — 75%; the cooling and heating capacity of the HVAC system, taking into account the operating conditions of the combine, are 7.8 and 6.3 kW respectively. The selection of the main equipment of the HVAC system for a unified cabin for a new family of unified cabins of combines — the BUHLER 1000 MFWD evaporator-heater and the Valeo TM16 compressor are chosen. CONCLUSION: The HVAC system designed in accordance with the presented methodology is capable of ensuring a comfortable cabin air temperature in the range of 22–24 °C at various operating modes of the combine harvester in different regions. In addition, the HVAC system parameters eliminate fogging of the cabin windows and the penetration of dusty air inside due to the created excessive pressure.

Comparing Feature and Trajectory-Based Remaining Useful Life Modeling of Electrical Resistance Heating Wires

Industrial heating significantly contributes to global greenhouse gas emissions, accounting for a substantial portion of annual emissions. The transition to fossil-free operations in the heating industry is closely linked to advancements in industrial electrical heating systems, especially those using resistance heating wires. In this context, Prognostics and Health Management is crucial for enhancing system reliability and sustainability through predictive maintenance strategies. The integration of machine learning technologies into Prognostics and Health Management has significantly improved the precision and applicability of Remaining Useful Life modeling. This improvement enables more accurate predictions of component lifespans, optimizes maintenance schedules, and enhances operational efficiency in industrial heating applications. These developments are essential for reducing greenhouse gas emissions in the sector. This paper serves as a guide for conducting Remaining Useful Life modeling for industrial batch processes. It evaluates and compares two methodologies: deep learning-based approaches using full time-series data, such as recurrent neural networks and their variants, and feature-engineering-based methods, including random forest regression and support vector machines. Our results show that the feature-oriented approach performs better overall in terms of predictive accuracy and computational efficiency. The study includes a detailed sensitivity analysis and hyperparameter estimation for each method, providing valuable insights into developing robust and transparent Prognostics and Health Management systems. These systems are crucial in supporting the heating industry’s move towards more sustainable and emission-free operations. The findings reveal that feature-oriented methods are both performant and robust, particularly excelling in handling outliers. The random forest regression model, in particular, demonstrated the highest performance on the test dataset according to the chosen evaluation metrics. Conversely, trajectory-oriented methods exhibited less bias across varying levels of degradation, a helpful characteristic for Prognostics and Health Management systems. While feature-oriented methods tend to systematically underestimate Remaining Useful Life at high true values and overestimate it at low actual values, this issue is less pronounced in trajectory-oriented models. Overall, these insights highlight the strengths and limitations of each approach, guiding the development of more effective and reliable predictive maintenance strategies.

Optimization of the integro-differential hyperbolic model

We consider a model of processes in viscoelastic media. The force arising inside the body may depend not only on the current values but also on the entire time history of the motion (for example, polymers, emulsions, and suspensions exhibit memory properties). Such processes often lead to the necessity of studying hyperbolic integro-differential equations with Volterra integral term. The main goal is to prove the existence and uniqueness of generalized solutions to the corresponding initial-boundary value problem, as well as to investigate the existence of optimal control for systems described by these models. The main results are obtained using the method of a priori inequalities in negative norms. In particular, we justify inequalities in negative norms for the integro-differential operator in certain functional spaces. The work begins with a short description of relevant results with similar formulations and referencing works with physical justification of the model. Afterwards, we describe the problem statement, constraints on equation parameters, and functional spaces used for the investigation. Below are the main results, in particular definitions of generalized solutions and properties on the well-posed of the initial-boundary value problem as well as the existence of optimal control. Control is carried out through the right-hand side of the equation using a certain special operator. It is possible to examine specific examples of such control operators (illustrating various control mechanisms) and the corresponding control spaces. By utilizing proven inequalities and relying on general results of S.I. Lyashko from the theory of a priori inequalities in negative norms, we establish sufficient conditions for the existence of optimal control. In particular, we impose the restrictions on the admissible set of controls and the quality criterion.

Investigating heterogeneity in IRTree models for multiple response processes with score-based partitioning.

Item response tree (IRTree) models form a family of psychometric models that allow researchers to control for multiple response processes, such as different sorts of response styles, in the measurement of latent traits. While IRTree models can capture quantitative individual differences in both the latent traits of interest and the use of response categories, they maintain the basic assumption that the nature and weighting of latent response processes are homogeneous across the entire population of respondents. In the present research, we therefore propose a novel approach for detecting heterogeneity in the parameters of IRTree models across subgroups that engage in different response behavior. The approach uses score-based tests to reveal violations of parameter heterogeneity along extraneous person covariates, and it can be employed as a model-based partitioning algorithm to identify sources of differences in the strength of trait-based responding or other response processes. Simulation studies demonstrate generally accurate Type I error rates and sufficient power for metric, ordinal, and categorical person covariates and for different types of test statistics, with the potential to differentiate between different types of parameter heterogeneity. An empirical application illustrates the use of score-based partitioning in the analysis of latent response processes with real data.

Dynamics of ionic liquid-polymer gel membranes-Insight from NMR relaxometry for [BMIM][BF4]-PVDF-HFP systems.

1H spin-lattice relaxation experiments have been performed for ionic liquid-polymer gel membranes, including 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]) and poly(vinylidene fluoride-co-hexafluoropropylene) (PVdF-HFP) with different proportions. The experiments have been performed in a broad range of resonance frequencies (from about 5Hz to 40MHz) vs temperature and complemented with analogous studies for [BMIM][BF4] in bulk as a reference. A model of the relaxation processes in the membranes has been proposed. The model includes two relaxation contributions. One of them corresponds to the concept of restricted, two-dimensional translation diffusion with a residence lifetime, while the second one has the form characteristic of polymers (mathematically similar to the limiting behavior of two-dimensional translation diffusion with a very long residence lifetime). The extensive dataset has been consistently interpreted in terms of the model, revealing two dynamical processes on the time scales of 10-7s (for the second relaxation contribution) and 10-9s (for the first one). The relationship of these relaxation contributions to the motion of the polymer or ionic liquid-polymer complexes and to the translation diffusion of BMIM cations in the matrix has been discussed.

Lipid-mediated adaptation of proteins and peptides in cell membranes

The paper overviews the results of computational studies of the molecular mechanisms underlying the adaptation of model cell membranes taking place during their interaction with proteins and peptides. We discuss changes in the structural and dynamic parameters of the water–lipid environment, the hydrophobic/hydrophilic organization of the lipid bilayer surface (the so-called “mosaicity”), etc. Taken together, these effects are called the “membrane response” (MR) and constitute the most important ability of the cell membranes to respond specifically and consistently to the incorporation of extraneous agents, primarily proteins and peptides, and their subsequent functioning. The results of the authors’ long-term research in the field of molecular modeling of MR processes with various spatial and temporal characteristics are described, from the effects of binding of individual lipid molecules to proteins to changes in the integral macroscopic parameters of membranes. The bulk of the results were obtained using the “dynamic molecular portrait” approach developed by the authors. The biological role of the observed phenomena and potential ways of rationally designing artificial membrane systems with specified MR characteristics are discussed. This, in turn, is important for targeted changes in the activity profile of proteins and peptides exerting action on biomembranes, not least as promising pharmacological agents.

Dynamic modeling of post-combustion carbon capture process based on multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder network

Solvent-based post-combustion carbon capture (PCC) technology is a promising, near-term solution for decarbonizing power generation and industrial facilities. Model-based process simulation is crucial for the optimal design and operation of the PCC process. Recently, data-driven models have gained attention due to their adaptability, efficient computation and high accuracy. However, the nonlinearity, strong couplings and multi-time scale features of the PCC process pose significant challenges for model identification. To this end, this paper proposes a multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder (MMoE-DAED) network for dynamic modeling of the PCC process under wide operating conditions. An encoder-decoder composed of long short-term memory (LSTM) network is employed to extract features from the time-dependent input data and learn the complex dynamic interactions caused by the inertial and delay properties of the process. Dual-stage attention mechanism is incorporated into the encoder and decoder respectively to select the most relevant input features and their correlations within the time series data. To enhance multi-output prediction accuracy, multi-gate mixture-of-experts (MMoE) framework that considers correlations of multitask learning is implemented. Simulation results using operating data from a PCC experimental setup indicate that the proposed modeling approach accurately predicts the steady-state values and dynamic trends of the CO2 capture rate and stripper bottom temperature over a wide operating range. The RMSE, MAPE and R2 indices for the CO2 capture rate are 2.1592, 0.0295, 0.9641, respectively, and for the stripper bottom temperature are 0.1491, 0.0003, 0.9833, respectively. Validations on a PCC simulator further verify the accuracy and efficiency of the MMoE-DAED model, which enables an 80.87% reduction in computation time compared to the simulator. This paper points to a new direction for the data-driven dynamic modeling of complex energy conversion processes.

Statistical Properties of SIS Processes with Heterogeneous Nodal Recovery Rates in Networks

The modeling and analysis of epidemic processes in networks have attracted much attention over the past few decades. A major underlying assumption is that the recovery process and infection process are homogeneous, allowing the associated theoretical studies to be conducted in a convenient manner. However, the recovery and infection processes usually exhibit heterogeneous rates in the real world, which makes it difficult to characterize the general relations between the dynamics and the underlying network structure. In this work, we focus on the susceptible–infected–susceptible (SIS) epidemic process with heterogeneous recovery rates in a finite-size complete graph. Specifically, we study the metastable-state statistical properties of SIS epidemic dynamics with two different nodal recovery rates in complete graphs. We propose approximate solutions to the metastable-state expectation and the variance in the number of infected nodes within the framework of the mean-field approximation method. We also derive several upper and lower bounds of the steady-state probability that a node is in the infected state. We verify the proposed approximate solutions of the mean and variance via simulations. This work provides insights into the fluctuations in the statistical properties of epidemic processes with complex dynamical behaviors in networks.

Study on the aerodynamic characteristics of reentry capsule with obtuse head inverted cone under hypersonic chemical nonequilibrium flow

Abstract During spacecraft reentry, the aerodynamic characteristics of the reentry capsule with a sizeable obtuse head inverted cone (OBHIC) in a hypersonic state are the key factors in the design of controlled reentry trajectory, the design of trim angle of attack (AoA), and the accuracy of fall point of capsules. This paper establishes a numerical simulation algorithm of a hypersonic chemical nonequilibrium flow field by considering high-temperature real gas effects. The reentry capsule’s aerodynamic and flow field characteristics are predicted and analyzed. The component transport equations for chemical reactions are added to the Navier-Stokes equations, enabling precise modeling of gas molecule vibrational excitation and ionization processes. A half-model multi-block structured mesh is employed to construct the aerodynamic profile of the reentry capsule with OBHIC. The aerodynamic characteristics and pressure distribution of the reentry capsule were calculated and analyzed under the typical conditions of flight altitude of 40~80 km, flight velocity of 6~27 Ma, and AoA of 15°~24°. The results of the aerodynamic characteristics prediction show that the aerodynamic characteristics of the reentry capsule are little affected by the incoming flow velocity in a hypersonic state but significantly affected by the AoA. As the AoA increases, the lift coefficient CL increases linearly, the drag coefficient CD decreases linearly, the lift-to-drag ratio K rises linearly, and K increases from 0.19 to 0.29. In its hypersonic state, the reentry capsule exhibits a low lift-to-drag ratio, characterized by high drag and low lift coefficients. The results of this paper play an essential role in the reentry flight dynamics and reentry trajectory design of the reentry capsule. Moreover, it can further ensure the safety and reliability of the reentry flight of spacecraft.

Fractal Modelling of Heterogeneous Catalytic Materials and Processes.

This review considers the use of fractal concepts to improve the development, fabrication, and characterisation of catalytic materials and supports. First, the theory of fractals is discussed, as well as how it can be used to better describe often highly complex catalytic materials and enhance structural characterisation via a variety of different methods, including gas sorption, mercury porosimetry, NMR, and several imaging modalities. The review then surveys various synthesis and fabrication methods that can be used to create catalytic materials that are fractals or possess fractal character. It then goes on to consider how the fractal properties of catalysts affect their performance, especially their overall activity, selectivity for desired products, and resistance to deactivation. Finally, this review describes how the optimum fractal catalyst material for a given reaction system can be designed on a computer.

Numerical modeling of hydro-mechanical processes during hydraulic testing of pre-existing fractures at the Grimsel Test Site, Switzerland

This study presents a fully coupled hydro-mechanical framework for modeling hydraulic shearing in a mesoscale reservoir located at the Grimsel Test Site, Switzerland. The experiment was conducted on a ductile–brittle fault embedded in low-permeable granite. We observe that normal fracture opening increases flow channel recoverably, while fracture sliding locks asperities leading to a non-recoverable increase in flow. To couple these processes, we use a poro-elasto-plastic constitutive framework and employ a permeability function that depends on several parameters, such as dilation angle, in-situ stresses, residual aperture and maximum aperture. Our results capture the recorded pressure responses well, and indicate that the permeability changes by one order of magnitude during the experiment.

Developing Prototype Digital Twins for biodiversity conservation and management: achievements, challenges and perspectives

The BioDT project, funded by the European Union Horizon Europe Programme, seeks to investigate and push the boundaries of methodological, intellectual, data, technical and relevance aspects of the digital twin (DT) approach for understanding and predicting biodiversity patterns and processes. In this special issue, we present the reasoning and goals of the project and the overall advancements made towards the development of ten prototype Digital Twins (pDTs). Based on models of ecosystems, species and biological processes, digital twins integrate diverse data sources with advanced modelling and simulation techniques to provide a comprehensive, dynamic and data-driven understanding of biodiversity. Leveraging the EuroHPC LUMI infrastructure and data and expertise from GBIF, eLTER, DiSSCo and LifeWatch ERIC research infrastructures, the project aims to develop hybrid models combining the strengths of statistical and process-based models for improved predictive capabilities. While there are specific challenges and limitations to biodiversity DTs, the pDTs of BioDT lay the foundation for model-data fusions that will offer potential benefits to a wide range of end-users, from researchers and conservation organisations to policy-makers, land managers, educators and private sector companies. By fostering global interoperability in biodiversity data and research, BioDT paves the way for future collaborative efforts in biodiversity conservation and management.

Advancing Proton Therapy: A Review of Geant4 Simulation for Enhanced Planning and Optimization in Hadron Therapy

Abstract Proton therapy is a cancer treatment method that uses high-energy proton beams to target and destroy cancer cells. In recent years, the use of proton therapy in cancer treatment has increased due to its advantages over traditional radiation methods, such as higher accuracy and reduced damage to healthy tissues. For accurate planning and delivery of proton therapy, advanced software tools are needed to model and simulate the interaction between the proton beam and the patient’s body. One of these tools is the Monte Carlo simulation software called Geant4, which provides accurate modeling of physical processes during radiation therapy. The purpose of this study is to investigate the effectiveness of the Geant4 toolbox in proton therapy in the conducted research. This review article searched for published articles between 2002 and 2023 in reputable international databases including Scopus, PubMed, Scholar, Google Web of Science, and ScienceDirect. Geant4 simulations are reliable and accurate and can be used to optimize and evaluate the performance of proton therapy systems. Obtaining some data from experiments carried out in the real world is very effective. This makes it easy to know how close the values obtained from simulations are to the behavior of ions in reality.