2. Sensitivity Analysis of Combustion Systems

The 2025 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA2025) was successfully held on September 27-28, 2025, in Changsha, China. The submissions represent the outcomes of conference participants in AMMSA2025, which have attracted a diverse and highly accomplished cohort of researchers, engineers, and scholars from around the globe. AMMSA 2025 attracted over 80 participants from across the globe, constituting a vigorous and engaging event, with a diverse program including 6 keynote reports (35-50 minutes). They are, respectively, Prof. Quanxin Zhu, Prof. Valery Zorkaltsev, Prof. Alexander G. Ramm, Prof. Alexander A. Zlotnik, Prof. Choonkil Park, and Prof. Javad A. Esfahani. 20 oral presentations (15 minutes + 3 minutes for Q&A) in five sessions (advances in mathematics and theory, statistical modelling and data analysis, computational intelligence and algorithms, mathematical modelling in engineering and mathematical modelling in physics) and 20 posters. This conference emphasizes the advancement of theories and applications and promotes interdisciplinary efforts among mathematicians, statisticians, scientists, engineers and researchers from other disciplines. All accepted manuscripts in AMMSA2025 underwent a rigorous double-blind peer-review process, with each paper evaluated by at least two reviewers. A total of 28 reviewers contributed to this conference, providing their expert and critical feedback to ensure the relevance and quality of the manuscripts. Each submission was assessed for similarity against previously published content using the iThenticate platform, with all sources of similarity thoroughly examined; only original contributions were accepted. This proceeding offers a contemporary overview of principles, techniques, and tools for mathematical simulation and modelling across various research fields. List of Conference Committees are available in this PDF.

Based on a successful conference, the 2022 International Conference on Applied Mathematics and Digital Simulation, AMDS 2022, held virtually via Zoom from November 26-28th, 2022 in Beijing, due to the continuing impact of COVID-19, the contributions are the results of submissions from the conference participants. They provide readers with a broader view of mathematical, statistical and computational science methods, ideas and tools.There are 88 participants in AMDS 2022 online conference, in which the program includes 4 keynote reports (35 minutes), 38 oral presentations (12 mins+3 Q&A) included in two sessions (advances in mathematical modelling and theory, mathematical modelling and computation in engineering and physical sciences) and 10 E-posts. This conference emphasizes the development of theories and applications and promotes interdisciplinary endeavours among mathematicians, statisticians, scientists, engineers and researchers from other disciplines.This volume comprises select proceedings of AMDS 2022 of 48 papers from 75 submissions. All accepted manuscripts were peer-reviewed by 2 reviewers at least in a double-blind review process. A total of 51 reviewers participated in this conference, who provided their professional and strict comments for authors to ensure the topic and quality of the manuscripts. There are eight topic partitions, including mathematical modelling and theory, statistical modelling and data analysis, fluid dynamics and mechanics, intelligent control, machine learning, medical physics, mathematical modelling physical engineering, and optics and engineering. This proceeding constitutes an up-to-date account of principles, techniques, and tools for mathematical simulation & modelling in a wide range of research fields.List of Scientific Committee is available in this Pdf.

Editorial: "Cyber Security Prevention, Defenses Driven by AI and Mathematical Modelling and Simulation Tools". The rise of the Smart Computation driven by the Artificial Intelligence with the ubiquitous access to Data Computer Communication Technologies and Internet by anyone, from anywhere at any time, has become a working, learning and socializing platform for people worldwide and if often known as Cyberspace. The past century technological advancements in the Data Communications Technologies , Semiconductor Industry (SI) and a Very Large Scale Integration (VLSI) contributed to significant increase of computer systems performance and computational capacity of small portable computers and cell phones that have become available at low cost for people anywhere in the world. Due to relatively low cost, anyone and anywhere in the world may purchase a PC, Laptop, Smart Phone, or Tablet. These smart-computational devices are capable of powerful computation and connecting almost any computer system in the world via Internet 24/7. Given the current unstable geopolitical ecosystem in conjunction with the dynamic development in the artificial intelligence (AI), smart computation and fast Internet with the ubiquitous access to Internet 24/7, the reliable and secure provision of confidentiality, integrity and accessibility of data available via Internet, has become essential for government, business, the industry and individual worldwide. The fast developments in the field of AI gave rise to a new era of smart computation that can easily be utilized by the governments, special groups or very well trained and skilled individual with malicious intend due to their political , economic or personal motivation. Given the rise of next generation of smart cyberattacks, the use of AI and Mathematical Modeling and Simulations Tools has become essential part of cybersecurity tool kit, to make sure that contra cyber measures are most effective and reliable in real-time 24/7. The Research Topic was received with great interest in the scholastic and professional community worldwide. The paper selection was very competitive based on rigorous reviews presenting seven papers. The Research Topic presents a collection of selected peer reviewed papers presenting most recent advances and the state-of-the-art in the field of. Applied AI and Mathematical Modelling in Cyber Security. The papers show evidence that cyberattacks are becoming much more sophisticated, computationally complex, and challenging for the current cyber security mechanisms, which drives the research, innovation, and development of the next generation of AI assisted Cyber Security imbedded with the Mathematical Modelling and Simulations tools. The first paper presents practical application of eXplainable AI (XAI) in cybersecurity while enabling a machine-human interaction. The second paper discusses effective use of AI applied in the cyberattacks prevention utilizing Intrusion Detection and Prevention Systems (IDPS). The third paper presents use of deep learning and machine learning algorithms to identify the malicious malware. The fourth paper discusses the use of encryption and cryptographic algorithms in creating an effective cyber defenses in the Internet of Thing Cyber Infrastructures. The fifth paper presents effective methods to identify possible cyberattacks utilizing the noisy audio evidence. The sixth paper illustrates utilization of simulation based on the dropout mechanisms to identify obfuscated malicious traffic. The seventh paper presents effective applications of Generative AI Influenced Spread, Extended Models and Awareness Spread Models to analyze fake news propagation and enable the intervention strategy assessment. The Research Topic brings to light most recent developments in the field of applied AI and Mathematical Modeling and Simulations in Cyber Security, while presenting current and future challenges presented by ongoing challenges in the field of studies. Given the current dynamic advancements in the field of AI, Smart Computation and AI, we are yet at the very beginning of new era of new generation of ultra-smart and most sophisticated cyberattacks that will require a new ultra-smart cyber defenses to make sure that the future cyberspace will be safe, reliable and beneficial to the betterment of mankind for future generations for many years to come. The Research Topic promotes creation of the AI Inspired Cyber Security Center, bringing together multidisciplinary research teams working together while seeking best solutions and mechanism to make the current and future cyberspace safe, secure and reliable 27/7 in the nation and worldwide. The Topic Editors would like to use this opportunity to express most sincere gratitude to contributors, reviewers, to Dr. Enrique Morillas, Dr. Nicola Zannone, Mr Sean O'Reilly Ms Alice Gooch, and Colleagues at Frontiers in Computer Science for their strong support, kind guidance and good counsel provided during the course of the project.

Modeling of various phenomena in engineering work is always a kind of simplification of real processes, aiming at a model where a certain level of mathematical theory and computational procedures is sufficient. If the complexity of the required theory corresponds to the general mathematical competence of engineers, then technical problems can be treated separately in engineering (or physical) models without regard to the mathematical background. However, in some advanced engineering fields, the harmonized development of engineering and mathematical models and toolboxes is necessary to find efficient solutions. For example, modeling variable structure systems in ideal sliding mode requires a mathematical toolbox that goes far beyond general engineering competence through the theory of discontinuous right-hand-side differential equations. Although sliding mode control is popular in practice and the concept of sliding mode allows a significant reduction of model complexity, its exact mathematical description is rarely encountered. The problem of friction compensation of a micro-telemanipulator using sliding mode control demonstrates a harmonized application of the mathematical and engineering approaches. Based on Filippov’s theory, the ideal sliding mode can be discussed. Although an ideal system cannot be implemented in reality, the real systems can be kept close enough to it; therefore, the discussion of the solution of the ideal model is important for practical applications. Although several elements of the topic are available in the literature, in this paper a unique complex approach is given for users of sliding mode control with experimental considerations, different engineering models, and codes. The paper concludes that sliding mode control is a case where engineering and mathematical modeling are inseparable and requires the competence of both fields.

The reduction in the duration of the development of new vehicles and the associated desire to reduce the costs of automotive plants contribute to a more active application of mathematical modeling to solve engineering problems. The tasks, which are analyzing and predicting the fatigue life of vehicle components and assemblies, can be solved using a set of methods and tools of mathematical modeling.
 This article discusses the development of a complex of mathematical models of the front and rear suspensions of a passenger automobile, which have the properties necessary for accurate reproduction of wheel loads and dynamic behavior of the suspension as a mechanical system. The complex of mathematical models is implemented in a multi-link modeling environment. The models are designed to carry out a subsequent validation study of the loading of individual suspension components during forced service life tests. In the future, this complex of mathematical models can be used for simulation of semi-natural forced resource tests of the suspension module, as well as a sample for the development of suspension models of other automobiles.
 The purpose of this work is to form sufficient requirements for mathematical models for the implementation of fatigue durability studies using mathematical modeling methods and obtaining reliable calculation results. Research engineers will be able to follow these requirements to determine and collect initial data for the development of their own mathematical models.

Background. In recent years, modern technical devices have been created so that to use in the practice of treating diabetes mellitus. These are systems for continuous monitoring of glycemia, which is a significant addition to the widely accepted measurements of glucose levels with a glucometer, various infusion systems, which significantly improve the doctor's decision-making process. However, such technical means are quite expensive and inaccessible to a wide range of users. In addition, their use is associated with both adverse reactions when wearing them and with patient compliance issues. In this case an alternative can be using mathematical modeling tools. Objective. The aim of the paper is to prove the possibility of using mathematical modeling to predict the glycemic profile as a certain degree of alternative to a sensor for continuous monitoring of blood glucose levels under conditions of limited irregular measurements. Methods. To solve the problem it is proposed to employ the technology of mathematical modeling. The structure of the model makes it possible to implement the mathematical formalism by analytical formulae. Results. As a result, the insulin-glucose-tolerance test has been developed that allows quantitatively assessing a patient's personal sensitivity to insulin-bolus therapy. We proposed the mathematical model for solving the problem by analytical formulae. Algorithms for identifying model parameters, an algorithm for calculating the insulin dose that compensates for the carbohydrate component in the intended meal, and an algorithm for predicting the daily glycemic profile have been developed. The software-algorithmic structure for the implementation of the mathematical formalism has been developed. Conclusions. The conducted simulation study employing the technology of mathematical modeling makes it possible to evaluate the functioning of the developed procedures at the preclinical stage. The simplicity of calculations using analytical formulae can be a prerequisite for the implementation of the algorithm in portable autonomous special-purpose devices or in smartdata under the Android OS, which is a definite contribution to development of digital diabetology.

These proceedings collect the academic and scientific contributions presented at the X International Conference Days of Mathematical Modeling in Sciences (X ICDMMS), which continues the legacy of the “Days of Applied Mathematics” (ICDAM) previously held at Universidad Simón Bolívar, San José de Cúcuta, Colombia. The X ICDMMS took place in Talca, Chile, on October 2–25, 2024. The primary objective of this conference is to promote the exchange of concrete experiences in applying mathematical knowledge to address complex problems across diverse scientific contexts. In line with this mission, the event has brought together a vibrant academic and scientific community, including postgraduate students, professors, and researchers from across Latin America, who have not only presented their research but have also had the opportunity to publish their work (since the fourth edition) in the Journal of Physics: Conference Series, the journal associated with the conference. This event is an initiative led by three academic research groups with a presence in Chile and Colombia: the Mathematical Modeling Group of Maule (GM3), the Scientific Modeling and Business Innovation Group of Universidad Simón Bolívar (GIMCINE), and the Mathematical Modeling Group of Biological Systems at Universidad Tecnológica Metropolitana (MATBIO-UTEM). Its purpose is to serve as a platform for disseminating knowledge in the basic and applied sciences through the lens of mathematical modeling. A key focus of the conference is to continue encouraging a space for the scientific contributions of graduate students and to promote interactions that strengthen their integration into the research community. The topics developed in the X ICDMMS were the following: (i) Computer Science in Mathematics and Engineering (ii) Modeling in Basic Sciences (iii) Modeling in Social Sciences (iv) Mathematical Biology (v) Modeling, Simulation, and Diagnostics (vi) Education and Communication in Science and Engineering The ICDMMS forms an integral part of the training and research processes within the academic programs in Applied Mathematical Modeling, Mathematics Education, and Experimental Sciences Education at the Faculty of Basic Sciences of the Universidad Católica del Maule. It aligns with the institution's commitment to high-quality education and responds to the demands of the digital era by emphasizing the modeling of scientific knowledge across diverse fields, including mathematics, physics, biology, and chemistry, as well as applications in medicine, engineering, management, business, and education. The ICDMMS offers emerging researchers opportunities for participation and growth through meaningful interactions and dialogue with established researchers from various scientific disciplines represented at the conference. It also allows students, faculty, and researchers a valuable space for professional development and knowledge acquisition through thematic sessions. List of Organizing Committee, National Scientific Committee, International Scientific Committee, Organizing groups and partners and Sponsors are available in this PDF.

A Fluid queue is a mathematical model used to describe fluid level in a reservoir subject to randomly determined periods of filling and emptying the system without interruption called a buffer, according to a randomly varying rate regulated by an external stochastic environment. Such fluid queues are used as a mathematical tool for modeling, for example, to approximate discrete models, model the spread of wildfires in ruin theory and to model high speed data networks, a router, computer networks including call admission control, traffic shaping and modeling of TCP and production and inventory systems. The fluid from the first phase (i.e, fluid output of the queue) goes to the second phase represents a buffer with a constant leak rate c. We always assume that service rate is greater than buffer, μ ˃ c. In this paper, a fluid queue driven by infinite queue with fixed –size batch arrivals. The generating function technique is used to obtain the expressions for steady-state distribution of both the buffer content and stationary state probabilities of background birth-death process. Hence, the performance measures are computed whereas the server utilization analysis and mean buffer content are investigated. . In addition, some numerical results are provided to illustrate the effect of various parameters on the distribution of the buffer content.

The research subject is development and testing for efficiency of technologies combining digital twin and the industrial Internet of Things for the biotechnological farm. The research object is a high-tech closed water circulation aquafarm. The information model is based on data from a specific high-intensity aquabiotechnological enterprise in Russia, the Kursk region. A pool of complex research methods was used and a data model was developed to describe this system. A digital model includes BIM, mathematical, engineering, and economic models. The information base is filled directly by real-time sensors data, as a dynamical digital twin was formed. Its interface allows to create of an image both for the current system state or for any previous times. While the predictive analytics methods were used, the simulation results were compared with the real data. The digital twin integrates heterogeneous components: (i) Mathematical model of the development of the population of commercial aquaculture was used. Mathematical models by different types of the aquaculture population were created during this research. (ii) An engineering model includes sets of sensors and actuators. They generated parameters of the aquaculture habitat, operating modes sensors for biological and engineering components. Additionally, resource consumption data counters were employed. (iii) The economic model should consider the real volumes of consumption and prices for all the used resources. Detailed online accounting of electricity, heat, water, supplies, and consumables costs is organized, with differentiation by engineering systems and aquaculture groups (cages). During this study, the sufficient model’s accuracy level was confirmed. We used such models to describe and predict the biological productivity of the aquafarm and its economic efficiency. The efficiency and prospects of the approach to the management of biotechnological production using digital twin technologies and the Internet of Things are additionally displayed. There has been a plan developed to continue the work in the future for intelligent assistant creation and robotic management for aquabiotechnological farms.

This paper presents the results of the combustion system test of the MS7001F installed at the Virginia Power Chesterfield station. Tests of water and steam injection for NOx control were performed. Results of emissions, combustor dynamics, and combustor hardware performance are presented. Emissions test results include NOx, CO, unburned hydrocarbons, VOC and formaldehyde levels. Combustor dynamic activity over a range of diluent injection ratios, and the performance of an actively cooled transition duct are also discussed. Combustion system mechanical performance is described following the first combustion system inspection.

The first Siemens AG SGT5-4000F engine with hybrid burner ring combustor (HBR) was introduced in 1996. Since then, frequent evolutionary design improvements of the combustion system were introduced to fulfill the continuously changing market requirements. The improvements particularly focused on increased thermodynamic performance, reduced emissions, and increasing operational flexibility in terms of load gradients, fuel flexibility, and turndown capability. According to the Siemens product development process, every design evolution had to pass several validation steps to ensure high reliability and best performance. The single steps included cold flow and mixing tests at atmospheric pressure, high-pressure combustion tests in full-scale sector combustion test rigs, and full engine tests at the Berlin test facility (BTF). After successful validation, the design improvements were gradually released for commercial operation. In a first step, cooling air reduction features have been implemented in 2005, followed by the introduction of a premixed pilot as second step in 2006. Both together resulted in a significant reduction of the NOx emissions of the system. In a third step, an aerodynamic burner modification was introduced in 2007, which improved the thermo-acoustic stability of the system towards higher turbine inlet temperatures and adapted to fuel preheating to allow for increased cycle efficiency. All three features together have been released as package in 2010 and to date accumulated more than 50,000 operating hours (fleet leader 24,000). This paper reports upon the steps towards this latest design status of the SGT5-4000F and presents results from typical focus areas of lean premixed combustion systems in gas turbines including aero-dynamical optimization, fuel/air mixing improvements and cooling air management in the combustor.

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Background: With an estimated 10.4 million incident tuberculosis (TB) cases in 2015, and the current trajectory of 1.5%/year incidence rate decline, new TB vaccines are urgently needed to help meet the WHO goal of tuberculosis elimination by 2050. However, insufficient epidemiological evidence exists to inform TB vaccine development strategies and to assist clinical trial site selection and design. Research to meet these data needs is critical to accelerate TB vaccine development. To maximise the future impact of new TB vaccines, estimates of the population-level impact of vaccine characteristics and implementation strategies are needed to inform design of TB vaccine target product profiles. To accelerate and de-risk clinical trials, appropriate epidemiological data are required to inform trial site selection, sample size calculations and recruitment strategy. However, data availability at trial sites is limited, and prospective studies are resource-intensive, so new methods are needed to collect appropriate data to inform TB vaccine trial design.
\nTo inform data-driven development strategies for new TB vaccines, the aims of this thesis were to 1) estimate the epidemiological impact of new TB vaccine characteristics and implementation in China to inform design of target product profiles; and 2) to develop a novel epidemiological spatial mapping tool capable of informing clinical trial design for new TB vaccines in low-income, high-burden settings.
\nMethods: A deterministic, age-structured, Mycobacterium tuberculosis transmission model was developed and calibrated to age-stratified epidemiological and demographic data from China. This was employed to estimate the population-level epidemiological impact of new TB vaccines over the 2025-2050 time horizon, through an exploration of potential vaccine characteristics and implementation strategies. 
\nA new methodology for empirical data collection to determine spatial distribution of TB notifications was developed. The electronic PArticipant Locator application (ePAL app) combined 3,243 community-identified points of interest with high resolution satellite maps, within an electronic tablet-based case report form. The app was integrated in to the National Tuberculosis Programme in Blantyre, Malawi, for collection of demographics, health status and coordinates of place of residence for patients ≥18 years initiating TB treatment. Accuracy of ePAL-recorded co-ordinates was evaluated against GPS coordinates obtained at the participant’s place of residence. 
\nResults: Mathematical modelling predicted a shift towards a reactivation-driven, ageing TB epidemic in China by 2050. Vaccines protective against disease, effective post-infection and providing at least 5 years protection were essential for achieving higher levels of impact. Vaccination of older adults provided greater population-level impact than vaccinating adolescents for all equivalent vaccines explored, even if much lower coverage were achieved in older adult vaccination. Recommendations for post-infection vaccines were robust to substantial reductions in efficacy and duration of protection in older adults, whereas for pre-infection vaccines in some of these scenarios adolescent vaccination may be equivalent or preferred. Vaccinating older adults with post-infection vaccines provided substantially higher impact than pre-infection vaccines.
\n1,899 TB patients were registered using ePAL in the 12-month study period, with high patient acceptance (98.7%, 1,899/1,924). ePAL achieved clinic-based collection of patient location of residence accurate to a median of 84 metres (IQR: 35-317 metres) in a high population density urban setting without a municipal address system. Advantages of the ePAL system included real-time availability of high-resolution spatial data, low set up costs, and ease of use by health staff as part of routine TB registration. Data were used to identify areas with high TB burden, potentially suitable for TB vaccine trials.
\nConclusions: The research presented in this thesis informs the development of appropriate TB vaccines and target populations to maximise future population-level impact. A prevention of disease vaccine efficacious post-infection and delivered to older adults would contribute towards maximising population-level impact in China. Adolescent-targeted tuberculosis vaccines are likely to have low impact in ageing, reactivation-driven epidemics like China, which suggests a modification of the current strategic focus on adolescents among certain funders. Clinical trials should assess disease endpoints, include M.tb-infected and older adult populations, and extend beyond the usual 2-3 years follow up. To support design of disease endpoint trials, ePAL may provide an accurate, easily implementable, low-cost tool for identification of areas of high TB burden in settings without addresses.

An important task in the development of training complexes is the implementation of physical processes in virtual space using the example of burning objects. To solve it, an adapted mathematical model is proposed that allows one to formalize the structure of combustion objects, their main properties and processes of changing states, starting from the ignition of the object and ending with their complete destruction or extinguishing. Original diagrams of the life cycle of virtual objects and a system of rules for changing their states have been developed, which allow tracing the entire life cycle of objects and the procedure for changing states. Each state and transition between them are clearly formalized and calculated on the basis of the properties of the object and external influences. The patterns of state change are formalized using a matrix representation of object properties. This approach is distinguished by its versatility and can be used to model other physical processes (smoke, flooding, freezing, spread of toxic gases, etc.). The practical implementation of the combustion process using the apparatus of life cycle diagrams of virtual objects has been carried out. The developed method has been successfully integrated into the Unity development environment. its use made it possible to reliably and efficiently implement the combustion process without losing performance, which is especially important for virtual reality systems.

In this work, two different reduction methods for simplification of chemical kinetics, the Global Quasi-linearization (GQL) and the Directed Relation Graph (DRG), are implemented and compared for ignition of CH/air homogeneous reacting system. The GQL approach is an automatic and scaling invariant method for a global analysis of the combustion system time-scale hierarchy that takes into account all the original species and elementary reactions, while the DRG is a reduction method for construction of skeletal mechanism that aims at removing unimportant species and thus elementary reactions from the original detailed kinetics. The auto-ignition process is used for validation in a wide range of system parameters and initial conditions which are important for gas turbines, dual-fuel diesel engines, etc. The present study shows that for methane combustion system studied in this work DRG requires dimensions at least two times higher than GQL to ensure the same level of accuracy for the whole range of considered system parameters. And, the advantages and disadvantages of both methods are discussed.