Mathematical Model For Computation Research Articles

Computational Math Modeling and AI Optimization for Better Decision-Making: Applications in Machine Learning

The integration of computational mathematical modeling, optimization, machine learning (ML), data analysis, and artificial intelligence (AI) offers transformative opportunities to solve complex problems across diverse fields. This paper delves into the development and application of advanced modeling techniques that blend traditional mathematical approaches with advanced AI algorithms to enhance predictive accuracy, optimize resource allocation, and improve decision-making processes in areas such as logistics, finance, engineering, and healthcare. By leveraging data-driven insights, these models can simulate real-world scenarios and identify optimal solutions more effectively than conventional methods. The paper also explores how AI-enhanced modeling can impact broader systems by reshaping industry practices, influencing frameworks, and potentially challenging established societal norms. Eventually, the paper argues for a balanced and informed deployment of AI-driven modeling techniques to maximize their benefits while addressing potential risks and limitations.

Numerical Simulation of “Submachinegun - Grenade Launcher” Dynamic System

In modern small arms, one of the most important technical characteristics is the survivability of barrel, which greatly affects the competitiveness of the product on the market. The task of increasing the survivability of the barrel of automatic small arms has not yet lost its relevance. Based on the modern nature of combat and the multitasking of combat operations, complexes of manual automatic weapons with the use of various external devices, such as sighting devices of various designs, under-barrel grenade launchers, low-noise shooting devices, etc., are currently being developed on the basis of a single reference sample. When designing new samples, several approximated mathematical models are currently used, that do not take into account the impact of external devices on the dynamic and oscillatory processes occurring during firing from modern hand-held automatic weapons. This article describes a physical and mathematical model of the dynamic interaction of an under-barrel grenade launcher and small arms (submachine gun), demonstrates a computational mathematical model for different types and methods of securing a device such as an under-barrel grenade launcher. The optimal computational model is revealed, allowing to describe the dynamic process of interaction of the system elements in the numerical simulation of the shot. Advantages and disadvantages have been identified, on the basis of which the goals and objectives of further work have been determined, consisting in studying the influence of external devices on the parameters of the barrel of automatic small arms, in order to improve the operational, tactical and technical characteristics of automatic small arms systems based on the development of a numerical and analytical approach, and a modular mathematical model of automatic small arms weapons.

Plasma volume response patterns and a physiologic model of ultrafiltration in hemodialysis.

Ultrafiltration (UF) is an essential process of restoring fluid homeostasis during hemodialysis (HD). Fluid shifts across the extracellular compartments during UF, predominantly across the capillary interface and between the macro- and microcirculation. A mismatch between UF and transcapillary fluid transport can lead to hemodynamic instability leading to cardiac morbidity. We wished to study intradialytic fluid transport characteristics and their variation during UF to identify factors that govern variability in transcapillary fluid movement in HD. Twenty-two patients undergoing stable HD sessions were studied to measure and monitor absolute blood and plasma volume throughout UF. A computational mathematical model of predicted plasma volume decay during UF was analyzed with respect to the intradialytic real-time data profile. Pre- and post-dialysis fluid status was assessed using multifrequency bioimpedance spectroscopy. Serum electrolytes, osmolality, and total protein concentration were measured pre- and post-dialysis and during the intradialytic phase. Two distinct profiles of PV responses were detected. 60% of the patients presented plasma volume decline, characterized by a high percentage of volume decrease during the first hour, and a subsequent slower decrease with early rebound. The model was modified to achieve a proper fit of these volume profiles, assuming time-dependent changes in selected parameters governing the refilling flow. Although the modified model could more accurately fit the data, the new parameter values often fell outside of a physiologically acceptable range, suggesting that other factors not included in the classic description of transcapillary fluid transport might be the cause of the observed patterns.

Integrated battery thermal management and CO2 control in electric buses using recirculated air and waste heat recovery

In recent years, electric vehicles (EVs) have significantly progressed due to their environmental protection and energy-saving advantages. However, maintaining indoor thermal comfort and effective battery thermal management are critical considerations for EVs. Unlike conventional vehicles, EVs cannot use waste heat and depend on heat pump systems for winter heating and summer cooling. This research proposes a novel system to reduce the energy consumption of heat pump systems by using recirculated air while addressing the challenges of battery temperature management and maintaining safe CO2 concentrations in the cabin. The primary objectives of this research are threefold: first, to ensure passenger comfort through an integrated approach that includes cabin CO2 concentration safety; second, to recover heat from recirculated cabin air to save energy and control battery temperature; and third, to comprehensively analyze the performance of the Electric Vehicle Thermal Management System (EVTMS) using precise Computational Fluid Dynamics (CFD) analysis and mathematical modeling. This study will significantly enhance the understanding of energy consumption related to CO2 concentration control; an aspect previously overlooked in the literature. It will develop a battery temperature management strategy that leverages bus exhaust heat. These contributions will aid in achieving both passenger comfort and battery stability in electric buses, providing practical insights for the design and optimization of thermal management systems for future electric buses.

Horizontally Scalable Implementation of a Distributed DBMS Delivering Causal Consistency via the Actor Model

Causal Consistency has been proven to be the strongest type of consistency that can be achieved in a fault-tolerant, distributed system. This paper describes an implementation of D-Thespis, which is an approach that employs the actor mathematical model of concurrent computation to establish a distributed middleware that enforces causal consistency on a widely used relational database management system (RDBMS). D-Thespis prioritises developer experience by encapsulating the intricacies of causal consistency behind an interface that is accessible over standard REST protocol. Here, we discuss several novel results. Firstly, we define a method that builds a causally consistent DBMS supporting elastic horizontal scalability. Secondly, we deliver a cloud-native implementation of the middleware and provide results and insights on 6804 benchmark configurations executed on our implementation while running on a public cloud infrastructure across several data centres. The evaluation concerns transaction processing performance, an evaluation of our implementation’s update visibility latency, and a memory profiling exercise. The results of our evaluation show that under a transactional workload, a single-node installation of our implementation of D-Thespis is 1.5 times faster than a relational DBMS running serialisable transaction processing, while the performance of the middleware can improve by more than three times when scaled horizontally within the same data centre. Our study of the memory profile of the D-Thespis implementation shows that the system distributes its memory requirements evenly across all the available machines, as it is scaled horizontally. Finally, we also illustrate how our middleware propagates data changes across geographically-distributed infrastructures in a timely manner: our tests show that most of the effects of data change operations in one data centre are available in a remote data centre within less than 300 ms over and above the network round trip latency between the two data centres.

Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection

Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include deducing the unknown properties of biological structures or tissues from the observed imaging data, presenting a unique challenge in decoding intricate biological phenomena. Regarding disease detection, this technique has played a critical role in optimizing diagnostic efficiency by extracting meaningful insights from different imaging modalities like molecular imaging, MRI, and CT scans. Inverse problems contribute to uncovering subtle abnormalities by employing iterative optimization techniques and sophisticated algorithms, enabling precise and early disease detection. Deep learning (DL) solutions have emerged as robust mechanisms for addressing inverse problems in biomedical image analysis, especially in disease recognition. Inverse problems involve reconstructing unknown structures or parameters from observed data, and the DL model excels in learning complex representations and mappings. This study develops a DL Solution for Inverse Problems in the Advanced Biomedical Image Analysis on Disease Detection (DLSIP-ABIADD) technique. The DLSIP-ABIADD technique exploits the DL approach to solve inverse problems and detect the presence of diseases on biomedical images. To solve the inverse problem, the DLSIP-ABIADD technique uses a direct mapping approach. Bilateral filtering (BF) is used for image preprocessing. Besides, the MobileNetv2 model derives feature vectors from the input images. Moreover, the Henry gas solubility optimization (HGSO) method is applied for optimal hyperparameter selection of the MobileNetv2 model. Furthermore, a bidirectional long short-term memory (BiLSTM) model is deployed to identify diseases in medical images. Extensive simulations have been involved to illustrate the better performance of the DLSIP-ABIADD technique. The experimentation outcomes stated that the DLSIP-ABIADD technique performs better than other models.

Hawking Temperature Modification and the Physical Dynamics of Black Holes: A Study of the Influence of Internal and Cosmological Variables

<p>The main objective of the study is to develop a model that modifies the traditional Hawking temperature by considering the influence of internal variables such as radius, mass, electric charge, angular momentum, and cosmological constant. The research method involves mathematical analysis and computational modeling based on the modified Hawking temperature equation. The results show that the modified Hawking temperature produces non-linear corrections that show the interaction between black holes and the quantum structure of spacetime. graphical representations visualize the variation of Hawking temperature with changes in area, electric charge, angular momentum, and cosmological parameters. The implications of the research extend to the understanding of the thermal properties of black holes in the context of gravitational and quantum theories. The research identifies gaps in the knowledge of the effects of cosmological parameters on black hole thermodynamics and introduces Hawking temperature modifications that have not been mapped in detail before. The study concludes that the Hawking temperature modification provides a strong foundation for further research in black hole physics, particularly in the effect of physical and cosmological parameters on the thermal properties of black holes.</p>

Computational model of the cancer necrotic core formation in a tumor-on-a-chip device

The mechanisms underlying the formation of necrotic regions within avascular tumors are complex and poorly understood. In this paper, we investigate the formation of a necrotic core in a 3D tumor cell culture within a microfluidic device, considering oxygen, nutrients, and the microenvironment acidification by means of a computational-mathematical model. Our objective is to simulate cell processes, including proliferation and death inside a microfluidic device, according to the microenvironmental conditions. We employed approximation utilizing finite element models taking into account glucose, oxygen, and hydrogen ions diffusion, consumption and production, as well as cell proliferation, migration and death, addressing how tumor cells evolve under different conditions. The resulting mathematical model was examined under different scenarios, being capable of reproducing cell death and proliferation under different cell concentrations, and the formation of a necrotic core, in good agreement with experimental data reported in the literature. This approach not only advances our fundamental understanding of necrotic core formation but also provides a robust computational platform to study personalized therapeutic strategies, offering an important tool in cancer research and treatment design.

Real Time-Cutting Algorithmic Trading

a. Brief overview of algorithmic trading. Algorithmic trading, also known as algo trading or automated trading, refers to the use of computer algorithms and mathematical models to execute trading orders in financial markets. The primary goal of algorithmic trading is to achieve efficient and optimized execution of trading strategies, leveraging the speed and precision of computer systems. Here is a brief overview of algorithmic trading: 1. **Automation:** Algorithmic trading involves automating the process of buying or selling financial instruments, such as stocks, bonds, currencies, or commodities. The trading decisions are based on predefined rules and algorithms, eliminating the need for manual intervention. 2. **Speed:** One of the key advantages of algorithmic trading is its ability to execute trades at extremely high speeds. Algorithms can analyze market conditions, make decisions, and execute trades in a fraction of a second, responding to market changes much faster than human traders. 3. **Quantitative Analysis:** Algorithmic trading relies heavily on quantitative analysis and statistical models. Traders develop algorithms that are based on mathematical formulas, historical price data, technical indicators, and other relevant factors to identify trading opportunities. 4. **Market Making:** Some algorithmic trading strategies are designed for market making, where traders provide liquidity to the market by quoting bid and ask prices. Market-making algorithms aim to profit from the spread between the buying and selling prices. 5. **Arbitrage:** Algorithmic trading is often employed for arbitrage strategies, which involve exploiting price discrepancies between different markets or instruments. These discrepancies may arise due to inefficiencies, delays in information dissemination, or other market imperfections. 6. **Risk Management:** Algo trading systems typically incorporate risk management measures to control exposure and mitigate potential losses. These measures may include setting stop-loss orders, position sizing algorithms, and other risk controls. 7. **Backtesting:** Before deploying algorithms in live markets, traders often perform extensive backtesting. This involves running historical market data through the algorithm to simulate how it would have performed in the past. This helps refine and optimize the algorithm for real-world conditions. 8. **Machine Learning:** Some advanced algorithmic trading strategies incorporate machine learning techniques to adapt and learn from changing market conditions. Machine learning algorithms can analyze vast amounts of data to identify patterns and make predictions. 9. **Regulatory Considerations:** Algorithmic trading is subject to various regulations to ensure fair and orderly markets. Regulatory bodies may impose rules regarding algorithmic trading practices, transparency, and risk management to prevent market manipulation and protect investors. Algorithmic trading has become increasingly prevalent in financial markets, with institutional investors, hedge funds, and proprietary trading firms utilizing sophisticated algorithms to execute trades efficiently and capitalize on market opportunities.

Comparisons of oxygen increment in a micropump considering the dissolved and released processes

Abstract Space micropump are an important component of the on-orbit refuelling system. The gas-liquid behaviour transition induced by dissolved and released processes in the micropump has obtained considerable attention. Our proposed mathematical model achieved the bidirectional dissolved-released processes in an on-orbit space micropump for the first time. The performance characteristics of the micropump and the mathematical computational model used by the plug-discharge flow have been validated. Herein, the correlated mechanism between the flow, mass transfer and solution status were analysed. Under the pressurization of the micropump impeller, the gas was constantly dissolved into the solution, and the higher concentration caused the gas to evolve from the solution. The oxygen increment decreased rapidly when the initial dissolved oxygen centration exceeded 23 mg/L because of the gas evolution occurred. Focused on this sudden decreasing state of oxygen increment from 23 to 24 mg/L, four concentration conditions were discussed in depth including flow field, oxygen increment and mass transfer rate. In the impeller, the equilibrium concentration increases controlled by the pressure was obviously larger than the increase of the real-time concentration; thus, the unidirectional absorbed processes occurred inside the impeller. Two near-rate mass-transfer band were found near the short blades, exhibiting the dynamic equilibrium characteristic. Moreover, affected by the strong disturbance of the impeller outlet, the mass transfer rate was the highest here.

Influence of Slip Boundary Conditions on Jeffrey Fluid Flow in Tapered Conduit with Hall Current and Soret–Dufour Effects

AbstractThe present work examines the computational analysis and mathematical modeling of peristaltic blood flow in a tapered channel, taking into account slip boundary conditions, Hall current, and Soret–Dufour forces. By neglecting the wave number and employing a long‐wavelength approximation to analyze the fluid model, the investigation was conducted within the framework of a low Reynolds number regime. The study reveals that the values of the Soret number, Dufour number, Prandtl number, and Schmidt number exhibit an upward trend as the fluid temperature rises. In contrast, a rise in the thermal slip parameter causes the fluid temperature to fall. It has been shown that when the mass slip parameter increases, the fluid's concentration rises. The skin friction coefficient rises within the range of x = 0.55 to x = 1 as the velocity, concentration slip parameter, Soret number, and Dufour number increase. Conversely, the skin friction coefficient decreases as the thermal slip parameter increases. The selected characteristics exhibit realism since they find use in many fields such as medical biology, biomechanics, heat exchangers, gas turbines, and several other domains.

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

Preface

The 2023 International Conference on Applied Mathematics and Digital Simulation (AMDS 2023) was held successfully on 28-29 December 2023 in Beijing, China. The contributions are the results of submissions from the conference participants, who provide readers with a broader view of mathematical, statistical and computational science methods, ideas and tools.There are 92 participants in AMDS 2023, in which the program includes 6 keynote reports (35-45 minutes), 48 oral presentations (15 mins+3 Q&A) included in four sessions (Mathematics and Computational Theory, Mathematical Modelling and Machine Learning Methods Statistics Modelling and Data Analysis, and Fluid Dynamics and Mechanics), 15 E-posts and 23 listeners. The point of the conference emphasizes the development of theories and applications and promotes interdisciplinary endeavors among mathematicians, statisticians, scientists, engineers and researchers from other related disciplines.This volume comprises selected papers from the AMDS2023 conference. Accepted manuscripts were peer-reviewed by 2 to 4 reviewers in a double-blind review process. A total of 48 reviewers participated in this conference and provided their professional comments to authors to ensure the quality of the manuscripts. There are six topic partitions, including Mathematics and Computational Theory, Statistics Modelling and Data Analysis, Mathematical Modelling and Machine Learning Methods, Fluid Dynamics and Mechanics, Mathematical Modelling in Physics and Control and Automation, which constitutes an up-to-date account of principles, techniques, and tools for mathematical simulation and modelling in a wide range of research fields.We would like to thank the authors of all papers and participants; the members of the Scientific Committee who assisted with the review of the papers submitted and presented at the conference; the four session chairs: Prof. Yichuan Zhao, Prof. Quanxin Zhu, Prof. P.M. Siva Raja, Prof. Rao Li; the six keynote speakers: Prof. Alexander G. Ramm, Prof. Ovidiu Radulescu, Prof. Hajime Urakawa, Prof. Yanjun Liu, Prof. Grienggrai Rajchakit and Prof. Oscar Eduardo Ruiz Salguero; the organzier and sponsors: the Hubei Mengshun Exhibition Service Center, the Research Center for Stochastic Configuration Machines (SCM), the National Laboratory for Scientific Computing (LNCC); the publisher: IOP Publishing Ltd agrees to publish the AMDS 2023 proceedings. We are grateful for your attendance and contribution to a successful AMDS 2023.Organizing Cmmittee of AMDS 2023Jiang Zhu (Advisory Chair)National Laboratory for Scientific Computing-LNCC, BraziList of Conference Committees are available in this Pdf.

Computational mathematical modeling in the cocoa fermentation process in four isolated systems

In the southern region of Bahia, the cocoa culture has been a source of income and moved the economy for a long time, but the emergence of the “witch's broom” (Moniliophthora Perniciosa) caused the decline of this productive sector. Given the importance of cocoa for the region, makes sense to use it as an object of study for the development of academic research that can foster the reheating of this branch of the economy. In this context, for the processing of cocoa to occur to be able to use in the manufacture of chocolate and derivatives it needs to go through 5 distinct stages, being: harvesting, breaking, fermentation, drying, and storage. The stages in question are responsible for more than 45% of the organoleptic properties of cocoa, i.e. affect the final quality of the product. Fermentation is one of the most important stages since it is through fermentation that cocoa develops most of its characteristics, such as color, flavor, and aroma. The step by step of cocoa fermentation is to let the mass ferment, without stirring, until the temperature reaches 32ºC or if it does not reach the ideal temperature, wait to complete 48h. There are cases in which temperatures can be reached between 40ºC to 60ºC. Thus, the idea arose to create a cocoa fermenter model that could increase the efficiency of the fermentation cycle, and boost its scale and quality. Nowadays the process is done manually, requiring the worker to perform the laborious task of monitoring and stirring the cocoa mass at short intervals, over hours. This work compares four fermenter models through the analysis of heat diffusion. The method is theoretical with the help of computer modeling, where the propagation of heat from a source throughout the system filled with a mass of cocoa is evaluated.

Integrating Computational Fluid Dynamics for Maneuverability Prediction in Dual Full Rotary Propulsion Ships: A 4-DOF Mathematical Model Approach

To predict the maneuverability of a dual full rotary propulsion ship quickly and accurately, the integrated computational fluid dynamics (CFD) and mathematical model approach is performed to simulate the ship turning and zigzag tests, which are then compared and validated against a full-scale trial carried out under actual sea conditions. Initially, the RANS equations are solved, employing the Volume of Fluid (VOF) method to capture the free water surface, while a numerical simulation of the captive model test is conducted using the rigid body motion module. Secondly, hydrodynamic derivatives for the MMG model are obtained from the CFD simulations and empirical formula. Lastly, a four-degree-of-freedom mathematical model group (MMG) maneuvering model is proposed for the dual full rotary propulsion ship, incorporating full-scale simulations of turning and zigzag tests followed by a full-scale trial for comparative validation. The results indicate that the proposed method has a high accuracy in predicting the maneuverability of dual full-rotary propulsion ships, with an average error of less than 10% from the full-scale trial data (and within 5% for the tactical diameters in particular) in spite of the influence of environmental factors such as wind and waves. It provides experience in predicting the maneuverability of a full-scale ship during the ship design stage.

COMPUTER SIMULATION OF THE DIAGNOSTIC SYSTEM OF BRAKING CIRCUITS OF FREQUENCY-CONTROLLED ASYNCHRONOUS ELECTRIC DRIVES CONVERTERS

This research is essential for improving the reliability and efficiency of frequency-controlled asynchronous electric drives, especially in industrial environments where uninterrupted production processes are critical. The primary goal of the research is to develop a computer model for a brake system diagnostic system that allows for the timely detection of issues and failures in these systems. One of the key aspects of your research is the use of computer modeling, particularly in the MatLab Simulink environment. This enables engineers and researchers to more easily develop and test models, reducing research time and costs. An important element of such a model is the use of logic-time functions and wavelet diagnostics. These methods allow for the analysis of signals from brake systems from different perspectives and the detection of anomalies in their operation. The research also focuses on mathematical models of brake systems that are integrated into computer programs for simulating the operation of electric drives. Analyzing critical parameters and characteristics of brake circuits helps identify potential problems or failures in advance. Diagnostic algorithms based on data obtained from computer models enable the timely determination of the brake system's condition and the necessary decision-making to enhance safety and efficiency in electric drive operation. The results obtained from your research have significant practical importance as they help maintain the reliability and safety of industrial systems at a high level. Brake system diagnostics in frequency-controlled asynchronous electric drives become more accessible and efficient through the use of mathematical models and computer modeling in MatLab Simulink. This contributes to increased productivity and reduced risk of accidents in industrial systems.

Artificial-Neural-Network-Based Predicted Model for Seam Strength of Five-Pocket Denim Jeans: A Review

This study explores previous research efforts concerning prediction models related to the textile and polymer industry, especially garment seam strength, emphasizing critical parameters such as stitch density, fabric GSM, thread type, thread count, stitch classes, and seam types. These parameters play a pivotal role in determining the durability and overall quality of denim jeans based on cellulosic polymer. A significant focus is dedicated to the mathematical computational models employed for predicting seam strength in five-pocket denim jeans. Herein, the discussion poses the application of AI for manufacturing industries, especially for textile and clothing sectors, and highlights the importance of using a machine learning prediction model for sewing thread consumption, seam strength analysis, and seam performance analysis. Therefore, the authors suggest the significant importance of the machine learning prediction model, as future trends anticipate advancements in AI-driven methodologies, potentially leading to high-profile predictions and superior manufacturing processes. The authors also describe the limitation of AI and address a comprehensive model of risk outlines of AI in the manufacturing-based industries, especially the garments industry. Put simply, this review serves as a bridge between the realms of AI, mathematics, and textile engineering, providing a clear understanding of how artificial-neural-network-based models will be shaping the future of seam strength prediction in the denim manufacturing landscape. This type of evolution, based on ANN, will support and enhance the accuracy and efficiency of seam strength predictions by allowing models to discern intricate patterns and relationships within vast and diverse datasets.

A Novel Computational Mathematical Model for Team and Route Selection of the Emergency Response Operations

During the daily operations of emergency response, the decision maker is faced with the complex challenge of selecting a team and route in a short time period to respond and attend to the emergency. This study presents the combined use of the Analytic Hierarchy Process (AHP), the Élimination Et Choix Traduisant la Realité II (ELECTRE II), and the Dijkstra algorithm to deal with such situations. First, the AHP method is implemented to rank the aspects that are most relevant to a given emergency. Subsequently, this ranking is employed in ELECTRE-II to determine which emergency response team is best prepared to provide support. In the last stage of the proposed model, regarding the geographic coordinates of the team and the emergency, the Geographic Information System (GIS) utilizes the Dijkstra algorithm to regulate the most suitable route for assistance.

Implementation of the Simple Additive Weighting (SAW) Method in Selection of Students Recipients of Single Tuition Fee Assistance

Higher education in Indonesia, including at Budi Darma University, continues to be committed to creating an inclusive and equitable learning environment for all students. In line with this determination, Budi Darma University is implementing the Single Tuition Assistance (BUKT) program to support educational accessibility. Obstacles in the selection process, which is subjective and lacks a structured framework, can lead to inequality in the distribution of aid. This can result in students who should receive greater support being overlooked, while those who may need less may receive greater aid. In the BUKT selection process, there are a number of requirements that must be met, such as parents' income, PKH card ownership, completeness of documents, parents' dependents and home ownership. It is hoped that the use of a decision support system can be a solution to overcome this challenge. Decision Support Systems (DSS) integrate computer technology, mathematical models, and data to provide structured and organized support within a decision-making framework. The Simple Additive Weighting (SAW) method is a multi-criteria decision making method that allows relative weighing between criteria to determine the final score for each alternative. By applying SAW in the selection of BUKT recipient students, it is hoped that more objective and data-based decisions can be obtained. The research results produced the best alternative with a value of 100.00 in the alternative with code A5 in Fitri's name, so that Fitri was declared entitled to receive single tuition assistance.

Digitalization of Physics Teaching: Using Computer Models and Animations

The article analyzes current trends in the introduction of information technologies into the physics teaching process – the use of computer modeling and visualization to improve the efficiency of mastering complex physical material by students. The relevance of the work is due to the importance of using innovative digital teaching tools in the context of the rapid modernization of education. Using the example of modeling the discharge of a capacitor through a resistor, the technique of creating mathematical computer models with software implementation in Python and graphical visualization using Matplotlib is examined in detail. Various approaches to computer animation (multi-screen, fragmentary, elemental, figurative) are considered for presenting dynamic systems. Specific examples of animation of physical processes are given. The systematic technique of developing interactive electronic posters for teaching physics is highlighted. Methodological recommendations for the use of computer models in different types of lessons and at different stages of the educational process are formulated. The materials of the article are addressed to physics teachers, methodologists and specialists in the field of modern educational technologies. The article provides a methodological rationale for the feasibility of introducing information and communication technologies into physics teaching, analyzes various approaches to computer modeling and visualization, and examines specific examples and stages of creating animation and modeling tools for schoolchildren.